International Conference on Advanced Intelligent Systems for Sustainable Development: Volume 4 - Advanced Intelligent Systems on Energy, Environment, ... (Lecture Notes in Networks and Systems, 714) 3031352440, 9783031352447
This book describes the potential contributions of emerging technologies in different fields as well as the opportunitie
122 65 66MB
English Pages 588 [584] Year 2023
Foreword
Preface
Organization
Acknowledgement
Contents
Modeling Tool for the Design of Municipal Solid Waste Transportation Systems
1 Introduction
2 Methodology
2.1 Representation of the Transportation Trajectories
2.2 Optmization Objectives
3 Results and Discussions
3.1 Application Example
3.2 Results and Discussions
3.3 Sensitivity Analysis
4 Conclusions
Appendix
References
Mathematical Modelling and Dynamic Simulation for Wastewater Treatment Plant Management: An Experimental Pilot Study
1 Introduction
2 Materials and Methods
2.1 WEST ® Program
2.2 Data Collection and Model Structure
2.3 Construction and Evaluation of the Model on WEST
3 Results and Discussion
3.1 Steady-State and Dynamic Experiment
3.2 Sensitivity Analysis
3.3 Model Calibration and Validation
3.4 Model Calibration
3.5 Model Validation
4 Conclusions
References
Solar Flat Plate Collector (FPC) in Series with Evacuated Tube Collector (ETC) in a Forced Circulation Water Heating Installations Used in Buildings
1 Introduction
2 Methods
2.1 Examined Site
2.2 System Description and Modeling
2.3 Performance Metrics
3 Results and Discussion
4 Conclusion
References
Application of Predictive Control to Multilevel Inverters Used in a WECS for a Harmonics Minimization
1 Introduction
2 Multilevel Inverter Model
2.1 Model of the 3 Phases 5 Level NPC Inverter
2.2 Command Strategy of the 3 Phases 5 Level NPC Inverter
3 Predictive Control of the Multilevel Inverter
3.1 PWM Control of the 5 Level NPC Inverter
3.2 Model Predictive Control of the 5 Level NPC Inverter
4 Simulation Results and Discussion
5 Conclusion
References
Study of a Simulator for the Diagnosis of Wind Farm Failures and the Development of Maintenance Strategies
1 Introduction
2 Component-Based Wind Farm Modeling
3 Diagnosis Based on Energy Balances
3.1 Power Curve
3.2 Energy Balance of Components
4 Optimal Maintenance Strategies
5 Diagnosis of Faults Based on the Simulation of Energy Balances
6 Experiments
7 Conclusion
References
Industry 4.0 Technologies on Demand Driven Material Requirement Planning: Theoretical Background and Impacts
1 Introduction
2 Theoretical Background
2.1 Demand Driven MRP
2.2 Context of Industry 4.0
3 Discussion
3.1 Impacts of Adopting Industry 4.0 in DDMRP
3.2 The Benefits of Smart DDMRP
4 Conclusion
References
Effects of Slow Vehicles on Carbon Dioxide Emission in a Two-Lane Cellular Automata Model
1 Introduction
2 Methodology
2.1 Carbon Dioxide Emission Model
3 Results and Discussion
3.1 Homogeneous Configuration
3.2 Inhomogeneous Configuration
4 Conclusion
References
CFD Modelling and Thermal Performance Analysis of Ventilated Double Skin Roof Structure
1 Introduction
2 Method
2.1 Model Description
2.2 CFD Modelling
3 Results and Discussion
3.1 Thermo-Aeraulic Performance of the Roof
3.2 Heat Flux Through the Roof Structure
4 Conclusion
References
Literature Review of Energy Consumption Modeling for Mobile Devices
1 Introduction
2 Backgrounds
3 Research Method
4 Discussions
4.1 Building the Energy Consumption Model
4.2 Detecting Energy Bugs
4.3 Optimized Code Structure with Energy-Saving Practices
5 Conclusion
References
Density and Thermal Properties of MWCNT/Glycerol Nanofluids
1 Introduction
2 Materials and Methods
3 Materials and Experimental Methods
3.1 Thermal Conductivity
3.2 Density of MWCNT/Glycerol Nanofluid
4 Conclusion
References
Rheological Properties of MWCNT/Glycerol Nanofluids
1 Introduction
2 Materials and Experimental Methods
3 Results and Discussion
3.1 Dynamic Viscosity of Glycerol
3.2 Dynamic Viscosity of the MWCNT/Glycerol Nanofluids
3.3 Rheological Behaviors of Glycerol Based MWCNT Nanofluidsnts
4 Conclusion
References
Modeling and Control of a Photovoltaic Systems in Grid-Connected AC Microgrid
1 Introduction
2 Structure and Modeling of Photovoltaic System in Microgrid
2.1 Structure of Microgrid
2.2 Modeling of the Photovoltaic Array
3 Control of Microgrid
3.1 MPPT Control
3.2 Grid Synchronization
3.3 Grid Current Control
3.4 DC Bus Voltage Control
4 Simulation Results
5 Conclusion
References
Integral Sliding Mode Control of a DFIG Based Wind Turbine Using PSO Algorithm
1 Introduction
2 Wind Energy System Modeling
2.1 Wind Turbine Modeling
2.2 DFIG Modeling
3 The Proposed Intelligent Control Method
3.1 Integral Sliding Mode Control
3.2 Particle Swarm Optimization
4 Simulation Result and Discussion
5 Conclusion
Appendix
References
Blade Profile Effect on the Impulse Radial Turbine Performances for OWC Wave Energy Converter
1 Introduction
2 Turbine Geometry
3 Numerical Modelling
3.1 Turbine Flow Conditions
3.2 CFD Model Validation
4 Results and Discussion
4.1 Turbines Overall Performances
4.2 Aerodynamic Losses Analysis
5 Conclusion
References
Active and Reactive Power Control for a Hybrid Microgrid Based on Doubly Fed Induction Generator and Hydrogen Fuel Cell Power Sources
1 Introduction
2 Microgridâs Modeling and Topology
3 Microgrid's Power Control
4 Simulation Results and Discussion
5 Conclusion
References
An Overview on Smart MicroGrids Managing Renewable Energies Resources in an Isolated Site
1 Introduction
2 Classifications of Smart-MicroGrids
2.1 AC MicroGrids
2.2 DC MicroGrids
2.3 AC/DC MicroGrids
3 Stand-Alone MicroGrids Control
4 Smart-MicroGrids Energy Management System
5 Smart-MicroGrids Challenges and Opportunıtıes
6 Conclusion
References
Industrial Automation PLC Implementation of MPPT Using P&O Algorithm for PV System Applications
1 Introduction
2 System Description
2.1 PV Modeling Using Automation PLC
2.2 DC-DC Boost Converter Modeling Using Automation PLC
2.3 The Proposed P&O Algorithm Using Automation PLC
3 System Description
3.1 Rapidly Changing Irradiation Conditions
3.2 Rapidly Changing Temperature Conditions
4 Conclusion
References
A Review of Different Structures Generators and Control Strategies Applied to the Wind Turbine
1 Introduction
2 Wind Energy Conversion Systems
3 Generators and Topologies
3.1 Asynchronous Generators
3.2 Synchronous Generators
4 Maximum Power Point Tracking Controller for WECS
4.1 Conventional IPC- Based MPPT Algorithms for WECS
4.2 Conventional DPC Based MPPT Algorithms for WECS
5 Review Result and Discussion
5.1 Comparison of PMSG Versus Others Types Generators
5.2 MPPT Controllers Comparison
6 Conclusion
References
Fraud Detection in Supply Chain 4.0: A Machine Learning Model
1 Introduction
2 Financial Fraud in Supply Chain
3 Fraud Detection Model: A Machine Learning Approach
3.1 Proposed Methodology
3.2 Model Deployment
3.3 Performance Measurement
4 Results
5 Conclusion
References
Environmental Management and Environmental Performance: A Bibliometric Review Study and Visualization Analysis
1 Introduction
2 Environmental Management and Environmental Performance: A Brief Literature Review
3 Research Methodology and Initial Data Statistics
3.1 Importance of the Bibliometric Analysis
3.2 Key Approach to Our Bibliometric Analysis
4 Results
4.1 Evolution of the Number of Research Works
4.2 Ranking of Results by Discipline
4.3 Ranking of Results by Country of Writing
4.4 Volume of Scientific Production in the Field
4.5 Co-occurrence Analysis
4.6 Cluster Analysis
5 Discussion
6 Conclusion
Appendix
References
Intelligent Multisensors System, Temperature, Gas and Sound, Using Arduino
1 Introduction
2 Components and Software
2.1 Components
2.2 Softwars
3 Development of a Simulation Model and Prototype
3.1 Simulation Model
3.2 Creating a Prototype
3.3 Block Diagram
4 Results and Discussions
4.1 Temperature Measurements
4.2 Gas Detection
4.3 Sound Detection
4.4 Discussions
5 Conclusion
References
Model of a Hybrid Energy Storage System Using Battery and Supercapacitor for Electric Vehicle
1 Introduction
1.1 Energy Hybridization
1.2 State of Charge (SOC) Indicators
1.3 Energy Recovery Management
2 Hybrid Energy Storage Systems
3 Studied System
4 Conclusion
References
Estimation of Port Air Emissions Inventory: The Case of Tanger Mediterranean Port Authority
1 Introduction
2 Material and Methods
3 Results
4 Discussion
5 Conclusion
References
Exemplarity of Public Administrations: An Important Lever for the Energy Efficiency of Buildings - Case of Morocco
1 Introduction
2 Exemplarity of the Administration - The Case of Morocco
3 Methodology
4 Results and Discussion
4.1 Results of Diagnosis
4.2 Recommendations
4.3 Analysis of Public Procurement
5 Conclusion
References
Offline Parameter Identification of the Battery Equivalent Circuit Model for Electric Vehicles Using Particle Swarm Optimization Method
1 Introduction
2 Method
2.1 Equivalent Circuit Model (ECM)
2.2 Particle Swarm Optimization (PSO)
2.3 Parameter Identification Model
3 Application and Simulation Results
4 Conclusion
References
Multi-horizon Short-Term Load Consumption Forecasting Using Deep Learning Models
1 Introduction
2 Related Works
3 The Deep Learning Models
3.1 Long Short Term Memory Networks (LSTM)
3.2 Bi-directional LSTM Networks (Bi-LSTM)
3.3 The Convolution Neural Network (CNN)
4 Household Energy Consumption Dataset
5 Forecasting Models Implementations and Results
5.1 Data Imputation
5.2 Correlation between Features
5.3 Multicollinearity Studying
5.4 Models Configurations
5.5 Results
6 Conclusion
References
Electric Motors and Control Strategies for Electric Vehicles: A Review
1 Introduction
2 Electric Motors
2.1 DC Motor
2.2 Permanent Magnet Brushless Motor
2.3 Synchronous Motor
2.4 Comparison of Motors of EV
3 Control Strategies
3.1 Pulse Width Modulation (PWM)
3.2 Direct Torque Control (DTC)
3.3 Field Oriented Control (FOC)
3.4 Comparison of Control Algorithm
4 Conclusion
References
The Lighting Master Plan is a Lever for Efficient and Sustainable Management of Public Lighting in Moroccan Cities
1 Introduction
2 Research Methodology
3 Definition of the Scope of the Study
4 Context Analysis, Identification, and Validation of Needs
5 Urban Analysis of the Street Lighting Network
5.1 Urban and Architectural Analysis
5.2 Light Analysis
5.3 Analysis of Public Lighting Assets
5.4 Energy and Environmental Analysis
6 Recommendations for Energy Efficiency and Performance of the Public Lighting Network
7 Costing of the Implementation of the Lighting Master Plan for the City of Casablanca
8 Multi-criteria Analysis for Renewal Prioritization
9 Results and Discussion
10 Conclusion
References
High Gain Observer Design for PEM Fuel Cell State Estimation in Electric Vehicles
1 Introduction
2 General Description of the Studied PEMFC Systems
3 High Gain Observer Design for Nonlinear Dynamic Model of PEMFC
4 Exploitation of the Estimated Variables
4.1 Determination of the Output Stack Voltage
4.2 Determination of the Excess Ratio of Oxygen
5 Simulation Results
6 Conclusion
Appendix 1
Appendix 2
References
Short-Term Electric Load Forecasting Model Based on SVR Technique
1 Introduction
2 Related Works
3 Data Preparation
3.1 Database Study
3.2 Features Selection
3.3 Data Processing
4 Prediction Approach
4.1 Data Description
4.2 SVR Model Development
4.3 KNN Model Development
5 Results and Discussion
5.1 Strategy and Procedure of Simulation
5.2 Performance Evaluation
5.3 Evaluation of SVR Model
5.4 SVR Prediction Model Performances Comparing to KNN
6 Conclusion
References
A Novel OPT-GBoost Approach for Predicting Direct Normal Irradiance
1 Introduction
2 Data Source
3 Methodology
3.1 Data Pre-processing
3.2 Feature Extraction
3.3 XGboost
3.4 GridSearchCV
3.5 Optuna: An Efficient Hyper-parameter Tuning Technique
4 Result and Discussion
5 Conclusion
References
Evaluation of Smartness Level in Local Maritime Ports
1 Introduction
2 Definition, Objectives and Models of Smart Ports
2.1 Classification of Smart Environments
2.2 Smart Port Model and a Proposed Extension
3 Modeling the Metric of Smartness Level in Smart Maritime Ports
4 Use Cases of Local Ports
4.1 Local Maritime Ports
4.2 Evaluation of Smartness Level in Local Ports
5 Conclusions
References
The Dynamic Impact of Renewable Energy Consumption on CO2 Emissions: The Case of Morocco
1 Introduction
2 Literature Review
3 Model Presentation
4 Results and Discussions
4.1 ARDL Cointegration Tests
4.2 Causality and Parameter Stability
5 Conclusion and Policy Implications
References
Energy Demand Management in a Residential Building Using Multi-objective Optimization Algorithms
1 Introduction
2 Proposed Energy Management Strategy
2.1 Resolution of the Multi-objective Optimization Problem
2.2 Electricity Bill Minimization
2.3 Peak to Average Ratio Minimization
2.4 Appliances's Operational Waiting Time Minimization
2.5 Summary of Constraints
3 Proposed Black Widow Optimization Algorithm
4 Simulation Results and Discussion
5 Conclusion and Perspectives
References
A Novel Approach of Hotspot Detection in PV Plant
1 Introduction
2 Related Work
2.1 Hotspot Causes
2.2 Description of PV Systems
3 Proposed Approach
3.1 Performance Analysis
3.2 Experimental Results
4 Conclusion
References
Categorizing Data Imperfections for Object Matching in Wastewater Networks Using Belief Theory
1 Introduction
2 Research Context
2.1 Concepts of the Belief Theory
2.2 Related Works
2.3 The Belief Theory in Matching Road Networks
3 Data Imperfections in a Wastewater Network Database
3.1 Wastewater Network Graph
3.2 Categorization of Imperfections in a Wastewater Networks
4 The Belief Theory in Wastewater Object Matching
5 Experiments and Results
6 Conclusions
References
Modeling and Simulation of Piezo-Composite Energy Harvesting from Beam Subjected to Moving Load
1 Introduction
2 Homogenization of Piezoelectric Composite Materials
3 Harvesting Model
3.1 DQM Implementation
4 Result and Discussion
4.1 Micromechanical Results
4.2 Application
5 Conclusion
References
Solar Radiation Forecasting Using Artificial Intelligence Techniques for Energy Management System
1 Introduction
2 Proposed Prediction System Structure
2.1 Collect the Data
2.2 Pre-processing
2.3 Processing the Data
3 Configurations
3.1 Introduction
3.2 MLP Architecture
3.3 Stacking Architecture
4 Results and Discussion
5 Conclusion and Perspectives
References
Towards Machine Learning Applications for Computational Fluid Dynamics Modeling in Chemical Engineering
1 Introduction
2 Machine Learning for CFD Models Optimization
2.1 Computer Aided Design Optimization
2.2 Mesh Optimization
2.3 Surrogate Modeling Approach
3 Machine Learning for Turbulence Modeling
3.1 RANS Models
3.2 DNS Turbulence Models
3.3 LES Turbulence Models
References
Numerical Prediction of Effect of Hardening Laws on Springback and Blank Thickness Distribution During Cylindrical Deep Drawing Process
1 Introduction
2 Methodology
2.1 Modelling Approach
2.2 Yield Criteria and Hardening Law
2.3 Hardening Law
2.4 Finite Element Model
3 Results and Discussion
3.1 Validation of the Model
3.2 3D Springback Prediction
3.3 The Spring Back of the Aluminum 5052-O
4 Conclusion
References
Comparison of MPPT Algorithms for Grid Connected PV System
1 Introduction
2 Description and Model of the System
3 Control Strategy
3.1 MPPT Controller
3.2 Inverter Controller
4 Simulation Results and Discussion
5 Conclusion
References
Identification of the Parameters of the Lithium-Ion Battery Used in Electric Vehicles for the SOC Estimation
1 Introduction
2 Battery Model and Parameter Identification
3 SOC Estimation System Design
4 Simulations and Results Discussion
5 Conclusion
References
Voltage Profile Improvement of IEEE14 Bus System Using SVC and STATCOM
1 Introduction
2 FACTS Devices
2.1 Static Synchronous Compensator STATCOM
2.2 Static Var Compensator SVC
3 Simulation and Discussion
3.1 Simulation of the Base Case
3.2 Simulation with SVC
3.3 Simulation with STATCOM
3.4 Discussion
4 Conclusion
References
Assessing the Impact of Digitalization on the Energy Transition
1 Introduction
2 Preparation Approach of the Simulation by the Concept of Digital Twin
3 Methodology
4 Modeling Autonomous Systems
4.1 Cyber Physical Production System (CPPS)
4.2 Model of Digital Twin Synchronization
4.3 Model of Autonomy in the Flow of Part
4.4 Model of Autonomy in Handling Breakdowns
5 Conclusion
References
Application of Controlled DC-Chopper to Improve the Dc-Link Voltage During a Fault Grid
1 Introduction
2 Proposed Protection of DC-Link Using Fuzzy Logic Controller
2.1 DC-Link Protection with DC-Chopper
2.2 Design of Fuzzy Logic Controller
3 Simulation Analysis and Discussion
4 Conclusion
References
Machine Learning Based Predictive Maintenance of Pharmaceutical Industry Equipment
1 Introduction
2 Study Case: The Cyclotron
2.1 Context and Objectives
2.2 Cyclotron: Overview
3 Proposed Approach
3.1 Develop FMECA for the Cyclotron
3.2 Feature for Fault Detection
3.3 Data Collection
4 Experiment Results and Discussion
4.1 Evaluation Metrics
4.2 Results Discussion
5 Conclusion
References
Weibull and Extreme Value Theory Approach to Estimate Wind Energy in the North Region
1 Introduction
2 Data Characteristics
3 Statistical Analysis
4 Likelihood Method
5 Weibull Distribution
6 Extreme Value Theory
7 Wind Energy
8 Conclusion
References
Selecting Key Product Characteristics to Improve the QMS in Automotive Sector
1 Introduction
2 Related Work
2.1 Data Mining Methodology
2.2 Problematic
3 Linear regression model and Least Square estimator
4 Case Study
5 Conclusion and Perspectives
References
Berth Allocation and Quay Crane Assignment and Scheduling Problem Under Energy Constraints: Literature Review
1 Introduction
2 Selection and Analysis Criteria
3 Problems and Approaches
3.1 Berth Allocation Problem
3.2 Quay Crane Assignment Problem
3.3 Quay Crane Scheduling Problem
4 General Overview and Solution Methods
5 Conclusions and Future Research
References
Simulation of Thermal Conductivity with Comsol Multiphysics of Clay
1 Introduction
2 Numerical Modelling
2.1 Thermal Transfer Module
2.2 Electric Module
3 System Design and Mesh
3.1 System Design
3.2 Mesh
4 3D Simulation Results
4.1 The Temperature Variation in the Wire
4.2 Calculates the Thermal Conductivity of Clay
4.3 Manufacture of Specimens
4.4 Experimental Apparatus
4.5 Thermal Conductivity Measurement
5 Conclusion
References
Author Index
Recommend Papers
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Lecture Notes in Networks and Systems 714
Janusz Kacprzyk Mostafa Ezziyyani Valentina Emilia Balas Editors
International Conference on Advanced Intelligent Systems for Sustainable Development Volume 4 - Advanced Intelligent Systems on Energy, Environment, and Industry 4.0
Lecture Notes in Networks and Systems
714
Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).
Janusz Kacprzyk · Mostafa Ezziyyani · Valentina Emilia Balas Editors
International Conference on Advanced Intelligent Systems for Sustainable Development Volume 4 - Advanced Intelligent Systems on Energy, Environment, and Industry 4.0
Editors Janusz Kacprzyk Polish Academy of Sciences Systems Research Institute Warsaw, Poland
Mostafa Ezziyyani Abdelmalek Essaâdi University Tangier, Morocco
Valentina Emilia Balas Department of Automatics and Applied Software Aurel Vlaicu University of Arad Arad, Romania
ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-35244-7 ISBN 978-3-031-35245-4 (eBook) https://doi.org/10.1007/978-3-031-35245-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Foreword
Within the framework of the International Initiative for Sustainable Development of innovations and scientific research in order to keep pace with the digital transformation in light of the fourth industrial revolution and to encourage development projects known to the world, ENSAM-Rabat of Mohammed V University in cooperation with ICESCO organized the fourth edition of the International Conference on Advanced Smart Systems for Sustainable Development and their applications in various fields through five specialized seminars during the period from May 22 to 28, 2022. The fourth edition of the International Conference on Advanced Smart Systems for Sustainable Development was a great success, under the high patronage of His Majesty King of Morocco, Mohammed VI, and the participation of scientists and experts from more than 36 countries around the world. The conference, in its fourth edition, also resulted in a set of agreements and partnerships that were signed between the various participating parties, thus contributing to achieving the goals set by the conference regarding the investment of smart systems for sustainable development in the sectors of education, health, environment, agriculture, industry, energy, economy and security. In view of the importance of the conference as a high-level annual forum, and in consideration of the scientific status that the conference enjoys nationally, continually and internationally. Based on the experience gained and accumulated through the previous editions, we look forward to the success of next edition at all organizational and scientific levels, like its predecessors, and hosting a distinguished presence and weighty personalities from all participating countries in order to move forward for cooperation in priority areas and common interest such as health, agriculture, energy and industry.
Preface
Science, technology and innovation have for a long time been recognized as one of the main drivers behind productivity increases and a key long-term lever for economic growth and prosperity. In the context of the International Conference on Advanced Intelligent Systems for Sustainable Development plays an even more central role. Actually, AI2SD features strongly in Sustainable Development Goal in different fields, as well as being a cross-cutting one to achieve several sectoral goals and targets: Agriculture, Energy, Health, Environment, Industry, Education, Economy and Security. An ambition of the AI2SD to become the global forerunner of sustainable development should, in particular, include integrating new technologies and artificial intelligence and smart systems in its overarching and sectoral strategies of research and development. In which it emphasizes that solutions discussed by experts are important drivers for researches and development. AI2SD is an interdisciplinary international conference that invites academics, independent scholars and researchers from around the world to meet and exchange the latest ideas and discuss technological issues concerning all fields Social Sciences and Humanities for Sustainable Development. Due to the nature of the conference with its focus on innovative ideas and developments, AI2SD provides the ideal opportunity to bring together professors, researchers and high education students of different disciplines, to discuss new issues, and discover the most recent developments, scientific researches proposing the panel discussion on Advanced Technologies and Intelligent Systems for Sustainable Development Applied to Education, Agriculture, Energy, Health, Environment, Industry, Economy and Security.
Organization
Chairs General Chairs Mostafa Ezziyyani Janusz Kacprzyk Valentina Emilia Balas
Abdelmalek Essaadi University, FST – Tangier, Morocco Polish Academy of Sciences, Poland Aurel Vlaicu University of Arad, Romania
Co-chairs Khalid El Bikri Wajih Rhalem Loubna Cherrat Omar Halli
ENSAM Rabat, Morocco ENSAM Rabat, Morocco ENCG of Tangier, Morocco Advisor to the Director General of ICESCO
Honorary Presidents Salim M. Almalik
Abdellatif Miraoui Younes Sekkouri Ghita Mezzour
Director General (DG) of the Islamic World Educational, Scientific and Cultural Organization (ICESCO) Minister of Higher Education, Scientific Research and Professional Training of Morocco Minister of Economic Inclusion, Small Business, Employment and Skills Minister Delegate to the Head of Government in Charge of Digital Transition and Administration Reform
Honorary Guests Thomas Druyen
Jochen Werner
Director and Founder of the Institute for Future Psychology and Future Management, Sigmund Freud University Medical Director and CEO, Medicine University of Essen, Germany
x
Organization
Ibrahim Adam Ahmed El-Dukheri Director General of the Arab Organization for Agricultural Development Stéphane Monney Mouandjo Director General of CAFRAD Jamila El Alami Director of the CNRST Rabat, Morocco Mostapha Bousmina President of the EuroMed University of Fez, Fez, Morocco Chakib Nejjari President of the Mohammed VI University of Health Sciences Casablanca, Morocco Noureddine Mouaddib President of International University of Rabat, Rabat, Morocco Azzedine Elmidaoui President of Ibn Tofail University, Kenitra, Morocco Lahcen Belyamani President of the Moroccan Society of Emergency Medicine SAMU Rabat, Morocco Karim Amor President of Moroccan Entrepreneurs and High Potentials of the World-CGEM Hicham El Abbadi Business Sales Manager, Afrique Francophone EPSON Ilham Berrada Director of ENSIAS Rabat, Morocco Mostafa Stito Director of the ENSA of Abdelmalek Essaadi University, Tetouan, Morocco Mohamed Addou Dean of FST Tangier, Morocco Ahmed Maghni Director of ENCG Tangier, Morocco
Keynote Speakers Chakib Nejjari Anas Doukkali Thomas Druyen
Jochen Werner Abdelhamid Errachid El Salhi Oussama Barakat Fatima Zahra Alaoui Issame Outaleb Rachid Yazami
President of the Mohammed VI University of Health Sciences Casablanca, Morocco Former Minister of Health, Morocco Director and Founder of the Institute for Future Psychology and Future Management Sigmund Freud University Medical Director and CEO, Medicine University of Essen, Germany Full Professor Class Exceptional Class, University Claude Bernard, Lyon, France University of Franche-Comté, Besançon, France Dean of the Faculty of Medicine of Laâyoune, Morocco CEO and Founder PharmaTrace, Munich, Germany Scientist, Engineer and Inventor, Morocco
Organization
Tarkan Gürbüz Plamen Kiradjiev Abdel Labbi
Mostafa Ezziyyani Ghizlane Bouskri Levent Trabzon Marius M. Balas Afef Bohli
Ahmed Allam (President)
Valentina Emilia Balas Faissal Sehbaoui Jaime Lloret Hanan Melkaoui Issa Mouhamed Hossana Twinomurinzi Abdelhafid Debbarh Hatim Rhalem Faeiz Gargouri (Vice President) Adil Boushib Nasser Kettani
Kaoutar El Menzhi Khairiah Mohd-Yusof (President) Nadja Bauer Badr Ikken Amin Bennouna Mohamed Essaaidi Hamid Ouadia
xi
Middle East Technical University (METU), Ankara, Turkey German Edge Cloud (GEC), Friedhelm Loh Group, Germany Head of Data & AI Platforms Research, IBM Distinguished Engineer, IBM Research – Europe FST – Tangier, Morocco Senior Data Scientist at Volkswagen Group, Germany Mechanical Engineering, Istanbul Technical University, Turkey Aurel Vlaicu University of Arad Assistant Professor at the Higher Institute of Computer Science and the Cofounder of Digi Smart Solutions World Association for Sustainable Development, Senior Policy Fellow, Queen Mary University of London, UK Aurel Vlaicu University of Arad, Romania CEO of AgriEDGE, Attached to the Mohammed VI Polytechnic University Department of Communications Polytechnic University of Valencia, Spain Yarmouk University, Irbid, Jordan Head|Centre for Applied Data Science at University of Johannesburg, South Africa Chief of Staff/Advisor to the President-UIR EPSON Sales Manager, Morocco University of Sfax, Tunisia Regional Manager Microsoft, Germany Entrepreneur, ExO Coach, Digital Transformation Expert, Exponential Thinker, Certified DPO, Accessibility Expert Head of Digital Learning Center UM5R, Morocco Johor Bahru, Johor, Malaysia Dortmund, Germany General Director of IRESEN, Rabat, Morocco Cadi Ayyad University, Marrakech, Morocco ENSIAS, Mohammed V University, Rabat, Morocco ENSAM, Mohammed V University, Rabat, Morocco
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Organization
Khalid Zinedine Brahim Benaji Youssef Taher Tarik Chafik Abdoulkader Ibrahim Idriss Loubna Cherrat Laila Ben Allal Najib Al Idrissi
Hassan Ghazal Muhammad Sharif Mounir Lougmani El Hassan Abdelwahid Mohamed Zeriab Es-Sadek Mustapha Mahdaoui M’Hamed Ait Kbir Mohammed Ahachad
Faculty of Sciences, Mohammed V University, Rabat, Morocco ENSAM, Mohammed V University, Rabat, Morocco Center of Guidance and Planning of Education, Morocco FST, Abdelmalek Essaadi University, Tangier, Morocco Dean of Faculty of Engineering – University of Djibouti, Djibouti Abdelmalek Essaadi University, Morocco FST Abdelmalek Essaadi University, Morocco Mohammed VI University of Health Sciences, General Secretary of the Moroccan Society of Digital Health, Morocco President of the Moroccan Association of Telemedicine and E-Health, Morocco Director and Founder of Advisor/Science and Technology at ICESCO General Secretary of the Association of German Moroccan Friends-DMF Cadi Ayyad University, Marrakech ENSAM, Mohammed V University in Rabat FST, Abdelmalek Essaadi University, Morocco Abdelmalek Essaadi University, Morocco Abdelmalek Essaadi University, Morocco
Course Leaders Adil Boushib Ghizlane Bouskri Nadja Bauer Hassan Moussif Abdelmounaim Fares Imad Hamoumi Ghizlane Sbai
Regional Manager Microsoft, Germany Senior Data Scientist at Volkswagen Group, Germany Dortmund, Germany Deutsche Telekom expert, Germany. General Director and Founder of M-tech Co-Founder and Chief Executive Officer Guard Technology, Germany Senior Data Scientist Engineer, Germany Product Owner, Technical Solution Owner at Pro7Sat1
Organization
Scientific Committee Christian Axiak, Malta Bougdira Abdeslam, Morocco Samar Kassim, Egypt Vasso Koufi, Greece Alberto Lazzero, France Charafeddine Ait Zaouiat, Morocco Mohammed Merzouki, Morocco Pedro Mauri, Spain Sandra Sendra, Spain Lorena Parra, Spain Oscar Romero, Spain Kayhan Ghafoor, China Jaime Lloret Mauri, Spain Yue Gao, UK Faiez Gargouri, Tunis Mohamed Turki, Tunis Abdelkader Adla, Algeria Souad Taleb Zouggar, Algeria El-Hami Khalil, Morocco Bakhta Nachet, Algeria Danda B. Rawat, USA Tayeb Lemlouma, France Mohcine Bennani Mechita, Morocco Tayeb Sadiki, Morocco Mhamed El Merzguioui, Morocco Abdelwahed Al Hassan, Morocco Mohamed Azzouazi, Morocco Mohammed Boulmalf, Morocco Abdellah Azmani, Morocco Kamal Labbassi, Morocco Jamal El Kafi, Morocco Dahmouni Abdellatif, Morocco Meriyem Chergui, Morocco El Hassan Abdelwahed, Morocco Mohamed Chabbi, Morocco Mohamed_Riduan Abid, Morocco Jbilou Mohammed, Morocco Salima Bourougaa-Tria, Algeria Zakaria Bendaoud, Algeria Noureddine En-Nahnahi, Morocco Mohammed Bahaj, Morocco Feddoul Khoukhi, Morocco Ahlem Hamdache, Morocco
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Organization
Mohammed Reda Britel, Morocco Houda El Ayadi, Morocco Youness Tabii, Morocco Mohamed El Brak, Morocco Abbou Ahmed, Morocco Elbacha Abdelhadi, Morocco Regragui Anissa, Morocco Samir Ahid, Morocco Anissa Regragui, Morocco Frederic Lievens, Belgium Emile Chimusa, South Africa Abdelbadeeh Salem, Egypt Mamadou Wele, Mali Cheikh Loukobar, Senegal Najeeb Al Shorbaji, Jordan Sergio Bella, Italy Siri Benayad, Morocco Mourad Tahajanan, Morocco Es-Sadek M. Zeriab, Morocco Wajih Rhalem, Morocco Nassim Kharmoum, Morocco Azrar Lahcen, Morocco Loubna Cherrat, Morocco Soumia El Hani, Morocco Essadki Ahmed, Morocco Hachem El Yousfi Alaoui, Morocco Jbari Atman, Morocco Ouadi Hamid, Morocco Tmiri Amal, Morocco Malika Zazi, Morocco Mohammed El Mahi, Morocco Jamal El Mhamdi, Morocco El Qadi Abderrahim, Morocco Bah Abdellah, Morocco Jalid Abdelilah, Morocco Feddi Mustapha, Morocco Lotfi Mostafa, Morocco Larbi Bellarbi, Morocco Mohamed Bennani, Morocco Ahlem Hamdache, Morocco Mohammed Haqiq, Morocco Abdeljabbar Cherkaoui, Morocco Rafik Bouaziz, Tunis Hanae El Kalkha, Morocco Hamid Harroud, Morocco
Organization
Joel Rodrigues, Portugal Ridda Laaouar, Algeria Mustapha El Jarroudi, Morocco Abdelouahid Lyhyaoui, Morocco Nasser Tamou, Morocco Bauer Nadja, Germany Peter Tonellato, USA Keith Crandall, USA Stacy Pirro, USA Tatiana Tatusova, USA Yooseph Shibu, USA Yunkap Kwankam, Switzerland Frank Lievens, Belgium Kazar Okba, Algeria Omar Akourri, Morocco Pascal Lorenz, France Puerto Molina, Spain Herminia Maria, Spain Driss Sarsri, Morocco Muhannad Quwaider, India Mohamed El Harzli, Morocco Wafae Baida, Morocco Mohammed Ezziyyani, Morocco Xindong Wu, China Sanae Khali Issa, Morocco Monir Azmani, Morocco El Metoui Mustapha, Morocco Mustapha Zbakh, Morocco Hajar Mousannif, Morocco Mohammad Essaaidi, Morocco Amal Maurady, Morocco Ben Allal Laila, Morocco Ouardouz Mustapha, Morocco Mustapha El Metoui, Morocco Said Ouatik El Alaoui, Morocco Lamiche Chaabane, Algeria Hakim El Boustani, Morocco Azeddine Wahbi, Morocco Nfaoui El Habib, Morocco Aouni Abdessamad, Morocco Ammari Mohammed, Morocco El Afia Abdelatif, Morocco Noureddine En-Nahnahi, Morocco Zakaria Bendaoud, Algeria Boukour Mustapha, Morocco
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Organization
El Maimouni Anas, Morocco Ziani Ahmed, Morocco Karim El Aarim, Morocco Imane Allali, Morocco Mounia Abik, Morocco Barrijal Said, Morocco Mohammed V., Rabat, Morocco Franccesco Sicurello, Italy Bouchra Chaouni, Morocco Charoute Hicham, Morocco Zakaria Bendaoud, Algeria Ahachad Mohammed, Morocco Abdessadek Aaroud, Morocco Mohammed Said Riffi, Morocco Abderrahim Abenihssane, Morocco Abdelmajid El Moutaouakkil, Morocco Silkan, Morocco Khalid El Asnaoui, France Salwa Belaqziz, Morocco Khalid Zine-Dine, Morocco Ahlame Begdouri, Morocco Mohamed Ouzzif, Morocco Essaid Elbachari, Morocco Mahmoud Nassar, Morocco Khalid Amechnoue, Morocco Hassan Samadi, Morocco Mohammed Yahyaoui, Morocco Hassan Badir, Morocco Ezzine Abdelhak, Morocco Mohammed Ghailan, Morocco Kaoutar Elhari, Morocco Mohammed El M’rabet, Morocco El Khatir Haimoudi, Morocco Mounia Ajdour, Morocco Lazaar Saiida, Morocco Mehdaoui Mustapha, Morocco Zoubir El Felsoufi, Morocco Khalil El Hami, Morocco Yousef Farhaoui, Morocco Mohammed Ahmed Moammed Ail, Sudan Abdelaaziz El Hibaoui, Morocco Othma Chakkor, Morocco Abdelali Astito, Morocco Mohamed Amine Boudia, Algeria Mebarka Yahlali, Algeria
Organization
Hasna Bouazza, Algeria Zakaria Bendaoud, Algeria Naila Fares, Spain Brahim Aksasse, Morocco Mustapha Maatouk, Morocco Abdel Ghani Laamyem, Morocco Abdessamad Bernoussi, Morocco
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Acknowledgement
This book is the result of many efforts combined with subtle and strong contributions more particularly from the General Chair of AI2SD’2022 Professor Mostafa EZZIYYANI from Adelmalek Essaadi University, the distinguished honorary Chair Academician Janusz KACPRZYK from the Polish Academy of Sciences, and Co-Chair Professor Valentina EMILIA BALAS, Aurel Vlaicu University of Arad, ROMANIA. The scientific contribution published throughout this book could never be so revolutionary without the perpetual help and the limitless collaboration of several actors who supreme is precisely the high patronage of his majesty King Mohammed VI, who in addition to his undeniable support in all the production and scientific inspiration processes, he provided us with all the logistical and technical means in the smallest needs presented during the organization of the event and the publication of this book. The deep acknowledgment addressed to ENSAM school embodied by its director Pr. Khalid BIKRI for his prestigious inputs and the valuable contributions provided by Pr. Wajih RHALEM and by all the faculty members and his engineering students have prepared a fertile ground for presentation and exchange resulting in rigorous articles which are published in this volume. Great thanks to the Director General of the Organization of the Islamic World for Education, Science, and Culture (ICESCO) presented by its Director General Dr. Salim M. Al MALIK for their collaboration, their support, and for the distinguished welcome of the researchers and guests from the AI2SD’2022 conference. The appreciation is addressed to Dr. Omar HALLI advisor of the Director General of ICESCO for His excellent role in coordinating the organization of the AI2SD’2022 edition at ICESCO. The dedication inevitably concerns the organizing committee managed by General Chair Professor Mostafa EZZIYYANI, the VIP coordinator Professor Mohammed Rida ECH-CHARRAT, the scientific committee coordinator Professor Loubna CHERRAT, the Ph.D. student organization committee coordinator Mr. Abderrahim EL YOUSSEFI, and all professors and doctoral students for their constant efforts for the organization, maintenance of the relationship with researchers and collaborators, and also in the publication process.
Contents
Modeling Tool for the Design of Municipal Solid Waste Transportation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Otman Ait Ihia and Driss Khomsi Mathematical Modelling and Dynamic Simulation for Wastewater Treatment Plant Management: An Experimental Pilot Study . . . . . . . . . . . . . . . . . Tawfik El Moussaoui, Redouane Elharbili, Mohamed Oussama Belloulid, Khalid El Ass, Laila Mandi, Fouad Zouhir, Hugues Jupsin, and Naaila Ouazzani Solar Flat Plate Collector (FPC) in Series with Evacuated Tube Collector (ETC) in a Forced Circulation Water Heating Installations Used in Buildings . . . H. Allouhi, A. Allouhi, and A. Jamil Application of Predictive Control to Multilevel Inverters Used in a WECS for a Harmonics Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maha Annoukoubi, Ahmed Essadki, Hammadi Laghridat, and Tamou Nasser Study of a Simulator for the Diagnosis of Wind Farm Failures and the Development of Maintenance Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . Brahim Sadki and Mourad Kaddiri Industry 4.0 Technologies on Demand Driven Material Requirement Planning: Theoretical Background and Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mustapha El Marzougui, Najat Messaoudi, Wafaa Dachry, and Bahloul Bensassi
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14
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Effects of Slow Vehicles on Carbon Dioxide Emission in a Two-Lane Cellular Automata Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Laarej, A. Karakhi, N. Lakouari, A. Khallouk, and H. Ez-Zahraouy
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CFD Modelling and Thermal Performance Analysis of Ventilated Double Skin Roof Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdou Idris, Abdoulkader Ibrahim, Assabo Mohamed, and Hamda Abdi
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Literature Review of Energy Consumption Modeling for Mobile Devices . . . . . . Ayyoub El Outmani, El Miloud Jaara, and Mostafa Azizi
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Contents
Density and Thermal Properties of MWCNT/Glycerol Nanofluids . . . . . . . . . . . . 113 Abdelhafid Abouharim, Abdelghafour El Moutarajji, Rachid Abdia, and Khalil El-Hami Rheological Properties of MWCNT/Glycerol Nanofluids . . . . . . . . . . . . . . . . . . . . 123 Abdelhafid Abouharim, Abdelghafour El Moutarajji, Elomari Youssef, and Khalil El-Hami Modeling and Control of a Photovoltaic Systems in Grid-Connected AC Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Youssef Akarne, Ahmed Essadki, Tamou Nasser, and Hammadi Laghridat Integral Sliding Mode Control of a DFIG Based Wind Turbine Using PSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Hasnae Elouatouat, Ahmed Essadki, and Tamou Nasser Blade Profile Effect on the Impulse Radial Turbine Performances for OWC Wave Energy Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Khalid Elatife and Abdellatif El Marjani Active and Reactive Power Control for a Hybrid Microgrid Based on Doubly Fed Induction Generator and Hydrogen Fuel Cell Power Sources . . . 162 Ouassima El Qouarti, Ahmed Essadki, Hammadi Laghridat, and Tamou Nasser An Overview on Smart MicroGrids Managing Renewable Energies Resources in an Isolated Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Mohammed Khayat, Mhamed El Mrabet, Zineb Mekrini, and Mohammed Boulaala Industrial Automation PLC Implementation of MPPT Using P&O Algorithm for PV System Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Hanane Yatimi, Youness Ouberri, Rim Marah, and Elhassan Aroudam A Review of Different Structures Generators and Control Strategies Applied to the Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Abdelfattah Dani, Zineb Mekrini, Mhamed El Mrabet, and Mohammed Boulaala Fraud Detection in Supply Chain 4.0: A Machine Learning Model . . . . . . . . . . . . 200 Houria Abouloifa and Mohamed Bahaj Environmental Management and Environmental Performance: A Bibliometric Review Study and Visualization Analysis . . . . . . . . . . . . . . . . . . . . 207 Fadoua Laghzaoui and Sarah Ferehoun
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Intelligent Multisensors System, Temperature, Gas and Sound, Using Arduino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Hind Mestouri, Saida Bahsine, and Kamal Baraka Model of a Hybrid Energy Storage System Using Battery and Supercapacitor for Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 Fatima El Bakkari and Hamid Mounir Estimation of Port Air Emissions Inventory: The Case of Tanger Mediterranean Port Authority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Farah Housni, Abdrazak Boumane, Ona Egube, Abdelfettah Sedqui, Kamal Lakhmas, and Amal Maurady Exemplarity of Public Administrations: An Important Lever for the Energy Efficiency of Buildings - Case of Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Salma El Majaty and Abdellatif Touzani Offline Parameter Identification of the Battery Equivalent Circuit Model for Electric Vehicles Using Particle Swarm Optimization Method . . . . . . . . . . . . . 272 Elmahdi Fadlaoui, Hamza Hboub, and Noureddine Masaif Multi-horizon Short-Term Load Consumption Forecasting Using Deep Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Ismael Jrhilifa, Hamid Ouadi, and Abdelilah Jilbab Electric Motors and Control Strategies for Electric Vehicles: A Review . . . . . . . . 293 Zineb Machhour, Mhamed El Mrabet, Zineb Mekrini, and Mohammed Boulaala The Lighting Master Plan is a Lever for Efficient and Sustainable Management of Public Lighting in Moroccan Cities . . . . . . . . . . . . . . . . . . . . . . . . 302 Youssef Kasseh and Abdellatif Touzani High Gain Observer Design for PEM Fuel Cell State Estimation in Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 Abdelaziz El Aoumari and Hamid Ouadi Short-Term Electric Load Forecasting Model Based on SVR Technique . . . . . . . 331 Nada Mounir and Hamid Ouadi A Novel OPT-GBoost Approach for Predicting Direct Normal Irradiance . . . . . . 343 Mohamed Khalifa Boutahir, Yousef Farhaoui, Mourade Azrour, Ahmed El Allaoui, and El Mahdi Boumait
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Contents
Evaluation of Smartness Level in Local Maritime Ports . . . . . . . . . . . . . . . . . . . . . 351 Ayoub El Idrissi, Abdelfatteh Haidine, Abdelmoula Ait-Allal, Abdelhak Aqqal, and Aziz Dahbi The Dynamic Impact of Renewable Energy Consumption on CO2 Emissions: The Case of Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 Soufiane Bouyghrissi, Salwa Bajja, Maha Khanniba, Hassan Radoine, and Jerome Chenal Energy Demand Management in a Residential Building Using Multi-objective Optimization Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 Saad Gheouany, Hamid Ouadi, and Saida El Bakali A Novel Approach of Hotspot Detection in PV Plant . . . . . . . . . . . . . . . . . . . . . . . 378 M. Limam El Hairach, Insaf Bellamine, Amal Tmiri, and Khalid Zine Dine Categorizing Data Imperfections for Object Matching in Wastewater Networks Using Belief Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 Omar Et-targuy, Yassine Belghaddar, Ahlame Begdouri, Nanée Chahinian, Abderrahmane Seriai, and Carole Delenne Modeling and Simulation of Piezo-Composite Energy Harvesting from Beam Subjected to Moving Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Yassin Belkourchia, Nada Tassi, and Lahcen Azrar Solar Radiation Forecasting Using Artificial Intelligence Techniques for Energy Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 Saida El Bakali, Hamid Ouadi, and Saad Gheouany Towards Machine Learning Applications for Computational Fluid Dynamics Modeling in Chemical Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422 Safae Elmisaoui, Sanae Elmisaoui, Lhachmi Khamar, and Hasnae Zerouaoui Numerical Prediction of Effect of Hardening Laws on Springback and Blank Thickness Distribution During Cylindrical Deep Drawing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 Sara Bendrhir, Kenza Bouchaala, Lahcen Azrar, Farah Abdoun, and Elhachmi Essadiqi Comparison of MPPT Algorithms for Grid Connected PV System . . . . . . . . . . . . 448 Mohamed Bahri, Mohamed Talea, Hicham Bahri, and Mohamed Aboulfatah
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Identification of the Parameters of the Lithium-Ion Battery Used in Electric Vehicles for the SOC Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Nasri Elmehdi, Jarou Tarik, Salma Benchikh, and Nabiha Saadi Voltage Profile Improvement of IEEE14 Bus System Using SVC and STATCOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Ismail Moufid, Zineb En-nay, Soukaina Naciri, Hassan EL Moussaoui, Tijani Lamhamdi, and Hassane El Markhi Assessing the Impact of Digitalization on the Energy Transition . . . . . . . . . . . . . . 480 Kawtar Agouzzal and Ahmed Abbou Application of Controlled DC-Chopper to Improve the Dc-Link Voltage During a Fault Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488 Zineb En-nay, Ismail Moufid, Hassan El Moussaoui, Tijani Lamhamdi, and Hassane El Markhi Machine Learning Based Predictive Maintenance of Pharmaceutical Industry Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Fatima-ezzahraa Ben-Bouazza, Oumaima Manchadi, Zineb El Otmani Dehbi, Wajih Rhalem, and Hassan Ghazal Weibull and Extreme Value Theory Approach to Estimate Wind Energy in the North Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Hind Sefian, Fatima Bahraoui, and Zuhair Bahraoui Selecting Key Product Characteristics to Improve the QMS in Automotive Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Laila Benzaza, Najlae Alfathi, and Abdelouahid Lyhyaoui Berth Allocation and Quay Crane Assignment and Scheduling Problem Under Energy Constraints: Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532 Mounir Ech-Charrat, Mofdi El Amrani, and Mostafa Ezziyyani Simulation of Thermal Conductivity with Comsol Multiphysics of Clay . . . . . . . 545 Zakaria Kbiri, Bouazza Tbib, Mohamed Faoussi, and Khalil El-Hami Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561
Modeling Tool for the Design of Municipal Solid Waste Transportation Systems Otman Ait Ihia(B) and Driss Khomsi Hydraulic Systems Analysis Team (EASH), Mohammadia School of Engineers, University Mohammed V in Rabat, Agdal, Rabat, Morocco [email protected]
Abstract. The transportation of municipal solid waste (MSW) and the selection of optimal locations for MSW landfills and transfer stations are considered the most important issues in every MSW management system. This paper presents a modeling tool to aid in the design of a MSW transportation system and for the identification of transfer station and landfill optimal locations using a multi-objective mathematical model that minimizes investment, operational and transportation costs, number of transportation vehicles and the emissions of pollutants. Matlab was used for the prgramming of the developed model enabling rapid yet realistic analysis of the MSW transportation systems, and can be used for analyzing either existing or proposed design systems. In order to demonstrate the principles of the model and the feasibility of its application, it is applied on a hypothetical example that represents a situation of a MSW management system composed of existing and planned facilities. A sensitivity analysis concluded that the use of compacting vehicles leads to a significant decrease in the number of vehicles and GHG emissions. Keywords: Modeling tool · Design · Municipal Solid Waste · Transportation Systems
1 Introduction In most countries, urban solid waste management is seen as a serious issue that must be addressed through an integrated system approach. The severity of the problem as a result of increasing urbanization, the growing number of technologies, and the wide range of possible paths from collection to disposal sites all contribute to the problem’s complexity [1, 2]. Municipal solid waste (MSW) management system, in general, Is composed of the sources of waste, transportation, and available treatment and recovery options. Waste transportation and disposal can result in a variety of environmental and economic issues, because if not managed properly, the generated wastes can affecte surface and groundwater, soil, and air [3–6]. The selection of a suitable MSW landfill site is seen as a difficult task in every MSW management system, and the circumstances, implications, and complexity make landfill site selection an essential issue in urban planning [7–10]. For MSW transfer stations, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 1–13, 2023. https://doi.org/10.1007/978-3-031-35245-4_1
2
O. A. Ihia and D. Khomsi
typical environmental impacts include noise, dust, stench, trash, and traffic congestion and they might be regarded as point sources of pollution and could be chosen using criteria or methods similar to those used for landfills [11, 12]. Therefore, the acquisition of an effective waste disposal strategy involves a manual identification of a set of selection criteria, which is a time-consuming task for a planner [13]. In order to facilitate MSW facilities’ locations and allocations, three basic methods are used: (1) Geographic Information Systems (GIS), (2) Multi-Criteria Decision Analysis (MCDA), and (3) Optimization models [10, 14]. Several studies have adopted GIS and MCDA considering environmental, social, and technical criteria. Ekmekçio˘glu et al. [7] have used a fuzzy TOPSIS methodology for the selection of appropriate MSW landfill sites while considering economic, environmental and technical criteria. While factors of topography and geology, natural resources, sociocultural, and economy and safety have been considered by Al-Jarrah and Abu-Qdais [15]. In the same context, Badi and Kridish [16] used criteria of wind direction, land cost, surface water level, site capacity, public acceptance, distance to the collection areas, and land accessibility as criteria. Some studies have adopted a combination of GIS and MCDA allowing the consideration of the geographical criteria, the distances between the different environmental and urban areas and the MSW management facilities, economic and technical criteria, and criteria linked to land characteristics [6, 8, 17–19]. Optimization models are capable of increasing MSW system performance by optimizing the location of their facilities while optimizing costs, transportation distances, or transportation time, and even greenhouse gas (GHG) emissions. Habibi et al. [4] proposed a multi-objective optimization model for the selection of MSW landfills and transfer stations considering the minimization of total cost and GHG emissions. Other similar studies are proposed by Eiselt and Marianov [20] and Erkut et al. [21] using linear programming while considering total cost and pollution minimization as objectives. While Monzambe et al. [22] proposed a Non-linear Mixed Integer mathematical model minimizing the time and cost of transportation. A combination of GIS and optimization model is adopted by Rathore and Sarmah [23] with the objective of total cost minimization. According to what has been discussed above, no study has focused on the selection of the optimal location of MSW management facilities and the identification of an optimal waste transportation system at the same time while considering economic criteria, environmental constraints, and transportation requirements. In this paper, a practical tool to aid in the design of MSW transportation systems and the optimal siting of landfills and transfer stations is presented. This modeling tool could be applied to existing or planned MSW management systems for the optimal transportation system that optimizes the total costs, the total number of transportation vehicles, and the total emissions of pollutants during the transportation of wastes. This paper is structured in three sections. The introduction and literature review are represented in the first section. The section section entails the methodology applied for the establishement of the mathematical model. A hypothetical example and its results are dicussed in the third section, and the paper is conluded in the last section.
Modeling Tool for the Design of Municipal Solid Waste Transportation Systems
3
2 Methodology The proposed methodology consists of the development of a modeling tool applied to existing and planned MSW management systems allowing the analysis of the data related to this system, which will help to identify the optimal locations of landfills and transfer stations and to define the most optimal MSW transportation system. A MSW management system proposed in Fig. 1 includes MSW sources, transfer station locations, as well as landfill locations. In this system, specific scenarios have been adopted which include: that the waste is not separated at sources, waste can be shipped either directly to landfills or to transfer station sites and then to landfill sites, the locations of existing and planned facilities are already determined, and vehicles can make several trips to transport the waste. The proposed modeling tool consists of the use of a multi-objective mathematical model based on linear programming where the decision variables are binary numbers allowing to select the most optimal transportation trajectories while considering three parameters including, the total MSW management system costs, the total number of transportation vehicles including collection and tranfer vehicles, the road traffic and the emissions of GHG. 2.1 Representation of the Transportation Trajectories The parameters used to represent transportation trajectories include: xij : equals 1 when the trajectory between a source (i) and a transfer station (j) will be crossed by the collection vehicles, and 0 otherwise. yjk : equals 1 when the trajectory between a transfer station (j) and a landfill (k) will be crossed by the transfer vehicles, and 0 otherwise. zik : equals 1 when the trajectory between a source (i) and a landfill (k) will be crossed by the collection vehicles, and 0 otherwise. 2.2 Optmization Objectives The three objectives of the model are illustrated under the following function objectives: Min
I J
invtsj × aij × xij × tsj +
j=1 i=1
+
J K
+
k=1 j=1
+
I J j=1 i=1
optsj × aij × xij
j=1 i=1
invl k × bjk × yjk × lk +
k=1 j=1 J K
I J
I K
invl k × cik × zik × lk
k=1 i=1
opl k × bjk × yjk +
I K
opl k × cik × zjk
k=1 i=1
cc × dij × aij × xij +
I K k=1 i=1
cc × dik × cik × zik
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O. A. Ihia and D. Khomsi
Fig. 1. The proposed MSW management system and transportation trajectories.
+
J K
tc × djk × bjk × yjk −
k=1 j=1
Min
I J
spj × aij × recj × xij
I J
Ncij × xij +
j=1 i=1
Min
K J
Ntjk × yjk +
k=1 j=1
I J
J K
I K
Ncik × zik
2 × d jk × Ntjk × Tr tjk ×
u × yjk KPLt
2 × d ik × Ncik × Tr cik ×
u × zik KPLc
k=1 i=1
(2)
k=1 i=1
u × xij KPLc
k=1 j=1
+
K I
2 × d ij × Ncij × Tr cij ×
j=1 i=1
+
(1)
j=1 i=1
(3)
Subject to: I
xij × aij ≤ captsj ; j = 1, 2, 3, . . . J
(4)
i=1 J j=1
yjk × bjk +
I
zik × cik ≤ capl k ; k = 1, 2, 3, . . . K
(5)
i=1
Equation (1) describes the total costs minimization including the investment and the operating costs for transfer stations, the investment and the operating costs for landfills, the cost of the transportation by collection and transfer transfer vehicles, As well as the earnings from the recyclable materials.
Modeling Tool for the Design of Municipal Solid Waste Transportation Systems
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Equation (2) describes the minimization of the total number of vehicles. The total number of vehicles required includes the collection vehicles operating for regions to transfer stations and region to landfills trajectories, and the transfer vehicles operating for transfer stations to landfills trajectories. Equation (3) consists of the minimization of GHG emissions expressed in (kgCO2 eq) calculated according to the Environmental Protection Agency [24]. Equation (4) allows the respect of the transfer station daily capacity. Equation (5) allows the respect of the landfill daily capacity.
3 Results and Discussions 3.1 Application Example The MSW management system reprresented in Fig. 2 describes a situation that includes an existing section and a planned section of the MSW management system. This system is composed of 4 regions (I = 4) representing MSW sources in which the quantity of waste generated is determined, 4 transfer stations (J = 4) which one existing transfer station with a maximum daily capacity of 300 tons is located in location j = 2, and an existing landfill in the location k = 1 with a maximum capacity of 1800 tons. Also, this MSW management system incorporates three proposed potential locations of new transfer stations in locations j = 1, j = 3, and j = 4, and a potential site for a new landfill is located in the site k = 2. For the implementation of the developed optimization model the data for the different parameters used were chosen according to the studies performed in [12, 25–28] and represented in Table 1 and Table 2. The distances used for this example are presented in Table 3, Table 4, and Table 5.
Fig. 2. The proposed MSW management system.
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O. A. Ihia and D. Khomsi Table 1. MSW collection and transfer vehicles data.
Parameter
Collection vehicles
Transfer vehicles
Working time
8h
8h
Preparation time
30 min
30 min
Cleaning time
30 min
30 min
Loading time
1.5 h
4h
Unloading time
12 min
12 min
Speed
40 km/h
50 km/h
Capacity
10 m3
40 m3
Loadig efficiency
80%
80%
MSW density
0.2 ton/m3
0.2 ton/m3
Compaction factor
No compaction
No compaction
MSW Density in the vehicles
0.2 ton/m3
0.2 ton/m3
Reserve rate
10%
10%
Transportation cost
5 MAD/ton/km
2 MAD/ton/km
KPL
2.5 km/l
2.5 km/l
Used Fuel
Gasoline
Gasoline
u
2.35 kgCO2 eq/l
2.35 kgCO2 eq/l
Table 2. Data for transfer stations. Parameter
Value
Transfer station investment cost
30 MAD/ton
Transfer station operating cost
10 MAD/ton
Rate of material recovery
No recovery
Selling price of recovered materials
0 MAD/ton
Landfill nvestment cost
90 MAD/ton
Landfill operating cost
135 MAD/ton
3.2 Results and Discussions The developed model programmed using Matlab treats a number of possible transportation scenarios of 2I ×J × 2J ×K × 2I ×K , where in our case it has treated 4294967296 possible transportation scenarios in 6 s. According to the graphical representation of the results for the application example and while comparing all the results given by the model and represented in Fig. 3, the most optimal transportation scenario gives a total daily cost of 129100 MAD, a number of required vehicles of 217 vehicles and GHG emissions of 8075 kgCO2 eq.
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Table 3. Distances of the trajectories source-transfer station. Transfer station locations Source locations
j=1
j=2
j=3
j=4
i=1
3 km
6 km
7 km
9 km
i=2
3 km
3 km
4 km
8 km
i=3
4 km
3 km
3 km
7 km
i=4
8 km
5 km
3 km
3 km
Table 4. Distances of the trajectories transfer station-landfill. Transfer station locations Landfill locations
j=1
j=2
j=3
j=4
k=1
30 km
31 km
40 km
42 km
k=2
34 km
31 km
39 km
35 km
Table 5. Distances from source locations to landfill locations. Source locations Landfill locations
i=1
i=2
i=3
i=4
k=1
36 km
37 km
40 km
42
k=2
34 km
37 km
39 km
35 km
The most optimal transportation scenario presented in Fig. 4 consists of the use of the transfer station in j = 2 serving sources i = 2 and i = 4 and then the waste brought to this transfer station will be transfered to the existing landfill in the location k = 1. As long as the capacity of the existing transfer station is not sufficient to serve all the waste from the four sources, a new transfer station will be built in j = 1 where the daily capacity is fixed at 300 tons receiving wastes from the sources in locations i = 1 and i = 3, and then shipped to the existing landfill in location k = 1. The optimal scenario for waste transportation that minimizes the three objective functions, as well as the road traffic generated and the GHG emissions for each transportation trajectory are summarized in Fig. 4. 3.3 Sensitivity Analysis A sensitivity analysis was applied for the application example. The compaction factor for the collection and transfer vehicles (fcc , and fct ), the material recovery rate (rec) with the selling price of those materials (sp), and volume of collection and transfer vehicles (Vc , and Vt ) are considered the input variables to this sensitivity analysis.
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O. A. Ihia and D. Khomsi
Fig. 3. Results for all the possible transportation scenarios and the results for the most optimal transportation scenario.
Fig. 4. Results of the optimal MSW transportation system.
The parameters examined in this sensitivity analysis do not change the configuration of the MSW transportation system or the locations of the operational transfer stations and landfills shown in Fig. 4. The results of this sensitivity analysis are shown in Table 6. The results of the sensitivity analysis showed that the recovery of materials of 8% and the selling of these materials by 100 MAD per ton results in a decrease of the total cost of about 9.84%, and reduces the amount of waste sent to the landfills, thus reducing the number of vehicles needed of 3.23%, and thereby reducing GHG emissions of 4.97%, making it a parameter that can affect the three objectives. The volumes of the collection and transfer vehicles should be greater than or equal to 10 m3 and 40 m3 respectively, because a small decrease leads to a significant increase in the total number of vehicles and the GHG emissions. The most important parameter that greatly affects the MSW transportation system is the compaction factor. The use of compacting vehicles with a compacting factor of
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9
Table 6. Sensitivity analysis results. Cost (MAD)
Vehicles
Pollutant emissions (kgCO2 eq)
Results for the actual inputs
129100
217
8075
Material recovery rate of 8% with a selling price of 100 MAD/ton
116400
210
7674
Collection vehicles capacity (+10%) (11 m3 )
129100
197
7323
129100
241
8964
129100
110
4091
Transfer vehicle capacity (+10%) (44 m3 ) Collection vehicles capacity (−10%) (9 m3 ) Transfer vehicle capacity (−10%) (36 m3 ) Compaction factor for all the collection and transfer vehicles of 2
2 reduces the number of vehicles required by 49.31% which results in a significant decrease in road traffic and therefore reduces the GHG emissions by 49.34%.
4 Conclusions The modeling tool presented here allows to identify an optimal system for the transportation of MSW and to determine the optimal locations of the operational transfer stations and landfills considering three parameters to be optimized including the total cost including the investement and operating costs for the facilities as welle as the transportation costs, the total number of collection and transfer vehicles and the emissions GHG during the transportation of MSW. Matlab was used for programming and results expression in a short period of time while treating a huge number of possible transportation scenarios. A graphical representation of all possible scenarios is given in order to compare between them and to give the user the possibility to select the transportation scenario desired according to its considerations. A sensitivity analysis was applied and concluded that using vehicles with compaction minimizes the number of vehicles, thus minimizes the road traffic and the GHG emissions. The model could be used for existing MSW management systems where the aim is to design the transportation system or for planned MSW management systems where the aim is to identify the optimal locations of the facilities and to identify the most economically and environmentally optimal transportation system. The proposed modeling tool requires that the locations of MSW facilities are already determined, for which the use of a combination with GIS and MCDA will be helpful
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while considering environmental and social constraints. In addition, this model is limited in terms of execution time which requires the development of methods and models for the study of MSW systems with huge transportation networks.
Appendix The following notations are used in this study for the expression of the following parameters: I: Sources; J: Transfer station locations; K: Landfill sites; aij : Amount in tons of MSW linked to the trajectory source-transfer station; bjk : Amount in tons of MSW linked to the trajectory tranfer station-landfill; bjk =
I
xij × aij × (1 − recj )
(6)
i=1
cik : Amount in tons of MSW linked to the trajectory source-landfill; invtsj : Investment cost in MAD/ton for a transfer station (j); optsj : Operating cost in MAD/ton for a transfer station (j); tsj : Equals 0 when a transfer station exists in the site (j) and 1 otherwise; invl k : Investment cost in MAD/ton for a landfill (k); opl k : Operating cost in MAD/ton for a landfill (k); lk : Equals 0 when a landfill exists in the site (k) and 1 otherwise; cc: Transportation cost in MAD/ton/km for collection vehicles; tc: Transportation cos in MAD/ton/km for transfer vehicles; spj : Selling price in DH/ton of the recyclable materials; recj : Rate in % for material recovery; Qwc : Capacity in tons of the collection vehicles: Qwc =
Vc × ec × Dc 1 + rc
(7)
Dc = fcc × Dw Vc : Capacity in m3 of the collection vehicle; ec : Collection vehicles loading efficiency in %; Dc : Collection vehicles MSW density in ton/m3 ; fcc : Collection vehicles MSW compaction factor; rc : Collection vehicles reserve rate in %; Qwt : Capacity in tons of the transfer vehicles; Qwt =
Vt × et × Dt 1 + rt
Dt = fct × Dw
(8)
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Vt : Capacity in m3 of the transfer vehicle; et : Transfer vehicles loading efficiency in %; Dt : Transfer vehicles MSW density in ton/m3 ; fct : Transfer vehicles MSW compaction factor; rt : Transfer vehicle reserve rate in %; Dw : MSW density in ton/m3 ; Tr cij : Number of trips for the trajectory source-transfer station calculated according to: Tr cij =
t 1 c − t 2 c + t3 c 2×dij vc
+ t4 c + t5 c
(9)
Tr cik : Number of trips for the trajectory source-landfill calculated according to: t 1 c − t 2 c + t3 c Tr cik = 2×d (10) ik v c + t4 c + t5 c t1c : Collection vehicle working time in hours; t2c : Collection vehicle preparation time in hours; t3c : Collection vehicle cleaning time in hours; t4c : Collection vehicle loading time in hours; t5c : Collection vehicle unloading time in hours; vc : Collection vehicle peed in km/h; Tr tjk : Number of trips for the trajectory transfer station-landfill calculated according to: Tr tjk =
t1t − (t2t + t3t ) 2×djk v t + t4 t + t5 t
(11)
t1t : Transfer vehicle working time in hours; t2t : Transfer vehicle preparation time in hours; t3t : Transfer vehicle cleaning time in hours; t4t : Transfer vehicle loading time in hours; t5t : Transfer vehicle unloading time in hours; vt : Transfer vehicle peed in km/h; dij : Diastance in km of the trajectory source-transfer station; djk : Distance in km of the trajectory transfer station-landfill; dik : Distance in km of the trajectory source-landfill; captsj : Transfer station maximum capacity in tons; capl k : Landfill maximum capacity in tons; Ncij : Number of vehicles for the trajectory source-transfer station: Ncij =
aij Qwc × Tr cij
(12)
Ntjk : Number of vehicles for the trajectory transfer site-landfill: Ntjk =
bjk Qwt × Tr tjk
(13)
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Ncik : Number of vehicles for the trajectory source-landfill: Ncik =
cik Qwc × Tr cik
(14)
KPLc : Collection vehicle distance traveled while using one liter of fuel; KPLt : Transfer vehicle distance traveled while using one liter of fuel; u: parameter that expresses the amount of GHG emissions generated per liter of fuel expressed in kgCO2 eq/l. It depends on the type of fuel used by the transportation vehicles.
References 1. Caputo, A.C., Pelagagge, P.M.: RDF production plants: I design and costs. Appl. Therm. Eng. 22(4), 423–437 (2002). https://doi.org/10.1016/S1359-4311(01)00100-4 2. Mavrotas, G., Skoulaxinou, S., Gakis, N., Katsouros, V., Georgopoulou, E.: A multiobjective programming model for assessment the GHG emissions in MSW management. Waste Manage. 33(9), 1934–1949 (2013). https://doi.org/10.1016/j.wasman.2013.04.012 3. Christensen, T.H.: Solid waste technology & management. Chichester, West Sussex, U.K. (2011) 4. Habibi, F., Asadi, E., Sadjadi, S.J., Barzinpour, F.: A multi-objective robust optimization model for site selection and capacity allocation of municipal solid waste facilities: a case study in Tehran. J. Clean. Prod. 166, 816–834 (2017). https://doi.org/10.1016/j.jclepro.2017. 08.063 5. Edderkaoui, R., Khomsi, D., Hamidi, A., Bennani Baiti, H., Souidi, H., Aqil, M.: Verification of the technical feasibility of composting: case study. In: E3S Web Conference, vol. 150 (2020). https://doi.org/10.1051/e3sconf/202015002018 6. A˘gaçsapan, B., Çabuk, S.N.: Determination of suitable waste transfer station areas for sustainable territories: Eskisehir case. Sustain. Cities Soc. 52, 1–38 (2019). https://doi.org/10. 1016/j.scs.2019.101829 7. Ekmekçio˘glu, M., Kaya, T., Kahraman, C.: Fuzzy multicriteria disposal method and site selection for municipal solid waste. Waste Manage. 30, 1729–1736 (2010). https://doi.org/ 10.1016/j.wasman.2010.02.031 8. Kontos, T.D., Komilis, D.P., Halvadakis, C.P.: Siting MSW landfills with a spatial multiple criteria analysis methodology. Waste Manage. 25(8), 818–832 (2005). https://doi.org/10. 1016/j.wasman.2005.04.002 9. Chang, N.B., Parvathinathan, G., Breeden, J.B.: Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region. J. Environ. Manage. 87(1), 139–153 (2008). https://doi.org/10.1016/j.jenvman.2007.01.011 10. Rezaeisabzevar, Y., Bazargan, A., Zohourian, B.: Landfill site selection using multi criteria decision making: influential factors for comparing locations. J. Environ. Sci. 93, 170–184 (2020). https://doi.org/10.1016/j.jes.2020.02.030 11. EPA: Waste Transfer Stations: A Manual for Decision-Making. USA (2002) 12. Chatzouridis, C., Komilis, D.: A methodology to optimally site and design municipal solid waste transfer stations using binary programming. Resour. Conserv. Recycl. 60, 89–98 (2012). https://doi.org/10.1016/j.resconrec.2011.12.004 13. Ghose, M.K., Dikshit, A.K., Sharma, S.K.: A GIS based transportation model for solid waste disposal – a case study on Asansol municipality. Waste Manage. 26(11), 1287–1293 (2006). https://doi.org/10.1016/j.wasman.2005.09.022
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14. Beliën, J., De Boeck, L., Van Ackere, J.: Municipal solid waste collection and management problems: a literature review. Transp. Sci. 48(1), 78–102 (2014). https://doi.org/10.1287/trsc. 1120.0448 15. Al-Jarrah, O., Abu-Qdais, H.: Municipal solid waste landfill siting using intelligent system. Waste Manage. 26(3), 299–306 (2006). https://doi.org/10.1016/j.wasman.2005.01.026 16. Badi, I., Kridish, M.: Landfill site selection using a novel FUCOM-CODAS model: a case study in Libya. Sci. African 9, 1–10 (2020). https://doi.org/10.1016/j.sciaf.2020.e00537 17. Sumathi, V.R., Natesan, U., Sarkar, C.: GIS-based approach for optimized siting of municipal solid waste landfill. Waste Manage. 28(11), 2146–2160 (2008). https://doi.org/10.1016/j.was man.2007.09.032 18. Demesouka, O.E., Vavatsikos, A.P., Anagnostopoulos, K.P.: Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: method, implementation and case study. Waste Manage. 33(5), 1190–1206 (2013). https://doi.org/10.1016/j.wasman. 2013.01.030 19. Wang, G., Qin, L., Li, G., Chen, L.: Landfill site selection using spatial information technologies and AHP: a case study in Beijing, China. J. Environ. Manage. 90(8), 2414–2421 (2009). https://doi.org/10.1016/j.jenvman.2008.12.008 20. Eiselt, H.A., Marianov, V.: A bi-objective model for the location of landfills for municipal solid waste. Eur. J. Oper. Res. 235(1), 187–194 (2014). https://doi.org/10.1016/j.ejor.2013. 10.005 21. Erkut, E., Karagiannidis, A., Perkoulidis, G., Tjandra, S.A.: A multicriteria facility location model for municipal solid waste management in North Greece. Eur. J. Oper. Res. 187(3), 1402–1421 (2008). https://doi.org/10.1016/j.ejor.2006.09.021 22. Monzambe, G.M., Mpofu, K., Daniyan, I.A.: Optimal location of landfills and transfer stations for municipal solid waste in developing countries using non-linear programming. Sustain. Futures 3, 1–9 (2021). https://doi.org/10.1016/j.sftr.2021.100046 23. Rathore, P., Sarmah, S.P.: Modeling transfer station locations considering source separation of solid waste in urban centers: a case study of Bilaspur city, India. J. Clean. Prod. 211, 44–60 (2019). https://doi.org/10.1016/j.jclepro.2018.11.100 24. EPA: Greenhouse Gas Emissions from a Typical Passenger Vehicle, EPA. USA (2018) 25. Badran, M.F., El-Haggar, S.M.: Optimization of municipal solid waste management in Port Said-Egypt. Waste Manage. 26(5), 534–545 (2006). https://doi.org/10.1016/j.wasman.2005. 05.005 26. Rahman M., Kuby M.: A multiobjective model for locating solid waste transfer facilities using an empirical opposition function. INFOR: Inf. Syst. Oper. Res. 33(1), 34–49 (1995). https://doi.org/10.1080/03155986.1995.11732265 27. Robinson, W.D.: The Solid Waste Handbook: A Practical Guide. United States of America (1986) 28. Komilis, D.P.: Conceptual modeling to optimize the haul and transfer of municipal solid waste. Waste Manage. 28(11), 2355–2365 (2008). https://doi.org/10.1016/j.wasman.2007.11.0
Mathematical Modelling and Dynamic Simulation for Wastewater Treatment Plant Management: An Experimental Pilot Study Tawfik El Moussaoui1,2(B) , Redouane Elharbili1 , Mohamed Oussama Belloulid2 , Khalid El Ass1 , Laila Mandi2,3 , Fouad Zouhir4 , Hugues Jupsin4 , and Naaila Ouazzani2,3 1 Resources Valorization, Environment and Sustainable Development Research Team (RVESD),
Department of Mines, Ecole Nationale Supérieure des Mines de Rabat (ENSMR), Ave Hadj Ahmed Cherkaoui, BP 753, Agdal, Rabat, Morocco [email protected], [email protected] 2 Laboratory of Water, Biodiversity and Climate Change, Faculty of Sciences Semlalia, Cadi Ayyad University, BP 2390, 40,000 Marrakech, Morocco 3 National Center for Studies and Research on Water and Energy (CNEREE), BP/511, Cadi Ayyad University, Av. Abdelkrim Elkhattabi, BP 511, Marrakech, Morocco 4 Sanitation and Environment Unit, University of Liege, 185 Avenue de Longwy, B6700 Arlon Campus Environment, Liège, Belgium
Abstract. Activated sludge process is the most commonly used biological method for domestic, industrial and urban wastewaters. However, wastewater treatment process are complex dynamic systems subjected to large variations in hydraulic and organic loads. The dynamic analysis of biological treatment processes is therefore crucial to determine the most suitable design, optimize the control and predict the process behavior. This research paper represent the dynamic simulation of urban pilot scale activated sludge system treating the urban wastewater of Marrakesh city, Morocco using WEST program. A dynamic model based on activated sludge model n°1 ASM1 describing the performance of the activated sludge process at an experimental wastewater treatment plant (WWTP) receiving urban wastewater is presented. The model was constructed by ASM1 equations and Takács model in the WEST platform. Calibration of the parameters was based on experimental data obtained from the conventional activated sludge pilot plant. The ASM1 developed model; calibrated and validated is a well proven and representative tool for the biological treatment of urban wastewater by the conventional activated sludge pilot. The ASM1 will allow to predict in a precise way the spatiotemporal process evolution and operation. Keywords: ASM1 · WEST · Activated sludge · Dynamic simulation · Urban wastewater
1 Introduction Wastewater treatment plants are complex dynamic systems subject to large and uncontrolled variations in hydraulic and organic load during wastewater processing. Modeling © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 14–27, 2023. https://doi.org/10.1007/978-3-031-35245-4_2
Mathematical Modelling and Dynamic Simulation
15
the operation of wastewater treatment plants is a widely used tool, particularly for the optimization and rehabilitation of wastewater treatment plant WWTP. Biological processes with biomass in suspension, of the activated sludge type, are applied for the treatment of urban or industrial wastewater. Generally, these processes are sized on the basis of material balance equations established on the assumption of a steady state. However, these systems are continually subjected to disturbances: significant variations in influent pollutant load, discontinuity of certain operations. The dynamic analysis of biological treatment processes is therefore essential to understand the current operation of the WWTP units and to optimize the sizing and operating parameters [1–3]. When the goal is to explain and predict the evolution of the system, this requires the use of knowledge models. This category of modeling, unlike black box modeling, keeps the physical information from state variables and thus explains what is happening within the process. For this, the overall behavior of the biological process is defined using material balances. However, the laws available to characterize the evolution of microorganisms are mainly empirical laws that are more specific and have a smaller scope of validity than classical physical laws. Establishing this model category therefore requires a good understanding of the phenomena occurring within the environment [4, 5]. Therefore, the model will be different depending on the studied process and the influent wastewater origin. Recently, a new way of thinking about modeling is emerging. Initiated by regulations, which are moving towards a more global approach to controlling the sanitation system (ie network, treatment plant and natural environment), namely that all entities interact with each other, latest generation program are now more and more like integrated modeling platforms, knowing that their open configuration makes them scalable systems [5–8]. The first tests of implementation and mathematical calculation were carried out on Matlab® using the SIMULINK code (The MathWorks, Inc.). This program is powerful, but given the complicity of use and the number of parameters to integrate. Thereby, it will be interesting to use of a platform characterized by its simplicity and flexibility of the implementation. Among the applied program for the simulation and dynamic modeling of biological water treatment processes, the WEST® Worldwide Engine for Simulation and Training program is the most widely used platform due to the advantages provided. This latest generation program is an open modeling platform; namely that everyone has access to the calculation code and that an informed user can integrate their own models into it relatively easily. Unlike other program which is based on IAWQ Model 1 equations, WEST® has all ASM models in its library. With the ASM1 and ASM3 models [9], we will be able to simulate the treatment of carbon and nitrogen pollution, and with the ASM2/2d versions [10–12], WEST® allows to model the elimination of phosphorus by chemical or biological means. Regarding the clarifier, WEST® has a wide range of settling models, including the multilayer Takács model [13]. However, WEST consists of a model database, a graphical user interface and ultimately an engine appropriate for this category of models. The main objective of this work focused on the development, calibration and validation of the mathematical bio-kinetic model ASM1 of the urban wastewater treatment plant, case study of urban wastewater of the Marrakesh city, Morocco. Simulations were performed using WEST ® dynamic simulation program. In a first step, we collected and
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validated the data to be used. Subsequently in the second phase to the construction and validation of the ASM1 built model. And finally, the ASM1 calibration and validation, which aim to manually or automatically optimize the adjustment of certain simulated variables to their measured values.
2 Materials and Methods 2.1 WEST ® Program The WEST®2017 version used in this work is the 5th edition of the re-designed and reengineered WEST® version, considered to be a powerful and user-friendly tool for the dynamic modeling and simulation of WWTPs and the integrated water system in urban areas. WEST® program extensive library of state of the art models allows the modeling and evaluation of almost any type of modern WWTP and a variety of integrated urban water systems (IUWS). 2.2 Data Collection and Model Structure The collection of data and measurements carried out on the experimental activated sludge treatment plant during the urban water treatment experiment is necessary on the one hand for the implementation of the parameters in the model for the numerical simulation. On the other hand, for the validation of the model by comparison with the simulation results. It is a validation in steady state and also characterization of the loads that the WWTP has undergone (initial conditions for the dynamic simulation). Short-term measures for the dynamic calibration of the model. 2.3 Construction and Evaluation of the Model on WEST The ASM1 model was implemented using WEST® program version 2017, 5th edition. WEST integrates ASM models (ASM1, ASM2, ASM2b…) into its library. The ASM1 biological model is used in the modeling of biological phenomena while the physical model of Takács [13] is used in the modeling of the parameters of the settling tank of the pilot activated sludge station. The program allows the creation of simplified diagrams of wastewater treatment plants using predefined tools in its drawing library, as presented in Fig. 1. Municipality: feed source; ASU.1_Anoxic tank: anoxic basin; ASU_2.Aeration tank: bioreactor/aerobic reactor; Clarifier: sedimentation basin; Splitters; Recirculation systems (internal recirculation).
Mathematical Modelling and Dynamic Simulation
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Fig. 1. Synoptic diagram of the pilot WWTP in WEST program
3 Results and Discussion 3.1 Steady-State and Dynamic Experiment The ASM1 model was implemented using the WEST program version 2017, 5th edition to determine the values of the input variables and parameters of the model, the results acquired in stabilized mode (on pilot and in a separate reactor) were used. For any layout built on WEST, two simulation experiments will be automatically created. The simulation in a steady state or stabilized [Steady-State Experiment] consists in simulating the evolution of the concentrations of macro-pollutants in the aeration basin until the values are stabilized (state of equilibrium). The dynamic regime simulation allows to characterize the behavior of the activated sludge process in response to variations in concentrations of macro-pollutants and micro-pollutants in the raw urban wastewater and in operating conditions. Likewise, the values of the derived variables obtained at the end of the steady-state simulation will be used as initial conditions of the dynamic simulation (although this behavior can optionally be filtered by the user if necessary). Figure 2 and Fig. 3 gives the results after the simulations of the two experiments for the soluble fractions in the anoxic and Arabian basin as a function of time (S_S, S_NO, S_NH) the concentration of biomass in the biological basin (X_BH, X_BA and X_TSS) and the dissolved oxygen concentration in the anoxic basin Anoxic_C (S_O) and in the biological basin Aerated_C (S_O).
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Fig. 2. Steady-state regime simulation result
Fig. 3. Dynamic regime simulation result
3.2 Sensitivity Analysis The module for analyzing the sensitivity of the parameters of a model built on the WEST® program is a tool for analyzing the sensitivities of a WWTP model to changes in parameters selected by applying a finite difference approach around nominal parameter values specified by the user. Two types of sensitivity analyzes on WEST ®, the analysis of local sensitivities and Analysis of global sensitivities. The (RSFs) were calculated according to Eq. 1, for each model output (y) relating to each selected model input (theta θ). These functions measure the relative change in the output value as a function of
Mathematical Modelling and Dynamic Simulation
19
a certain change in the input value and subsequently allow easy comparison between parameters whose magnitudes were different. RSF =
θ y yθ
(1)
All model evaluations used to calculate the sensitivity functions were performed using the Runge Kutta 4 Adaptive Stepsize Control integrator (RK4ASC). For analyzes of local sensitivities found in the literature, the selected inputs are generally the kinetic and stoichiometric parameters of the model and the operational parameters such as volume, temperature, flow rates, sludge retention time (SRT) and dissolved oxygen (DO) [14–16]. The parameters evaluated for the present study are the concentrations of NH4, dissolved oxygen (DO), chemical oxygen demand (COD), and biomass concentration (X_BH and X_BA). Figure 4 shows the local sensitivities analysis functions for X_BH, X_BA and NH4. Sensitivity analysis on parameters influencing NH4 revealed that ASU_2.theta_mu_A has the greatest effect on NH4 concentration. While the ASU_2.Y_A has little influence on this parameter. The conversion rate of ASU_2.Y_H heterotrophs had a significant effect on the concentration of heterotrophs in the biological pond. Also noted that the parameters related to the growth of heterotrophs such as ASU_2.b_H have a significant effect. The conversion rate of ASU_2.Y_A autotrophs has the greatest effect on the concentration of autotrophs in the biological pond. As with heterotrophs, parameters related to the growth of autotrophs have a significant effect, while other parameters such as ASU_2.Y_H have no significant effect. 3.3 Model Calibration and Validation All modeling requires phases of parameterization and verification of the model. The calibration phase aims to manually or automatically optimize the adjustment of certain simulated variables to their measured values [17–19]. The validation step, for its part, aims to verify the quality of the model calibrated on other series of measurements not used during the calibration. The successive realization of the calibration/validation constitutes the stage of development of the model on a given site. Using Virtual Experiments menu the Parameter Estimation Experiment (PE) command in the WEST environment. A Parameter Estimation (PE) experiment runs a series of simulations based on the same model, using automatically generated values for a set of parameters selected “parameters” for each simulation cycle, to minimize an objective function. However, the latter can be any objective function consisting of a combination of individual sub-objectives (such as the average, ... etc.) of one or more variables, or one of the four special objectives «Mean Difference», «Maximum Difference», «Thiel’s Inequality Coefficient» and «End Value Difference». The steps for calibrating and validating the ASM1 model of the urban wastewater type were carried out according to the method described by [20]. In our case, the calibration of the model is carried out on the basis of the comparison of the values simulated at the output of the system with the values actually measured during the period of operation of the pilot urban wastewater treatment system. By setting as a special objective “Mean Difference”.
20
T. El Moussaoui et al.
NH4 .ASU_2.Y_H .ASU_2.Y_A .ASU_2.b_H .ASU_2.b_A .ASU_2.mu_H .ASU_2.mu_A .ASU_2.theta_k_h .ASU_2.theta_b_A .ASU_2.theta_mu_H .ASU_2.theta_mu_A
-8
-6
-4
-2
0
2
4
6
8
X_BA .ASU_2.Y_H .ASU_2.Y_A .ASU_2.b_H .ASU_2.b_A .ASU_2.mu_H .ASU_2.mu_A .ASU_2.theta_k_h .ASU_2.theta_b_A .ASU_2.theta_mu_H .ASU_2.theta_mu_A
-3
-2
-1
0
1
2
3
X_BH .ASU_2.Y_H .ASU_2.Y_A .ASU_2.b_H .ASU_2.b_A .ASU_2.mu_H .ASU_2.mu_A .ASU_2.theta_k_h .ASU_2.theta_b_A .ASU_2.theta_mu_H .ASU_2.theta_mu_A
-2
-1
0
1
Fig. 4. Relative sensitivity functions for X_BH, X_BA and NH4
2
Mathematical Modelling and Dynamic Simulation
21
3.4 Model Calibration The calibration is carried out visually in order to minimize the differences between simulated values and measured values. Then, the precision of the simulations is evaluated by calculating two criteria: the average of the sum of the absolute values of the deviations between measurement and simulation (MAE) and the average of the absolute value of the relative deviations between measurement and simulation (MARE). During calibration, the coefficients providing the lowest values of MAE and MARE are sought. They are obtained after various attempts (trial-error). The calibration was made by trying to optimize the concentration of NH4 in the biological basin. Table 1 represents the values of the parameters after setting the parameters. After 370 simulation (runs), the values of the measured objectives are stabilized indicating that the overall objective value seems to have reached its minimum value (After about 20-25 runs, the overall objective value seems to have reached its minimum value). 3.5 Model Validation The evolution curves of the state variables relating to ASM1 carbon and nitrogen are shown in Figure 5 and Figure 6, respectively. The soluble chemical oxygen demand COD concentration at the outlet measured during the measurement campaign was compared with the values simulated by the calibrated ASM1 model (Figure 5). The results show a good agreement between the simulated values and that actually measured at the exit of the pilot. Regarding the nitrogen indicators of purified water, the differences between the measurements and the simulations are tolerable for NH4. The concentrations (S_NH4) and (S_ND) at the output measured during the measurement campaign were compared with the values simulated by the calibrated ASM1 model (Figure 6). The simulations were carried out with the parameters of the mean calibration, which makes it possible to explain the differences noted between the simulated results and the measurements.
1
2
1
1
1
1
1.2579964
1.4000886
1.6573146
2.3821175
9.5770664
9.6813945
9.7830431
9.7437389
9.6707487
9.7015058
9.7095658
9.6972059
9.713333
9.7280781
9.7918214
9.8142426
2
3
4
5
6
7
8
9
10
20
30
40
50
60
70
80
90
100
110
120
130
.ASU_2.K_NH
1
#
#RunNo
0.52272406
0.51723395
0.51014738
0.50265309
0.49276392
0.48535421
0.48249333
0.47846889
0.49308668
0.51263497
0.42047647
0.30188794
0.29915094
0.27883599
0.26741067
0.25938675
0.24
0.24
0.24
0.29
0.24
0.24
.ASU_2.Y_A
0.47088035
0.48676846
0.50940482
0.52839193
0.54546452
0.56132548
0.56442305
0.57358582
0.54572832
0.52155578
0.55861841
0.58277177
0.73056395
0.75243466
0.76548369
0.71858909
0.8
0.8
0.9
0.8
0.8
0.8
.ASU_2.mu_A
1.0727145
1.0718629
1.0724404
1.0691404
1.0686439
1.0635583
1.0630282
1.0637526
1.0550402
1.0457168
1.0813058
1.126707
1.1276419
1.1239338
1.1217338
1.1201283
1.116
1.126
1.116
1.116
1.116
1.116
.ASU_2.theta_b_A
1.1585688
1.1532095
1.1422574
1.1337188
1.1265461
1.117812
1.1151989
1.1108997
1.111016
1.1076754
1.1083311
1.0913109
1.1003143
1.1012101
1.0944637
1.1070459
1.113
1.103
1.103
1.103
1.103
1.103
.ASU_2 theta_mu_A
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.114138
10.114138
10.114138
Max. Obj Value
Table 1. Results for the parameter estimation experiment
8.9400291
8.9539862
8.9704624
8.9900391
9.0037151
9.0315605
9.0668444
9.1134498
9.1596656
9.2481766
9.3913162
9.5996384
10.03879
10.064171
10.076187
10.084978
10.092133
10.086084
10.080277
10.062207
10.045209
10.114138
Mean. Obj Value
8.770466
8.770466
8.7736451
8.7751213
8.7784266
8.7824997
8.7831361
8.7860538
8.7887368
8.7911086
8.8585949
8.9491466
9.8103615
9.9680347
9.9762791
9.9762791
9.9762791
9.9762791
9.9762791
9.9762791
9.9762791
10.114138
Min.Obj Value
(continued)
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
Error
22 T. El Moussaoui et al.
.ASU_2.K_NH
9.8248516
9.862915
9.8864313
9.876906
9.8814774
9.8780887
9.8772752
9.8761868
9.8763528
9.8763685
9.8763477
9.8763025
9.8763428
9.87636
9.8763654
9.8763679
9.8763701
9.8763707
9.8763706
9.8763704
9.8763704
#RunNo
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
0.5206218
0.52062177
0.52062179
0.52062178
0.52062183
0.52062156
0.52062197
0.52062237
0.52062297
0.52061966
0.52062763
0.52064511
0.52059425
0.52057253
0.52024543
0.5202992
0.520085
0.52085461
0.52369119
0.52105462
0.51774177
.ASU_2.Y_A
0.47051937
0.47051942
0.47051933
0.47051928
0.47051934
0.47052033
0.47051996
0.4705204
0.4705252
0.47054165
0.47052491
0.47048033
0.470621
0.47064445
0.4708809
0.47047475
0.47042647
0.46919741
0.46030125
0.4689269
0.48394568
.ASU_2.mu_A
1.0757442
1.0757442
1.0757442
1.0757443
1.0757443
1.0757443
1.075744
1.0757437
1.0757433
1.0757395
1.0757495
1.0757542
1.0757563
1.0757648
1.0756613
1.0757174
1.0765588
1.0762908
1.0769042
1.0740025
1.0715161
.ASU_2.theta_b_A
1.1703868
1.1703868
1.1703868
1.1703868
1.1703867
1.1703863
1.1703858
1.1703847
1.1703815
1.1703723
1.1703858
1.1703933
1.170384
1.1703519
1.1704816
1.170644
1.1715115
1.1706614
1.1730183
1.1672469
1.1585222
.ASU_2 theta_mu_A
Table 1. (continued)
1.7976931e+308
1.7976931e+308
1.7976931e+308
1.7976931e+308
1.7976931e+308
1.7976931e+308
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
10.134489
Max. Obj Value
5.2873327e+05
5.447555e+305
5.617791e+305
5.7990101e+305
5.9923104e+305
6.1989418e+305
8.858788
8.862324
8.8661352
8.8666316
8.8709377
8.8756146
8.8807202
8.8863092
8.892461
8.8992543
8.9017906
8.8992836
8.9077129
8.9171796
8.9278691
Mean. Obj Value
8.7630818
8.7630888
8.7630888
8.7630888
8.7630932
8.7630932
8.7630932
8.7630932
8.7630932
8.7630932
8.7630932
8.7631045
8.7631045
8.7631045
8.7631066
8.7632406
8.7633315
8.7637435
8.7642037
8.7650411
8.7677497
Min.Obj Value
(continued)
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
Error
Mathematical Modelling and Dynamic Simulation 23
.ASU_2.K_NH
9.8763704
9.8763705
9.8763705
#RunNo
350
360
370
0.52062179
0.52062179
0.52062179
.ASU_2.Y_A
0.47051936
0.47051936
0.47051936
.ASU_2.mu_A
1.0757442
1.0757442
1.0757442
.ASU_2.theta_b_A
1.1703868
1.1703868
1.1703868
.ASU_2 theta_mu_A
Table 1. (continued)
1.7976931e+308
1.7976931e+308
1.7976931e+308
Max. Obj Value
4.8586301e+305
4.993592e+305
5.1362661e+305
Mean. Obj Value
8.7630818
8.7630818
8.7630818
Min.Obj Value
OK
OK
OK
Error
24 T. El Moussaoui et al.
Mathematical Modelling and Dynamic Simulation
25
Fig. 5. CODoutput concentration at the output simulated by the model developed and measured
Fig. 6. Simulated and measured S_NH4 (a) and S_ND (b) concentrations
4 Conclusions An activated sludge pilot plant treating the urban wastewater of Marrakesh city, Morocco was simulated, calibrated and validated by mathematical bio-kinetic model ASM1 using the WEST® software package. The model built was calibrated and validated on the basis of the comparison of the values simulated at the system output compared to the values actually measured during the operation period of the experimental urban wastewater treatment system. The calibrated and validated ASM1 model is a correctly tested tool representative of the pilot of activated sludge for the treatment of urban wastewater, which will be able to be used in predictive mode. Thus, from potentially feasible situations, the simulation will allow us to consider and test different management modes in response to a change in the influent or/and influent wastewater or/and to a malfunction in the process.
26
T. El Moussaoui et al.
Acknowledgments. The authors are grateful to the Sanitation and Environment Unit, University of Liege, Belgium and to the Bilateral WALLONIA-BRUSSELS International cooperation project (WBI.2.3).
References 1. Gernaey, K.V., van Loosdrecht, M.C., Henze, M., Lind, M., Jørgensen, S.B.: Activated sludge wastewater treatment plant modelling and simulation: state of the art. Environ. Model. Softw. 19(9), 763–783 (2004). https://doi.org/10.1016/j.envsoft.2003.03.005 2. Rieger, L., et al.: Guidelines for Using Activated Sludge Models. IWA Publishing (2012) 3. Water Environment Federation: Wastewater Treatment Process Modeling (2014). MOP31, 2nd Edition 2014. McGraw-Hill Professional, Access Engineering 4. Langergraber, G., et al.: A guideline for simulation studies of wastewater treatment plants. Water Sci. Technol. 50, 131–138 (2004). https://doi.org/10.2166/wst.2004.0436 5. Makinia, J., Zaborowska, E.: Mathematical Modelling and Computer Simulation of Activated Sludge Systems. IWA Publishing (2020) 6. Wu, X., Yang, Y., Wu, G., Mao, J., Zhou, T.: Simulation and optimization of a coking wastewater biological treatment process by activated sludge models (ASM). J. Environ. Manage. 165, 235–242 (2016). https://doi.org/10.1016/j.jenvman.2015.09.041 7. Hassen, E.B., Asmare, A.M.: Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks. Chem. Int. 5(1), 87 (2019) 8. Savun-hekimo˘glu, B.: On the use of mathematical models for wastewater treatment: a review and analysis of activated sludge models ASM1 and ASM3. JEGEO 8(1), 1–18 (2021). https:// doi.org/10.30897/ijegeo.794643 9. Gujer, W., Henze, M., Mino, T., Van Loosdrecht, M.: Activated sludge model No. 3. Water Sci. Technol. 39(1), 183–193 (1999). https://doi.org/10.1016/S0273-1223(98)00785-9 10. Henze, M., Gujer, W., Mino, T., van Loosdrecht, M.C.M. (eds.): Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. Scientific and Technical Report No. 9. IWA Publishing, London (2000) 11. Henze, M., van Loosdrecht, M.C., Ekama, G.A., Brdjanovic, D. (eds.): Biological Wastewater Treatment. IWA Publishing (2008) 12. Van Loosdrecht, M.C.M., Lopez-Vazquez, C.M., Meijer, S.C.F., Hooijmans, C.M., Brdjanovic, D.: Twenty-five years of ASM1: past, present and future of wastewater treatment modelling. J. Hydroinformatics. 17(5), 697–718 (2015). https://doi.org/10.2166/hydro.201 5.006 13. Takács, I., Patry, G.G., Nolasco, D.: A dynamic model of the clarification-thickening process. Water Res. 25(10), 1263–1271 (1991). https://doi.org/10.1016/0043-1354(91)90066-Y 14. Kim, Y., Pipes, W.O.: Factors influencing suspended solids concentrations in activated sludge settling tanks. J. Hazard. Mater. 67(1), 95–109 (1999). https://doi.org/10.1016/S0304-389 4(99)00028-X 15. Donoso-Bravo, A., Mailier, J., Ruiz-Filippi, G., Wouwer, A.V.: Identification in an anaerobic batch system: global sensitivity analysis, multi-start strategy and optimization criterion selection. Bioprocess Biosyst. Eng. 36(1), 35–43 (2013). https://doi.org/10.1007/s00449-0120758-5 16. Ruiz, L.M., Rodelas, P., Pérez, J.I., Gómez, M.A.: Sensitivity analyses and simulations of a full-scale experimental membrane bioreactor system using the activated sludge model No.3 (ASM3). J. Environ. Sci. Health A. 50(3), 317–324 (2015). https://doi.org/10.1080/10934529. 2015.981122
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17. Hulsbeek, J.J.W., Kruit, J., Roeleveld, P.J., van Loosdrecht, M.C.M.: A practical protocol for dynamic modeling of activated sludge systems. Water Sci. Technol. 45(6), 127–136 (2002). https://doi.org/10.2166/wst.2002.0100 18. Andraka, D., Piszczatowska, I.K., Dawidowicz, J., Kruszy´nski, W.: Calibration of activated sludge model with scarce data sets. J. Ecol. Eng. 19(6) (2018). https://doi.org/10.1038/s41 598-022-07071-0 19. Solon, K., Volcke, E.I., Spérandio, M., Van Loosdrecht, M.C.: Resource recovery and wastewater treatment modelling. Environ. Sci.: Water Res. Technol. 5(4), 631–642 (2019). https:// doi.org/10.1039/C8EW00765A 20. Spérandio, M., Espinosa, M.C.: Modelling an aerobic submerged membrane bioreactor with ASM models on a large range of sludge retention time. Desalination 231(1–3), 82–90 (2008). https://doi.org/10.1016/j.desal.2007.11.040
Solar Flat Plate Collector (FPC) in Series with Evacuated Tube Collector (ETC) in a Forced Circulation Water Heating Installations Used in Buildings H. Allouhi(B) , A. Allouhi, and A. Jamil Ecole Supérieure de Technologie de Fès, U.S.M.B.A, Route d’Imouzzer, BP 242, Fez, Morocco [email protected]
Abstract. The main objective of the present work is to assess the integration potential of the different solar water heating collectors for building use. The evaluation was performed for FES, Morocco based on a dynamic simulation which consider the temporary variation of meteorological data. Firstly, the investigation includes the characterization of the different solar systems performances, namely: Flat plat collector (FPC), Evacuated tube collector (ETC) in order to meet the hot water demand of 200 l/day using a collector area of 2 m2 and a storage tank with a capacity of 300 l. Secondly, the study reveals the advantages of the FPC and ETC serial coupling to further ensure the domestic hot water demand at an encouraging price ratio. Keywords: Solar collectors · SDHW · Auxiliary energy · Solar Fraction · Simple Payback Period
1 Introduction Recently, global increase in greenhouse gases due to various anthropogenic activities with devastating environmental impacts encouraged scientific community to conduct more researchers in order to provide sustainable and eco-friendly energy [1–3]. As a result, many countries have made great efforts to increase their energy independence and reduce their carbon dioxide emissions [4]. Most of household energy demand is to fill heating and cooling loads. Commercial and residential buildings consume nearly 40% of primary energy in the United States or in Europe, and almost 30% in China [5]. In order to reduce the building dependence on primary energy, a number of studies on energy efficient technologies have been carried out worldwide. On the other hand, the use of renewable energies has been considered as a reasonable and promising way to reduce conventional energy use and find solution to global warming, air pollution and energy security [6]. The integration of Solar Domestic Heat Water systems for use in buildings, and specifically in NZEB (net zero energy building), can considerably improve the overall sustainability at encouraging costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 28–39, 2023. https://doi.org/10.1007/978-3-031-35245-4_3
Solar Flat Plate Collector (FPC)
29
Morocco, despite its strategic position, is the largest energy importer in the region. Currently, about 96% of Moroccan energy needs come from outside. Oil accounts for almost 61% of total national energy consumption. The current status and the rapid growth in energy consumption have prompted Moroccan decision-makers to implement the national energy strategy with renewable energies and the energy efficiency plan in 2008. Throughout its strategy, Morocco aims to promote renewable energies in order to reach the objective of ensuring 42% of total electricity production by 2020 and 52% by 2030 from renewable alternatives [7]. The NZEB in Morocco has recognized a great interest by the government with regard to innovative energy efficiency plans. By 2030, Morocco seeks to achieve more than 15% of primary energy savings, a solution that will help to better control energy demand in the coming years [8]. Actually, the most promising policies adopted for residential sector is the integration of solar thermal collectors to meet the domestic hot water needs and reduce their related amount of CO2 emissions [9]. Global installed capacity worldwide increased from 160 GWth to 185 GWth for the period between 2010 and 2011 [10]. Tiwari et al. [11] analyzed the energy performances of a solar hot water system in Ahmadabad, India through a dynamic simulation. The suggested system based on 5 m2 can cover over than 70% of the household demand and the best working period was detected during winter. Mohammed et al. [12] revealed the potential of 10 m2 of solar Flat Plate Collector (FPC) to satisfy the domestic heat water demand for 24 person in Baghdad, Iraq. The annual value of solar fraction attained 69% and the maximum value of auxiliary energy needed in December was about 1025 MJ. The study conducted by Tanha et al. [13] shows that the solar domestic hot water system can annually provide a thermal energy of about 2038 kWh using FPC and a value of about 1383 kWh when using Evacuated Tube Collector (ETC). Allouhi et al. [14] evaluated the technical performances of the solar water heaters under Moroccan conditions. According to the solar fraction results, ETC technology has been considered as the most promising solution for hot water production. Sokhansefat et al. [15] assessed the thermal and economic performances of FPC and ETC technologies under cold climate conditions in Iran. It has been deduced that ETC technology is cost-effective and the overall system performances are 41% better compared to ETC system. Greco et al. [16] established a comparative study of FPCs and ETCs integrated for solar domestic hot water systems in various climate locations. They highlighted that the energy performances of ETCs are higher only in locations with cold climate. In opposition, it turned out that FPC is strongly recommended in warm seasons. The main objective of this work is to assess a solar domestic heat water system based on FPC and ETC technologies using TRNSYS software and predict the temperature profiles along the year. An innovative aspect of the present work consists on exploring the benefits of connecting FPC-ETC in series as an uncommon system architecture with unknown performance that requires investigation before practical implementation.
30
H. Allouhi et al.
2 Methods 2.1 Examined Site Fez is a city in northern inland Morocco (34° 01 59.27 north latitude, -5° 00 1.01 western longitude). Fez has a hot-summer Mediterranean climate with a strong continental influence, shifting from relatively cool and wet in the winter to dry and hot days in the summer months between June and September. It is considered among the cities which enjoy a huge solar potential and the use of solar water heating systems seems to be a promising option [17]. 2.2 System Description and Modeling TRNSYS is a transient simulation tool that is specifically intended to model and evaluate the performance of various renewable energy systems along with their combination with buildings. The performance and behaviour of the studied systems are visualized as a function of time [11]. The suggested system was modelled in TRNSYS, and the weather data was provided by METEONORM in TMY2 form. The file summarizes all necessary weather data required to simulate the energy performance of the studied system. Regarding the mathematical formulation describing the operational work of the solar collector, storage tank as well as the energy performance of the system, readers are referred to Ref. [9]. The serial coupling of FPC- ETC in a forced circulation operation mode is depicted in Fig. 1. The series connection consist on linking output of the FPC with the input of the ETC in order to further increase the hot water temperature supplied to the user. It should be mentioned that this novel configuration is compared to reference cases for which FPC and ETC are operating individually using the same total installed surface (2 m2 ). The proposed system is composed of two loops: – The first is a solar loop and which consists of a thermal collector (FPC connected to ETC), a pump to circulate water from the collector to the storage tank and a flow deflector. – The second loop is the user loop starting from the outlet of the tank and the Tee piece model. The tee piece component contains two inlet liquid streams which are mixed into a single outlet to deliver hot water to the user at the set temperature. Before lunching dynamic simulations, a daily hot water load profile has been considered. It represents the hourly water consumption required by the user during the day (Fig. 4). The pump activation was controlled using a regulation system. For the studied case, a hot water consumption of 200 litters per day will be considered to satisfy the thermal load demand for a household of 4 people each consuming 50 L/day. The diverter allowed supplying water that will be heated inside the tank as well as the cold water directed to the Tee piece component when it is requested by the user. The technical characteristics of the simulated solar thermal collectors are given below (Table 1).
Solar Flat Plate Collector (FPC)
31
Table 1. Main parameters of the flat plate collector (FPC) and the evacuated tube collector (ETC) Parameters
FPC
ETC
Tested flow rate
72 kg h−1 m−2
83 kg h−1 m−2
Tank loss coefficient
0.043 kW m−2 K−1
0.043 kW m−2 K−1
a0: Intercept efficiency
0.79
0.821
a1: Efficiency slope
3.48 W m−2 K−1 0.0164 W m−2 K−1
2.82 W m−2 K−1
a2: Efficiency curvature
0.0047 W m−2 K−1
Fig. 1. Schematic model of the SDHW system based on serial coupling of FPC- ETC technologies in TRNSYS simulation software.
2.3 Performance Metrics – Quadratic efficiency of the solar thermal collector The quadratic efficiency of the solar thermal collector is computed in terms of the intercept efficiency (a0 ), the efficiency slope (a1 ), the efficiency curvature (a2 ), the inlet temperature of the fluid to collector (Ti ), the ambient temperature (Ta ) and the global incident radiation (IT ). The values of the main parameters used in the thermal efficiency of the solar thermal collectors are indicated in Table 1. The thermal efficiency can be defined as follow: ηc = a0 − a1
Ti − Ta (Ti − Ta )2 − a2 IT IT
(1)
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H. Allouhi et al.
– Solar fraction and auxiliary energy: In this study, the performance of the solar heating system will be expressed as a function of its solar fraction. The solar fraction formula can be expressed as follow: SF =
QS QS + Qaux
(2)
with (QS ) is the amount of energy supplied by the solar system and (Qaux ) is the amount of the auxiliary energy delivered by the back-up system. The auxiliary energy is defined in terms of the amount of energy required by the user (QL ) and the amount of the solar energy that contributes to the water heating operation (QS ): QL = QS + Qaux
(3)
– Total cost:
The total cost is computed in terms of the specific collector cost (Ccollector ), the area of the collector (Acollector ) which is equal to 2 m2 , the cost (Ctank ) and the volume (Vtank ) of the tank. The volume and the cost of the tank are fixed 0.3 m3 and 1500$/m3 , respectively. The costs of FPC and ETC are estimated 600$/m2 and 700$/m2 , respectively. The total installation cost is given in the following equation: Ctotal = Ccollector · Acollector + Ctank · Vtank · CBOS + Cins (4)
The sum of the balance of system cost (CBOS ) and the installation cost (Cins ) has been supposed to be equal to 25% of the equipment cost. – Simple Payback Period: The simple payback (SPP) period is the time taken for the global initial investment of the proposed system to be recovered by the global accumulated savings. The (SPP) can be calculated by taking the ratio of the total installation cost over the cost of energy saving (CES ): that can be estimated as follows: SPP =
Ctotal CES
(5)
The (CES ) is computed as function of the electrical cost (Celec ) and the amount of the energy obtained by the solar system (QS ). The electrical cost is fixed 0.13$/kWh:
CES = QS · Celec
(6)
Solar Flat Plate Collector (FPC)
33
3 Results and Discussion In this section, the results derived from the simulations of the introduced solar domestic hot water system are presented. Figure 2 and Fig. 3 depict the Global Horizontal radiation (Gh), Diffuse radiation (Dh), the Beam Normal radiation (Bn), Wind Velocity (WV) as well as the Ambient Temperature (Ta) in Fez. During July, the average beam normal irradiation and the average ambient temperature can reach over than 250 kWh/m2 and 25 °C, respectively. The maximum values of wind velocity are detected during April and December.
Solar Irradiation (kWh/m²)
300 250 200 150 100 50
Gh (kWh/m²) Dh (kWh/m²) Bn (kWh/m²)
0
Fig. 2. Monthly average variation of the Solar Irradiation for Fez city
Figure 4 shows the daily load profile based on user demand for a single day. The trend of the water flow rate as function of time shows that the load profile is not distributed over the entire day but concentrated in the morning period from 8 h to 9 h, from noon to 13 h and during the night period.
H. Allouhi et al.
Ta (C°)
WV(m/s)
Temperature (°C)
30
3.7 3.6 3.5 3.4 3.3 3.2 3.1 3 2.9 2.8
25 20 15 10 5 0
Wind Velocity (m/s)
34
Fig. 3. Monthly average profile of the Ambient Temperature and the Wind Velocity for Fez city
45
FlowRate (kg/h)
40 35 30 25 20 15 10 5 0
0
2
4
6
8
10 12 14 16 18 20 22 24
Time (h) Fig. 4. Daily load profile (200l/day) distributed for a period between 8 h and 22 h
Reliable and efficient operation of any solar water heating system requires optimum adjustment of the tilt angle as well as the orientation. Tilt angle of examined solar thermal collectors was optimized by carrying parametric studies in terms of the average daily incident solar energy. The optimal value that maximizes the average solar incident radiation was detected to be 30° (Fig. 5). Regarding the collector orientation, it is fairly recognized among installers that orientation towards the south allowed for the best performances. The surface of the collectors was chosen using dual objective functions
Solar Flat Plate Collector (FPC)
35
Daily average solar irradiation on collector (kWh/m2/day)
optimization in order to ensure the best tradeoff between the cost of the installation and the energy performances. 7 6 5 4 3 2 1 0
0
10
20
30
40 50 60 Tilt angle ( °)
70
80
90
Fig. 5. Daily average solar irradiation versus the tilt angle.
The dynamic results of the simulation below are obtained for the last three days of December and July months in Fez city. The illustration includes the temperature at the outlet of each collector configuration. Figure 6 shows the variation of temperature profiles for SDHW system using the FPC and ETC individually and the coupling series of FPC and ETC during December and July, respectively. The temperatures coming out of the FPC technology attain a value of about 42 °C and 75 °C during the morning hours for December and July, respectively. In the other hand, ETC technology can deliver a maximum temperature of about 50 °C and 90 °C during the months of December and July, respectively. The incident radiation during the month of December as well as the ambient temperature are not very high, which will influence the temperature at the outlet of thermal collectors and subsequently, the intervention of the back-up system will be imperative to provide the hot water temperature as required by the user. In the serial coupling case, the trend of the outlet temperature variation shows that the suggested system allowed for satisfactory results and could be considered as a promising solution for domestic heat water supply (Fig. 6). The temperature at the outlet of the FPC-ETC in series can reach a maximum value of more than 55 °C during December, so the need for auxiliary energy will be much reduced especially during the months when the ambient temperature and the solar irradiation are not high. During the July month, the outlet temperature exceeds the average temperature requested by the user and consequently, there will no longer be any need for the auxiliary energy intervention.
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70.00
Temperature (°C)
60.00 50.00 40.00 FPC
30.00
ETC
20.00
FPC+ETC
0.00
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0 70.0
10.00
Time (h) (a) 120.00
Temperature (°C)
100.00 80.00 FPC
60.00
ETC 40.00
FPC+ETC
0.00
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0 70.0
20.00
Time (h) (b) Fig. 6. Hourly temperature profile of the SDHW system during the last three days of December (a) and July (b)
Solar Flat Plate Collector (FPC)
37
According to the results obtained, several remarks can be mentioned. Firstly, two kinds of temperature drop at the outlet of the system was observed: – Normal falls which can be easily distinguished, and which are due to the radiation absence and the drop in ambient temperature at the end of the day. – Temperature drops coinciding with the drawing of cold water. When the hot water is drawn by the user, the same amount of water, this time cold, enters from the bottom of the tank, which decrease the temperature.
Annual Auxiliary Energy (kWh)
To extend the results of the analysis carried out for the three days, annual numerical integration over the complete year was established in order to compute the annual auxiliary energy consumption expressed in (kWh). The annual auxiliary energy is the energy required by a back-up electric boiler to increase water temperature up to the set value when the solar resource is not able to do so. The annual auxiliary energy for each configuration was calculated and depicted in Fig. 7. 500
461.0254
400
447.2525 343.2325
300 200 100 0
FPC
ETC
FPC+ETC
Fig. 7. Annual Auxiliary Energy outputs for each configuration
The main finding point out that coupling FPC and ETC allowed minimizing auxiliary energy compared to FPC alone for the same collector area. The relative decrease of auxiliary energy compared to FPC is around 3% with an increment cost of about 6%. The FPC technology is the most suitable configuration because of its lower SPP value which is due to the low specific collector cost. Regarding the case scenario of the system based FPC in series with ETC, it is clearly observed that the cost of the system is relatively reduced in comparison with the case of which ETC operated solely. Accordingly, an intermediate value of the SPP has been detected. Table 2 summarizes the main annual energetic and economic findings.
38
H. Allouhi et al. Table 2. Annual energy and economic results SF (%)
QS (kWh)
Qaux (kWh)
C total ($)
C ES ($)
SPP (years)
FPC
81.91
2087.98
ETC
86.53
2205.86
461.03
2062.5
271.44
7.598
343.23
2312.5
286.76
8.064
FPC+ETC
82.45
2101.49
447.25
2187.5
273.19
8.007
4 Conclusion The main objective of this work is to characterize the energy and economic performance of a novel solar forced-circulation water heating system coupling the FPC to an ETC. This uncommon configuration is not discussed in the available literature and its economic viability needs investigation. Dynamic simulations of the presented system were developed, and the performance corresponding configuration was compared to reference systems using FPC and ETC alone. The following conclusions can be made: – ETC-FPC in series configuration minimized the need for auxiliary energy compared to the FPC due to higher performance of ETC collectors. – Simple payback period in the case of FPC-ETC in series was reduced compared to ETC collectors from 8.064 to 8.007 years. – The idea of coupling the FPC and the ETC in series makes it possible to conserve the auxiliary energy and even minimize the cost partially. Sensitivity analyzes is further needed to analyze more comprehensively the effectiveness of FPC-ETC in series.
References 1. Aljerf, L.: Reduction of gas emission resulting from thermal ceramic manufacturing processes through development of industrial conditions. Sci. J. King Faisal Univ. 17(1), 1–10 (2016) 2. Byrne, J., Hughes, K., Rickerson, W., Kurdgelashvili, L.: American policy conflict in the greenhouse: divergent trends in federal, regional, state, and local green energy and climate change policy. Energy Policy 35(9), 4555–4573 (2007) 3. Lau, L.C., Lee, K.T., Mohamed, A.R.: Global warming mitigation and renewable energy policy development from the Kyoto protocol to the Copenhagen Accord—a comment. Renew. Sustain. Energy Rev. 16(7), 5280–5284 (2012). https://doi.org/10.1016/J.RSER.2012.04.006 4. Duarte, R., Mainar, A., Sánchez-Chóliz, J.: The impact of household consumption patterns on emissions in Spain. Energy Econ. 32(1), 176–185 (2010) 5. Pless, S., Torcellini, P.: Getting to net zero, no. September (2009) 6. Jacobson, M.Z.: Review of solutions to global warming, air pollution, and energy security, pp. 148–173 (2009). https://doi.org/10.1039/b809990c 7. Allouhi, A., Kousksou, T., Jamil, A., El Rhafiki, T., Mourad, Y., Zeraouli, Y.: Economic and environmental assessment of solar air-conditioning systems in Morocco. Renew. Sustain. Energy Rev. 50, 770–781 (2015). https://doi.org/10.1016/J.RSER.2015.05.044 8. Abdou, N., Mghouchi, Y.E.L., Hamdaoui, S., Asri, N.E.L., Mouqallid, M.: Multi-objective optimization of passive energy efficiency measures for net-zero energy building in Morocco. Build. Environ. 204, 108141 (2021)
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9. Gargab, F.Z., Allouhi, A., Kousksou, T., El-Houari, H., Jamil, A., Benbassou, A.: A new project for a much more diverse moroccan strategic version: the generalization of solar water heater. Inventions 6(1), 2 (2021) 10. Shukla, R., Sumathy, K., Erickson, P., Gong, J.: Recent advances in the solar water heating systems: a review. Renew. Sustain. Energy Rev. 19, 173–190 (2013) 11. Tiwari, A.K., Gupta, S., Joshi, A.K., Raval, F., Sojitra, M.: TRNSYS simulation of flat plate solar collector based water heating system in Indian climatic condition. Mater. Today Proc. 46, 5360–5365 (2021) 12. Mohammed, M.N., et al.: TRNSYS simulation of solar water heating system in Iraq. Recent Res. Geogr. Geol. Energy, Environ. Biomed. 153–156 (2011) 13. Tanha, K., Fung, A.S., Kumar, R.: Performance of two domestic solar water heaters with drain water heat recovery units: simulation and experimental investigation. Appl. Therm. Eng. 90, 444–459 (2015) 14. Allouhi, A., Jamil, A., Kousksou, T., El Rhafiki, T., Mourad, Y., Zeraouli, Y.: Solar domestic heating water systems in Morocco: an energy analysis. Energy Convers. Manag. 92, 105–113 (2015) 15. Sokhansefat, T., Kasaeian, A., Rahmani, K., Heidari, A.H., Aghakhani, F., Mahian, O.: Thermoeconomic and environmental analysis of solar flat plate and evacuated tube collectors in cold climatic conditions. Renew. Energy 115, 501–508 (2018) 16. Greco, A., Gundabattini, E., Gnanaraj, D.S., Masselli, C.: A comparative study on the performances of flat plate and evacuated tube collectors deployable in domestic solar water heating systems in different climate areas. Climate 8(6), 78 (2020) 17. https://en.wikipedia.org/wiki/Fez,_Morocco
Application of Predictive Control to Multilevel Inverters Used in a WECS for a Harmonics Minimization Maha Annoukoubi1(B) , Ahmed Essadki1 , Hammadi Laghridat1 , and Tamou Nasser2 1 National School of Arts and Crafts in Rabat (ENSAM), Mohammed V University, Rabat,
Morocco [email protected] 2 National High School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco
Abstract. Wind Energy Conversion Systems are using power electronic devices in order to inject the generated power to the electrical grid. The increasing use of those powers electronic component has led to more and more problems related to harmonic distortions of the electrical grid. As Total Harmonic distortion is one of the major parameters for determining WECS output voltage quality, this paper aims to apply the model predictive control to the multilevel inverter used in our WECS instead of the conventional PWM control in order to minimize the harmonics of the generated WECS power for a better integration to the electrical grid. The theoretical analyze, study and results will be confirmed by simulation using MATLAB/Simulink tools, to verify the feasibility and good performance of the proposed systems. Keywords: Model Predictive Control · Multilevel Inverter · Wind Energy Conversion System · PWM · Total Harmonic Distortion
1 Introduction In the recent years, the global consumption of electrical energy has increased considerably and that’s due to demographic changes, the development of new technologies and growth in emerging countries. Current systems of electrical energy production are mostly using fossil sources (oil, gas and their derivatives…). Only these sources are not inexhaustible, therefore these sources will not be able to respond to all our long-term energy needs. Moreover, the transformation of the sources fossils into electrical energy is accompanied by a release of CO2 which has a significant impact on the environment. It is therefore urgent to turn to a safer energy that allows us to continue to live normally without degrading our environment and without risk to populations. One of the cleanest ways of electrical energy generation is the use of Wind Energy Conversion Systems which use the wind to produce electrical energy and injected it to electrical grid. However, the intermittent and random nature of this source of renewable energy ensures that we can’t directly connect the WECS to the grid without using a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 40–49, 2023. https://doi.org/10.1007/978-3-031-35245-4_4
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41
power electronics interface. To this end, our work consists on developing the optimal configuration of an electrical power inverter and its control in order to ensure a better integration of the generated power to electrical grid and by respecting the system services in terms of reactive power, frequency, voltage and harmonics [1]. In order to raise the Wind energy integration, we need to maximize the power generated by WECS. However, the power transmitted through IGBT power electronic converters is limited by the characteristics of the IGBTs (the maximum voltage and current supported). The series association of these power modules increases the overall maximum voltage. This solution gave rise to a new topology called multilevel inverter. They are a developed approach used to have a high quality output voltage with a reduced harmonics. Multilevel inverters use several power semiconductors and capacitor voltage sources in order to generate a multilevel smoother output voltage (Fig. 1).
Fig. 1. Output voltage Waveform
Many controllers have been proposed for these multilevel inverters, including hysteresis control, proportional-integral controllers using pulse-width modulation (PWM), dead-beat control and multiloop feedback control. And to generate the state signal for the inverter switches in order to obtain a reduced harmonics output voltage. For our previous work [2, 5] we used the PI controller using PWM to control 3, 5, 7 and 9 level inverter but we finally conclude that the complexity of controller raises with the level of inverter. That’s why for this work we mainly focused on developing and using a new controller topology which the Predictive control in order to minimize the THD of the output voltage of the multilevel inverter that will be injected to the electrical grid. The reminder of this article is organized as follow. Section 2 presents the model of the proposed multilevel inverter. A description of the used predictive control will be given is Sect. 3. Simulation results using MATLAB/SIMULINK that compares the performances of the proposed control strategy with the already used PWM control is presented in the Sect. 4 and then in last section we conclude about robustness, cost and THD of our work.
2 Multilevel Inverter Model Electrical energy is usually distributed as sinusoidal voltages. Power electronics can modify the presentation of electrical energy to adapt it to different needs. Thanks to the technological progress made in recent years in the field of power electronics, a new technology of inverters have been developed, it’s called multilevel inverter. It has been introduced at 1980 by Nabae [3] with the aim of generating a staircase output waveform,
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which has a reduced harmonic distortion from a continuous input voltage. By increasing the number of levels in the inverter, the output voltages become smother and with less harmonics. 3 different topologies of multilevel inverters have been proposed: diodeclamped (neutral-clamped) [4]; capacitor-clamped (flying capacitors) [5]; and cascaded H-Bridge. In this part we will present the model of the conventional 2 level inverter and the 5 level NPC inverter that we will use for our WECS in order to minimize the harmonics. The control strategy of these inverters will be presented in the next section. 2.1 Model of the 3 Phases 5 Level NPC Inverter A q-phase n-level NPC inverter consists of q arms, switches and a source of continuous voltage for each arm [6]. Each switch is composed of a transistor and a floating diode that connect the output to the intermediate levels of the continuous input voltage: – Number of power switches = 2(n-1) – Number of DC bus capacitors = (n-1) – Number of floating diodes = 2(n-2) For our 3 phases 5 level NPC inverter we will need 8 switches for each arm, 4 DC inputs and 6 floating diode. The scheme of our multilevel inverter is presented in the Fig. 2.
Fig. 2. 5 level NPC inverter Model
Each switch Sij has two possible states that we can express by the following equation Fij : Fij = i = 1, 2, 3 and j = 1, 2,3,4,5,6,7,8
1 if Sij is closed 0 if Sij is open
(1)
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In order to ensure a good command and to avoid the appearance of a short circuit if multiples switches conduct at same time we adopt a complementary command. So for each arms i of our 5 level inverter we defined a complementary command as follow: ⎧ Fi5 ⎪ ⎪ ⎨ Fi6 ⎪ F ⎪ ⎩ i7 Fi8
= 1 − Fi1 = 1 − Fi2 = 1 − Fi3 = 1 − Fi4
(2)
2.2 Command Strategy of the 3 Phases 5 Level NPC Inverter So depending on the complementary condition we can resume all the possible switching state of an amr i of our 5 level NPC inverter in the following table, each arm can only have 5 states which forms the 5 level output waveform. Table 1. Switching states of the 5 level NPC inverter State
Fi1
Fi2
Fi3
Fi4
Fi5
Fi6
Fi7
Fi8
Vout
P2
1
1
1
1
0
0
0
0
Vdc/2
P1
0
1
1
1
1
0
0
0
Vdc/4
O
0
0
1
1
1
1
0
0
0
N1
0
0
0
1
1
1
1
0
-Vdc/4
N2
0
0
0
0
1
1
1
1
-Vdc/2
The 3 phases 5 level NPC can have 125 states, depending on the combination between the state of each one of the three arms and the condition of the three-phase power which consist of having a 120◦ out of phase between the 3 output voltage of the 3 arms. Means that if the 1st arm is at the state P1, the second one should be at state N2 and the third one at O. And these lead us to look for the adaptive command strategies that enable us to have the best output voltage waveform with the fewer Harmonic possible. The Fig. 3 present the simulation result of the 3 phases output voltage of the NPC 5 level inverter using the defined switching states. Although this command strategies present harmonics, the harmonics analyses is presented in the last section. That’s why in the next section we are going to introduce an adaptative command of our inverter.
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Fig. 3. 3 phases 5 level output voltage
3 Predictive Control of the Multilevel Inverter The aim our work is to improve the output voltage of our inverter that will be injected to the electrical grid in order to have a THD that is less than 5%. This can be done either by improving the inverter circuits and component or by looking for the best control strategy. In this section we will discuss and apply one of the advanced control strategy called the predictive control which is used in industrial applications to solve regulation problems of complex industrial systems [7]. The term predictive control does not indicate a specific control strategy but rather a set of algorithms that explicitly use the model of the system, in order to determinate an optimal control sequence satisfying the constraints of the system and the performance formulated through a certain optimization criterion. So in this part we will study the Model Predictive Control and compare its use with the NPC 5 level inverter to using the usual PWM command of NPC switch in terms of the quality of the output voltage and its THD. 3.1 PWM Control of the 5 Level NPC Inverter Pulse width modulation (PWM) controller enables a control of their output voltages and frequencies at same time. Therefore, PWM control is commonly used in many industrial applications, such as renewable energy sources, electrical machine drives, and power conversion systems. In our previous works we concluded that either the uses of PWM remain a simple and efficient control strategy for inverter but the more we raise the level of multilevel inverter the control strategy became complex and present higher THD. The Fig. 4 presents the used PWM control strategy for our 5 level NPC inverter. As we can see we start by comparing measured output voltage Vout with the input reference ∗ . The generated difference is injected to a PI controller for a regulation, output voltage Vref in order to minimize the steady-state error and the rise time of our inverter output voltage for keeping the output voltage equal to the reference voltage. We used a modulator to generate signals for the switches. The parameters of the component of this controller are the main influence of its performances. Although it can be a simple control strategy but ∗ and it present an output voltage with a significant it can present errors in terms of the Vref THD. The simulations results of the FFT analyze of this control strategy used with the 5 level NPC and 2 level inverter is presented in Sect. 4.
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45
Fig. 4. Bloc scheme of PWM control strategy
3.2 Model Predictive Control of the 5 Level NPC Inverter In this part we will describe the model of the proposed predictive control. First we start by modeling our system in order to predict the behavior of the system for all the switching state described at Table 1 then we will define a cost function J and finally define an optimized algorithm to minimize this function. The electrical output voltage of the 5 level NPC inverter could be expressed as follow: ⎤ ⎤ ⎡ ⎡ ⎡ ⎤ ⎤ ⎡ ⎤⎧⎡ ⎤ ⎪ F11 F11 + F11 F10 + F10 F v1 2 −1 −1 ⎪ ⎨ ⎢ ⎥ ⎥ ⎥ ⎢ ⎢ ⎢ 11 ⎥ 1⎢ ⎥ ⎢ ⎥ ⎥ ⎢ F + F ⎥ ∗U c2 −⎢ F + F ⎥ ∗U c3 −⎢ F ⎥ ∗U c4 ∗U + ⎣ v2 ⎦ = ⎣ −1 2 −1 ⎦ ⎢ F c1 ⎣ 21 ⎦ ⎣ 21 ⎣ 20 ⎣ 21 ⎦ 21 ⎦ 20 ⎦ ⎪ 3 ⎪ −1 −1 2 ⎩ F v3 F31 + F31 F30 + F30 F31 31 ⎡
where
⎧ ⎪ ⎪ Fi1 ⎪ ⎨ Fi0 ⎪ Fi1 ⎪ ⎪ ⎩ Fi0
= Fi1 .Fi2 .Fi3 .Fi4 = Fi5 .Fi6 .Fi7 .Fi8 and i = 1, 2, 3 = Fi5 .Fi6 .Fi7 .Fi8 = Fi1 .Fi2 .Fi3 .Fi4
⎫ ⎪ ⎪ ⎬ ⎪ ⎪ ⎭
(3)
(4)
The input current of our inverter is expressed using the output currents and the switching functions as: ⎤ ⎤ ⎡ ⎡ F11 F21 F31 ⎡ ⎤ iin1 ⎥ io1 ⎢ iin2 ⎥ ⎢ F11 F21 F31 ⎥⎣ ⎦ ⎥=⎢ ⎢ (5) ⎢ ⎥ io2 ⎣ iin3 ⎦ ⎣ F F F ⎦ 10 20 30 i o3 iin4 F10 F20 F30 The current I = io1 + io2 + io3 − iin1 − iin2 − iin3 − iin4 . Using 5 levels NPC inverter model in Fig. 2 we determinate the current and voltage of the 4 capacitors C 1 , C 2 , C 3 , C 4 :
(6)
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From Eq. 6 we can deduce and predict the future inputs voltages that we will use for our MPC:
(7)
And finally we define the cost function ε using the future input voltage as: ⎧ ⎨ ε ji = Ucref − U cjnew ε k = vrefk − vk ⎩ ε i = ε ji + ε k
(8)
And ⎧ ⎨
vref 1 = V msin(ωt) vref 2 = V m sinωt + 2π 3 ⎩ vref 3 = V m sin ωt − 4π 3
(9)
where j = 1, 2, 3, 4; i = 1…125 and k = 1, 2, 3 The Fig. 5 resume our model predictive controller used for our NPC 5 level inverter.
Fig. 5. Bloc scheme of 5 levels NPC inverter
4 Simulation Results and Discussion In this last section we will present the simulation results of the described control strategy using MATLAB/Simulink. At first we start by modeling the 5 level NPC inverter using the electrical power switches, 24 IGBT/8 for each arm and 18 floating diode/6 for each arm. Then we applied at firs the PWM control and used the FFT analyze to obtain the THD of the output voltage, we applied the PWM also to control a 2 level inverter in order to compare THD and conclude about multilevel performances. Figure 6 present THD result of NPC 5 level inverter output voltage which is equal to 10.56% and less by about 10.66% than the THD of 2 level inverter presented at Fig. 7.
Application of Predictive Control
Fig. 6. THD of NPC 5 level inverter V out with PWM
47
Fig. 7. THD of 2 level inverter V out with PWM
In second part we used the same model of 5 level NPC inverter but we changed the control strategy and used the Model of predictive control described at the last section. For that we defined and algorithm describing the control strategy as shown in Fig. 8 and applied it to control 5 level and 2 level inverters in order to compare their THD results with results obtained using PWM control.
Fig. 8. Algorithm of predictive control
Figures 9 and 10 present THD results of using the predictive control with both 2 levels and 5 level inverter. As we can see that the MPC has ameliorated the THD of 2 level inverter by 9.85% and 5 level inverter by 6.81% to make it less than 5% imposed by IEEE-519 standards.
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Fig. 9. THD of 2 level inverter V out with MPC
Fig. 10. THD of 5 level inverter V out with MPC
5 Conclusion Results of MATLAB/SIMULINK simulation obtained using the two control strategies PWM and Predictive control of 2 level and 5 level inverter show that the command of inverters switches decrease harmonics of output voltage and makes it smoother. As presented in simulation results the THD obtained using a multilevel is less than the one using the conventional 2 level inverter which confirm the importance of using Multilevel for a harmonic reduction but either PWM control is a sample control strategy it becomes more and more complex by raising the level of inverter which impacts negatively the THD of output voltage. Therefore we look up for a control strategy in order to have better reduction of harmonics as shown by using the predictive control developed which ameliorate significantly the THD and makes it less than 5% for 5 level inverter, it respected IEEE-519 standards and improved the integration of the generated power of our WECS to the electrical grid.
References 1. Rashid, M.H.: Power Electronics, circuit, devices, and application, Third Edition, pp. 253–255 (2009) 2. Annoukoubi, M., Essadki, A., Laghridat, H.: Comparative study between the performances of a three-level and two-level converter for a Wind Energy Conversion System. WITS (2018) 3. Nabae, A., Takahashi, I., Akagi, H.: A new neutral-point-clamped PWM inverter. IEEE Trans. Ind. Appl. (1981) 4. Rodriguez, J., Lai, J.S., Peng, F.Z.: Multilevel Inverters: a Survey of topologies ,controls and applications. IEEE Trans. Ind. Electron. (2002) 5. Annoukoubi, M., Essadki, A., Nasser, T.: Cascade h-bridge multilevel inverter for a wind energy conversion system applications. In: IRSEC (2021) 6. Sahu, V., Kaushik, S.: A new five-level diode clamp multilevel inverter topology. Int. J. Creat. Res. Thoughts (2013) 7. Mohamed, I., Zaid, S., Elzayeed, M.: Implementation of model predictive control forthreephase inverter with output LC filter one Zdsp F28335 Kit using HIL simulation. Int. J. Model. Identif. Control 25(4) (2016) 8. Corzine, K.A., Wielebski, M.W., Peng, F.l., Wang, J.: Control of cascaded multilevel inverters. IEEE Trans. Power Electron. 19(3), 732–738 (2004) 9. Mukand Patel, R.: Wind and Solar Power Systems. CRC Press, Boca Raton (1999)
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10. Mohamed, I., Ahmed Zaid, S., Abu-Zayed, M.F.: Three-phase inverter with output LC filter using predictive control for UPS applications Conference on Control, Decision and Information Technologies (CoDIT) (2013) 11. Rodriguez, J., Bernet, S., Steimer, P.K., Lizama, I.E.: A survey on neutral-point-clamped inverters. IEEE Trans. Ind. Electron. 57(7), 2219–2230 (2010) 12. Ebrahimi, J., Babaei, E., Gharehpetian, G.B.: A new multilevel converter topology with reduced number of power electronic components. IEEE Trans. Ind. Electron. 59(2), 655–667 (2012) 13. Adefarati, T., Bansal, R.C.: Integration of renewable distributed generators into the distribution system: a review. IET Renew. Power Gener. 10(7), 873–884 (2016)
Study of a Simulator for the Diagnosis of Wind Farm Failures and the Development of Maintenance Strategies Brahim Sadki(B) and Mourad Kaddiri Laboratory of Industrial Engineering, Mechanical Engineering Department, Faculty of Sciences and Technologies, Sultan Moulay Slimane University, Beni Mellal, Morocco [email protected]
Abstract. Wind turbines transform the kinetic energy of the wind into mechanical energy, which is then transformed into electrical energy and injected into the electrical network. The wind turbine is characterized by the relationship between the wind speed and the power delivered. This relationship is called the power curve, which is the most widely used tool for monitoring wind turbine performance. This curve must make it possible to detect the presence of failures and its severity. Because of the flaws in fault detection by power curve, studies have used the energy balances of the various components of wind turbines. The contribution of this article consists in simulating the energy balances within the different components of the wind farm, as well as on the electrical energy injection network. This simulation makes it possible to extract knowledge about diagnosing faults within the wind farm and also makes it possible to plan maintenance tasks. In wind farms, most maintenance planning tools are responsive. The adopted approach makes it possible to adapt the maintenance strategy to the meteorological conditions and to the degrees of degradation of the components in a preventive manner. The first simulation steps are promising to improve this work and take into consideration the different dimensions of wind farm maintenance. Keywords: Wind Farm · Maintenance Strategies · Diagnosis · Energy Balances · Simulation · Software Components
1 Introduction Wind energy is one of the most popular green technologies. It is experiencing rapid growth and increased competition among its operators. These operators use several wind turbine maintenance techniques to optimize costs. They monitor the operation of the parks and use analysis tools to gain efficiency and optimize the budget. Different tools are used including CMS (Condition Monitoring System) and SCADA (Supervisory Control and Data Acquisition). CMS indicates equipment status to prevent failures before they occur [1, 2]. This system includes sensors installed in the wind turbine. They make it possible to generate © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 50–58, 2023. https://doi.org/10.1007/978-3-031-35245-4_5
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indicators linked to physical quantities selected for their relationship with the origin of potential failures. SCADA is a remote management system; this system analyzes the data of the sensors already present in the wind turbine. Supervision by the SCADA system is carried out using an automated alarm system. Each of these tools (SCADA or CMS) has its own disadvantages [3]. The failure of SCADA is because variations in the operating conditions of wind turbines are not taken into account [4]. The information collected by SCADA is important for failure analysis, but it requires too much processing to use it for maintenance planning [5]. For CMS, the non-stationary regime of wind turbines is a source of false alarms [4]. Indeed, wind speed variations generate significant mechanical stresses on the wind turbine; this appears on CMS as a defect and results in a false alarm. This type of situation leads to unnecessary interventions and requires more in-depth analyzes of the information. The costs of setting up maintenance strategies using CMS are high. The CMS requires the intervention of experts and visual inspections of defects. The objective of this article is to optimize maintenance; we seek to reduce the intervention of experts and the analysis time to predict defects. Scheduling of maintenance tasks should avoid shutdown of wind turbines during periods of maximum production. Efficiency, better than effectiveness, makes it possible to rationalize the resources available. The efficiency of the maintenance strategy must be increased according to the constraints of the environment and the resources. The contribution of this work is the development of a new dynamic planning strategy for the maintenance of wind turbines. Our approach is that the intervention decision will be based on the degradation levels of wind turbine components, these levels will be extracted from the wind turbine monitoring information stream. Indeed, each wind turbine will be equipped with a number of sensors for collecting information on phenomena related to the appearance of faults. Maintenance plan renewal is based on monitoring data and maintenance cost. The rest of this article is organized as follows. Section 2 presents the importance of subdividing the wind turbine into a set of components. In Sect. 3, we study the energy balance role of each component in the diagnosis of the wind system. Section 4 gives examples of maintenance strategy design criteria. Section 5 presents the way to exploit the energy aspect in diagnosis and supervision of wind turbines. In Sect. 6, we discuss the proposed method for maintenance management in wind farms and the first results obtained. Conclusions and indications for future work are described in Sect. 7.
2 Component-Based Wind Farm Modeling Wind farms are modeled as comprising several components: gearboxes, power generators, blades, control systems, etc. Each component can be divided into sub-components according to maintenance needs and to facilitate fault location. The wind turbine is subject to stochastic forces, which constitutes an obstacle to the prediction of the degradation levels of its components for maintenance purposes. The isolation of a set of materials to constitute a component makes it possible to model its behavior and facilitate the prediction of its state.
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The component model makes it possible to compare the current state of the component with respect to that during a period without faults. The difference between the measured observation and the one predicted by the model allows the detection of faults. A component is defined as a set of spatially related materials to which measurements are associated. A component has a set of output variables whose evolution can be explained by a set of measurements from the same component. The useful variables in this study are those that explain the energy balance of the component. Indeed, each component has its contribution in generating energy. The energy balance is the means of fault diagnosis by global simulation of the energy system. The energy system can be either all the wind installations connected to the electricity grid or just the wind turbine alone. In this paper, the variables and measures considered must reflect the energy balance of each component. If the division into components and sub-components is fine enough, faults can be located geographically in the wind turbine. But it requires a high cost in terms of the number of measurements needed. For each component, a compromise must be sought between the degree of granularity of each component and the cost of the necessary physical measurements of the same component. It is considered that the level of degradation of a component is assessable. This variable allows us to know the state of the system, its reliability and its availability. Reliability and availability are two parameters for evaluating the performance of the wind turbine considered as a multi-component system. The values of reliability and availability depend on the structure of the wind turbine as well as the reliability and availability of each of its components.
3 Diagnosis Based on Energy Balances 3.1 Power Curve The power curve of a wind turbine is a curve that represents the power delivered by this wind turbine as a function of the wind speed. In general, the manufacturer of the wind turbine provides this curve. This curve is used to predict the energy that the wind turbine will produce over a day, over a month and over a year with statistical methods. Figure 1 below shows an example of the power curve [6]. The power curve makes it possible to predict the profitability of a wind turbine installation on a site from meteorological data. The power curve is also an important tool for monitoring wind turbines, It detects the presence of defects. Indeed, the power curve makes it possible to evaluate the overall operation of the turbine; it provides an indication of the expected theoretical efficiency between the mechanical power supplied by the wind and the electrical power delivered by the machine. If a fault occurs in the wind turbine, the efficiency of the machine decreases. This reduction generates performance losses related to the seriousness of the defect, which means the presence of faults at the level of the entire wind turbine. Analysis of the power curve does not identify and locate the fault [4]. 3.2 Energy Balance of Components Our approach is based on modeling the energy performance of wind turbine components; this modeling is based on energy measurements at the input and output of each
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Fig. 1. Power of wind turbine as a function of wind speed [6].
component. Reference [7] focuses on modeling losses due to thermal heating of wind turbine components and models are constructed to predict the temperature of one or more components of the wind turbine. The wind turbine drivetrain is the most critical in terms of cost and repair time. This is why monitoring systems first focused on the components of the drivetrain [7]. Several components of the drivetrain are studied: the rotor, the multiplier and the generator. Reference [7] presents a method to study variations in the behavior of drivetrain components. Figure 2 represents the energy balance within a component of the drivetrain which helps to explain the choice of variables used in modeling. It can be seen that three sources of energy losses are represented, losses linked to friction, losses linked to the environment and losses linked to the cooling system. Each loss is linked to a variable used as input to the component model. The temperature of the monitored component makes it possible to follow the evolution of these energy losses and therefore informs about the state of the component.
Fig. 2. Energy balance of a wind turbine drivetrain component [7]
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The temperature is therefore chosen to constitute the fault indicator. The fault indicator is the difference between the temperature value predicted by the model and the measured temperature value. The temperature of the component being monitored is modeled as a function of several variables such as the electrical power generated, shaft rotation speed, room temperature, etc. Each energy loss within the component is linked to a variable measured by sensors. This variable is taken into account in the component model. The component modeling in [7] is interested in the components of the drivetrain but it will be general for the other components according to their nature: electrical components, mechanical components, etc.
4 Optimal Maintenance Strategies The degradation of wind turbine components is due to the interactions of the components with each other and environmental influences. We seek to control the reliability and availability of wind turbines through adaptive maintenance strategies while seeking to minimize the cost. References [8, 23] present a review of opportunistic maintenance models for multi-component systems such as wind turbines. In [9], an opportunistic and imperfect maintenance policy for a multi-component system is presented. This policy is based on the ability to replace or repair a component while minimizing the cost, when the system stops. For our approach, the wind turbine is subdivided into several components and subcomponents. Based on this principle, fault diagnosis is carried out and at the same time the maintenance strategy to be applied is dynamically sought. The maintenance of multi-component systems is modeled based on the dependence of the components on each other [10, 11]. This dependency makes modeling and maintenance planning quite complex. Reference [12] gives several types of dependencies between components. In general, failure of one component affects the failure rate of other components. The organization of wind turbine maintenance is corrective and preventive [3]. Indeed, wind farms use several maintenance techniques simultaneously in order to optimize costs and take advantage of different techniques. In maintenance planning, the simultaneous maintenance of several components would save costs, requires less time and improves the availability of the wind turbine. Indeed, if the various maintenance tasks require the wind turbines to be stopped, then simultaneous interventions by the maintenance teams can enormously reduce the downtime. Preparatory time of maintenance actions require upstream work from the teams (travel, parts to be provided, etc.). With a grouping of maintenance, the preparation time will be reduced enormously. In addition, the duration of a maintenance action or a failure of a component can represent an opportunity to carry out preventive maintenance operations on the other components. In [13], the authors used swarm optimization to find the optimal combination of maintenance actions. In this paper, the proposed maintenance strategy makes it possible to update the maintenance plan taking into account short-term information and using combinations of maintenance activities. For wind systems, it is important to choose the
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optimal maintenance periods and combinations of activities according to the degrees of degradation of the components and the weather conditions. Indeed, it is best to avoid maintenance work during periods of high production, which correspond to suitable wind speeds.
5 Diagnosis of Faults Based on the Simulation of Energy Balances Simulation has been widely used to deal with system problems [15]. In particular, integrated modeling aims to integrate and simulate models from several disciplines to represent the complexities and interdependencies of a system components [16, 17]. Our approach to evaluating the behavioral performance of wind turbines is based on simulation by integrated modeling which aims to combine models from several different disciplines. In our case, we are interested in the fields of weather, mechanics, electronics, etc. The hardware components and the weather will be represented by models in the form of software components having inputs and outputs and which are linked in the form of a network [15, 20]. Pynsim is a Python library that speeds up the development of network-based simulators. Pynsim has been used in several fields and mainly that of water, transport and energy networks [15]. It aims to facilitate model integration, agent-based and component-based modeling. Components here can be added and removed easily. The components of the wind farm are represented by interconnected software components. The structure of Pynsim is as follows: Figure 3 presents the UML (Unified Modeling Language) schema of Pynsim components and their extensions. The components are represented by the classes in green, which are the building blocks of a Pynsim network. The pink class is the engine, it executes processing on the components. The Simulator class controls the sequencing of model execution by defining execution time periods and acting on the network and engines. The bottom section of Fig. 3 illustrates an example of a generic model for a simple water resources system, using a single engine [15]. Our simulation case concerns the monitoring of energy flows from the kinetic energy of the wind to its electrical form, always with the aim of diagnosing wind turbines and managing maintenance. Pynsim gives the possibility to use the component-based approach or the agent-based approach or both at the same time. This facilitates the integration of decision-making functions at the component level. Indeed, in Pynsim each component is able to execute its own decision-making code autonomously. In this case the component is an agent and Pynsim is a multi-agent simulation framework. Pynsim allows to take advantage of maintenance models based on multi-agent systems [21, 22], these are approaches that make it possible to model the dynamic interactions between several entities. Each Pynsim component that mirrors a hardware component belonging to the wind turbine must make decisions about its maintenance needs and the timelines for performing them. The simulator class elaborates a general decision-making on the maintenance strategy resulting from the simulation.
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Fig. 3. The generic model of the Pynsim components and their extensions [15].
6 Experiments Initial simulation is run for a diverse set of drivetrain component maintenance scenarios. All components and subcomponents of the drivetrain are represented by software components based on Pynsim. First, we only simulate the economic sustainability of part of the wind farms. This work can be considerably improved to prevent all types of damage and optimize the performance of wind farms. An algorithm is developed to allow the analysis and selection of different maintenance strategies taking into consideration the different criteria. We have evaluated the influence of component maintenance actions on the costs. We studied the possibilities of grouping the tasks by simultaneous interventions of the maintenance teams. Depending on the degradation of the components, the insertion of preventive maintenance operations makes it possible to reduce the costs due to the unavailability of wind turbines and shutdowns for maintenance. The cost of maintenance is sensitive to the maintenance strategies. Our first simulations show that we can reduce maintenance costs by up to 5% compared to ordinary maintenance strategies.
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7 Conclusion Reducing maintenance costs is important for the future of wind farms. In simulation, we start by reasoning on the cost to select the maintenance strategies, but it is possible to choose other factors such as the weather which also influences the cost of production and maintenance. We adopted Pynsim for the design of our simulator thanks to its flexibility and maintainability. The software components of simulations are reusable and considered as public Python libraries. Pynsim allows to keep the history of component properties during each simulation step. This history can be queried for advanced processing. The use of algorithms for the study of the modifications made to these properties over time makes it possible to extract knowledge on the maintenance of wind turbines, which will be the subject of future work. Another perspective is to move towards real-time simulation by reducing the simulation time step.
References 1. Yang, W., et al.: Wind turbine condition monitoring: technical and commercial challenges. Wind Energy 17(5), 673–693 (2014). https://doi.org/10.1002/we.1508 2. Yingning, Q., et al.: Model based wind turbine gearbox fault detection on SCADA data (2014). https://doi.org/10.1049/cp.2014.0820 3. Tavner, P.: Offshore wind turbines: reliability, availability and maintenance, IET (2012). https://doi.org/10.1049/pbrn013e_ch8 4. Lebranchu, A.: Analyse de données de surveillance et synthèse d’indicateurs de défauts et de dégradation pour l’aide à la maintenance prédictive de parcs de turbines éoliennes, Université Grenoble Alpes (2016) 5. Yang, W., Jiang, J.: Wind turbine condition monitoring and reliability analysis by SCADA information. In: 2011 Second International Conference on Mechanic Automation and Control Engineering, IEEE (2011). https://doi.org/10.1109/mace.2011.5987329 6. Jaohindy, P.: Modélisation des systèmes éoliens verticaux intégrés aux bâtiments: modélisation du couple production/Bâtiment, La Réunion (2012) 7. Wilkinson, M., et al.: Comparison of methods for wind turbine condition monitoring with SCADA data. IET Renew. Power Gener. 8(4), 390–397 (2014). https://doi.org/10.1049/ietrpg.2013.0318 8. Nowakowski, T., Werbi´nka, S.: On problems of multicomponent system maintenance modelling. Int. J. Autom. Comput. 6(4), 364–378 (2009). https://doi.org/10.1007/s11633-0090364-4 9. Hou, W., Jiang, Z.: An opportunistic maintenance policy of multi-unit series production system with consideration of imperfect maintenance. Appl. Math. Inf. Sci. 7(1L), 283–290 (2013). https://doi.org/10.12785/amis/071l37 10. Thomas, L.: A survey of maintenance and replacement models for maintainability and reliability of multi-item systems. Reliab. Eng. 16(4), 297–309 (1986). https://doi.org/10.1016/ 0143-8174(86)90099-5 11. Cho, D.I., Parlar, M.: A survey of maintenance models for multi-unit systems. Eur. J. Oper. Res. 51(1), 1–23 (1991). https://doi.org/10.1016/0377-2217(91)90141-h 12. Nicolai, R.P., Dekker, R.: Optimal maintenance of multi-component systems: a review. In: Complex system maintenance handbook, pp. 263–286 (2008).https://doi.org/10.1007/978-184800-011-7_11
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Industry 4.0 Technologies on Demand Driven Material Requirement Planning: Theoretical Background and Impacts Mustapha El Marzougui1(B) , Najat Messaoudi1 , Wafaa Dachry2 , and Bahloul Bensassi1 1 Laboratory of Industrial Engineering, Information Processing and Logistics, Faculty of
Sciences Ain Chock, Hassan II University, Casablanca, Morocco [email protected] 2 Laboratory of Engineering, Industrial Management and Innovation, Hassan 1 University, Settat, Morocco [email protected]
Abstract. The innovations such as digitalization and industry 4.0 (I4.0) affect the development of new processes, concepts, and models in supply chain management. A recently invented method, Demand Driven Material Requirements Planning (DDMRP), is proposed as an innovative approach for production planning systems dealing with the dynamic and volatile market. However, the literature review points out some critical issues related to the subjectivity choice of some key factors that can restrict its application and performance. This paper discusses the importance of linking I4.0 technologies and DDMRP methods and presents some of the main I4.0 tools that impact positively on some key DDMRP factors. In this context, the purpose of this paper is to identify and investigate the novel digital technologies of industry 4.0 that are applicable to DDMRP through a literature review and its impact on the planning and control process of demand-driven models. As a result, this paper shows that industry 4.0 innovations such as digital technologies can provide new value potentials such as smart network effects and increased autonomy in the DDMRP approach. Keywords: DDMRP · Industry 4.0 · Supply chain · Digitalization · Smart DDMRP
1 Introduction Nowadays, new methods have developed aiming to face companies’ processes in highly volatile environments. Industry 4.0 and DDMRP are important concepts for companies principally, the influence of the two philosophies, on the main pillars of production: process, product, and people. The research in industry 4.0 and DDMRP is relatively recent since the two topics started to be disseminated in 2011. This study covered important key principles and the technologies of industry 4.0 that are used in the supply chain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 59–69, 2023. https://doi.org/10.1007/978-3-031-35245-4_6
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context and how these principles pillars are applied to provide a sustainable competitive advantage for the DDMRP approach. According to the literature review done in this work, the impact of industry 4.0 technologies on DDMRP processes is not well known. To address this gap, this work explores how industry 4.0 digital technologies will affect the DDMRP approach positively to improve the operational performance of organizations. The principal reasons and motivations for developing this study are related to: (1) there are only a few studies about the general repercussions of industry 4.0 on the supply chain precisely on demand-driven MRP; (2) because the knowledge of these potential correlations can influence important decisions for the industrial companies and their stakeholders to adopt this new concept. In order to understand the opportunities and possible threats from the introduction of industry 4.0 technologies, it is necessary to study the impact of industry 4.0 on the DDMRP Method. To accomplish this, the paper uses a literature review, which is a valid and structured methodology to understand and discover a research field. A literature review is a common method for thoroughly investigating a research field. This theoretical analysis has been undertaken with the main objective of identifying the opportunities and interests as a result of the introduction of industry 4.0 at each component of the DDMRP and aims to provide a thought towards a smart DDMRP. There is a need for studies providing the impact of industry 4.0 technologies on demanddriven models, leading to an important question: What are the implications of Industry 4.0 technologies for demand-driven MRP processes? In order to answer this question, the paper is organized as follows: In Sect. 2, we introduce the main points of the method DDMRP and a brief literature review of industry 4.0. In Sect. 3: we discuss the main implications of industry 4.0 technologies on the DDMRP process. Lastly, Sect. 4 provides the conclusion and future work.
2 Theoretical Background The current complexity and volatility of the market have been defined by the VUCA (Volatility, Uncertainty, Complexity and Ambiguity) environment. Organizations must adopt suitable approaches to be applied, which can provide a comprehensive response to manage this new reality. In this context, the DDMRP approach and industry 4.0 technology are two new concepts created to manage the four VUCA threats. This section analyses the theoretical background of the concepts involved in the present study, such as the context of industry 4.0 and the DDMRP Model. 2.1 Demand Driven MRP A Demand Driven Operating Model (DDOM) is a supply order generation, operational scheduling and execution model utilizing actual demand in combination with strategic decoupling and control points with stock, time and capacity buffers in order to create a predictable and agile system. Figure 1 presents a DDOM’s key parameters that are set through the Demand Driven Sales & Operations Planning (DDS&OP) process to meet the stated business and market objectives while minimizing working capital and expedite-related expenses [1]. The core of the DDOM is the innovative method of supply
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order generation and execution known as DDMRP [1]. According to capacities, driven capacity-scheduling carries out the detailed scheduling of manufacturing orders to be launched, and demand driven execution, which manages launched orders.
Fig. 1. Demand Driven Operating Model [1]
The objective of DDMRP is to amortize these variabilities of customer requests via buffers. These buffers are our shock absorbers. They will be placed and dimensioned strategically to optimize the supply chain and cushion customer demand. DDMRP is an innovative multi-echelon material and inventory planning and execution solution. DDMRP consists to place strategically decoupling points within the product structure and supply chain to absorb variability and compress lead-time. It enables a company to build more closely to actual market requirements and promotes better and quicker decisions and actions at the planning and execution level. DDMRP was introduced to the world in the book Orlicky’s Material Requirements Planning, Third Revised Edition by Carol Ptak and Chad Smith [2]. The concept of the DDMRP philosophy is to promote and protect the flow of relevant information and materials, synchronize complex and dynamic environments, pace to actual demand and provide a clear signal for every resource [1]. Figure 2 shows the five primary components of DDMRP process considered in this research. These components are interconnected: from strategic inventory positioning to visible and collaborative execution. Each of the components is defined and represented by some important parameters or factors to obtain quantifiable measures to compare if there are changes over time.
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Fig. 2. DDMRP components [1]
Strategic inventory positioning: determine where the decoupling points are placed in the supply chain to create independence between processes or entities. These decoupling points will be the only way to stop the bullwhip effect, reduce variability and compress lead times. The key factors of positioning are customer tolerance time, market potential lead-time, sales order visibility horizon, external variability, inventory leverage and flexibility and critical operation protection [1]. Buffer Profiles and Levels: determine the amount of inventory contained at those decoupling points (buffers). In this case, buffers are the heart of the DDMRP concept. Buffer sizing is the shock absorber to mitigate both supply and demand variability and reduce or eliminate the bullwhip effect. The size of the buffer is the summation of three calculated zones: red, yellow and red. The key factors are decoupled lead-time (DLT), minimum order quantity (MOQ), average daily usage (ADU), lead-time factor (LTF) and variability factor (VF). Dynamic adjustments: Dynamic adjustments define how that level of protection flexes up or down based on operating parameters, market changes and/or planned or known future events. This step of DDMRP process calms the supply chain and smooths operations for all variabilities within a set period. There are two forms of dynamic adjustment: recalculated adjustment and planned adjustment factors (PAF). The critical factors are minimum order quantity (MOQ), average daily usage (ADU), lead-time, profile change, product transitions, and seasonality. Demand-Driven planning: is the process by which supply orders (purchase orders, manufacturing orders and stock transfer orders) are generated by evaluating actual inventory, a stock that has been ordered but not received, and qualified sales order demand. Supply order generation is created by the use of the net flow equation (NFE). The key factors are inventory on hand, stock on order but not received yet, and qualified sales order demands (sales orders past due, sales orders due today and qualified spikes).
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Visible and collaborative execution: is the process by which a DDMRP system manages open supply orders. In this step, DDMRP execution focuses on current and projected inventory on hand position across the DDMRP model. The key factors are: buffer status alert (current on hand alert; projected on hand alert) and synchronization alert (material synchronization alert; lead-time alert). 2.2 Context of Industry 4.0 The concept of industry 4.0 also called the Fourth Industrial Revolution was first used in the Hannover Fair in Germany in 2011. It has been shown and described that the emergence of a new paradigm in manufacturing and production processes is based on the digitalization of factories [3]. Industry 4.0 represents a new concept of smart manufacturing networking where machines and products interact with each other without human control [4]. Industry 4.0 facilitates strategic and operational decision-making through flexible manufacturing and the analysis of large amounts of data in real-time. Thus, the industry enables the collection and connectivity of data to improve business performance by establishing intelligent and highly collaborative networks [5]. Several of studies [6-7-8, 9] address a wide range of the impacts of Industry 4.0 solutions on production practices in terms of operational productivity and efficiency, quality and flexibility, driving changes in the nature of work organization that will improve competitiveness and revenue growth. The introduction of industry 4.0 technologies into the manufacturing system has many influences on the whole supply chain [10]. Industry 4.0 improves overall industrial performance by establishing intelligent and highly collaborative networks using data and connectivity as its main characteristics [5]. Industry 4.0 is considered a system that integrates technologies, organizational concepts, and management principles and adapts dynamically and structurally to market changes in demand and supply to have a profitable, responsive, resilient and efficient network [11]. Industry 4.0 is “an integrity of technologies, organizational concepts and management principles underlying a costefficient, responsive, resilient and sustainable network, data-driven and dynamically and structurally adaptable to changes in the demand and supply environment through rapid rearrangement and reallocation of its components and capabilities” [11]. According to [12], Fig. 3 represents the technologies that are most cited in Industry 4.0 digitalization using Industry 4.0 technologies, such as Internet of Things, enforce data interchange standardization and forecasting abilities, to higher order execution ratios, leading to better coordination via improved data exchange between customers and partners. This study focuses on DDMRP modules to show which Industry 4.0 technologies and elements can be used to increase efficiency within this planning and control execution.
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cyber physical system
Big Data Cloud Compung
Simulaon
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Cyber security Industry 4.0
Virual Reality
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Fig. 3. Advanced technologies of industry 4.0 [13, 14]
3 Discussion 3.1 Impacts of Adopting Industry 4.0 in DDMRP In recent years triggered by events such as Covid-19, it appears more organizations are now applying different features of digitalization. Most tools used such as RFID (radio frequency identification), big data, cloud computing, Internet of Things, and artificial intelligence to create integrated and smart supply chain systems enabling them to synchronize to changing of markets [15]. DDMRP is an alternative to the traditional production planning and control (PPC) systems. We have identified several studies that talk about smart PPC system, that emerging digital capabilities of industry 4.0 can create new opportunities for PPC [6]. Recent surveys identified the core elements of different digital technologies and their impacts on supply chain management (SCM). The digital technologies of Industry 4.0 that are presented in the literature are Internet of Things (IoT), Internet of Services (IoS), Cyberphysical Systems (CPS), big data, advanced manufacturing technologies with sensors and Smart Factory [16]. According to the review of literature and industry 4 .0 benefits within this field, the main interpretations can be presented for a better understanding of DDMRP digitalization. DDMRP means a philosophy of flow synchronization in which industry 4.0 can give technological support to achieve excellence in manufacturing processes. Technological and digital advancements reinforce and support the transparent flow of information amongst an organization, its suppliers, and customers. The coordination between the different steps involved in the supply chain is necessary. However, the integration and coordination of all the processes in the demand-driven MRP approach are crucial to synchronizing supply and demand. Based on the past sections, it is possible to suppose the impact that implementation of industry 4.0 enabling technologies and their potential as stated in theory has on the DDMRP process.
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Currently, there are many studies showing that industry 4.0 implies changes in a different ways of manufacturing, process, product, human factors [17]. To confirm this, an analysis of how each technology of industry 4.0 affects each factor of the DDMRP component was carried out. Based on the results obtained from the literature, the next step was the formulation of propositions for the opportunities for each component in DDMRP. An initial template was built (Table 1) to link each technology with each factor and some arguments for why the technology affected the factor are presented. Table 1. Impact of Industry 4.0 enabling-technologies on DDMRP processes and the resulting factors improvements Main Industry 4.0 Technologies
Contributions on DDMRP Process
Sources
DDMRP Factors Improvements
Radio Frequency Identification (RFID)
- Real-time identification and traceability of raw material and final products - Reduce search time of product - Eliminate inaccurately or fault information flow
[18–20]
- Enables continuous stock monitoring which eventually results in reduced inventory levels -Visual control for the managers, planners, workers
Cyber physical systems (CPSs)
- Sharing information and communication between machine and system - Real-time and automatic data collection from machines, processes, and business
[4, 21]
- Capability of real-time calculations of the main factors: NFE, ADU, DLT, PAF - Allows a reliable calculation of the (NFE) of reliability on hand inventory and stock on order - Sharing databases with customers and suppliers, specifically: ADU, orders and scheduling
Internet of Things (IoT)
- Shorter lead time and better capacity - Flexibility at level process - Self-monitoring capabilities - Visibility into operations
[4–21]
- Improved decision making of buffers position using factors - Help workers with good visibility of planning and avoid mistakes and increase their productivity execution by giving a near perfect signal of demand
Big data analytics (BDA)
- Facilitate the consolidation of information - Improve planning and execution - Collaboration with suppliers/customers through real-time data sharing
[4, 20]
-Increase the visibility of buffers inventory (Measuring KPI in real time: Factors of positing and level of buffers) -Butter customer experience promotion of seasonality, product changement - Helps to identify trends and transition of adjustments of buffers
(continued)
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Main Industry 4.0 Technologies
Contributions on DDMRP Process
Sources
DDMRP Factors Improvements
Digital automation with smart sensors
- Collecting data, measuring, analyzing and triggering other processes - Smart sensors give autonomous feedback
[22]
- Collecting data from processes and products using smart sensors and analyzing them -Visual control for the planner with smart sensors and RFID - Better manage priorities of planning and execution
Robotic stations on automated production line
- Shorter lead time and better capacity - Automatic data collection
[4]
- Automate calculation about level and adjustment of buffers - Speed flow of product and information
Cloud computing technologies
- High capacity of storage - Supports systems for decision-making - Enables smart automation of customer service
[20]
- Speed decision making about: factors of positioning and adjustment of buffers - Storage capacity of relevant information about customers and supply variability
Augmented reality (AR)
- Shorter lead time - Better capacity
[4]
- Reduce internal variabilities - Get a good position of the buffers: optimization of level of inventory
Additive manufacturing (AM) rapid prototyping or 3D printing
- Reduce setup times and lead time [18, 20] - Enables agile production - Increases manufacturing flexibility and - Enables rapid prototyping
- Reduce internal variabilities: increase reliability - Real time traceability of: ADU, order spike horizon - Synchronization operations of planning and execution - Efficiency of inventory management
Artificial intelligence and machine learning algorithms
- Rapid implementation customer requirements by a high level of flexibility - Supports systems for decision-making - Optimizes inventory management - Enables smart automation of customer service
- Predict material flows and maintenance - Speed calculation of key factors: ADU, LTF, VF - Adapt production more to customers’ demands - Synchronize of demand and supply
[20]
3.2 The Benefits of Smart DDMRP While the field of digitalization is in the process of evolution and especially for the concept of DDMRP. Therefore, the objective of this review is to propose an approach for building an effective smart DDMRP model. The smart DDMRP is defined as a process that moves at the cadence of the market has new features associated with digital technology enablers. There are major opportunities for DDMRP process efficiency and implementation presented by digitalization that include increased information availability and visibility and transparency through end-to-end real-time information access and control, integration and collaboration, and efficient inventory management.
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Based on [15], DDMRP’s new digitalization model will take full advantage of capacity information, massive data and connectivity to make components intelligent: speed, flexibility, global connectivity, intelligence, transparency, cost effective, scalability. The main benefits of smart DDMRP are: • Real-time visibility: in addition to making DDMRP synchronize with current demand, it will also have real-time visibility, which will allow it to work with higher synchronization and share information throughout the whole supply chain. This visibility will help to get a precise of the key factors for implementing DDMRP process. • Continuous collaboration: collaboration and harmonization will be increased both between the DDMRP process steps and with the partners involved in the chain (supplier and customer) to improve service levels. • Adopting advanced analytics and analytics tools: real-time data storage and analysis helps all supply chain stakeholders make reliable and timely decisions to solve complex problems • Flexibility: in case of aberrant events, the capability to take suitable measures to adapt and react efficiently and effectively • Speed and enhanced responsiveness: the first principle of the supply chain is the speed of flow, with the new model the speed of material and information will be increased in addition to quick turnaround intelligent system. In short, a supply chain that is driven by demand, smart and armed with the tools of Industry 4.0 can help increase customer satisfaction and operational performance. It is the key to success for organizations to face market changes in the long term.
4 Conclusion According to the results shown in the previous section, this paper presents a review of industry 4.0 interest in the DDMRP process. This work presented reflection research based on the literature, regarding those two practices, as well as their relationship with digital technologies. DDMRP and Industry 4.0 represent two types of concepts that aim to improve operational performance, although they rely on different types of principles and tools to achieve these goals. Hence, these results can contribute to important decision support for organizations. In this context, the implementation of Industry 4.0 will affect practices that are typically related to DDMRP. This paper attempts to support companies in better understanding the impact of the relevant industry 4.0 technologies towards the achievement of the digital DDMRP. We believe that this study can be a good starting point for organizations to become involved in digitalization of supply chain projects. With regard to the limitations of this study, it must be highlighted that the theoretical implication of this paper indeed showed the impact of I4.0 tools on the digitalization of the DDMRP process. Based on the findings of this study, and in order to gain more objective results, a conceptual framework that will allow this implementation, testing of the results obtained, and assessment of how companies should digitally integrate their demand driven operating model is therefore required for future research.
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Effects of Slow Vehicles on Carbon Dioxide Emission in a Two-Lane Cellular Automata Model A. Laarej1(B) , A. Karakhi1 , N. Lakouari2,3 , A. Khallouk1 , and H. Ez-Zahraouy1 1 Laboratoire de Matière Condensée et Sciences Interdisciplinaries (LaMCScl), Faculty of
Sciences, Mohammed V University of Rabat, P. O. Box 1014, Rabat, Morocco [email protected] 2 Cátedra CONACyT, Instituto Nacional de Astrofísica, Óptica y Electrónica, (INAOE), Tonantzintla, 72840 Puebla, Mexico 3 Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, (INAOE), Tonantzintla, 72840 Puebla, Mexico
Abstract. The high-speed distribution caused by slow vehicles on a two-lane road increases the carbon dioxide emissions. In this paper, a two-lane cellular automata model was investigated to show the effect of slow-moving vehicles on carbon dioxide emissions. The dependence of emission to the density as well as the impact of a homogeneous and inhomogeneous initial configuration on the CO2 emissions were studied. The results devoted that slow vehicles have a great effect on carbon dioxide emissions, especially for low and intermediate densities. Hence, for large densities, slow vehicles have no effect, and the longitudinal interaction between vehicles contributes to the variation of CO2 emissions. Keywords: CO2 emissions · Cellular automata · Slow vehicles · Two-lane · NaSch model
1 Introduction Over the past decade, the world has witnessed significant economic and urban activity, also many factories and huge projects have been established. The quality of living has been enhanced where the number of residential buildings, water sanitation networks, and use vehicles has increased. As the result of this huge increase, the industrial and transportation emissions, among other effects became the main causes of environmental pollution. Moreover, the air quality has been affected which affect the health of the population [1–3]. This situation is expected to be exacerbated in the coming years, as the continuous rise in the standard of living leads to a significant increase in air pollutants due to fuel combustion. One of the principal causes of gas pollutants in the air is automobile engines. The list of atmospheric pollutants of automobile origin is long, among of them, one finds the oxides of carbons (CO and CO2 ), the oxides of nitrogen (NO and NO2 regrouped under the name NOx ), volatile organic compounds (VOCs) and particulate matter (PM). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 70–86, 2023. https://doi.org/10.1007/978-3-031-35245-4_7
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These substances cause diseases on humans, including cancer, respiratory infections, eye pain, and nervous system dysfunction [4, 5]. Besides the pace of experiments and research aimed at reducing harmful emissions from vehicles, it has not subsided, and the field of research has exceeded the modifications made to the vehicle, to reduce fuel consumption and thus extend to the road and driving behaviors [6, 7]. It is well established that vehicle emissions are intrinsically linked to traffic flow status, transient driving patterns, which are created by repeated decelerations and accelerations of the vehicle and frequently occur at junctions or during self-organized jamming phases on roadways (e.g., stop-and-go), produce a significant extra quantity of released exhaust pollution [8]. As a result, microscopic models must be used to examine the precise evolution of vehicular flow, which gives a strong approach to anticipating vehicle emissions. Micro-traffic flow simulations may provide a comprehensive description of a moving vehicle queue [9–12]. Traditionally, car-following models have been employed to replicate the behavior of single vehicles [13]. Recently, cellular automata (CA) models, which are discrete in both space and time, have emerged as a viable technique for micro-simulation based on the Nagel-Schreckenberg model (NaSch), a stochastic CA model for single-lane roadway [14]. Despite the simplicity of this model, it can simulate the basic phases of real-world traffic, namely free flow and traffic jams. Since then, the NaSch model has been extended to a two-lane traffic model induced by lane-changing maneuvers in numerous ways and used to analyze a variety of real-world traffic problems [15–18]. Some scholars have studied the PM emissions due to their health and environmental implications [1, 4, 5, 19]. Hence, many researchers have relied on microscopic models to investigate the vehicles emissions. Qiao et al. [20] studied the PM emission in a single lane using two types of slow-to-start models namely VDR [21] and TT [22] under periodic boundary and open boundary conditions. However, the PM effects can be mild if it was considered far from the cities like highways or expressway. In another hand, the effect of CO2 emission on global warming cannot be neglected even though far from cities. As we know the global warming is a serious problem that can induce climatic change [23]. Scholar studied the effect of many metrics in vehicular traffic on the CO2 emission. Binoua et al. [24] studied the effect of two types of traffic lights controlling methods in a single lane CA model on the CO2 emission and energy dissipation. Pérez-Sansalvador et al. [25] investigated the influence of speed humps on traffic flow and on the exhaust emissions of CO2 , NOx , VOC, and PM. Recently, Wang et al. [26], studied the pollutant emissions of mixed traffic flow which is composed of vehicles with different length and maximum speed. Li-Dong and Wen-Xing [27] analyzed the signal control strategy for reducing CO2 emission based on proposing a new relative speed discrete traffic flow model, which is derived from traditional coupled map car-following model. As we know the heterogeneity of traffic increases the energy consumption which negatively affect vehicles emissions. The heterogeneity of speed can be induced by slow vehicles or with fixed bottlenecks. In two lanes traffic the vehicles that drive over time in one lane can induce a change in the emission of vehicles. This paper aims to investigate the CO2 emission of a mixture traffic flow model which is composed of vehicles distinguished by their lengths and their maximum speed in two lanes CA model. The dependence of emission to the density as
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well as the impact of a homogeneous and inhomogeneous initial configuration on the CO2 emissions were studied. Speed frequency is also investigated. The remainder of this paper is structured as follows. In the next section, the motion model that describe the straightforward movement, lane changing rules and the traffic emission model are presented, the simulation results are discussed in Sect. 3. Conclusions are given in Sect. 4.
2 Methodology In this section, the model used for the simulation of the car-following and lane-changing rules will be described, and the method used in the calculation of the CO2 emission is also showed. We considered the model proposed by Nagel-Schreckenberg [14] where the space and time are discrete. The road is considered as a lattice of L sites, here we consider two parallel lattices to mimic the two-lane road scenario. Each site can either be empty or occupied by one vehicle, the accumulation is forbidden. In this paper we consider the heterogeneity of vehicles which can be long-slow vehicles or short-fast vehicles. The slow vehicle occupies two cells while the fast one occupies one cell where fs is the fraction of slow vehicles in the system. Hence, the periodic boundary condition is adopted where the conservation of the number of vehicles is assured. The length δx for each site of the lattice represents 7.5 m in real world. The straightforward motion is described by the NaSch model where the update of the speed and position is prescribed with the following steps: f or s S1 : Speed adaptation: vi → Min vi + 1, vmax . S2 : Safety braking: vi → Min(vi , gi ). S3 : Disturbance : vi → Max(vi − 1, 0). With probability p. S4 : Motion: xi → xi + vi . Where vi is the speed of the vehicle i, xi is the position of the vehicle i while the sorf f f s vmax is the maximum allowed speed (whether it is slow vmax or fast vmax where vmax > i+1 s ), and g = x vmax i i+1 − xi − lfors is the distance between two consecutive vehicles, where the lf or s is the length of the vehicle (i.e. fast lf = 1or slow ls = 2). sorf When a vehicle is obstructed or cannot move with its desired speed (i.e., vmax ) in its actual lanes, in real traffic driver try to find solution to enhance his individual situation by lane changing. In our computation model we are going to adopt the rules proposed by Rickert et al. [17]: • Incentive criteria S1 : gi (t) < vd . S2 : go (t) > gi (t). • Safety criterion (fors) S3 : gb (t) ≥ vmax .
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Where gb (t), go (t) are the distances between actual vehicle and back vehicle, forward (fors) vehicle respectively sited in the destination lane at instant t, and vd = min vi + 1, vmax is the hoping speed of the vehicle i in its home lane (see Fig. 1).
Fig. 1. A schematic illustration and the quantities relevant for lane change on a two-lane expressway.
Overall if those criteria were satisfied the process of the lane changing is stochastic, i.e., even though the criteria were met the vehicles can stay in their home lane or decide fors f f with a probability chlane1 or 2 to change its lane. Here we should note that chlane1 chlane2 means the probability of a fast vehicle changing lane 1(lane 2) going to lane 2 (lane 1), and the same for slow vehicles (chslane1 or 2 ). 2.1 Carbon Dioxide Emission Model The global form of a function of emission rates is expressed by a different parameter (average speed, type, instantaneous acceleration, age and other properties of the vehicles). In this paper we will consider the model proposed by Panis et al. [28] that depends on the instantaneous acceleration, velocity of vehicles and six coefficients. ei (t) = max e0 , f1 + f2 vi (t) + f3 vi2 (t) + f4 ai (t) + f5 ai2 (t) + f6 vi (t)ai (t) (1) Wherevi (t) and ai (t) are the speed and the acceleration of a i-th vehicle at time t. e0 present the specified minimum emission (g/s) for each type of vehicles and pollutant (e0 = 0), f1 to f6 are the specific constants of emission for each kind of vehicles, and contaminant, the values of those parameters are presented in the Table 1. Each emission amount varies according to the speed and the acceleration of the vehicle i. Thus, the average emission rate is: s or f Em
t0 +T N 1 1 = ei (t). TN
(2)
t=t0 +1 i=1
where N is the number of vehicles in the lane and t 0 is the relaxation time (i.e., taken as a long enough time for the system to reach a steady state).
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Pollutant of vehicles
f1
f2
f3
f4
CO2 (Pretrol) 5.53 × 10−1 1.61 × 10−1 −2.89 × 10−3 2.66 × 10−1 CO2 (Diesel)
3.24 × 10−1
8.59 × 10−2
4.96 × 10−3
−5.86 × 10−2
f5
f6
5.11 × 10−1 1.83 × 10−1 4.48 × 10−1 2.30 × 10−1
3 Results and Discussion Simulations are conducted to examine the exhaust emissions in two-lane model. In our computational study, we consider two lattices of L = 5000 sites. The simulation was performed with two categories of vehicles distinguished by their length and maximum s = 3) and fast ones with the couple (lf = 1; speed (i.e., slow vehicles (l s = 2; vmax f vmax = 5)). Vehicles at the beginning of the simulation are distributed randomly all along the two lanes according to their fractions (i.e., f f = 0.8 for fast and f s = 0.2 for slow one). The closed boundary condition is adopted which means the number of vehicles in the lattices is conserved. We carry out the simulation with the same value of randomization p = 0.2. We focused on the situation where the lane-changing are asymmetric, where f f the following measures are assumed (chlane1 = 1, chslane2 = 1, chlane2 = 1), the slow vehicles in the lane 1 have a different probability of lane-changing (i.e., this mimics the real case where slow-moving vehicles remain in the slow lane). The system runs for 80,000 steps of time. The calculation is done after the last 10 000 iterations, where the results are averaged over 100 different initial configurations. We should note that fast vehicles use petrol as a source of energy, while the slow ones use diesel as a source of energy. 3.1 Homogeneous Configuration In this subsection we will investigate the homogeneous configuration which means that the slow vehicles are distributed overall both lanes. Slow vehicles in real traffic have a great effect on traffic flow since they condense the vehicles after them into a cluster of slow vehicles. The only way to escape from this situation is to find adequate space to do a safety lane-changing, however, in some cases, the slow vehicles also change lanes to increase their speed. In that case, fast vehicles can be trapped behind slow-moving vehicles in both lanes. This situation affects the traffic flow as well as the CO2 emissions. Hence, the distribution of the slow vehicles along the two-lane can drastically affect the fast vehicles flow. One of the ways that change the distribution of the slow vehicles in the two lanes model is the lane changing probability that mimics the aim of the vehicles to change the lane when the conditions are met (i.e., the safety and incentive criterion). Therefore, as a preliminary step we focus on the effect of mixture types of vehicles on the CO2 emissions in two-lane system. We investigate the effect of the chslane1 on the CO2 emissions on both lanes. Figure 2 shows the CO2 emission for several values of chslane1 (chslane1 = 0.1, chslane1 = 0). Here, the probability of lane changing of slow vehicles in lane 1 is evaluated. In this case, we evaluate the
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restriction of some countries where it is prohibited for slow vehicles to occupy a lane continuously (or prohibited those slow vehicles occupy the right lane) on the road with multilane. Figure 2(a) displays the average CO2 emissions against density ρ for fast and slow vehicles in two-lane for chslane1 = 0.1. For slow vehicles, we can see that the CO2 emissions increases with a small slope as the density increases, until a critical density ρ = 0.2, then we observe a dramatic rise in CO2 emission to a maximum value, followed by a sharp drop to a value close to zero. We should note that for large densities the curves of CO2 emissions match which means that the CO2 emissions depend only on the longitudinal interactions of vehicles. For fast vehicles, the CO2 emissions show qualitatively and quantitatively the same behavior for all densities ranges except where ρ p [0.15; 0.22]. For the case of chslane1 = 0, Fig. 2(b) depicts the CO2 emission as a function of the density ρ. In the lane 1, the CO2 emissions of the slow vehicles increase with ρ, till reaching a maximum value then decays and converges to a close to zero value. In the lane 2, the CO2 emissions were almost unchanged in the density range [0, 0.2] for slow vehicles. As the density increases the CO2 emissions drop rapidly and reach zero values in the density range [0.21, 0.51]. Subsequently, the CO2 emissions rise rapidly pass through a maximum then started to decrease gradually till reaching the f 1 which corresponds to the CO2 emissions in the standstill situation. For the CO2 emission of the fast vehicles in both lanes show similar feature expect in intermediate densities (i.e., 0.5 < ρ < 0.7) where the CO2 emission in the lane 1 is higher than that in lane 2. To understand Fig. 2, one must analyze the microscopic interaction between vehicles which is the main cause of the change in the CO2 emission. Hereafter, in order to understand the difference between the CO2 emission of the two lanes we investigate the spacetime configurations where the evolution of the vehicles in space and time is exploited. For lane 1, when chslane1 = 0.1 and ρ = 0.2, the number of slow vehicles is slightly bigger than that in lane 2 (see the red color in Fig. 3 (a), (b)). That can explain why the CO2 emissions in lane 1 are slightly higher than that in lane 2 (see Fig. 2(a)). Here the low changing lane probability of the slow vehicles in lane 1, increases the number of slow vehicles which imped the fast vehicles to move at their desired speed, thus increase moderately CO2 emissions of fast vehicles in lane 1. For the case when chslane1 = 0 and ρ = 0.21, the lane change probability prohibits the slow vehicles to
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change their lane even though the situation in lane 1 is not suitable for them to move at their desired speed. In this case, all slow vehicles in lane 2 merge into lane 1, which means that lane 2 is controlled by fast vehicles, this explains why CO2 emissions in lane 2 for the slow vehicles equals zero in the density range [0.21, 0.51]. In the same way, the presence of slow vehicles in one lane can increase CO2 emissions of the fast vehicles in this lane, which explains why lane 1 shows higher CO2 emissions compared to lane 2.
Fig. 3. Space time diagram for the two-lane. For chslane1 = 0.1 and ρ = 0.2 (a) lane 1 (b) lane2. For chslane1 = 0 and ρ = 0.21 (c) lane 1. (d) lane2. Where the white color is the free-space and the black color corresponds to fast vehicles, orange color corresponds to slow vehicles.
As the lane changing affect drastically the distribution of vehicles in both lanes. The lane changing frequency can show us when the longitudinal interaction is the responsible for the CO2 emission. Figure 4 depicts the lane-changing frequency of the vehicles in two-lane for a mixture traffic for chslane1 = 0. As we can see in Fig. 4(a) at low-density, the lane-changing frequency of fast vehicles from lane 1 to lane 2 is larger than that for lane 2 to lane 1. In this situation the presence of slow vehicles in lane 1 increases the lane changing frequency. At high density CO2 emissions become the same for all vehicles, we explain that by the impossibility of lane changing for both types of vehicles in the two lanes, because of that the longitudinal interactions are the main cause of the CO2
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emissions. Another important note to discuss in Fig. 4(b) where in the density range [0.21, 0.51] the lane changing frequency equals zero, here the slow vehicles back to lane 1 over the time before steady state and then for that they become trapped in lane 1(we recall the initial configuration of both vehicles is homogenous which means that the slow vehicles are distributed equiprobably in both lanes). However, for ρ > 0.5 the slow vehicles that initialized at the beginning of the simulation in lane 2 cannot find adequate space to do a safe lane changing which explains the presence of the slow vehicles in the lane 2 permanently even though if we increase the calculation time, and this mimics the real case near fixed bottleneck or in jams cases. 0,012
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One of the main causes of lane changing of fast vehicles is the variability of speed or the uneven speed distribution overall the two-lane which may provoke an increase in the CO2 emission. Slow vehicles play the role of a moving obstacle for fast vehicles and the speeds of the slow vehicles have a great effect on the CO2 emissions. Therefore, subsequently, we are going to study the effect of changing the speed of slow vehicles on the CO2 emission in both lanes for the asymmetric case, where the slow vehicles in lane 1 have a low changing lane probability. This restriction causes slow vehicles to have a preferred lane which is in this case lane 1. 50
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As we can see from Fig. 5 the CO2 emission for the slow vehicles that have the maximum allowed speed equal to 1 is the lowest, slow vehicles keep a stable emission rate even the density increase, however near to the value ρ = 0.45 the CO2 emission shows a slight increase till a maximum value then start to decrease with the increasing of the density. In this case, the slow vehicles have a speed of 1 that only affected when the stopped vehicles appear in the system (by the large density or with the braking probability s increases the CO2 emission for slow vehicles, in addition, effect). The increase of the vmax the critical density that correspond to the maximum values of the CO2 emission shift toward the lowest values of the density. As shown in the spacetime diagrams (see Fig. 6) s can drive with their desired speed, the perturbation the slow vehicles in the case of vmax observed (see the slight deviation of the orange line in the space time for Fig. 6, 7, 8 (a) and (b)) is due to the braking probability that can affect the motion of slow vehicles. However, in intermediate density the distance shrunk, and the stopped vehicles appears which increase the stop and go process that explain the rise of the CO2 emissions. Hence, as the density increase the number of stop vehicles become predominant which meanly decrease the CO2 emissions. We should note that the main cause of CO2 emissions in s is the speed of vehicles. However, the contribution low densities for low values of vmax of the acceleration process become important when the stopped vehicles appear, and the heterogeneity of speed become highest. As comparted to CO2 emissions of slow vehicles in both lanes, the lane 1 shows the highest emission this is simply explained by the fact that the slow vehicles in the lane 1 have a lower changing probability chslane1 = 0.1, which create a denser space even for low densities. However, the slow vehicles have the same CO2 emissions as the fast ones in the high density (see the graph that become identic in the high density ρ > 0.8). As for fast vehicles, in the high density even though we change the speed of slow vehicles the CO2 emission is the same, in this case the spacetimes configurations show almost the same features for different values of slow vehicles speed (see Fig. 6, 7, 8 (e) and (f)). Hence, for intermediate densities we can observe a non-linear feature in s = 1 is the the CO2 emissions of the fast vehicles where the CO2 emissions for vmax s s lowest and the highest for vmax = 2. Here, slow vehicles in the case of vmax = 1 have a local effect on fast vehicles. As seen in the spacetime the slow cluster provoked by slow vehicles is only observed behind slow ones and moderate free space is observed between s = 2 the presence of the clusters (see Fig. 6 (c) and (d)). However, for the case of vmax s =3 slow vehicles provokes the perturbation overall the lane. Hence for the case of vmax a small number of slow vehicles is observed in lane 2 this provokes a low CO2 emission s = 2 show higher CO2 for the fast vehicles. Hence, in order to understand why the vmax s emissions than the case of vmax = 3, Fig. 9 depict the speed frequency of vehicles in s = 2, the number of slow-moving vehicles is higher than both lanes. For the case of vmax s that for the case of vmax = 3, thus provokes the stop and go more often for the case of s = 2 which explain why the CO2 emissions are higher as compared to the case of vmax s = 3. vmax
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Fig. 6. Space time plots of the proposed homogeneous traffic in both lanes, white color is the empty space, and the black color corresponds to fast vehicles, red color corresponds to slow s = 1. Lane 1: (a) ρ = 0.27 (c) ρ = 0.35 (e) ρ = 0.83. Lane 2: (b) ρ = 0.27 (d) vehicles for vmax ρ = 0.35 (f) ρ = 0.83. With chslane1 = 0.1.
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Fig. 7. Space time plots of the proposed homogeneous traffic in both lanes, white color is the empty space, and the black color corresponds to fast vehicles, red color corresponds to slow s = 2. Lane 1: (a) ρ = 0.27 (c) ρ = 0.35 (e) ρ = 0.83. Lane 2: (b) ρ = 0.27 (d) vehicles for vmax ρ = 0.35 (f) ρ = 0.83. With chslane1 = 0.1.
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Fig. 8. Space time plots of the proposed homogeneous traffic in both lanes, white color is the empty space and the black color corresponds to fast vehicles, red color corresponds to slow vehicles s = 3. Lane 1: (a) ρ = 0.27 (c) ρ = 0.35 (e) ρ = 0.83. Lane 2: (b) ρ = 0.27 (d) ρ = 0.35 for vmax (f) ρ = 0.83. With chslane1 = 0.1.
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3.2 Inhomogeneous Configuration In this subsection, we will investigate inhomogeneous initial configurations, which means that slow vehicles are distributed randomly only on lane 1 while fast ones are distributed on both lanes. 50
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Figure 10(a) depicts the CO2 emission of vehicles in both lanes in the case where the slow vehicles in lane 1 have a lane-changing probability of 0.1. As we can see the CO2 emissions of both kinds of vehicles in two lanes display a similar feature as seen in Fig. 2(a) except in the intermediate density range, where the CO2 emission in the lane 2 is slightly bigger than that in the lane 1. When the density increases, the situation of finding an adequate space to do a safe lane changing become smaller. We explain this by the fact that the initial configuration of slow vehicles in lane 1 reduces drastically the speed of vehicles which reduces the heterogeneity of speed that affect the acceleration process thereby reducing the CO2 emissions in this lane. We should note that the CO2 emissions are higher when the traffic status is heterogeneous. Hence, for the large density (i.e., ρ > 0.85), the slow vehicles cannot change their lanes which make the slow vehicles confined in the lane 1. That explain why we get zero in CO2 emissions value for slow vehicles of lane 2. As the chslane1 decrease (see Fig. 10(b)) the slow vehicles become confined in lane 1, which affects the speed of the fast vehicles, which explain the higher values of CO2 emission, especially in the intermediate densities. Hence, as the density increase, the effect of the slow vehicles becomes seldom. Hereafter, the only cause that determinate the CO2 emission of the vehicles is the longitudinal interaction between vehicles no matter how type the vehicles is, because the traffic is denser. We should note that the CO2 emission of the slow vehicles in lane 2 equals a zero because there are no slow vehicles in this lane because all slow vehicles keep moving in the lane 1. For low density (see Fig. 11, 12 (a), (b)) the cluster is only observed for the case chslane1 = 0.1, fast vehicles get trapped behind slow ones in both lanes. In other hand for chslane1 = 0 fast vehicles can easily escape from slow ones by changing their lane and because the lane 2 has no slow vehicles this reduce the CO2 emissions. For intermediate density (see Fig. 11, 12 (c), (d)), traffic situation in both lanes is similar, as the stop and go are observed all over the two lanes. For extremely high density (see Fig. 11, 12 (e), (f)) slow vehicles become confined in lane 1 even though that we change the probability of lane changing, where CO2 emissions are the same for both lanes (types of vehicles).
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Fig. 11. Space time diagram for inhomogeneous configuration in two-lane traffic model. For chslane1 = 0. Lane 1: (a) ρ = 0.1 (c) ρ = 0.4 (e) ρ = 0.9. Lane 2: (b) ρ = 0.1 (d) ρ = 0.4 (f) ρ = 0.9. Where the white color is the free-space and the black color corresponds to fast vehicles, red color corresponds to slow vehicles.
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Fig. 12. Space time diagram for inhomogeneous configuration in two-lane traffic model. For chslane1 = 0.1. Lane 1: (a) ρ = 0.1 (c) ρ = 0.4 (e) ρ = 0.9. Lane 2: (b) ρ = 0.1 (d) ρ = 0.4 (f) ρ = 0.9. Where the white color is the free-space and the black color corresponds to fast vehicles, red color corresponds to slow vehicles.
4 Conclusion Traffic heterogeneity impedes fast vehicles from reaching their target speed and increases carbon dioxide emissions. In this paper we propose a cellular automata model to investigate the effect of slow vehicles on the CO2 emissions and on the microscopic structure of traffic. The asymmetric scenario is evaluated, where the slow vehicles have a different lane changing probability. This situation mimics the real case where slow vehicles are prohibited to move continuously in the fast lane. The initial configuration is evaluated, where we call homogenous case when the slow vehicles likewise the fast vehicles can be distributed over the two lanes. Hence, in the inhomogeneous configuration the slow vehicles can start in slow lane only, however for both cases the fast vehicles are disturbed randomly in both lanes. We found that in both initial configurations, fast vehicles emit
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more pollutants than slow in terms of CO2 especially in intermediates density. This fact is due to the drastic effect of the slow vehicles on the fast ones.
References 1. Zhang, K., Batterman, S.: Sci. Total Environ. 450, 307 (2013) 2. Hagenbjörk, A., Malmqvist, E., Mattisson, K., Sommar, N.J., Modig, L.: Monit. Assess. 189, 161 (2017) 3. Han, S., et al.: Aerosol Air Qual. Res. 11, 128 (2011) 4. Harrison, R.M., Yin, J.: Sci. Total Environ. 249, 85 (2000) 5. Novi, C.D.: J. Socio-Econ. 44, 27 (2013) 6. Pandian, S., Gokhale, S., Ghoshal, A.K.: Transp. Res. D: Transp. Environ. 14, 180 (2009) 7. Hallmark, S., Wang, B., Mudgal, A., Isebrands, H.: Transp. Res. Rec. J. Transp. Res. Board. 2265, 226 (2011) 8. Xue, W., Yu, X., Bing-ling, C., Peng, Z., Hong-Di, H.: Phys. A. 537, 122686 (2020) 9. Vandaele, N., Woensel, T., Van Verbruggen, A.: Transp. Res. D 5, 121 (2000) 10. Heidemann, D.: Transp. Sci. 35, 405 (2001) 11. Helbing, D.: J. Phys. A. 36, L593 (2003) 12. Newell, G.F.: Transp. Res. B. 27 289 (1993) 13. Brackstone, M., McDonald, M.: Transp. Res. F Traffic Psychol. Behav. 2, 181 (1999) 14. Nagel, K., Schreckenberg, M.: J. Phys. I 2, 2221 (1992) 15. Chang, G.L., Kao, Y.M.: Trans. Res. A 25, 375 (1991) 16. Hall, F.L., Lam, T.N.: Trans. Res. A 22, 45 (1988) 17. Rickert, M., Nagel, K., Schreckenberg, M., Latour, A.: Phys. A. 231, 534 (1996) 18. Nagatani, T.: J. Phys. A Math. Gen. 29, 6531 (1996) 19. Stafoggia, M., Faustini, A., Rognoni, M., et al.: Epidemiol. Prev. 33, 65 (2009) 20. Qiao, Y.F., et al.: Phys. A 574, 125996 (2021) 21. Barlovic, R., Santen, L., Schadschneider, A., Schreckenberg, M.: Eur. Phys. J. B 5, 793 (1998) 22. Takayasu, M., Takayasu, H.: Fractals 01, 860 (1993) 23. Wigley, T.M.L., Jones, P.D.: Nature 292, 205 (1981) 24. Binoua, H., Ez-Zahraouy, H., Khallouk, A., Lakouari, N.: Int. J. Mod. Phys. C. 31, 2050154 (2020) 25. Pérez-Sansalvador, J.C., Lakouari, N., Garcia-Diaz, J., Hernández, S.E.P.: Appl. Sci. 10, 1592 (2020) 26. Zhang, L.-D., Zhu, W.-X. : Phys. A. 428, 481 (2015) 27. Wang, X., Xue, Y., Cen, B.L., Zhang, P., He, H.D.: Phys. A. 537, 122686 (2020) 28. Panis, L.I., Broekx, S., Liu, R.: Sci. Total Environ. 371, 270 (2006)
CFD Modelling and Thermal Performance Analysis of Ventilated Double Skin Roof Structure Abdou Idris(B) , Abdoulkader Ibrahim, Assabo Mohamed, and Hamda Abdi Laboratoire de Recherche en Science et Technologies Industrielles (GRE), Faculty of Engineering, Department of Electrical and Energy Engineering, Université de Djibouti, Djibouti 1904, Djibouti {abdou_idriss_omar,abdoulkader_ibrahim_idriss, assabo_mohamed_djama}@univ.edu.dj Abstract. Air conditioning is a serious issue in hot areas. The demand to live in more comfortable buildings, which is understandable, has resulted in an increase in energy consumption by air conditioning. However, in Djibouti, one of the world’s most expensive electricity countries, this demand is exacerbated by building that is inadequate and unfit for the climate. This paper investigates the design of the roof which is the surface receiving the most solar radiation and which determines the general behavior of the building. The energy performance of a double skin ventilated roof is modeled and analyzed using Computational Fluid Dynamics (CFD). This study looks at Djibouti’s climate, which is hot and humid in the winter and extremely hot and humid in the summer. To characterize the flow and heat transfer induced in the ventilated roof in a steady-state, roof simulations are carried out using the Ansys Fluent software. The effects of numerous parameters on heat gain through the roof are compared, including the internal emissivity of the upper surface, the thickness of the roof insulation, and the thickness of the vented channel. The energy-saving potential is also studied and presented in comparison to the current constructions in Djibouti. Keywords: Double skin roof · CFD · Energy Saving · Forced Convection · Natural Convection
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1 Introduction Construction elements such as double-skin roofing can be used for more than one purpose at the same time and can reduce the energy requirements of a building. Such structures © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 87–98, 2023. https://doi.org/10.1007/978-3-031-35245-4_8
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have the potential to be used in a bioclimatic approach, especially in Djibouti and other high-solar-radiation areas. With 90 percent of overall electricity usage in Djibouti, the construction sector is by far the largest [1]. Cooling demands, such as air conditioning and ventilation, continue to dominate electricity demand, contributing for more than 70% of total consumption. The study of ventilated structures is quite complicated and is dependent on many variables such as airflow rate, thermo-physical properties of materials, external conditions, and so on. Since Fracastoro et al. in 1997 [2], who examined the idea of reducing heat gain in buildings by employing under-roof cavities, much research has been done to study the performance of a ventilated roof. They performed a steady-state thermal analysis of ventilated and unventilated light roofs using a numerical model. The relationship between solar heat input and induced cavity ventilation rate was studied by Sandberg and Moshfegh [3]. Hirunlabh et al. [4] investigated different Nusselt number correlations as a function of solar radiation for various roof tilt degrees. Khedari et al. [5] experimental investigation of free convection in a roof solar collector reveals a Nusselt number dependence law on the Rayleigh number, the angle of the channel, and the aspect ratio of the ventilation cavity, i.e., the ratio of the channel’s width to its length. Ciampi et al. [6], then, Dimoudi ˇ et al. [7] and Cerne and Medved [8] observed that the ventilated roof contributes to keeping the temperature of the inner surface closer to ambient conditions, reducing the impact of solar radiation on the building. Chang et al. [9] investigated the energy savings achieved by adding a radiant barrier system in a double-skin roof in an experimental setting. Biwole et al. [10] suggested that the appropriate width for the ventilation channel must be between 6 cm and 10 cm based on numerical and experimental modeling. Using an open-ended inclined model with parallel plates to replicate the ventilated roof structure receiving solar radiation, Lai et al. [11] examined the ideal spacing as a function of the Grashof number. For their part, Villi et al. [12] constructed correlations for describing airflow and heat transport phenomena in the ventilated cavity. Gagliano et al. [13] concluded that roof ventilation can significantly lower heat fluxes (up to 50%) during the summer season based on research of the thermo-fluid dynamic behavior of air within ventilated roofs and heat fluxes through ventilated roofs. The thermal analysis of a ventilated roof requires both a complete thermo-aeraulic analysis of the air gap and accurate knowledge of the heat transfer coefficients as well as the thermo-physical properties of the materials. Most of the studies presented previously use simplified numerical models whose boundary conditions have not been completely mastered. The uncertainty of the internal and external heat transfer coefficients of the ventilated roof and of the head losses within the channel, for example, can reduce the reliability of the CFD analysis. This study completes our previous paper [14] and differs from all previous studies because we considered the entire building to analyze the thermal behavior of the ventilated roof. The objective of this paper is to study the thermal performance of the ventilated roofs solution in Djibouti. The airflow and heat transfer processes in the ventilated cavity with buoyancy-driven airflow and forced convection were characterized using CFD.
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2 Method 2.1 Model Description Building typology in Djibouti has traditionally focused on the growth of two sectors: residential and tertiary. The residential sector consists of the following categories of structures: • In most cases, a standard home’s roof is made composed of sheet metal or tiles. This kind accounts for 90% of the residential sector in Djibouti, according to DISED, the Djiboutian Agency for demographic and statistical data (DISED, 2015). (a). Figure 1 (b) shows an example of a building front face with a pitch angle of at least 5 degrees. • Residential buildings made up 2% of the total construction and had many floors and a concrete roof. • Villas, which are single-family dwellings with a tiled or concrete roof, account for 3% of all structures. • The previous structures’ walls are made out of external and internal cement plaster, as well as bricks or concrete blocks. • Tukuls are unofficial settlements that make up more than 5% of all buildings. They are highly fragile structures located in an urban slum. The current model of construction in Djibouti offers quite of potential for improving thermal efficiency. Despite the lack of traditional architecture in Djibouti, which could have served as the foundation for a sober bioclimatic design in terms of resource use and energy consumption, the crossing of the city of Djibouti still makes it easy to discover bioclimatic architectural details: Natural ventilation forcing in buildings which use perforated bricks, shading by shutters, brise-soleil for windows shading, among others. In this study, we are interested in the roof of a standard house since it represents the largest part of the building typology in Djibouti. The roof of this style of structure is usually made of peaked galvanized sheet metal. Two model configurations to reduce the heat fluxes through the roof were considered. The first configuration, as illustrated in Fig. 2, is a ventilated structure made up of two flat parts separated by an air gap that allows air to pass. The conventional roof is covered with a 6-mm screen (outside surface). Insulation is applied to the inner slab in the second configuration.
Fig. 1. (a) Different types of construction in Djibouti; (b) Example of the building with the largest share
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The Thermo-physical characteristics and geometry of the ventilated roofs are shown in Table 1. All materials have been assumed to be homogeneous and isotropic. A non-ventilated air gap separates the roof from the inside. The air gap’s thermal resistance was calculated to be 0.18 m2 /(K.W), according to ISO EN UNI 6946 norms [15]. CFD prediction also confirmed this figure. 2.2 CFD Modelling Local wind data, building density, and geometry all influence wind conditions in the urban environment [16]. These can make the contribution of wind difficult to estimate. However, despite the difficulty, it may be possible to study a case of calculation in forced convection in the present analysis. Then, in order to achieve more general conclusions, only the flow of air due to density differences caused by being at different temperatures was investigated, with the wind contribution assumed to be null, which corresponds to the worst-case scenario. We used Fluent Software to determine the thermodynamic parameters of the air within the ventilated layer and to investigate the impact of the
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various configurations mentioned above on the energy performance of the double skin roof using the CFD approach. It is a thorough modeling technique for solving the system of partial differential equations for mass, momentum, and energy conservation in an enclosed space, such as the solution domain shown in Fig. 2. A finite volume technique was used to solve the time-averaged Navier-Stokes differential equations for steady and incompressible flows while considering turbulent phenomena. Turbulence modeling is especially important in CFD simulation since incorrect modeling can be a major source of inaccuracy. By Reynolds decomposition of the equations governing fluid motion, flow is analyzed in two parts, an average part, and a fluctuating component. Flow equations are then time-averaged to yield an expression for the mean properties of the flow. All details concerning the state of the flow contained in instantaneous fluctuations are neglected. The process of time-averaging introduces additional unknown terms in the conservation equations. They are the so-called “Reynolds stresses”. The main task of turbulence modeling deals with the development of a computational procedure that enables the definition of these stresses. One of the most frequent types of turbulence models is the so-called “two-equation model.” They also include two additional transport equations to represent the flow’s turbulent properties. The standard k-”realizable” model was utilized in this paper; therefore the turbulent kinetic energy k and the turbulent dissipation rate are the additional transported variables. The precision of the flow inside the cavity is closely tied to the choice of this model [17]. The impacts of the side walls were neglected because the ventilation channel length is significantly higher than its breadth, and the calculations were carried out assuming that the flow in the hollow is two-dimensional. Within the solid parts, the no-slip condition is assumed, and we performed a simulation by imposing an energy source reproducing the quantity of solar radiation absorbed by the outer surface of the roof. In free convection, a constant relative pressure of 0 Pa has been imposed across all the surfaces surrounding the environment. These boundaries are assumed to be in ambient temperature but the exchange in long-wave radiation with the roof at the sky temperature. In the case of forced convection, we defined the inlet velocity and turbulence profile of the wind based on the measurements from our local weather station and considering the logarithmic law derived from the standard NF EN 1991–1-4 dated November 2005. To resolve the near-wall flow, CFD simulations were run using the enhanced wall treatment approach, which aims to achieve near-wall modeling accuracy comparable to a two-layer approach [18] without imposing a sufficiently fine mesh everywhere, which would have required too many computational resources. As a result, only the mesh in the near-wall region must be fine enough to allow full resolution of the viscosity-affected region. A coarser mesh is allowed elsewhere. The demarcation of the two regions is determined by a non-dimensional wall distance y + which is taken equal to 5 in this simulation. Meeting this requirement has been obtained as a result of a repeated mesh refinement process performed on the cells sited along the channel boundaries. Such adaptation is
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iterated for each of the cases that have been simulated. An example of the results on mesh dimensions for such an iterative process is displayed in Fig. 3. The simulations were carried out by considering the air as a Newtonian fluid, incompressible, of constant viscosity, and subjected to the gravity field. The discretization was based on the finite volume method and the resolution of the equations was based on the PISO algorithm in transient.
Fig. 3. Results of the meshing.
The different tested cases are presented in Table 2. Three thicknesses of the air gap were tested for different solar radiation, and three insulation thicknesses were also studied. The outside temperature (Ta) is assumed to be equal to 40 °C and the internal temperature (Ti) is maintained at 26 °C. To study the effect of the emissivity (ε) of the internal surface of the roof screen, an extreme value of the emissivity (0.1) was applied to case 4 to test the effect of a coating on the heat transfer through the roof.
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Table 2. Studied Configurations N°
d, m
Insulation
di , m
G, W/m2
ε
wind, m/s
1
0.2
No
-
800
0.8
-
2
0.2
No
-
600
0.8
-
3
0.2
No
-
300
0.8
-
4
0.2
No
-
800
0.1
-
5
0.1
No
-
800
0.8
-
6
0.1
No
-
600
0.8
-
7
0.1
No
-
300
0.8
-
8
0.05
No
-
800
0.8
-
9
0.05
No
-
600
0.8
-
10
0.05
No
-
300
0.8
-
11
0.2
Yes
0.02
800
0.8
-
12
0.2
Yes
0.05
800
0.8
-
13
0.2
Yes
0.1
800
0.8
-
14
0.2
No
-
800
0.8
Yes
3 Results and Discussion 3.1 Thermo-Aeraulic Performance of the Roof In the interests of simplification and simplicity, the flow in the air gap is analyzed based on velocities and temperature profile of air using cases 1, 4, 5, 8, 13, and 14. The simulations show that the air goes through the cavity of the roof. Its temperature is close to the outdoor temperature. The objective is to obtain a roof with a low temperature. Figure 4 (b) reveals mainly, for all simulations that the outdoor air is heated along the roof cavity. As this air is heated, it rises by buoyancy forces before being evacuated by the exit of the cavity. This evacuation of air brings fresh air that is introduced into the cavity of the roof. The velocity contours in Fig. 4 (a) indicate, for all simulations, a higher air velocity near the roof cavity outlet. For the worst case, without wind effect, this speed remains low (maximum of 0.45 m/s). In addition, it is interesting to note some differences between the configurations. The temperature contours of case 4 are blue on the inner surface of the roof compared to the case. Therefore, applying a treatment of low-emissivity in the internal surface of the screen in addition to the double skin roof can contribute to the reduction of the inner surface temperature. The heat transfer is thus essentially made by the radiation in the air gap. The choice of the surface to be treated is motivated by the fact that the ambient air is charged with the dust in Djibouti, so it is practically impossible to have such a low emissivity on the other surfaces of the roof. Simulation 13 also shows that the use of insulation with respect to case 1 greatly reduces the temperature on the inner surface. Furthermore, the lower the thickness of
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the cavity, the more difficult it is for the air to evacuate the heat from the roof surfaces. From these results, it can be concluded that the double skin roof transfers its heat to the ambient air. This indicates a favorable behavior for the thermal discharge of the building’s roof and thus being able to improve the thermal comfort inside and reduce the energy consumption due to the air conditioning.
Fig. 4. (a) Velocities contours for the case 1, 5, 8 and 13; (b) Temperature contours for the case 1, 4, 5, 8 and 13.
The temperature and air velocity profiles within the cavity are presented in Fig. 5. Two, thermal and dynamic, boundary layers are developed in correspondence with the surfaces. We note that the air in the middle of the cavity remains at the outdoor temperature for the simple reason that the thermal boundary layer of the two surfaces have not merged and the temperature strongly increases near the upper surface of the roof for cases 1, 4, 5 and 13 (see Fig. 5- (b), (d) and (f)). For case 8 where the cavity is thinner, the thermal boundary layers of the two surfaces are merged, and therefore, the air of the cavity is warmer. The average temperature of the upper surface of the roof varies from 367 K for case 4 to 357 K for case 1 while the temperature ranges from 308 K to 329 K for case 8. Although case 4 favours the reduction of the temperature of the inner surface of the roof, simulation 13 showed a better efficiency with a temperature of the inner surface of 300 K.
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Fig. 5. Velocity and temperature comparison for the case 1, 4, 5, 8, 13 (a) Velocity profiles at the inlet of the cavity (x/L = 0); (b) Temperature profiles at the inlet of the cavity (x/L = 0); (c) Velocity profiles at the middle of the cavity (x/L = 0.5); (d) Temperature profiles at the middle of the cavity (x/L = 0.5); (e) Velocity profiles at the outlet of the cavity (x/L = 1); (f) Temperature profiles at the outlet of the cavity (x/L = 1).
The air velocity profile on each cross-section also shows two dynamic boundary layers which are developed along the inner and upper surfaces except for case 8 (Fig. 5 (a), (c), (e)), with Zero value on both sides due to the no-slip boundary condition. The inlet velocities are disturbed by the edge effects and the air is accelerated in the outlet. Within the cavity, the maximum velocity is observed near the screen. This is caused by the buoyancy force, which is proportional to the difference between the plate’s temperature and the outdoor temperature. The air increases along the direction of the motion with a maximum velocity of 0.25 m/s near the outlet and 0.45 m/s near the upper surface. Similar profiles were obtained for each simulation.
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We also carried out a simulation with the effect of the wind (case 14) to estimate its effects on the flow of air in the cavity (see Fig. 6). This case is the most favorable configuration comparing to other cases. The velocity in Fig. 6 (a) and 6 (c) show velocities of up to 1.8 m/s in the cavity. The effects of the buoyancy force disappear with the contribution of the even if it weakly felt. It is also interesting to see an overpressure on the facade facing the wind (see Fig. 6 (b)).
Fig. 6. (a) Velocity contours for the case 14, (b) Pressure contours for the case 14, (c) Velocity profiles for the case 14, (d) Temperature profiles for the case 14; V_0% → x/L = 0; V_25% → x/L = 0.25; V_50% → x/L = 0.5; V_75% → x/L = 0.75; V_100% → x/L = 1.
On the contrary, the leeward facade and the roof are in a depression. Consequently, there is a difference in pressure between the windward and leeward facades, allowing ventilation in the cavity. The temperature of the air in the middle of the channel remains at the outside temperature. The temperature of the screen varies from 313 K to 327 K along the cavity; while for the inner surface the temperature increases from 307 K to 313 K along the channel (see Fig. 6 (d)). 3.2 Heat Flux Through the Roof Structure To quantify the thermal efficiency of the ventilated double skin roof, it is necessary to compare the total flux through the inner surface for the different configurations presented
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above (see Fig. 7). Only cases with the highest solar radiation value (G = 800 W/m2 ) in free convection are analyzed and compared to the case “0”. This corresponds to the standard configuration shown in Fig. 1 (b). It’s important to note that using a ventilated roof reduces the heat flux into the building by nearly half. These findings appear to be in line with those given before. When compared to a typical roof, the system’s inner surface has a low emissivity, which reduces heat input by 82%. Case 13 has the best thermal performance, reducing total flow by 91% as compared to the standard roof 0.
Fig. 7. Heat flux comparison for different configuration
4 Conclusion Numerous CFD simulations have been carried out to characterize the thermo-aeraulic behavior of a ventilated double-skinned roof under the climatic conditions of Djibouti. The results obtained by the CFD simulations evaluate the energy performance of the system by varying different parameters such as the thickness of the cavity, the insulation, or the emissivity of the internal surface of the screen. The analysis concludes that in the worst case of free convection, the performance of the system is much better if, in addition to the double skin, the internal surface of the screen is treated. The thickness of the cavity seems to have a negligible impact on the total flux through the inner surface of the roof. The addition of the insulation considerably reduces the heat flux through the roof, and the best configuration, therefore, corresponds to case 13 with a 91% reduction compared to the standard roof (good insulation and low emissivity). These results indicate the importance of using double-skin roofing as an effective way to minimize heat transfer through the roof, in hot areas such as Djibouti. Therefore, this bioclimatic technique might be considered an efficient and inexpensive technique to improve the energy performance of the building.
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References 1. Abdou, I.O.: Études de l’amélioration de la performance énergétique et de stratégies de conception architecturale bioclimatique en climat chaud et humide, phdthesis, Université de Lyon (2020). https://tel.archives-ouvertes.fr/tel-03346755. Accessed 12 Jan 2022 2. Fracastoro, G.L.G.V.: Reducing cooling loads with under roof air cavities. In: AIVC (1997). https://www.aivc.org/resource/reducing-cooling-loads-under-roof-air-cavities. Accessed 20 Mar 2022 3. Sandberg, M., Moshfegh, B.: Ventilated-solar roof air flow and heat transfer investigation. Renew. Energy 15(1–4), 287–292 (1998). https://doi.org/10.1016/S0960-1481(98)00175-X 4. Hirunlabh, J., Wachirapuwadon, S., Pratinthong, N., Khedari, J.: New configurations of a roof solar collector maximizing natural ventilation. Build. Environ. 36(3), 383–391 (2001). https://doi.org/10.1016/S0360-1323(00)00016-0 5. Khedari, J., Yimsamerjit, P., Hirunlabh, J.: Experimental investigation of free convection in roof solar collector. Build. Environ. 37(5), 455–459 (2002). https://doi.org/10.1016/S03601323(01)00054-3 6. Ciampi, M., Leccese, F., Tuoni, G.: Energy analysis of ventilated and microventilated roofs. Solar Energy 79(2), Art. no 2 (2005). https://doi.org/10.1016/j.solener.2004.08.014 7. Dimoudi, A., Androutsopoulos, A., Lykoudis, S.: Summer performance of a ventilated roof component. Energy Build. 38(6), Art. no 6 (2006). https://doi.org/10.1016/j.enbuild.2005. 09.006 ˇ 8. Cerne, B., Medved, S.: Determination of transient two-dimensional heat transfer in ventilated lightweight low sloped roof using Fourier series. Build. Environ. 42(6), 2279–2288 (2007). https://doi.org/10.1016/j.buildenv.2006.04.022 9. Chang, P.-C., Chiang, C.-M., Lai, C.-M.: Development and preliminary evaluation of double roof prototypes incorporating RBS (radiant barrier system). Energy Build. 40(2), 140–147 (2008). https://doi.org/10.1016/j.enbuild.2007.01.021 10. Biwole, P.H., Woloszyn, M., Pompeo, C.: Heat transfers in a double-skin roof ventilated by natural convection in summer time. Energy Build. 40(8), Art. no 8 (2008). https://doi.org/10. 1016/j.enbuild.2008.02.004 11. Lai, C., Huang, J.Y., Chiou, J.S.: Optimal spacing for double-skin roofs. Build. Environ. 43(10), p. 1749–1754 (2008). https://doi.org/10.1016/j.buildenv.2007.11.008 12. Villi, G., Pasut, W., Carli, M.D.: CFD modelling and thermal performance analysis of a wooden ventilated roof structure. Build. Simul. 2(3), 215–228 (2009). https://doi.org/10. 1007/s12273-009-9414-7 13. Gagliano, A., Patania, F., Nocera, F., Ferlito, A., Galesi, A.: Thermal performance of ventilated roofs during summer period. Energy Build. 49, 611–618 (2012). https://doi.org/10.1016/j.enb uild.2012.03.007 14. Omar, A.I., Virgone, J., Vergnault, E., David, D., Idriss, A.I.: Energy saving potential with a double-skin roof ventilated by natural convection in Djibouti. Energy Procedia 140, 361–373 (2017). https://doi.org/10.1016/j.egypro.2017.11.149 15. CEN, ISO 6946 - Building components and building elements — Thermal resistance and thermal transmittance — Calculation method, ISO 2007 16. Antvorskov, S.: Introduction to integration of renewable energy in demand controlled hybrid ventilation systems for residential buildings. Build. Environ. 43(8), 1350–1353 (2008). https:// doi.org/10.1016/j.buildenv.2007.01.045 17. Shih, T.-H., Liou, W.W., Shabbir, A., Yang, Z., Zhu, J.: A new k- eddy viscosity model for high reynolds number turbulent flows. Comput. Fluids 24(3), 227–238 (1995). https://doi. org/10.1016/0045-7930(94)00032-T 18. James, D., Webb, S.: Turbulent Natural Convection Heat Transfer in a Square Enclosure: Turbulence Model Comparisons, January 2004. https://doi.org/10.1115/HT-FED2004-56063
Literature Review of Energy Consumption Modeling for Mobile Devices Ayyoub El Outmani1(B) , El Miloud Jaara1 , and Mostafa Azizi2 1 Computer Science Research Laboratory (LARI), Faculty of Sciences (FSO), ESTO University
Mohammed First (UMP), Oujda, Morocco {ayyoub.eloutmani,e.jaara}@ump.ac.ma 2 MATSI Research Lab, ESTO University Mohammed First (UMP), Oujda, Morocco [email protected]
Abstract. The recent mobile devices consist of software and hardware components, such as screens, cameras, sensors (accelerometer, fingerprint reader, GPS …), connectivity adaptors (Bluetooth, WIFI …), different radio cells (H+, 3G, 4G, 5G…), processors with multiple cores, etc. The availability of these elements simultaneously on a given smartphone/tablet provides a rich experience for end users. However, the recent components consume a lot of energy, which severely limits the duration of their use, and reduces the autonomy of the device batteries. It is why the optimization of the energy consumption management becomes of crucial importance. However, most of the mobile applications developed so far have been designed unconsciously from their actual energy consumption and ignoring the energy bugs during the development process that could drain the battery later after deployment. Unfortunately, there are no quantitative approaches to detect specifically these energy bugs introduced in this fast-paced development process. Through this paper, we target to study and compare the different existing energy profilers for mobile devices, available in the literature. We report about the various techniques of energy consumption modelling, detecting energy bugs, and optimizing code structures with energy-saving practices. Keywords: Energy consumption · power/energy profilers · power models · battery monitoring · power estimation · mobile devices
1 Introduction Nowadays, there are approximately 6 billion smartphone subscriptions worldwide out of a population of 7.8 billions, representing an estimated penetration rate of over 78% [3]. This number shows that the smartphones have become an essential part of our daily lives. These devices are evolving exponentially over the last decades. They have a lot of processing power that could surpass high-end laptops of a few years ago. In addition to making traditional calls, they behave as real handheld computers: easy browsing in the internet, creating and sharing multimedia files, taking photos and videos with a good quality, doing data processing, and running very beneficial applications. The availability of these elements simultaneously on a given smartphone/tablet provides a rich experience © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 99–112, 2023. https://doi.org/10.1007/978-3-031-35245-4_9
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for end users[1]. Additionally, users prefer to continue their social, entertainment, and business activities while on the move [4]. Unfortunately, the autonomy of the batteries does not follow Moore’s Law, which poses a major problem regarding the availability of services. The only way to upper the capacity of a battery is to increase its size, which is somehow limited by the dimension of the global device. Up-to-now, there is no revolutionary solution capable to deal radically with this problem. The total energy consumption among the various hardware components has been the subject of several research publications, in order to improve the energy efficiency of applications. Several recent smartphones contain already more energy efficient components, such as displays and CPU. The improvement of OLED displays efficiency has reduced abundantly power consumption. While the efficiency factor improves from 60% to 140%, the power consumption of the 4.8-inch panel declines from 4.28 W to 1.57 W [5]. Samsung and TSMC started in 2020 volume production of 5 nm-chips, manufactured for companies such as Apple, Marvell, Huawei and Qualcomm. In today’s world, the N3 process is expected to increase performance by 10 to 15% or power consumption by 25 to 35% over the 3 nm process [6]. In this paper, we make a literature review of published works that focus on modelling and reducing energy consumption in mobile devices. We organize the remainder of this paper as follows. We present in Sect. 2 some domain terminologies. Section 3 explains the methodology of our literature review. In Sect. 4, we discuss our findings on energy saving techniques in Mobile Computing, and finally Sect. 5 concludes this paper.
2 Backgrounds According to the first quarter of 2021 statistics, Android users had a choice of 3.48 million apps from Google Play, while iOS users had a choice of 2.22 million apps from Apple [7]. Despite this high number of applications, the smartphones are still suffering from the shortness of battery life. Developers need so to look at energy optimisation during the development process. Unfortunately, a recent statistic show that most the applications developed until today are developed unconsciously on the energy side. About 18% of Android apps available on the Google Play store have energy consumption issues. Both freely available and commercial applications suffer from the problem [2]. Energy profiling enables mobile application energy optimization by pinpointing the contribution of each component of the application to the total energy budget [4]. Without it, it is so complicated to optimize and determine the causes of energy leaks [8]. In the literature, several solutions address the topic of energy profiling. To understand them, we should recall some technical concepts and definitions. For instance, in some cases, there is a fine line between power measurements and model-based profiling [9], therefore, it is important to clearly define some terms. Power measurements: This is the process of obtaining power measurements, or the current draws with external hardware such as the Monsoon Power Monitor or with internal special purpose on-board electronics such as the Nokia Energy Profiler (NEP) [9]. Power model: This mathematical formula represents power draw in terms of variables, measuring the impact of factors such as the level of use, state of the hardware
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component, usage time, and inter-component dependence on power consumption [4]. In general, these variables can be measured directly on a smartphone [9]. The model can be based on a single subsystem, a set of them, or all of the phone (system-level models). A basic example of a subsystem power model is the power model of the screen function with a single variable: the brightness level. The authors in [10] create a statistical model that breaks down energy consumption into sub-systems as the sum of all consumed energy in each subsystem: Power used = cpu + display + graphics + gps + audio + mic + . . . + wifi. The authors of [11] present the weighted power model of the device as follows: N ci pi Pest = coffset + i
where Pest is the total power consumption of the device. N is the number of hardware components. pi is a parameter that covers the power consumption of hardware component i. coffset and ci are coefficients to be determined through multiple regression analysis. Coffset could be considered as base power consumption of device that is independent of uses of such components. The authors also show the process of power characterization, to generate the power model (Fig. 1). As in the existing scheme, power characterization is done by multiple regression analysis using the above equation and training programs to collect training data used in the analysis [11].
Fig. 1. The process of power characterization [11]
Power estimation: reporting the power consumption of a mobile device or its subsystem based on one or more power models. Accuracy of power estimates is strongly
Audio
Wi-Fi
0,1
0,1
CPU_on
0,1
0,1
Wi − Fil
Wi − Fih
0,1
1–54
Rchannel
Audio_on
0-∞
npackets, Rdata
3G FACH 3G DCH
βWi−Fi_h : Equation1 βaudio : 384.62
3G DCH
Uplink_queue 3G idle
βcr βWi−Fi_l : 20
Downlink_queue
n.a
GPS.sl date_rate
n.a βCPU : 121.46 Cellular
GPS.on
GPS
freql , freqh
brightness
LCD
βuh : 4.34 βul : 3.42
1–100
0,1
0,1
0,1
0-∞
0-∞
0-∞
0,1
0,1
0–255
β3G_DCH : 570
β3G_FACH : 401
β3G_idle : 10
n.a
n.a
n.a
βGsl : 173.55
βGon : 429.55
βbr : 2.40
Power coefficient
util
Range
CPU
System variable
Category
Power Coefficient
System variable
Category
Range
(βuh ∗ f reqh + βul ∗ f reql ) ∗ util + βCPU ∗ CPU_on + βbr ∗ brightness + βGon ∗ GPS_on + βGsl ∗ GPS_sl + βwi−f i ∗ Wi − Fil + βWi−Fih ∗ Wi − Fih + β3G.idle ∗ 3Gidle + β3GFACH ∗ 3GFACH + β3G_DCH ∗ 3GDCH
Model
Table 1: HTC Dream Power Model.
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influenced by how relevant the power model is [9].The Table 1 illustrates a power model for the HTC Dream presented by the authors in [12]. Energy profiler: An energy profiler is a system that uses power models of mobile components (Display, CPU, Wi-Fi, LTE …) to estimate the energy consumption of applications on different abstraction levels, such as system, application, process, or task [9].The setup of the power profiling sometimes is difficult due to higher required expertise and lack of hardware measurement tools [13]. Figure 2 illustrates the functional design of energy profiler, which contains four steps:
Fig. 2. The functional design of energy profiler [9].
Step 1: The expert chooses the modelling method based on this domain specific knowledge. Step 2: We select the variables to generate the power models for each component. Step 3: The models are set and trained using power measures and system logs. The system logs can be used to reference the real variables involved in the power models and static learning techniques can be used to determine the coefficients of the model variables. Step 4: the final step consists of evaluating the two previous steps. Depending on the models chosen and trained, the profiler returns energy consumption results. Usually, the experts in the field check these results with external measurements in order to have good results, if this is not the case, the choice of the modelling approach and/or the selection of variables can be re-evaluated.
3 Research Method This review literature aimed to identify and analyse research works on modelling, profiling, and optimizations of the energy consumption in mobile devices like smartphones. Studied papers were first identified through manual searches over online documentary databases (Scopus, ScienceDirect, Google Scholar and IEEE Xplore) by using following keywords: Energy Profilers OR Power Estimation OR Energy Consumption OR Power profiler OR Battery Monitoring OR Power Model AND “Mobile devices”.
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Number of results
Scopus
9 780
IEEE Xplore
7,682
ScienceDirect
10,408
Google Scholar
22,300
Based on our research, we came across 9780 documents that were related to our criteria (energy profilers or power estimation or energy consumption or power profiler or battery monitoring or power model, and mobile devices), which demonstrates the considerable popularity of the topic among researchers from the revolution of smartphones in 2007 until 2020, then we limited our research by adding to our criteria the parameter subject area by selecting “engineering” and “computer science”, accordingly we got around 5358 documents between 2016–2020. Some results were excluded because they were just conference proceedings first pages and not actual papers and some irrelevant papers were eliminated due to their relevance of energy consumption of mobile devices (Fig. 3 and Fig. 4). These data (in Table 2.) were extracted from the Scopus database, which is the largest abstract and indexing database of peer-reviewed literature. it contains both publications and conference proceedings, patent records, and web sites of most important subjects.
Fig. 3. Scopus indexed papers per year.
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Fig. 4. Papers search process.
4 Discussions We focused our search on recent papers that address energy consumption issues in mobile devices, as shown in Table 4, we have classified them into three approaches: the first approach is work that focuses on building and modelling energy consumption, the second approach focuses on detecting energy bugs in applications or their commits, and the last approach targets works that recommend the implementation of an optimised code structure, with good practices for developers to have a more efficient energy consumption. 4.1 Building the Energy Consumption Model As mentioned in Sect. 2, we are interested in the process of obtaining power measurements, such as the current draws with external hardware (oscilloscope), or internal instrument such as NEP (Nokia Energy Profiler). In direct measurement, voltage and current are periodically measured using external power meters or embedded sensors (e.g., power meter). A power is calculated as the result of multiplying the two values, while total energy is calculated by integrating the power over the duration of execution. Hardware-level estimation is simple to implement and provides real-time results. However, during the execution of an application, it is not possible to know how energy is consumed inside the application and which components consume the most energy. During our research we found articles that use this method to estimate energy costs, we have listed them in Table 4. The indirect measurement correlates the power consumption with hardware performance counters using an energy consumption model. As explained in Sect. 2 building an energy consumption model is a complicated task and has several levels (modelling method, generate the power models, determine the coefficients of the model, etc..). The author’s in [2] and [9] categorizes power models into three types: system-call-based models, utilization-based models and instruction-based models. Models based on utilization: the power consumption is correlated with the usage of hardware components. For an application that uses multiple components, the power model includes all of the active subcomponents while the application is running [9].
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Android uses this method to estimates the power consumption of various applications and components based on its own energy models, while BatteryStats tracks the power consumption values for individual hardware components [14, 15]. Using timestamps, BatteryStats estimates the amount of energy consumed by the various hardware[9]. Despite the fact that the model is optimized for specific brands of devices, in the case of a new hardware model, the previous parameters of the model may need to be retrained [16]. Models based on instruction: An instruction-based model analyses the program code to be run. In this process, static analysis of the code is used to correlate energy consumption with APIs or instructions in the application. Bergel et al. [17] propose a code profiler called Pharo based on instruction models to measure the power consumption of any code expression, their results indicate that the way memory is used has a significant impact on power consumption. Models based on system calls: System call-based models correlate energy consumption with the number of system calls triggered by an application. However, utilizationbased and instruction-based models cannot capture non-linear characteristics of energy consumption, such as tail-energy ( Several components, e.g., disk, Wi-Fi, 3G, GPS, in smartphones exhibit the tail power behaviour, where activities in one entity, e.g., a routine, can trigger a component to enter a high-power state and stay in that power state long beyond the end of the routine) [18]. The Table 3 summarises the comparison between the models: Table 3. Comparison of power models Model Type
Measures
Accuracy
Overhead
Utilization
On-device
++
++
System calls
On-device
+++
+
Instruction
Off-device
+
+++
To summary, the instruction based Model is rarely used, because as a result, it is difficult to know what the energy consumption will be until the program code is run in a real device (On-device), as an example, poor wireless connection quality can cause a file to take longer to transmit and use more energy, in this case it would be better to use a usage-based model that measures the number of bits sent over time [9], its advantage of not requiring external equipment to measure energy consumption or running the software on a real machine and measurements are made Off-device. The utilization-based model is great for capturing linear relationships between resources and energy consumed by the hardware being modelled. In contrast, few smartphone components, however, have nonlinear characteristics in terms of energy consumption [19], In the case of tail-energy, for instance. The system call model can be adopted to characterize the asynchronous behaviour of the energy, using the Eprof tool, Pathak et al. presented a system-call-based approach to improving the accuracy of application energy estimation [18]. Models based on system calls provide a quantitative measure of how applications access hardware in order to overcome the limitations of utilization-based models [2], and use of these metrics
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helps build energy consumption models that are more accurate [2, 9, 19], the utilization based approaches could have an error rate as high as about 20% [20]. Finally, among these three models, the most accurate model is the one based on system calls but the latter requires overhead. 4.2 Detecting Energy Bugs Apps that consume large amounts of energy are classified as either energy hotspots or energy bugs: an energy hotspot is a situation in which executing a certain application causes the phone to consume excessive amounts of battery power, despite low utilization of its hardware resources [21], On the other hand, an energy bug is bug that causes the reduction of battery life in smartphones [22], is the case when a malfunctioning application prevents the smartphone from becoming idle, even after it has finished execution and there is no user activity. As opposed to identifying the bug in the code Zhu et al.[2], their procedure identifies the commit that introduced the energy bug or hotspots in the revision history, making it easier for developers to find the bug in the revision history. Liu et al. have developed NavyDroid [23], a tool to assess energy inefficiency problems, specifically to detect bugs in Android applications, such as sensor misuse, wake-up lock misuse, and sensor data underuse. First, the tool which consists of a simulation part; this is an application execution model that precisely specifies the event handler scheduling rules, Second, the creation of a monitor part the application execution engine simulates the behaviour of an application by generating synchronization events, state exploration, and event handler scheduling, reaching different application states. Gao et al. Developed E-Android [24]: A tool reveals six new threat, called energy collateral attacks, in particular, These attacks are capable of divulging battery information stealthily through inter-process communication, wakelock, and screen. Finally, until today, there is no radical solution capable of detecting all energy bugs and/or hotspots, but application developers can combine one or more of these or other tools to solve energy bugs and/or hotspots in their applications during the development process or after each commit for energy efficiency of their application. 4.3 Optimized Code Structure with Energy-Saving Practices Many approaches have been proposed for optimizing energy-saving applications, based on the analysis of code and on good energy-saving practices, among these factors that directly influence battery life and autonomy: firstly, the way in which the battery is charged, Horvath et al. [25] found that in cases where the users did not completely drain the battery and never let it reach 100% during charging, the drain coefficient was lower (1.42%) than in the case of a full charge, secondly we can use WI-FI instead of cellular networks as soon as we have the possibility: Zou et al. [26] developed a platform with an Arduino board and an open-source Java application, to monitoring the power consumption of video streaming, they observe that the energy efficiency (average throughput/average power consumption) with WIFI and for different locations is at least 54% higher than that of LTE, they also find that the power consumption increases when the video quality also increases, as well as the density of users and the quality of the channels (signal strength). For developers as well, several researches have been launched
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and recommendations have been drawn: Nguyen et al. [27] propose an interesting way to reduce the energy consumption of smartphones, their main idea is to offload heavy computational tasks to the cloud (more powerful computers) rather than performing them locally and downloading the results, For Machine Learning McIntosh et al. [28] present an empirical study of different machine learning algorithms that developers can use as a guide. The developers need to be aware of the trade-offs between different machine learning algorithms in terms of energy and accuracy, training more accurate models also has an energy cost [29], Mammeri et al. [30] compare image detection algorithms (Lbp-AdaBoost, Haar-AdaBoost, HOG-AdaBoost and HOG-SVM) using battery-limited devices, they deduce that a faster detector consumes less energy and vice versa. Cruz et al.[31] extract energy patterns from existing mobile applications in 1027 Android applications and 756 iOS, to identify design practices to improve the energy efficiency of mobile applications. In the end an analysis of the differences between Android and iOS devices shows, that the Android community is more energy-aware [31]. Table 4. Summary of reviewed papers Paper Approach
Date
Paper Title
Energy models/tools
Building the energy consumption model
2016
Android Power Profiler[15]
Usage-based model techniques/BatteryStats
2016
Android App Energy Efficiency: The Impact of Language, Runtime, Compiler and Implementation.[16]
AEP: Android Energy Profiler
2016
Power and Energy Code Profiling in Pharo [17]
Pharo
On Energy Security of Smartphones [24]
E-Android
Detecting energy bugs with 2016 code analysis 2018
NavyDroid: an efficient tool undefined of energy inefficiency problem diagnosis for Android applications [23]
2019
Evaluation of Machine Learning Approaches for Android Energy Bugs Detection With Revision Commits [2]
system-call-based models/CPU jiffies
(continued)
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Table 4. (continued) Paper Approach
Date
Paper Title
Energy models/tools
Optimizing code structures with energy-saving practices
2016
Evaluation of the Power Consumption of Image descriptors on Smartphone Platforms [30]
PowerTutor
2016
Studying the Energy Consumption in Mobile Devices [33]
PowerTutor and AmobiSense
2016
The Accuracy of Android Energy Measurements for Offloading Computational Expensive Tasks: Poster [27]
Using External Instruments/oscilloscope Tektronix TDS1012B Usage
2017
Battery consumption of smartphone sensors [25]
Lanoga Ltd/Windows Battery, Battery Doctor
2017
Smart Mobile Device Power Consumption Measurement for Video Streaming in Wireless Environments: WiFi vs. LTE [26]
The Arduino-based Power Monitor/Android Video Streaming Player application
2018
iBtryMntr [32]
iBtryMntr
2019
What can Android mobile app developers do about the energy consumption of machine learning? [28]
GreenMiner/Raspberry
2019
Catalog of energy patterns for mobile applications [31]
Undefined
2019
Estimation of energy consumption in machine learning [29]
real-time estimations/Intel Power Gadget and ARM Streamline Performance Analyser
5 Conclusion In this paper, we focus our review on recent papers that address energy consumption issues in mobile devices. The aim was to study and compare the existing energy profiling tools available for this kind of devices, published in existing literature. Indeed, we have studied some technical concepts and terminologies regarding mainly power measurements, power models, power estimation and power/energy profilers. Then, we show how we can build a new energy profiler, which is a real challenge for energy expert. Power models are divided into three categories, namely utilization-based models, instruction-based models, and system call-based models. According to our investigation, we conclude that the models based on system calls are the best ones in terms of accuracy.
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These models can characterize better the asynchronous behaviour of the energy than the others. On the other hand, there are also approaches that target detecting energy bugs, and optimizing codes’ energy consumption. Through this study, developers can clearly perceive the relationship between codes and their consumed energies.
References 1. Hamzaoui, K.I.: Contribution à la modélisation de la consommation d’énergie dans un dispositif mobile (2018). https://www.theses.fr/2018LIL1I030 2. Zhu, C., Zhu, Z., Xie, Y., Jiang, W., Zhang, G.: Evaluation of machine learning approaches for android energy bugs detection with revision commits. IEEE Access. 7, 85241–85252 (2019). https://doi.org/10.1109/ACCESS.2019.2925350 3. Smartphone penetration worldwide, https://www.statista.com/statistics/203734/global-sma rtphone-penetration-per-capita-since-2005/. Accessed 23 July 2021 4. Ahmad, R.W., Gani, A., Hamid, S.H.A., Xia, F., Shiraz, M.: A Review on mobile application energy profiling: Taxonomy, state-of-the-art, and open research issues. J. Netw. Comput. Appl. 58, 42–59 (2015). https://doi.org/10.1016/j.jnca.2015.09.002 5. Zhou, L., et al.: Power consumption model for AMOLED display panel based on 2T–1C pixel circuit. J. Disp. Technol. 12, 1064–1069 (2016). https://doi.org/10.1109/JDT.2016.2583665 6. Bae, G., et al.: 3 nm GAA technology featuring multi-bridge-channel FET for low power and high performance applications. In: 2018 IEEE International Electron Devices Meeting (IEDM), pp. 28.7.1–28.7.4 (2018). https://doi.org/10.1109/IEDM.2018.8614629. 7. Biggest app stores in the world 2020. https://www.statista.com/statistics/276623/number-ofapps-available-in-leading-app-stores/. Accessed 17 Aug 2021 8. Di Nucci, D., Palomba, F., Prota, A., Panichella, A., Zaidman, A., De Lucia, A.: Softwarebased energy profiling of Android apps: Simple, efficient and reliable? In: 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 103–114. IEEE, Klagenfurt (2017). https://doi.org/10.1109/SANER.2017.7884613 9. Hoque, M.A., Siekkinen, M., Khan, K.N., Xiao, Y., Tarkoma, S.: Modeling, profiling, and debugging the energy consumption of mobile devices. ACM Comput. Surv. 48, 1–40 (2016). https://doi.org/10.1145/2840723 10. Murmuria, R., Medsger, J., Stavrou, A., Voas, J.M.: Mobile application and device power usage measurements. In: 2012 IEEE Sixth International Conference on Software Security and Reliability, pp. 147–156. IEEE, Gaithersburg (2012). https://doi.org/10.1109/SERE.201 2.19 11. Kamiyama, T., Inamura, H., Ohta, K.: A model-based energy profiler using online logging for Android applications. In: 2014 Seventh International Conference on Mobile Computing and Ubiquitous Networking (ICMU), pp. 7–13. IEEE, Singapore (2014). https://doi.org/10. 1109/ICMU.2014.6799050 12. Zhang, L., et al.: Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis - CODES/ISSS 2010, p. 105. ACM Press, Scottsdale (2010). https://doi.org/10.1145/1878961.1878982 13. Jofri, M.H., Fudzee, M.F., Ismail, M.N., Kasim, S., Abawajy, J.: Quality of Experience (QOE) aware video attributes determination for mobile streaming using hybrid profiling. Ind. J. Electr. Eng. Comput. Sci. 8, 597 (2017). https://doi.org/10.11591/ijeecs.v8.i3.pp597-609 14. Android 5.0 APIs. https://developer.android.com/about/versions/lollipop/android-5.0?hl=fr. Accessed 30 Oct 2021
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15. Profils d’alimentation pour Android. https://source.android.com/devices/tech/power?hl=fr. Accessed 30 Oct 2021 16. Chen, X., Zong, Z.: Android app energy efficiency: the impact of language, runtime, compiler, and implementation. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp. 485–492. IEEE, Atlanta (2016). https://doi.org/10.1109/BDCloud-SocialCom-SustainCom.2016.77 17. Bergel, A.: Power and energy code profiling in pharo. In: Proceedings of the 11th Edition of the International Workshop on Smalltalk Technologies, pp. 1–7. ACM, Prague (2016). https:// doi.org/10.1145/2991041.2991065 18. Pathak, A., Hu, Y.C., Zhang, M.: Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof. In: Proceedings of the 7th ACM European Conference on Computer Systems - EuroSys 2012, p. 29. ACM Press, Bern (2012). https:// doi.org/10.1145/2168836.2168841 19. Pathak, A., Hu, Y.C., Zhang, M., Bahl, P., Wang, Y.-M.: Fine-grained power modeling for smartphones using system call tracing. In: Proceedings of the sixth conference on Computer systems - EuroSys 2011, pp. 153. ACM Press, Salzburg (2011). https://doi.org/10.1145/196 6445.1966460 20. Gao, X., Liu, D., Liu, D., Wang, H., Stavrou, A.: E-android: a new energy profiling tool for smartphones. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 492–502. IEEE, Atlanta (2017). https://doi.org/10.1109/ICDCS.201 7.218 21. Banerjee, A., Chong, L.K., Chattopadhyay, S., Roychoudhury, A.: Detecting energy bugs and hotspots in mobile apps. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 588–598. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2635868.2635871 22. Zhang, J., Musa, A., Le, W.: A comparison of energy bugs for smartphone platforms. In: 2013 1st International Workshop on the Engineering of Mobile-Enabled Systems (MOBS), pp. 25–30. IEEE, San Francisco (2013). https://doi.org/10.1109/MOBS.2013.6614219 23. Liu, Y., Wang, J., Xu, C., Ma, X., Lü, J.: NavyDroid: an efficient tool of energy inefficiency problem diagnosis for Android applications. Sci. China Inf. Sci. 61(5), 1–20 (2018). https:// doi.org/10.1007/s11432-017-9400-y 24. Gao, X., Liu, D., Liu, D., Wang, H.: On energy security of smartphones. In: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, pp. 148–150. ACM, New Orleans (2016). https://doi.org/10.1145/2857705.2857738 25. Horvath, Z., Jenak, I., Brachmann, F.: Battery consumption of smartphone sensors. J. Reliable Intell. Environ. 3(2), 131–136 (2017). https://doi.org/10.1007/s40860-017-0034-1 26. Zou, L., Javed, A., Muntean, G.-M.: Smart mobile device power consumption measurement for video streaming in wireless environments: WiFi vs. LTE. In: 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp. 1–6. IEEE, Cagliari (2017). https://doi.org/10.1109/BMSB.2017.7986151 27. Nguyen, Q.-H., Dressler, F.: The accuracy of Android energy measurements for offloading computational expensive tasks: poster. In: Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 393–394. ACM, Paderborn (2016). https://doi.org/10.1145/2942358.2942412 28. McIntosh, A., Hassan, S., Hindle, A.: What can Android mobile app developers do about the energy consumption of machine learning? Empir. Softw. Eng. 24(2), 562–601 (2018). https:// doi.org/10.1007/s10664-018-9629-2 29. García-Martín, E., Rodrigues, C.F., Riley, G., Grahn, H.: Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75–88 (2019). https://doi.org/10.1016/ j.jpdc.2019.07.007
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30. Mammeri, A., Boukerche, A., Zhou, D.: Evaluation of the Power consumption of image descriptors on smartphone platforms. In: Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks, pp. 119–124. ACM, Malta (2016). https:// doi.org/10.1145/2988272.2988286 31. Cruz, L., Abreu, R.: Catalog of energy patterns for mobile applications. Empir. Softw. Eng. 24(4), 2209–2235 (2019). https://doi.org/10.1007/s10664-019-09682-0 32. Gokhale, A., Wang, W., Deuskar, G.: iBtryMntr: experimental study of power consumption characteristics for wireless multimedia communication apps in iOS devices. In: Proceedings of the 11th EAI International Conference on Mobile Multimedia Communications. EAI, Qingdao (2018). https://doi.org/10.4108/eai.21-6-2018.2276546 33. Tawalbeh, M., Eardley, A., Tawalbeh, L.: Studying the energy consumption in mobile devices. Procedia Comput. Sci. 94, 183–189 (2016). https://doi.org/10.1016/j.procs.2016.08.028
Density and Thermal Properties of MWCNT/Glycerol Nanofluids Abdelhafid Abouharim(B) , Abdelghafour El Moutarajji, Rachid Abdia, and Khalil El-Hami Scientific Institute, University of Mohammed V, Rabat, BP 703, Av. Ibn Batouta, Agdal, Rabat, Morocco [email protected]
Abstract. In this paper, density and thermal conductivity of MWCNT/Glycerol nanofluids were measured at different volumes fractions (0.0025% ≤ F ≤ 0.01%) and temperatures (20 °C ≤ ≤ 40 °C). The experimental results, about the thermal conductivity that is measured by 3ω method, show clearly that the thermal conductivity increases slightly with temperature and the volumetric fraction of MWCNT nanoparticles while the density increased when volume concentration was increased and decreased with the temperature rise. Our results clearly showed an 85% enhancement in thermal conductivity of Glycerol was achieved by loading of 0.01% MWCNT. The classical mixing law makes it possible to estimate and correctly predict the evolution of the density of suspensions at volume fractions less than 0.01% with a relative error of the order of 2,4 .10–4 %. Keywords: MWCNT · Glycerol · Nanofluid · Density · Thermal conductivity
1 Introduction Thermal conductivity is one of the thermophysical properties of nanofluid and depends on several parameters such as thermal conductivities of the base fluid and the nanoparticles, the volume fraction, the shape and kind of the nanoparticles, the surface area, and the temperature [1]. Nanofluids are composed of nanoparticles or nanofibers suspended in a liquid whose typical size is between 1 and 100 nm. This type of dispersion has aroused great interest in recent years since the discovery of their thermal properties particular. Indeed the addition in a liquid of certain types of nanoparticles, even in very small proportion ( 1979 AND > 1979 AND PUBYEAR < 2022 PUBYEAR < 2022 9 387 82 410 Publication titles Topic "Environmental performance" OR "environmental management" Timespan: 1980-01-01 to 2021-12-31 (Publication Date) 6 064 32 529
The steps followed for our bibliometric analysis are represented in the figure (Fig. 3). 1 This database gathers a large number of “academic” references, recognized and unavoidable. It
allows us to know who does what in the world of science. We chose these databases because we wanted to have a fairly exhaustive look at the scientific production concerning “environmental performance”. These databases are international, multi-publisher and multidisciplinary.
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Step 1: Search Criteria Definition of keywords and search criteria
‘’environmental performance’’ OR ‘’environmental management’’
Step 2 : Database selection Web of Science (Wos) and Scopus database
6064 on WOS export in ‘’CSV’’
Step 3 : Data collection and cleaning Date from 1980 to 2021
9387 On Scopus export in ‘’txt’’
Step 4 : Methodology and software Software : VOS viewer and MS Excel
MS Excel to clean the data and delete works that are not related to the research object
A bibliometric approach based on keywords, using VOS viewer Software for visualization
Step 5 : Presentation and analysis of results
Volume of scientific production
Co-occurrence analysis (keywords)
Cluster generation (Network and Overlay Visualisation)
Fig. 3. Flowchart of our bibliometric analysis (Source: ourselves)
The collection of data was done separately for each database, but using the same methodological criteria, the works on EP and ME began to appear around the 70s, but it is only from 1980 that the number of works became significant exceeding the fifty works per year, we have selected only the works carried out between 1980 and the end of 2021, we have limited ourselves to 2021 to have a complete year and not to bias the results. The document collection focuses on the TITLE fields. The abstract and keywords were omitted because it provided results that were not consistent with the topic of interest. Scopus and WOS data were used as input for keyword co-occurrence analysis performed using VOSviewer. Note that VoSviewer provides two types of keyword analysis: original keywords or Author Keywords (provided by contributors) and index keywords
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or Index Keywords (provided by VoSviewer) which are called “keywords plus” [23]. We chose to use the “keywords plus” because they are passed through indexing, duplicate or similar term reduction. Before proceeding with the analysis, a number of general terms of little relevance to the topic (e.g., “questionnaire,” “methodology,” “comparative study,” “interview,” “literature review”, etc.) were manually eliminated. The minimum number of relationships with terms using Vosviewer was set to 10 terms. Keyword analysis focuses on the distribution of the most frequent keywords by keyword co-occurrence (keywords that appear together in the same article). The goal is to observe and highlight the most important research topics in the field. In this case, only the author keywords under the title are considered. The tool counts the number of documents in which two keywords appear together (highlighted by the authors in each document). The figures show the main keywords and the size of the nodes: the larger the node and the keyword, the higher the weight; thicker lines mean more frequent cooccurrence; the smaller the distance between nodes, the stronger the relationship. The program indicates a group of related keywords, or a group of keywords, with the same colour. Specifically, the program identifies nine groups. Using a threshold of five cooccurrences. The closer two keywords are to each other, the thicker the lines between them, the more frequent the co-occurrence (the number of documents in which they appear together) [3, 24].
4 Results 4.1 Evolution of the Number of Research Works From the bibliometric examination conducted in the Scopus database 9387 works were identified against 6064 works on WOS, which gave the following results (Fig. 4):
Scopus
Web Of science
800 700 600 500 400 300 200 100
Fig. 4. Evolution of scientific productions related to EP and EM per year
2020
2018
2014
2016
2012
2010
2008
2006
2004
2000
2002
1998
1996
1992
1994
1990
1988
1986
1984
1982
1980
0
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From the graph, we can see that research on the environmental management and environmental performance differ from one database to another, from the results collected on Scopus we can conclude that this is a topical axis, since the number of publications is growing, which justifies the enthusiasm of researchers to this theme. On the other hand, on WOS the year 2020 being the year where the most publications were made. 4.2 Ranking of Results by Discipline Figures 5 and 6 show the research production that has been conducted according to the research areas. On Scopus the research is conducted more in the environmental sciences (25%), engineering (14%), social sciences (12%), business, economics, and finance (11%). On the other hand, on WOS, research is more oriented to the scientific field, namely: Environmental sciences (26%), environmental studies (12%), engineering (12%) and science technology (10%). However, it is important to note that there is no specific field in which most of the research related to EM and EP is conducted. This is because both terms can be referenced in any field of knowledge (on Scopus we find more importance given to the discipline of management compared to WOS), since they refer to diverse topics that are associated with several issues, diverse contexts, and research approaches in which the research is conducted.
Ecology, 3.671
Engineering Civil, 3.475 Water Resources, 3.928 Energy Fuels, 5.348 Economics, 6.119
Others Categories, 8
Environmental Sciences, 31.138
Business, 7.614 Green Sustainable Management, Science 10.394 Technology, 11.845
Environmental Studies, 14.987 Engineering Environmental , 13.975
Fig. 5. Analysis by research area according to WOS
4.3 Ranking of Results by Country of Writing In addition to the bibliometric analysis according to the research areas, the results were analysed according to the country where the work was written. Figure 6 shows which countries have the greatest number of publications related to Environmental management
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Computer Science, 2.8
Decision Sciences, 2.3 Other, 11.4
Agricultural and Biological Sciences, 3.9
Environmental Science, 25.1
Economics, Econometrics and Finance, 4.9 Earth and Planetary Sciences, 5
Energy, 7.7
Business, Management and Accounting, 11 5
Engineering, 13.6 Social Sciences, 12
Fig. 6. Analysis by research area according to Scopus
and environmental performance. The country that publishes the most on these topics is the United States, the United Kingdom, followed by Australia, China, and Spain. Thus, 98% of the documents are written in English (Fig. 7).
2000 1500 1000 Scopus
500
WOS
0
Fig. 7. Country of origin of the works written on Scopus and WOS
We also find that there are many authors. The articles are written by several hands. The journals are numerous and the treatment of EM and EP concerns broad fields and themes. The authors are of very varied nationalities. However, we note that American and Asian authors are the most numerous, followed by English, Spanish, Italian and Canadian.
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4.4 Volume of Scientific Production in the Field To measure this element, we examined the number of citations that these publications received, we selected the ten works that received the highest number of citations in the two databases chosen, and the result is represented in Table 2.. Table 2. Ranking of the ten most cited works on WOS and Scopus (13/02/2022) Title of the work
Authors
Year of publication
Number of citations
Rank
Wos
Scopus
Wos
A resource-based Russo, perspective on corporate MV and Fouts, PA environmental performance and profitability
1997
2 236
2630
1
Stakeholder participation for environmental management: A literature review
Reed, M.S
2008
2 024
2 232
2
The impact of environmental management on firm performance
Klassen, R.D., McLaughlin, C.P
1996
1 279
1513
3
Revisiting the relation Clarkson, PM; Li, between environmental Y; (…); Vasvari, FP performance and environmental disclosure: An empirical analysis
2008
1096
1286
4
Effects of “best practices” of environmental management on cost advantage: The role of complementary assets
Christmann, P
2000
1 110
1218
5
Environmental management and manufacturing performance: The role of collaboration in the supply chain
Vachon, S., Klassen, R.D
2008
1 110
1048
7
Scopus
6
(continued)
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Title of the work
Number of citations
Rank
Wos
Scopus
Wos
Scopus
2004
873
986
6
7
Assessing the impact of Melnyk, SA; Sroufe, 2003 environmental RP and Calantone, R management systems on corporate and environmental performance
675
825
8
739
-----
9
The relations among environmental disclosures environmental performance, and economic performance: a simultaneous equations approach
Authors
Al-Tuwaijri, SA; Christensen, TE and Hughes, KE
Year of publication
Stakeholders and environmental management practices: An institutional framework
Delmas, M., Toffel, M.W
2004
The relation between environmental performance and environmental disclosure: a research note
Patten, DM
2002
621
735
9
10
Does the market value environmental performance?
Konar, S and Cohen, 2001 RA
596
679
10
12
The selected works have a high number of citations, which shows the importance and the interest of researchers in the themes related to environmental performance and environmental management. However, for both databases much of the selected works are focused on environmental disclosure and stakeholders, while the other works are related to the resource-based perspective, firm performance, and environmental best practices. Thus, since the founding of the Management Institute for Environment and Business and Organizations and the Natural Environment research communities in 1990, a large body of work has begun to emerge dealing with and linking both environmental performance and environmental management. However, this academic work has involved several disciplines such as sociology, economics, and philosophy, and has subsequently been anchored in management through theories of organizations. These works were strongly dominated by empirical research and a minority by case studies, which is
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why quantitative indicators were used to the detriment of organizational and qualitative indicators. Three periods [25] can be distinguished to present the evolution of the characteristics of the research (Fig. 8):
During the 1992-2000 period, research focused on quantitative indicators, mainly used in empirical studies. The main research question is whether (it pays to be green) and under what conditions companies can reconcile the objectives of economic competitiveness with their environmental responsibility
During the period 20012007, academic research has moved quite significantly towards a more sustained mobilization of organizational indicators mainly in case studies. In this case, EP reflects the environmental practices used by companies to reduce their ecological footprint.
During the period 20082017, academic research has opened up to mixed and normative research methods where the indicators mobilized combine quantitative and organizational measures. The studies are particularly interested in proactive environmental strategies, EMS certification and the integration of stakeholders in environmental management.
Fig. 8. Evolution of the awareness of environmental issues by researchers by identifying EP and EM as key research words
4.5 Co-occurrence Analysis The bibliometric analysis of the output of the software used with the data collected on Scopus shows us that there are five clusters, the main keywords of which have been selected (Appendix 1). The result of the software from the WOS data is different from the one from Scopus, the analysis of the co-occurrences of the keywords from Wos gave us 7 clusters, whose main keywords are represented in the Appendix 2. 4.6 Cluster Analysis → According to Scopus After being analysed with VosViewer, there were 5 clusters (red, green, yellow, blue and purple) that showed the relationship between one topic and another, on the 1,970 keywords found 23,440 links (Figs. 9, 10). The overlay visualization shows that research currently focuses on: Environmental management practices; Environmental strategy; Integrated management systems; Financial performance; Environmental performance evaluations; Environmental indicators;
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Fig. 9. Co-occurrence network of keywords in the research domain according to ER1 on VOS based on Scopus data using “network visualization”
Fig. 10. Co-occurrence network of keywords in the research domain according to ER1 on VOS on the Scopus database using “overlay visualization”
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Sustainability; Performance evaluation; Environmental assessment; Environmental technology; Carbon dioxide; Energy efficiency; Global warming; Energy use; Environmental performance; Life cycle and life cycle assessment. → According to Web of Science There is a large body of work linking environmental performance and environmental management. The first article associating the concepts of environmental performance and environmental management dates from 1992 [25]. However, when analysing the output of our results and focusing on the managerial aspect, we find that most of the works in this field deal with environmental performance and finance, management and EP, institutional pressures, governance and policies, and the technical aspect of the environment. The co-occurrence of the authors’ keywords is analysed with a minimum threshold of 5 per author. Of the 1146 keywords, 15419 links are found (Figs. 11, 12).
Fig. 11. Co-occurrence network of the keywords in the research domain according to ER1 on VOS based on the WOS data using “network visualization”
The overlay visualization shows that the research is currently focused on: Corporate social responsibility; CO2 emissions; Economic growth; Human resource management; Product innovation; Institutional pressures; Green product innovation; Eco-innovation and Stakeholder pressure.
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Fig. 12. Co-occurrence network of keywords in the research domain according to ER1 on VOS based on WOS data using “overlay visualization”
5 Discussion Together with the growing concern about EM and EP among practitioners, there is also a significant increase of scientific production since 2000. As all these terms are generic and transversal, their intersection is discussed in the literature from many different perspectives and diverse levels of generalization. Additionally, there is a considerable number of terms that appear within the analysed intersection, and a multitude of different combinations in which these two general concepts are combined in the scientific discourse. In addition, the work conducted shows that the results obtained are not similar between the two databases used and that the most relevant authors are similar in the two resources consulted. Regarding the main trends of the topic, the study has identified a double evolution. First, we note that, in the beginning, the research was focused on EM, but today, we observe a shift towards EP and the importance of sustainability and life cycle assessment. However, the numerous citations received by the main documents reflects the relevance of the topic. For instance, the most cited document in EP has a little more than 2630 citations, and another document that focuses on stakeholder participation for environmental management has more than 2232 citations. In addition, other many documents have more than 500 citations. The citations per year also indicate the importance of the topic. We may also analyse the research trends using the topic nodes that have been identified. Therefore, this study reveals the importance of the concentration of articles that
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seek to provide an overview of the EM and EP. However, it is significant that we can observe nodes linked to more organizational and business management aspects, such as environmental management systems, sustainability, efficiency, and decision making, and those technical such as pollution. Considering the multitude of thematic and grammatical configurations in which the terms analysed in this article may be used together and the abovementioned problems resulting from the use of two partially incompatible databases, it is not surprising that the bibliometric analysis of the discussed research field is complex and time-consuming. On the other hand, only an incredibly detailed and in-depth analysis may allow for a reliable assessment of the scientific productivity in this field. The article simply provides a broad overview of the issues under investigation, leaving more intricate in-depth analytical work, including a systematic literature review, to subsequent studies. Other restrictions on the results discussed above should also be addressed. It is crucial to note right away that not all articles in the study’s field are included in the data that was pulled from Scopus and WoS. The article simply provides a broad overview of the issues under investigation, leaving more intricate in-depth analytical work, including a systematic literature review, to subsequent studies. Other restrictions on the results discussed above should also be addressed. It is crucial to note right away that not all articles in the study’s field are included in the data that was pulled from Scopus and WoS. This study is not without limitations, some of which are good opportunities for further research. First, the use of a purely objective technique, the collection of keywords, may lead to confusing interpretations if it is not complemented with qualitative analyses. In this case, the relatively small sample of documents analysed could limit the interpretation of the results. Secondly, this study focuses on the material provided by the WoS et Scopus database, this procedure can help to guarantee the inclusion of the most important and consistent works. Nevertheless, further research could use other materials such as doctoral theses or other reports such as conference proceedings. The software used in this study and other bibliographic tools, methodologies and software could improve or extend the conclusions. Finally, future research could apply a qualitative approach to the analysis of the identified clusters.
6 Conclusion In contrast to other types of studies.This study discusses bibliometric analysis and analysis concepts compared to classical studying theoretical literature in management sciences–rules, data, methods, foundations and software. The bibliometric study’s process and stages discuss EM and EP related to the emergence of words, the most influential researchers in this field, critical research work, reference sources, reference countries, and references. Research institutions rely on the VOSviewer network, density software outputs, research results, and suggestions. There is a relatively rich literature where the terms “environmental management” and “environmental performance” appear together or separately, the scientific production in this area of research has grown significantly and it has attracted increasing interest over the last decade, we can highlight the importance of the environmental management and
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environmental performance as a research area due to the number of publications that have been indexed in the most important bibliometric databases. Moreover, through bibliometric examination, it has been possible to identify that EM and EP is a term applied by multiple disciplines and that adapts to all kinds of methods and academic contexts, which makes its definition and classification difficult. As a result, on Scopus the research is more oriented towards environmental management and the managerial aspect of the environment, while on WOS the research is more technical and oriented towards environmental performance. Finally, the results of this study can aid the decision making of researchers, politicians, and institutions. The paper provides guidance for researchers to help them focus their publication efforts and identify scholars who conduct research in common fields, facilitating networking between researchers. It also helps politicians and institutions by providing a reference for their decisions on whether to finance certain fields of research. Acknowledgments. The authors would like to thank the anonymous reviewers for their contributions.
Appendix Appendix Table 1. The Clusters identified by bibliometric analysis of Scopus data via VosViewer with the most frequently occurring keywords Thematic group with central keyword
Keywords
Occurrences
Link
Cluster 1: Red (156 items) Environmental management systems
Benchmarking environmental engineering environmental management system environmental management system (ems) environmental management systems environmental protection industrial management industry iso 14001 laws and legislation management manufacture performance planning pollution control project management public policy risk assessment societies and institutions standards
110 226 182 112 404 561 94 98 165 141 210 121 96 121 161 100 104 104 106 142
282 286 263 255 418 469 212 277 259 260 322 264 194 311 334 221 233 245 217 223
(continued)
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(continued) Thematic group with central keyword
Keywords
Occurrences
Link
Cluster 2: green (150 items) Environmental performance
carbon dioxide climate change costs economic and social effects emission control energy efficiency energy utilization environmental assessment environmental impact environmental impact assessment environmental performance environmental technology gas emissions global warming greenhouse gases life cycle life cycle analysis life cycle assessment (lca)
155 150 113 114 121 157 172 125 888 99 1597 151 112 125 128 420 122 462
324 360 280 293 304 317 330 304 575 266 593 337 308 285 324 448 288 420
Cluster 3: blue (148 items) decision making
decision making ecology economics environment environmental planning environmental sustainability pollution waste management
322 110 136 223 88 106 111 170
477 232 353 346 190 269 293 352 (continued)
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(continued) Thematic group with central keyword
Keywords
Occurrences
Link
Cluster 4: Yellow (131 items) Environmental management
commerce competition corporate environmental performance corporate social responsibility developing countries environmental innovation environmental management accounting environmental management practices environmental strategy financial performance human resource management information management innovation stakeholders corporate strategy environmental economics environmental management environmental policy environmental regulations performance assessment sustainability
58 69 73 69 60 28 84 93 50 99 50 86 79 131 66 160 3167 174 132 250 366
195 214 133 114 190 48 100 195 116 151 146 223 199 196 132 270 623 289 285 410 445
Cluster 5: Purple (39 item) Efficiency
construction industry Efficiency Environmental conditions environmental performance assessment environmental performance evaluations sustainable development
88 74 38 52 47 769
232 192 112 190 158 583
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Appendix Table 2. The Clusters identified by bibliometric analysis of Web of Science data via VosViewer with the most frequently occurring keywords Thematic group with central keyword
Keywords
Occurrences
Link
Cluster 1: Red (185items) Environmental management
Climate-change Environmental management Environment governance conservation indicators quality sustainable development participation policy biodiversity stakeholders decision-making
72 825 175 150 120 121 134 219 70 145 67 60 67
196 538 248 302 217 240 710 348 180 289 136 188 185
Cluster 2: green (148 items) Life cycle assessment
Life cycle assessment Emissions Energy Technologies Consumption Environmental impact Impacts Optimization systems
284 173 187 66 70 93 105 82 218
928 304 278 194 196 148 237 148 383
Cluster 3: blue (107 items) Sustainability
business cleaner production competitive advantage eco-innovation environmental management practices firm performance green impact innovation resource-based view strategy supply chain management sustainability
46 54 102 39 40 137 231 502 177 126 149 88 564
161 142 218 135 121 280 367 549 326 254 311 218 574
Cluster 4: Yellow (90 items) Pollution
co2 emissions eco-efficiency pollution productivity technology self-regulation energy-consumption energy efficiency
78 61 147 65 42 39 38 59
160 170 288 179 147 128 114 164 (continued)
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(continued) Thematic group with central keyword
Keywords
Occurrences
Link
Cluster 5: Purple (84 item) Environmental performance
Financial performance Behaviour corporate environmental performance corporate social responsibility corporate social-responsibility disclosure economic performance environmental performance environmental policy responsibility risk
218 68 52 49 146 68 65 747 59 60 57
326 179 154 136 272 185 189 555 158 183 140
Cluster 6: light blue (59 item) Environmental management accounting
Organizations Environmental environmental management accounting smes environmental performance evaluation
60 38 61 52 28
183 99 122 146 58
Cluster 7: Orange (54 item) Environmental management system
Environmental management system Iso 14001 Performance Corporate Emas Industry Benefits Adoption Certification implementation
197 81 300 114 44 125 50 100 86 112
282 183 399 240 114 282 152 244 196 296
References 1. Gavronski, I., Ferrer, G., Paiva, E.L.: ISO 14001 certification in Brazil: motivations and benefits. J. Clean. Prod. 16, 87–94 (2008). https://doi.org/10.1016/j.jclepro.2006.11.002 2. González, P., Sarkis, J., Adenso-Díaz, B.: Environmental management system certification and its influence on corporate practices: evidence from the automotive industry. Int. J. Oper. Prod. Manag. 28, 1021–1041 (2008). https://doi.org/10.1108/01443570810910179 3. Waltman, L., van Eck, N.J., Noyons, E.C.M.: A unified approach to mapping and clustering of bibliometric networks. J. Informetr. 4, 629–635 (2010) 4. Gendron, C.: La gestion environnementale et la norme ISO 14001 (2004) 5. Gilpin, M.E.: Metapopulation Biology: Ecology, Genetics, and Evolution, Elsevier (1997) 6. Brouwer, M., Van Koppen, C.: The soul of the machine: continual improvement in ISO 14001. J. Clean. Prod. 16, 450–457 (2008). https://doi.org/10.1016/j.jclepro.2006.08.022 7. Hertin, J., Berkhout, F., Wagner, M., Tyteca, D.: Are EMS environmentally effective? the link between environmental management systems and environmental performance in European companies. J. Environ. Plan. Manag. 51, 259–283 (2008) 8. ISO International Organization for Standardization: ISO 14031: 1999. Environmental management—environmental performance evaluation—guidelines. https://www.iso.org/cms/ren der/live/en/sites/isoorg/contents/data/standard/02/31/23149.html
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9. Turki, A.: Comment mesurer la performance environnementale? Gestion 34, 68–77 (2009) 10. Denning, K., Sharstri, K.: Environmental Performance and Corporate Behavior (2000) 11. Boiral, O.: Managing with ISO systems: Lessons from practice. Long Range Plann. 197-220 (2011) 12. Darnall, N., Henriques, I., Sadorsky, P.: Adopting proactive environmental strategy: the influence of stakeholders and firm size. J. Manag. Stud. 47, 1072–1094 (2010). https://doi.org/10. 1111/j.1467-6486.2009.00873.x 13. Hughey, K., Tait, S., O’Connell, M.: Qualitative evaluation of three ‘environmental management systems’ in the New Zealand wine industry. J. Clean. Prod. 13, 1175–1187 (2005). https://doi.org/10.1016/j.jclepro.2004.07.002 14. Comoglio, C., Botta, S.: The use of indicators and the role of environmental management systems for environmental performances improvement: a survey on ISO 14001 certified companies in the automotive sector. J. Clean. Prod. 20, 92–102 (2012). https://doi.org/10.1016/j. jclepro.2011.08.022 15. Chiappetta-Jabbour, C., Govindan, K., Teixeira, A., Freitas, W.: Environmental management and operational performance in automotive companies in Brazil: the role of human resource management and lean manufacturing. J. Clean. Prod. 47, 129–140 (2013). https://doi.org/10. 1016/j.jclepro.2012.07.010 16. Gomez, A., Rodriguez, M.: The effect of ISO 14001 certification on toxic emissions: an analysis of industrial facilities in the north of Spain. J. Clean. Prod. - J CLEAN PROD. 19, 1091-1095 (2011). https://doi.org/10.1016/j.jclepro.2011.01.012 17. Link, S., Naveh, E.: Standardization and discretion: does the environmental standard iso 14001 lead to performance benefits? Eng. Manag. IEEE Trans. On. 53, 508–519 (2006). https://doi. org/10.1109/TEM.2006.883704 18. Massoud, M., Fayad, R., Kamleh, R., El-Fadel, M.: Environmental Management System (ISO 14001) certification in developing countries: challenges and implementation Strategies. Environ. Sci. Technol. 44, 1884–1887 (2010). https://doi.org/10.1021/es902714u 19. Rao, P., O’Castillo, O.L., Intal, P., Sajid, A.: Environmental indicators for small and medium enterprises in the Philippines: empirical research. J. Clean. Prod. 14, 505–515 (2006). https:// doi.org/10.1016/j.jclepro.2005.03.016 20. Curkovic, S., Sroufe, R., Melnyk, S.: Identifying the factors which affect the decision to attain ISO 14000. Energy 30, 1387–1407 (2005). https://doi.org/10.1016/j.energy.2004.02.016 ˇ 21. Zupic, I., Cater, T.: Bibliometric methods in management and organization. Organ. Res. Methods. 18, 429–472 (2015). https://doi.org/10.1177/1094428114562629 22. Mulet-Forteza, C., Genovart-Balaguer, J., Mauleon-Mendez, E., Merigó, J.M.: A bibliometric research in the tourism, leisure, and hospitality fields. J. Bus. Res. 101, 819–827 (2019). https:// doi.org/10.1016/j.jbusres.2018.12.002 23. Liu, Z., Yin, Y., Liu, W., Dunford, M.: Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis. Scientometrics 103(1), 135–158 (2015). https://doi.org/10.1007/s11192-014-1517-y 24. Botella-Carrubí, M.D., Garrigos-Simon, F.J., Gonzalez-Cruz, T.: Social Capital, Human Capital, and Sustainability: A Bibliometric and Visualization analysis (2018). https://doi.org/10. 20944/preprints201810.0748.v1 25. Jaggi, B., Freedman, M.: Un examen de l’impact de la performance de la pollution sur la performance économique et du marché: Entreprises de pâtes et papiers (1992)
Intelligent Multisensors System, Temperature, Gas and Sound, Using Arduino Hind Mestouri1(B) , Saida Bahsine2 , and Kamal Baraka1 1 Laboratory of Mathematical Computer and Communication System UCA, Safi, Morocco
[email protected] 2 Laboratory of Fundamental and Applied Physics UCA, Safi, Morocco
Abstract. This project proposes an intelligent sensor system, using multiple sensors, for monitoring applications, using Arduino. The system includes a gas sensor for detecting gas leakage (such as smoke and butane), and sends an alert. A temperature sensor is used, in case of fire, to send an alert or trigger a water system, and a sound sensor, to detect a presence in case of theft (for example) and send an alert. The system is fixed but it explores its surroundings by turning on itself with a servomotor. An initial prototype is established; different detection results are obtained and discussed. A more efficient system, mobile and accessible via Wifi, connected to a computer or a Smarphone is proposed. Keywords: Gas sensor · Temperature sensor · Sound sensor · Servomotor · Arduino board · Arduino Sorfward · Fritzing
1 Introduction Connected objects, internet of things, are a recent efficient way to collect data and even to process them in some cases. The explosion of this technology is due to the miniaturization of electronic components but also to their low cost and eases of implementation [1]. The human being is always in front of situations where his response time is considered long, for example, a gas leak that can lead to asphyxiation or fire, either in buildings or in factories, hence the importance of a system that helps us to detect these situations and react before the aggravation of the problems so as to prevent catastrophic consequences. The main objective of this work is to realize an intelligent system, using an electronic board Arduino programmed to start a fan, or trigger an alarm. Display the detected temperature values and trigger an alarm in case of fire. The detection of unusual noise, the sound sensor will send a noise detection message for the case of unwanted presence. In this article we start by presenting the characteristics of the different components used, and the software used. A simulation model and a first prototype are presented, we have performed several calibrations, and we will present the different results and discussions.
2 Components and Software In this part, we will present the components and the different software used for the realization of our intelligent sensor system [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 230–239, 2023. https://doi.org/10.1007/978-3-031-35245-4_21
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2.1 Components • Arduino UNO Arduino is an open-source project that provides a platform for prototyping interactive objects, consisting of an electronic board and a programming environment. The electronic board is not equipped with any type of connector other than the USB port, so it uses a different module (Wifi, Bluetooth, memory card reader, etc.) which makes it cheap, extremely adaptable to different projects and economical from an energy point of view [2] (Fig. 1).
Fig. 1. Arduino UNO
There are different types of Arduino boards of different sizes and with different features. For our project we chose the Arduino UNO board, one of the most standard boards and one that meets our needs perfectly. The Arduino UNO board is based on an ATMega328 microcontroller clocked at 16 MHz. It is the most economical microcontroller board of Arduino. Connectors located on the outer edges of the board allow plugging a series of complementary modules. • Temperature sensor LM35 The LM35 is integrated circuit, calibrated at the factory, for use as a precision temperature sensor (Fig. 2). Specifications [2, 4]: • • • • •
Typicalconsumption 60µA. Accuracy: ±0.75 °C (typical). Calibrated directly in degrees Celsius. Probe gain (output): 10 mV/°C. Supply voltage: +4 to +30 V (+20 V recommended).
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Fig. 2. Temperature sensor LM35
• Gas Sensor MQ-2 Gas Sensor MQ-2 (Fig. 3) is a sensor with an analog output (A0) that signals the presence of smoke by raising the output voltage. The sensor delivers a voltage proportional to the gas concentration. When the gas concentration is higher, the output voltage is higher to. In our case, the reading of the reference voltage on the Arduino is in reference condition, output A0 returns a value around 200 mV and this value rises quickly in the presence of gas. Specifications [2, 4]: • • • • •
Vcc 5 V input; GND input; Analog output A0; Digital output D0; Detects: smoke, propane, butane, methane, alcohol, hydrogen, LPG.
Fig. 3. MQ-2 gas sensor
• Sound Sensor W104 The W104 (Fig. 4) sound sensor uses a very sensitive microphone for good detection quality. This didactic sound sensor consists of an electrets microphone and an amplifier. This sensor delivers an analog and a digital signal according to the received sound intensity. The sensitivity is adjustable via an adjustable potentiometer. Specifications [5]:
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Frequency band: from 50 Hz to 20 kHz. Input Vcc 3,3 to about 5 V; GND input; Analog output A0; Digital output D0;
Fig. 4. Sound sensor W104
• LEDs A light-emitting diode (LED) (Fig. 5) is a semiconductor device that emits visible light when an electric current passes through it. The light is monochromatic, occurring at a single wavelength, and is not particularly bright. They will be used as actuators for gas and sound detection.
Fig. 5. LEDs
• Breadboard The breadboard (Fig. 6) is a solderless device for making brief models with equipment and for testing circuit designs. Most electronic sections of electronic circuits can be interconnected by installing their wires or terminals in the openings and, a little later,
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establishing a relationship through wires, if necessary. The test plate consists of metal strips placed underneath the plate and associated with the openings located at the most noticeable location on the plate. The metal strips are arranged in a fan-like pattern as shown below. Note that the top and base openings are connected evenly and separated in the middle in an upward direction.
Fig. 6. Breadboard
• Servomotor Servomotors are actuators. Widely used in model making and industry, their main characteristic is their “torque”, i.e. the rotational force they can exert. The more torque a servomotor has, the more it will be able to operate heavy “members” such as moving an arm that carries a load (Fig. 7).
Fig. 7. Servomotor
2.2 Softwars • Arduino software The Arduino software is a free hardware board with a microcontroller on it that can be programmed to analyze and produce electrical signals in order to perform a variety of tasks such as controlling a robot.
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• Fritzing software The Fritzing software [6] is an open-source hardware initiative that makes electronics accessible as a creative material for everyone. It offers a software tool, a community site, and services in the spirit of Arduino and processing, fostering a creative ecosystem that allows users to document their prototypes, share them with others, teach electronics in a classroom, and layout and build.
3 Development of a Simulation Model and Prototype 3.1 Simulation Model For this study we have established a simulation model of our system, thanks to the software Fritzing, knowing that we have the different characteristics of each component that we will use (Fig. 8).
Fig. 8. Simulation of the global connection diagram
3.2 Creating a Prototype We started by assembling and connecting the components of our system using the connection cables and the breadboard. As shown in Fig. 9. The presented assembly allows measuring continuously the temperature; we can visualize the detected values during an interval of time that we will determine in the test. This visualization is done at the level of the Arduino IDE. It can still detect the presence of gas, thanks to a gas sensor, which measures the concentration of smoke. An LED will react according to the value detected by the sensor. The other sensor presented in the assembly is the sound sensor; it has the task of powering an LED to the detection of sound. A servomotor is used to monitor 180° of detection area.
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Fig. 9. Assembly of the prototype system
3.3 Block Diagram In order to understand the functioning of the program, and to make it more readable, we have preferred to put it in the form of a flow chart (Fig. 10). We had to create the Arduino program to read the detected data and generate the appropriate outputs. Once the quantities are detected, it reads them and displays them in IDE Arduino, as it turns on the LEDs for gas detection and sound and displays the results of temperature detect, while turning the servo motor to explore the whole surroundings of the system.
Fig. 10. Flowchart of the system
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4 Results and Discussions 4.1 Temperature Measurements We first measured the initial value of the temperature in the absence of flame, the maximum value that the sensor can detect is 100 °C, in order not to damage it, we measure the maximum distance corresponding to this value, we find 5 cm, we make vary this distance until 45 cm by a step of 5 cm and we measure. The calibration is repeated several times, varying the intensity of the fire. Normally the temperature value detected is displayed in mV, and then converted into degrees Celsius, but we have modified the code to display it in Celsius directly for better readability and ease of measurement. The results are shown in Fig. 11.
Fig. 11. Graph showing the calibration of the temperature sensor
We note that the variation of the measured value and the distance are inversely proportional. Between 5 cm and 15 cm and between 25 cm and 45 cm the measured value is almost stable, it undergoes a small variation. Between 15 cm and 25 cm the relationship between the distance and the measured value is approximately linear. The temperature measurements start to take the same value of 25.2 °C from appropriate fire intensity. For this sensor we have done, however, an angular calibration. As before, we measure the initial value that the sensor detects in the absence of flame, then we measure the distance corresponding to the maximum value of the sensor at a well-defined angle, the angular interval varies from 0° to +40° by a step of 5°. We repeat the calibration several times by varying the intensity of the fire. We found that the sensor calibration measurements begin to take on the same 252 mV value at the appropriate fire intensity. The maximum value is at the angle Ø° to
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the sensor, which means that the sensor reaches its maximum response when the flame source is in front of it. 4.2 Gas Detection Similarly for the gas sensor we measure the initial value captured by the sensor in the absence of smoke, then we measure the distance corresponding to the maximum value of the sensor at a well defined angle. We note that this sensor has a very wide angular range which does not help us to direct us to the source of gas with precision, also the vapor state of gas forces us to vary the distance of the source of gas vertically from the sensor. We find that the higher the gas concentration, the higher the output voltage. Also the LED lights up in the presence of gas, we can also connect an alarm. 4.3 Sound Detection For the values detected by the sound sensor, we notice that 1’s for each distance variation, and the LED remains lit all the time. Indeed, the LED takes as input value the result of the sensor, our sensor has a high sensitivity, which led to the permanent supply of the LED because of the existence of noise in the place of experiment. 4.4 Discussions The system is able to detect different signals at the same time, and we can combine the different detected results of temperature and gas, to shout an intelligent system, which will analyze the results, for example if the temperature is high and no presence of gas, in this case it will not send an alert of presence of fire, it will just display the value of temperature detected. On the other hand in the case of detection of high temperature and presence of smoke, it will trigger the alarm. For the sound detection, a study is in progress so that the system can distinguish the human voice from the parasite noise.
5 Conclusion After successfully completing the different steps described above, we were able to create a kind of monitoring prototype. We have managed to create a first version of the system that performs its main functions, but with the appropriate modifications. Our next objective is to add to our system a Wifi ESP8266 model that has the function of sending the data detected by the sensors using Wifi to an Android platform installed on computer or smartphone, this platform will allow to visualize the results, giving it an IP address that exists in the same network as the address of the wifi card. Moreover, a mobility is aimed to make the system more usable, for example in rescue and surveillance situations, in case of disasters, where the access for a human being contains risk, this mobility will be controlled and manipulated by the same platform mentioned before, adding a DC motor, manipulated remotely [7].
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References 1. Gannavaram, T.K., Kandhikonda, U.M., Bejgam, R., Keshipeddi, S.B., Sunkari, S.: A Brief Review on Internet of Things (IoT). In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6 (2021). https://doi.org/10.1109/ICCCI50826.2021. 9451163 2. https://learn.adafruit.com/dht 3. https://www.arduino.cc 4. Karvinen, T., et al.: Les capteurs pour Arduino et Raspberry Pi. Tutoriels et projets. Dunod., p.304 (2014) 5. Gannavaram, T.K., Bejgam, R., Keshipeddi, S.B., Sunkari, S., Aluvala, V.K.: Conversion of sound energy into electrical energy in highly populated areas. In: 2021 6th International Conference on Communication and Electronics Systems (ICCES), pp. 32–36 (2021). https://doi. org/10.1109/ICCES51350.2021.9489219 6. https://fritzing.org 7. Bejgam, R.: Integrating machine to machine communication (M2M) and MQTT protocol techniques for conversion of water motor pump into a smart system. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 982–987 (2021). https://doi.org/10.1109/ICESC51422.2021.9532848
Model of a Hybrid Energy Storage System Using Battery and Supercapacitor for Electric Vehicle Fatima El Bakkari(B) and Hamid Mounir EMISys Research Team, Mohammed V University in Rabat, Engineering 3S Research Center Mohammadia School of Engineers, Rabat, Morocco [email protected], [email protected]
Abstract. With the recent environmental issues, the adoption of EVs and HEVs has expanded. Because of the increased demand, manufacturers are investing in new technologies. The goal is to address environmental issues by reducing transportation emissions and developing powertrains that recover energy during different driving cycles. Furthermore, manufacturers can ensure an easy and simplified driving experience by combining intelligent and secure devices. One of the issues impacting transportation electrification is energy production, conversion, storage, and recovery. For example, the kinetic energy is converted and dissipated as heat when using conventional brakes. It demonstrates the importance of recycling energy to improve efficiency and safety in complex situations. In addition, one of the main problems to be solved is autonomy, which will increase through energy regeneration during various driving cycles and modes. This article is divided into four sections. The first section will describe the latest technological aspects, the second section will explain the hybrid energy storage system characteristics with the main configurations, and the third section will present the studied model and results from the interpretation. Finally, we will conclude with the importance of combining two energy storage devices in system performance. Keywords: Electric vehicle (EV) · Hybrid electric vehicle (HEV) · Energy storage system (ESS) · Hybrid energy storage system (HESS) · Permanent magnet brushless DC motor (BLDCM) · Regenerative braking (RegenB) · State of charge (SOC)
1 Introduction EVs are a long-term alternative to internal combustion engine vehicles because they help reduce emissions and the number of exhaust gases in the atmosphere. In recent years, the popularity of EVs has grown, with systems featuring two and four-in-wheeled motor driving mechanisms, both geared and gearless [1]. The goal is to reduce the speed to the desired wheel while shortening the transmission path from the electric machine to the driving wheel. These variations in transmission modes and mechanical configurations must be compatible with the ESS’s electronic/electric facilities. The battery is the most common © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 240–249, 2023. https://doi.org/10.1007/978-3-031-35245-4_22
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energy source used as the primary power source for propulsion. Utilizing a battery as a storage device provides greater flexibility in terms of energy recovery. Besides, batteries have become widely used in ground vehicles due to their high energy density, compact size, and dependability [2]. The most common types of batteries are lead-acid batteries, nickel-metal hydride (NiMH) batteries, lithium-ion (Li-Ion) batteries, and nickel-zinc (Ni-Zn) batteries, and nickel-cadmium (Ni-Cd) batteries. Some new technologies are nanobolt lithium tungsten batteries, zinc-manganese oxide batteries, organosilicon electrolyte batteries, gold nanowire gel electrolyte batteries, and tank two string cell batteries [3]. The majority of EVs and HEVs share common load profile characteristics. Achieving such requirements necessitates a high power and a high energy density source. Modern batteries may have one of these characteristics but not both; lithium-ion batteries have the highest energy density of any actual battery and are used commonly in such applications [4]. Furthermore, Li-Ion batteries offer an adequate voltage range depending on vehicle performance. In opposition, lead-acid batteries are limited to low-cost applications, and Zinc-based batteries are utilized for experimental demonstrations only (Figs. 1 and 2).
Fig. 1. The operation of a lithium-ion battery (lithium ions are represented in blue)
Fig. 2. Comparison of energy and power density of various energy storage devices [5]
Ultra-capacitors are a type of energy storage technology similar to batteries. They use a double-layer technology to increase capacitance to farad levels. A supercapacitor is a device with relatively high energy density, a long lifespan, and efficient performance that
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can withstand millions of charging/discharging cycles due to the storage mechanism [6]. The benefits of a supercapacitor include a high specific power, high energy density, and infinite cycle life. Moreover, they are simple to charge, and measuring the energy storage capacity is simple. They are, however, limited by their low specific energy, low maximum voltage, and high cost. Ultra-capacitors can provide the high-power density required for short-term acceleration and regenerative braking energy in EVs and HEVs, saving energy and protecting batteries from the high-frequency rapid charging/discharging process [7]. Because of their high specific power rate, supercapacitors can be integrated with an electrochemical energy source to supply the power peaks required by the vehicle during the mission [8]. The potential benefits include: improving vehicle efficiency and energy economy under variable power driving conditions, ensuring high performance and good vehicle behavioral response, and reducing the energy source’s reliance on high-rate power demand [8] (Fig. 3).
Fig. 3. Supercapacitor main components (Source: Electronics Tutorials)
1.1 Energy Hybridization Energy storage devices such as batteries, Supercapacitors, and flywheels cannot meet the demand for high specific energy and high specific power at the same time. In this regard, EVs can use the HESS by combining two energy devices in the same architecture. This feature helps meet energy demand requirements while increasing the system’s complexity and costs (Fig. 4). By combining the benefits of the battery and the supercapacitor, HESS can attain greater efficiency and reach the vehicle’s power requirements [9]. This energy management strategy allows energy to be divided between the two sources based on the SOC of each source and the vehicle displacement state, such as stopping, acceleration, cruising down and uphill, and deceleration [10]. Passive configuration is the elementary approach because there are no intermediary power electronic devices. This approach is simple to implement, requires minimal control, and is inexpensive, but it does not ensure effective use of the ultra-capacitor stored energy [11]. Figure 5) b) shows that the ultra-capacitor provides direct voltage to the bidirectional DC-DC converter; if the ultra-capacitor is discharged, the battery supplies the other components, and vice versa [12]. Afterward, in battery/ultra-capacitor
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Fig. 4. Cascaded HESS configuration using the battery, supercapacitor, and bidirectional converter
Fig. 5. HESS configurations: a) basic passive parallel configuration, b) ultra-capacitor/battery configuration, c) battery/ultra-capacitor configuration, d) multiple converter configuration
architecture, the voltage levels are the same, and the charging and discharging current profiles of the battery are smooth because SOC is dependent on voltage and does not vary considerably because power management is disabled [13]. For better performances, we use a cascaded configuration with bidirectional converters and inverters. Figure 5) d) depicts this topology. Other architectures, such as multiple-input device configurations, employ multiple converters [14]. 1.2 State of Charge (SOC) Indicators SOC indicators provide the driver with the necessary energy information. Therefore, he can manage the driving range and determine the adequate recharging time. The SOC of a cell denotes the available capacity as a function of the rated capacity; the SOC value ranges from 0% to 100% [15]. Most battery SOC calculation methods are based on the electrochemical characteristics of the battery and real-time loading conditions [16]. The determination of the battery SOC is more complicated than Supercapacitors
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based on the measurement of capacitor voltage. The accuracy of the SOC estimation results will impact the battery management system performance [17]. The precision of SOC estimation becomes increasingly crucial as energy storage devices are highlighted in electronics and electric vehicle applications [18]. Energy management is a critical issue in battery–supercapacitor systems. It entails determining the best power flows to share to extend battery life. Controlling the battery current to limit battery stress is one relevant way to achieve this goal [19]. 1.3 Energy Recovery Management To overcome the problem of EV models’ limited range, automakers develop new systems to recuperate wasted energy during the driving process. Regenerative braking enables the vehicles to convert kinetic energy into electrical energy. The regenerated energy is stored in energy storage devices such as batteries, supercapacitors, or flywheels. Generally, the vehicle is equipped with both conventional and regenerative braking for emergency cases. When an EV travels, energy is extracted from the battery and fed to the load, which is the motor; during regenerative braking, energy flows from the wheels to the battery, i.e., in the opposite direction as it does when propelling. As a result of the induced counter electromotive force, the battery can be thought of as the load and the motor as the source [20]. Regarding motorization type, BLDCMs are becoming the optimized choice for EVs due to their higher speed ranges, higher efficiency, better speed versus torque characteristics, and noiseless operation [21]. As a result, the most recent models employ this type of electric motor, which can function in the opposite sense as a generator to recover energy during the braking process. Actual research focuses on improving energy recovery by controlling the braking force distribution. This methodology considers the various braking requirements and constraints parameters.
2 Hybrid Energy Storage Systems HESS operates based on high specific energy and high specific power. The advantage is to separate energy densities and power requirements. Consequently, this comes with the drawbacks of increased complexity and cost. The main components of the studied system are battery, supercapacitor, boost DC/DC converter, buck/boost DC/DC converter, universal bridge, and the BLDCM. A boost converter is a DC-to-DC power converter that increases voltage while decreasing current from its input (supply) to its output (load). The primary function of such a system is to provide a regulated output voltage higher than its input voltage; the boost accomplishes a using switching element that governs the energy transfer from the input to the output [22]. Traditional boost converters can achieve a high gain on a high-duty cycle, resulting in a reverse recovery problem and high-voltage stress across the switching devices, lowering efficiency [23]. Moreover, the buck-boost converter is a DC-DC converter with an output voltage magnitude greater than or less than the input voltage magnitude. It is analogous to a flyback converter that uses a single inductor instead of a transformer. Buck converters are built with a fast MOSFET for electronic
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switching, a diode, an inductor, and a capacitor [24]. The output voltage in an inverting topology has the opposite polarity as the input voltage. This is a switched-mode power supply with a circuit topology similar to the boost and buck converters. The output voltage is adjustable based on the switching transistor’s duty cycle. One potential disadvantage of this converter is that the switch lacks a ground terminal, which complicates the driving circuitry. The Universal Bridge block simulates converters using naturally commutated (or line-commutated) power electronic devices and forced-commutated devices (gate turnoff thyristor, insulated-gate bipolar transistor, metal-oxyd-semiconductor field-effect transistor). Finally, the BLDCM is a type of permanent magnet brushless drive. The BLDC possesses high torque and power densities because of the interaction between the trapezoidal field and rectangular current. BLDCMs are beneficial compared to brushed DC and induction motors because they are efficient, reliable, have lower noise, longer lifetime (no brush and commutator erosion), and have no ionizing sparks from the commutator [25]. BLDC machines can operate in three modes: motor, generator, and hybrid. In motor mode, several control methods have been proposed [26]. These motors rely on electronic commutation to function. In addition, they use sensors (often Hall effect sensors) to detect the position of the shaft at any given time, the pulse-width-modulation (PWM), and other circuit controllers to switch the current to each winding at the appropriate time. As a result, It is the electrical equivalent of the brush/commutator rings on a brushed DC motor used to switch the current from winding to winding mechanically. As a result, BLDCMs are frictionless, emit no brushing dust, and are quieter than brushed DC motors.
3 Studied System The model consists of a HESS combining battery and supercapacitor, associated with a BLDC motor. The three-phase inverter is driven using a six-step commutation. The selection of motorization is based on comparing different motors in terms of power density, efficiency, controllability, maturity, cost level, noise level, and maintenance requirements (Fig. 6).
Fig. 6. Simulation setup for the HESS studied model
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The scheme above represents a cascaded architecture of battery and ultra-capacitor. The overall system includes a battery, supercapacitor, bidirectional DCDC converter, inverter, and BLDC motor. A speed regulator controls the DC bus voltage. The inverter gate signals are generated by decoding the Hall effect signals from the motor. Hall sensors are used to provide feedback on rotor position to a motor controller. BLDC motor control system is a complicated circuit in which several components collaborate to move the motor in the desired direction. Engineers are most concerned with efficiency, durability, and performance when designing such a system [26]. A BLDC motor typically has three Hall-effect Sensors mounted on the rotor or stator. These Hall sensors are 120° apart, giving a 0 to 360° angle position. When these hall sensors are exposed to the rotor’s magnetic field, they generate a digital pulse of 1 and 0. These hall sensors can provide the motor position in six steps. Simulations were carried out on the Matlab Simulink application. The results of the implemented scopes are as follows: comparison of the supercapacitor and the battery power, comparison of the total power with the power required, and the SOCs for the supercapacitor and battery.
Fig. 7. Simulation results for the studied model: a) comparison of the primary power with the total generated power, b) ultra-capacitor and battery powers, c) battery characteristics, and d) ultra-capacitor characteristics
From the Fig. 7., it is clear that total power follows the same path as primary power. The power drops from 2000 W to 1000 W in 3 s, then remains constant at 1000 W between 3 s and 6 s and eventually drops to zero. The battery’s SOC decreases continuously to supply the required energy to the other components, whereas the ultra-capacitor’s SOC increases after 8 s to recover energy. Regenerated currents can be effectively stored and reused by including an ultra-capacitor in an EV to assist batteries. Ultra-capacitors can also handle high current demands during rapid acceleration. As a result, this reduces battery size and thus improves vehicle performance. Battery life will be extended because the battery can operate within safe limits.
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Fig. 8. Simulation results for the BLDCM: a) stator current in (A), b) rotor speed in RPM, c) hall effect signals, and d) electromagnetic torque
Observing current variation, the initial current is high and decreases as the speed increases to the nominal speed. To maintain speed, the stator current increases when the nominal torque is applied.
Fig. 9. Simulation results for the studied system using battery only: a) battery characteristics, b) stator current in (A), c) electromagnetic torque, d) rotor speed in RPM
By comparing results from Fig. 8 and Fig. 9. it is clear that the use of a battery and ultra-capacitor pack allows energy to divide between the two sources based on their SOC. The BLDCM provides a large speed margin in this configuration. The Fig. 8. Depicts the variation of speed. It drops from 400 to 300 rpm in [0–3 s] and then to
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200 rpm in 8 s. We can create a system that regenerates while braking. In HESS, the ultra-capacitor pack provides high peak power while receiving regenerative braking power. During normal driving conditions, the stored energy will power the battery. In the second configuration, the battery provides only a constant nominal speed while the SOC decreases continuously. And this necessitates a regular charge in order to recover energy for vehicle movement.
4 Conclusion Both environmental concerns and market demand have contributed to the popularity of EVs, but electrochemical energy storage systems are still far behind expectations for competing with fuel-powered vehicles [27]. HESS technology currently provides the most suitable expansion option, owing to its moderate weight and relatively small size and volume. The HESS mass could be reduced without reducing EV performance. Moreover, the combination of a parallel battery and an ultra-capacitor provides numerous advantages. As for efficiency and performance boost, it is well suited for light EVs. The results of this study are theoretical, and the optimal configuration parameters must be determined. The power source for a HESS real-world application should be a sufficiently commercial model based on the best theoretical values; additionally, it is possible to design a battery using a combination of cells in order to achieve the desired voltage and capacity.
References 1. Jamadar, N.M., Jadhav, H.T.: A review on braking control and optimization techniques for electric vehicle. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 095440702199690 (2021) 2. Khaligh, A., Li, Z.: Battery, ultracapacitor, fuel cell, and hybrid energy storage systems for electric, hybrid electric, fuel cell, and plug-in hybrid electric vehicles: state of the art. IEEE Trans. Veh. Technol. 59(6), 2806–2814 (2010) 3. New Battery Technology That Will Change The future (2022). https://www.gray.com/ins ights/5-new-battery-technologies-that-will-change-the-future/ 4. Kuperman, A., Aharon, I.: Battery–ultracapacitor hybrids for pulsed current loads: a review. Renew. Sustain. Energy Rev. 15(2), 981–992 (2011) 5. Zhang, Y., et al.: Sodium-ion capacitors: materials, Mechanism, and Challenges. Chemsuschem 13, 2522–2539 (2020) 6. Kumar, D., Nema, R.K., Gupta, S.: A comparative review on power conversion topologies and energy storage system for electric vehicles. Int. J. Energy Res. 44(10), 7863–7885 (2020) 7. Sharma, K., Arora, A., Tripathi, S.K.: Review of supercapacitors: materials and devices. Journal of Energy Storage 21, 801–825 (2019) 8. Faggioli, E., Rena, P., Danel, V., Andrieu, X., Mallant, R., Kahlen, H.: Supercapacitors for the energy management of electric vehicles. J. Power Sources 84(2), 261–269 (1999) 9. Liang, J., Zhang, J.-L., Zhang, X., Yuan, S.-F., Yin, C.-L.: Energy management strategy for a parallel hybrid electric vehicle equipped with a battery/ultraapacitor hybrid energy storage system. J. Zhejiang Univ., Sci., A 14(8), 535–553 (2013) 10. Azizi, I., Radjeai, H.: A new strategy for battery and supercapacitor energy management for an urban electric vehicle. Electr. Eng. 100(2), 667–676 (2017)
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11. Cao, J., Emadi, A.: A new battery/ultracapacitor hybrid energy storage system for electric, hybrid, and plug-in hybrid electric vehicles. IEEE Trans. Power Electron. 27(1), 122–132 (2012) 12. Gopikrishnan, M.: Battery/ultra capacitor hybrid energy storage system for electric, hybrid and plug-in hybrid electric vehicles. Middle-East J. Sci. Res. 20(9), 1122–1126 (2014) 13. Geetha, A., Subramani, C.: A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles. Int. J. Energy Res. 41(13), 1817–1834 (2017) 14. Muragani, V.K., Rajagiri, A.K. Analysis of hybrid energy storage system for hybrid electric, battery electric and plugin hybrid electric vehicles using bidirectional DC/DC converter. E3S Web of Conferences. 309, 01064 (2021). https://doi.org/10.1051/e3sconf/202130901064 15. Abdi, H., Mohammadi-ivatloo, B., Javadi, S., Khodaei, A.R., Dehnavi, E.: Energy Storage Systems. Distributed Generation Systems, 333–368 (2017) 16. Ogura, K., Kolhe, M.L.: Battery technologies for electric vehicles. Electric Vehicles: Prospects and Challenges, 139–167 (2017) 17. Xing, L., Ling, L., Gong, B., Zhang, M.: State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models. Connect. Sci. 34(1), 162–184 (2022) 18. Chang, W.-Y.: The state of charge estimating methods for battery: a review. ISRN Applied Mathematics 2013, 1–7 (2013) 19. Castaings, A., Lhomme, W., Trigui, R., Bouscayrol, A.: Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints. Appl. Energy 163, 190–200 (2016) 20. Totev, V., Gueorgiev, V.: Efficiency of Regenerative Braking in Electric Vehicles. 2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA) (2020) 21. Billah, S.M.B., Jakaria, M., Nath, P.: A novel regenerative braking system of BLDC motor for lightweight electric vehicles: an analysis of braking characteristics. 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE) (2017) 22. Cunha, B.F., Pagano, J.D.: Limitations in the control of a dc-dc boost converter. IFAC Proceedings Volumes 35(1), 211–216 (2002) 23. Yaseen, M., Farooq, A., Malik, M.Z., Usman, M., Hafeez, G., Ali, M.: Design of a High Step-Up DC-DC Converter with Voltage Doubler and Tripler Circuits for Photovoltaic Systems. International Journal of Photoenergy 2021, 111 (2021) 24. Cristri, A.W., Iskandar, R.F.: Analysis and design of dynamic buck converter with change in value of load impedance. Procedia Engineering 170, 398–403 (2017) 25. Janpan, I., Chaisricharoen, R., Boonyanant, P.: Control of the brushless DC motor in combine mode. Procedia Engineering 32, 279–285 (2012) 26. Hall effect sensor and its role in a motor controller (2021). https://www.embitel.com/blog/ embedded-blog/hall-effect-sensor-and-its-role-in-a-motor-controller#:~:text=How%20H all%20Effect%20Sensor%20Works,to%20360%20degree%20angle%20position 27. Kouchachvili, L., Yaïci, W., Entchev, E.: Hybrid battery/supercapacitor energy storage system for the electric vehicles. J. Power Sources 374, 237–248 (2018)
Estimation of Port Air Emissions Inventory: The Case of Tanger Mediterranean Port Authority Farah Housni1(B) , Abdrazak Boumane1 , Ona Egube2 , Abdelfettah Sedqui1 , Kamal Lakhmas1 , and Amal Maurady1 1 Innovative Technology Laboratory, University of Abdelmalek Essaadi, Tangier, Morocco
[email protected] 2 Department of Informatics and Engineering Systems, University of South of Carolina,
Upstate, SouthCarolina, USA
Abstract. The motivation for this study arises from the need of ports to become more sustainable through the continuous improvement of processes to reduce air pollution. This research focuses on the emissions of contaminants such as nitrogen oxide (NOx), carbon monoxide (CO), hydrocarbons (HC), particulate matter 10 µm or less in diameter (PM10 ), particulate matter 2.5 µm or less in diameter (PM2.5 ), and sulfur dioxide (SO2 ). Analysis of data collected in Tanger Mediterranean Port Authority for the year of 2019 using the methodology proposed by the United States Environmental Protection Agency (US EPA) to estimate the emissions of vessels, trucks, tugboats and containers handling equipment is presented. Currently The Port of Tanger Mediterranean performs analysis of the air quality in the surrounding area. However the results of this study will acquaint the port with the percentage of its emissions by source category in order to identify the most polluting processes to target in its efforts to reduce its air emissions. We consider examples of initiatives adopted by other ports in the world that reduce their air pollution to suggest recommendations for the Port of Tanger Mediterranean to improve its air quality. Keywords: Air pollution · Sustainable port · Air contaminants · Environmental management · Environmental policy
1 Introduction Due to globalization, the importance of maritime transport has grown significantly, and ports have become an essential element of global production and supply chains [1]. The maritime transport continues to increase, and the ports operations have been forced to expand and develop more efficient facilities and conditions [2]. According to Tzannatos (2010), pollution related to shipping raise attention particularly the contribution to coastal emissions and pollution [3]. The International Maritime Organization has included ship pollution in the International Convention for the Prevention of Pollution from Ships (MARPOL) in 1973 which requires a gradual decrease of nitrogen oxide (NOX ), sulfur dioxide (SO2 ) and particulate matter (PM) from marine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 250–258, 2023. https://doi.org/10.1007/978-3-031-35245-4_23
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engines [4, 5]. The regulation of air pollution by ships defined in MARPOL Annex VI, includes a progressive reduction of (SO2 )and (NOX ) and (PM) in Emission Control Areas (ECA) of the ports [6, 7]. Therefore, according to the Top 10 environmental priorities of 2016 and 2020 of the ESPO (European Sea Ports Organization), air quality is the main concern of European ports environmental management [8]. Tanger Mediterranean Port Authority (TMPA) is ranked by highest of the top five of African ports and has the highest level of connectivity, due to the services provided by the port and its location [9]. TMPA (Fig. 1) was inaugurated in July 2007, located at the crossing of two maritime routes allowing it to be linked to more than 180 ports in 70 countries for container activity, and to become a part of Morocco’s economic policy based on exports [10]. TMPA is the first port capacity in the Mediterranean, composed of two container terminals of 3 million twenty-foot equivalent units in Tanger Med 1, and two containers terminals with 6 million TEU in Tanger Med 2 [10].
Fig. 1. Tanger Med Port Authority Complex
This study focus on the air emissions of TMPA, which complies with the international convention MARPOL 73/78. However, this paper focuses on contaminants cited in Annex VI of MARPOL and calculates the percentage of these contaminants by source category: vessels, truck, tugboats and cargo handling equipment (CHE) to determine the distribution of emissions of these contaminants caused by the different sources in the port. Furthermore, this research determines how the vessels emissions compare to other air emissions sources within the port. The rest of the paper is organized as follows: The first section presents the methodology to estimate emissions of (NOX ), (CO), (HC), (PM10 ), (PM2, 5 ), and (SO2 ) by source category. The second section presents the results. For confidentiality reasons actual numbers are not declared, and the results are presented as diagrams showing percentages. The third section discusses the result and proposes recommendations to TMPA by providing examples of successful initiatives which could be taken into consideration to improve the port air quality and lastly, the study conclusion is presented.
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2 Material and Methods In order to assist TMPA to reduce its air emission and improve air quality.This study select all sources of emissions in the ECA of the port of Tangier for the year of 2019. Furthermore, the contaminants considered in this paper were selected because they are required to be decreased by the MARPOL convention which TMPA complies with. TMPA is also focused on the performance of its environmental management system and applies not only the national regulations but also international regulations and standards and has selected among its environmental goals to reduce its air emissions. Therefore, this study will inform the port environmental manager on the most polluting processes to target in its efforts to reduce its air emissions. Emissions will be estimated in accordance with methodologies specified in the (US EPA) [11]. Equation (1) presents the methodology to estimate the emissions of the vessels per year [12]: EE = (NVC/year) × (HM /call) × ES × LF × EF × LLFA × (1lb/453, 5g) × (1ton/(2, 000lb)
(1)
Equation (2) presents the emissions estimation of the Tugboats per year [13]: EE = (HE/year) × ES × LF × EF × (1ton/907, 185g)
(2)
Equation (3) estimates the emissions of CHE per year [14]: EE = HM × EF × F × G × (1pound /453, 592g) × (1ton/2000lb)
(3)
Equation (4) estimates the trucks emissions per year [15]: EE = (VMT ) × EF(g/VMT ) × (1pound /453, 592g) × (1ton/2, 000pounds)
(4)
where: EE indicate the Estimations of Emissions in tons per year, NVC is the number of vessel calls, HM represent hours in mode, ES indicates the engine size, LF represent the load factor, EF refers to the emission factor and LLFA represents the low load adjustment factor, HE indicate hours in mode per engine, F represents the rated horsepower, G refers to the fraction of available rated power and VMT represents the Vehicle miles traveled by the trucks per year. The low load adjustment and emissions factors, engine size, of each brand of vessels, tugboats and CHE are obtained from [16-19];
3 Results The air emissions estimated of (NOX ), (PM10), (CO), (PM2, 5), (HC) and (SO2 ) generated by Vessels, tugboats, trucks and CHE are presented below (Figs. 2, 3, 4, 5, 6 and 7):
Estimation of Port Air Emissions Inventory
CHE 4% Trucks 25%
Tugboats 4%
Vessels 67%
Fig. 2. (NOX ) emissions estimation by source category
CHE 4%
Tugboats 2%
Trucks 19% Vessels 75%
Fig. 3. (PM10 ) emissions estimation by source category
CHE 9%
Trucks 40%
Tugboats 4%
Vessels 47%
Fig. 4. (CO) emissions estimation by source category
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CHE 4%
Tugboats 2%
Trucks 19% Vessels 75%
Fig. 5. (PM2, 5 ) emissions estimation by source category
Tugsboo at 1%
CHE 4% Trucks 21%
Vessels 74%
Fig. 6. (HC) emissions estimation by source category
Trucks 2%
Tugboats 3%
Vessels 95%
Fig. 7. (SO2 ) emissions estimation by source category
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The results shown in the figures above indicate that vessels were the primary sources of emissions of (NOx ), (PM10), (CO), (PM2, 5 ), (HC) and (SO2 ). These results explain why MARPOL requires reduction of the sulfur content in the marine gas oil or converting vessels to liquefied natural gas LNG [20]. Ports can encourage this by giving facilities and price reduction to the vessels that work with the LNG.
4 Discussion The main challenge for ports is to reduce their air emissions. Therefore, all users of ports should be implicated in order to realize this challenge. TMPA could use the example of the new port area Maasvlakte 2 in the Port of Rotterdam which aimed to build the most sustainable terminal in the world in Rotterdam. The sustainability requirements of the port targets are: • Major focus on automation and efficiency; • Adopting Green energy; • Achieving zero energy buildings [21]. Concerning the reduction of fuel consumption and (CO2 ) emissions, another example is the Port Authority of Singapore which launched an initiative of three programs: • First, the Green Ship Program is a program that encourages the use of energy efficient ship design in order to reduce fuel consumption and (CO2 ) emissions. The MPAS (Maritime and Port Authority of Singapore) provides incentives to ship owners whose ships go beyond the Energy Efficiency Design Index (EEDI) requirements of the IMO; • Secondly, the Green Port Program is intended for ships using type-approved abatement/scrubber technology or clean fuels and allows them a benefit of 15% reduction in port dues; • Lastly, the Green Technology Program intended for local maritime companies who develop and adopt green technologies [22]. As shown earlier, the pollutants emitted by shipping operations are (SO2 ), (PM), (CO) and (NOX ) [23, 24]. According to Alexander and Monios (2018) the efficient approach to reduce the emissions of these contaminants is the initiative of onshore power supply/cold ironing [25]. The cold ironing is also effective to reduce noise pollution generated by the auxiliary engines of the berthed ships in the ports [26]. Figure 4 shows that trucks account for a significant percentage of carbon monoxide CO at 40%, on the other side percentages ranging from 20% to 25% of emissions of (NOX ), (PM10 ) and (PM2,5 ). As shown in Fig. 7, trucks represent a very low percentage of (SO2 ). In order to reduce trucks emissions TMPA could take the example of the Port of Los Angeles, which started a ban on polluting trucks in the ECA of the port; as a result the air pollution generated by the trucks in the port decreased by more than 80% from 2007 to 2012 [27, 28]. And in order to became neutral port, TMPA could encourage the use of electric vehicles while Peterson, Whitacre, and Apt (2011) study shows that electric vehicles will result in reductions in (NOX ) and (CO2 ) emissions, nevertheless the majority of models show an increase in (SO2 ) emissions[29].
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Figures 2, 3, 4, 5 ,6 and 7 above also indicate that tugboats emissions represent lower than 5% of emissions produced by the activities of the port for all the contaminants CHE accounts for approximately 4% of emissions of (NOX ), (CO),(PM10 ) and (PM2,5 ). In addition, as shown in Fig. 5, CHE is responsible for 9% (HC) emissions and is not presents on Fig. 7 of (SO2 ) emissions. In order to monitor and improve air quality, ports should adopt sustainable attitudes and behaviors. For example, the involvement in the successful initiatives addressed to reduce air emissions in the ports such as the maritime Singapore green initiative, the innovative terminal operations used in Rotterdam port, the approach of cold ironing and the adoption of clean truck program applied by the port of Los Angeles [30].
5 Conclusion Maritime transport is taking a very important role in international commerce. Therefore, ports activities, particularly the number of ships received, is projected to increase. In this work we analyze the TMPA air contaminants such as (NOX ), (CO), (HC), (PM10 ), (PM2.5 ), and (SO2 ), to study air emission by source category (vessels, tugboats, CHE, trucks). The results of this study show why the reduction of air emissions has become a priority for the ports due to the fact that vessels are the principal contributors to air emissions. Therefore, it is necessary for ports to engage in voluntary initiatives to reduce their emission of contaminants and greenhouse gasses. TMPA engages in several voluntary environmental initiatives to reduce (SO2 ) and (NOX ) and (PM) in Emission Control Areas of the ports. In addition, TMPA plans to become more sustainable and has selected the goal of reducing air emission. This paper presented examples of effective initiatives to improve air quality that could be taken into consideration by TMPA.
References 1. Corbett, J., Winebrake, J.: International maritime shipping: the impact of globalisation on activity levels. Chap 3. Globalisation, transport and the environment OECD, pp. 56–79 (2008). https://doi.org/10.1787/9789264072916-en 2. Laxe, F.G., Bermúdez, F.M., Palmero, F.M., Novo-Corti, I.: Sustainability at spanish ports specialized in liquid bulk: evolution in times of crisis (2010–2015). Marit. Policy Manag. 46, pp. 491–507 (2019). https://doi.org/10.1080/03088839.2019.1569766 3. Tzannatos, E.: Costs and benefits of reducing so2 emissions from shipping in the greek seas. Marit. Econ. Logist 12, 280–294 (2010) 4. IMO (International Maritime Organization) (2019a). https://www.imo.org/en/About/Conven tions/Pages/International-Convention-for-the-Prevention-of-Pollution-from-Ships-(MAR POL).aspx 5. European Commission (2011) Clean air at sea – promoting solutions for sustainable and competitive shipping.https://ec.europa.eu/transport/sites/transport/files/modes/maritime/eve nts/doc/2011_06_01_stakeholder-event/item2.pdf 6. Imran, S.J.B.: Greenhouse Gas Emissions from Shipping: Existing Regulations and Regulatory Challenges (Master’s thesis) (2020)
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7. IMO (International Maritime Organization). International convention for the prevention of pollution from ships (MARPOL) (2019b). https://www.imo.org/en/About/Conventions/ Pages/International-Convention-for-the-Prevention-of-Pollution-from-Ships-(MARPOL). aspx 8. ESPO (European Sea Ports Organization). Top 10 environmental priorities 2020 final (2020). https://www.espo.be/media/Top%2010%20environmental%20priorities%202020% 20FINAL.pdf 9. UNCTAD (United Nations Conference on Trade and Development) (2020) UNCTAD’s review of maritime transport 2020: highlights and figures on Africa. https://unctad.org/press-material/ unctads-review-maritime-transport-2020-highlights-and-figures-africa 10. TMPA (Tanger Med Port Authority). Tanger med, port authority, port complex (2020). http:// www.tmpa.ma/en/activites-services/activite-conteneurs/ 11. US EPA (United States Environmental Protection Agency). Current methodologies in preparing mobile source port-related emission inventories final report (2009) 12. California Air Resources Board Planning and Technical Support Division. Emissions estimation methodology for ocean-going vessels (2006). https://ww3.arb.ca.gov/regact/2008/fuelog v08/appdfuel.pdf 13. US EPA (United States Environmental Protection Agency). Office of mobile sources, assessment & modeling division, compression ignition, nonroad technical report nr-009d (2010) 14. SCPA (South Carolina Ports Authority). Air emissions inventory (2018). https://dc.statelibr ary.sc.gov/bitstream/handle/10827/33625/SPA_Air_Emissions_Inventory_2018-10.pdf?seq uence=1&isAllowed=y 15. US EPA (United States Environmental Protection Agency). MOVES Onroad technical reports (2014) 16. US EPA (United States Environmental Protection Agency). Office of mobile sources, assessment & modeling division, exhaust and crankcase emission factors for nonroad engine modeling (2010) 17. Markit, I.H.S.: Lloyd’s register of ships 2020 (2020). https://ihsmarkit.com/products/mar itime-ships-register.html 18. US EPA (United States Environmental Protection Agency). Office of mobile sources, assessment & modeling division, conversion factors for hydrocarbon emissions components nonroad (2010) 19. US EPA (United States Environmental Protection Agency). Office of mobile sources, assessment & modeling division, median life, annual activity, and load factor values for nonroad engine (2010) 20. Wang, S., Notteboom, T.: The adoption of liquefied natural gas as a ship fuel: a systematic review of perspectives and challenges. Transp. Rev 34, 749–774 (2014). https://doi.org/10. 1080/01441647.2014.981884 21. Port of Rotterdam. Maasvlakte (2019). https://www.portofrotterdam.com/nl/onze-haven/hav enontwikkeling/maasvlakte-2 22. MPAS (Maritime and Port Authority of Singapore). Maritime Singapore green initiative (2019). https://www.mpa.gov.sg/web/portal/home/maritime-singapore/green-efforts/mar itime-singapore-green-initiative 23. European Commission. Study on differentiated port infrastructure charges to promote environmentally friendly maritime transport activities and sustainable transportation executive summary (2017). https://ec.europa.eu/transport/sites/transport/files/2017-06-differentiated-portinfrastructure-charges-exec-summary.pdf 24. Environmental Navigation Commission. PIANC report n° 150: Sustainable ports, a guide for port authorities (2014)
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Exemplarity of Public Administrations: An Important Lever for the Energy Efficiency of Buildings - Case of Morocco Salma El Majaty(B) and Abdellatif Touzani Casablanca, Morocco [email protected]
Abstract. Engaged for many years in a strategy to control climate change, Morocco is committed to leverage on a sustainable development as a new development model and as a true project for the society. This commitment resulted in implementing several reforms targeting the consolidation of a developed economic, the improvement of social conditions, and the acceleration of environmental positive changes. The public administration developed the Administration Exemplarity Pact (AEP) as a concrete action to lead by example the implementation of the National Strategy for Sustainable Development. Developed in accordance with the main stake of the National Strategy of Sustainable Development, its goal is to promote the governance of sustainable development in our country through several strategic focus areas. This document presents the experience of a Moroccan administration that has implemented the guidelines described in the AEP. The approach and the results obtained are detailed and could be used as an example for other Moroccan companies. The first step of the methodology consists of a diagnostic phase to establish the current situation. The second step is related to the strategy to put in place to define the main orientations of the approach and the action plan. These key steps allow us to identify area of improvement and to build a roadmap adapted to the current context and constraints. One of the best practices for this approach is to define the main orientations to act by positive contagion on the ecosystem. Keywords: National Strategy for Sustainable Development (SNDD) · Administration Exemplarity Pact (AEP) · Moroccan administration, regulatory audits of the AEP
1 Introduction After the warning from of the Club of Rome in 1975 [1], it is mandatory today to well manage the natural resources. Since the oil shocks of 1973 to 1979, developed countries have realized that their financial sustainability was based on the intensive use © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 259–271, 2023. https://doi.org/10.1007/978-3-031-35245-4_24
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of limited natural resources. Nevertheless, one important aspect has been neglected: the environment [2]. The Sustainable Development Goals (SDGs) were defined as the seventeen goals established by the member states of the United Nations. They are assembled in the 2030 Agenda [1]. This agenda was adopted by the Organization of United Nations in September 2015 after two years of negotiations including governments and civil society. It defines targets to be achieved by 2030. The implementation of the SDGs within a State requires the active commitment of governments and all stakeholders such as companies, communities, associations and researchers. Morocco has very limited energy resources and imports more than 95% of its need [3]. In this context, Moroccan government gives primary importance to the energy sector as the main driver of economic development and social progress. Several strategic actions have been developed [4]: • The Constitution of Morocco recognizes sustainable development as a right for all citizens to achieve better democratic governance. • A national charter for the environment and sustainable development was deployed. • The law No. 99–12 was adopted and requires the government to develop and implement a national strategy for sustainable development. • Morocco is showing real commitments in international conventions around the environment and sustainable development [5]. The adoption of the National Strategy for Sustainable Development by the Council of Ministers under the presidency of His Majesty King Mohammed VI [5] was a key step in the process of consecrating sustainable development at the national level. The first axis of this strategy aims to make the exemplarity of the administration a lever for the implementation of sustainable development. Thus, the public administration has developed the Administration Exemplarity Pact (AEP) as a concrete action to set an example for the implementation of the National Strategy of Sustainable Development [6]. This paper presents the experience of a Moroccan administration that implemented the guidelines described in the AEP. The approach and the results obtained are detailed and could be used as an example for other Moroccan companies.
2 Exemplarity of the Administration - The Case of Morocco In the frame of the Administration Exemplarity Pact (AEP), the public administration has the challenge to lead by example in the implementation of national sustainable development strategies [6]. Adopted by the National Committee for Sustainable Development in February 2019, the Administration Exemplarity Pact (AEP) reflects the commitment of Moroccan administration in sustainable development. For the effective implementation of the AEP, a circular from the Head of Government has been sent to all public administrations concerned.
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Since its launch, the deployment of the AEP aims to encourage and promote best practices of sustainable development among all economic and social stakeholders at national level. The main objectives of the Administration Exemplarity Pact [7] can be summarized in 6 items, as describe in Fig. 1:
Fig. 1. AEP Objectives
In order to support the ministerial departments in the process of preparing their Exemplarity Pact, the Environment Department within the Ministry of Energy, Mines and the Environment has carried out several actions. These actions include: • the development and dissemination of a methodological guide on the Exemplarity of the Administration, • orientation sheets by domain (water, waste, mobility, energy efficiency…), • and standardized terms of reference on environmental auditing in public buildings [7]. In addition, technical assistance has been made available to ministerial departments within the framework of international cooperation to accelerate the pace of implementation and generalize these measures to public entities and local authorities. The exemplarity of the administration concerns several domains: energy (mainly electricity), water, waste, public procurement, mobility and Office consumables.
3 Methodology The Moroccan authorities are convinced that the State, in the broadest sense, and the public administration in particular have a decisive role to play in the process of the Kingdom’s energy transition [5]. The AEP is a disposition put in place by the Moroccan government to encourage the exemplarity of administrations in terms of sustainable development.
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The purpose of this study is to present the methodology and results of a Moroccan administration that has implemented the AEP guidelines. The objective of this study is to provide a methodology for the implementation of PEA tested in a public administration in Morocco. The article focuses on two components of this study: the results of the diagnosis and the actions implemented. The study also demonstrates the need to involve stakeholders as early as possible in the process to ensure wide dissemination of these principles. This study could be extended to other administrations and companies to address the challenges of climate change, economic development and energy security. To carry out this mission, a methodology in 3 steps was developed. As explained in the Fig. 2, an initial phase related to the diagnosis and inventory of the equipment, analysis and treatment of the preliminary data by carrying out field visits and working meetings with the concerned. The second phase is devoted to the realization of the field audit and to make the synthesis of it to propose tracks of improvement. The third phase was devoted to the study of the technical and economic feasibility of the recommendations and the implementation of an action plan. This was an important phase of the mission, which allowed the definition of the strategy and monitoring framework (analysis of operational objectives, monitoring indicators, etc.) and the definition of an action plan.
Fig. 2. The methodology of the study
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According to the AEP requirements, the audit [8] covered: energy, water, waste and paper use. During these audits, the following elements were analyzed: Energy contracts, use of renewable energy, lighting, office equipment, heating/ventilation/air conditioning, kitchen equipment and hot water. For the water audit, the water consumption, the quantities of water discharged, the characteristics of the installed facilities and the wastewater collection were examined. It was also a question of evaluating the quantity of waste produced and to make a point on the use of paper.
4 Results and Discussion 4.1 Results of Diagnosis Current the stage of diagnosis, it was important to analyze the data to understand the energy situation at the site. It is often useful to graph the consumption data to visualize the changes in consumption. Establishing relationships between energy consumption and influencing factors provides a clear understanding of what is happening at the site. Electricity consumption: Electricity consumption varied from 2,090,678 kWh in 2018 to 1,766,671 kWh in 2019 and 1,526,111 in 2020, a change of −15% and −14% respectively Fig. 3:
Fig. 3. Evolution of electricity consumption 2018, 2019, 2020
Water consumption:Water consumption varied from 4,950 m3 in 2018 to 2,207 m3 in 2019 and 2,080 m3 in 2020, i.e., a change of −55% and -6% respectively, Fig. 4:
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Fig. 4. Evolution of water consumption, 2018, 2019, 2020
Climate of Casablanca: The climate of the city of Casablanca is temperate, influenced by the Atlantic. The average temperatures do not vary much, the temperature profiles of the years 2018, 2019 and 2020 seem to be close, Fig. 5.
Fig. 5. Average temperatures in Casablanca (2018, 2019 and 2020) (Source: www.infoclimat.fr)
On the other hand, the heating and cooling degree days do show that 2020 was warmer than 2019 and 2018, Fig. 6.
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Fig. 6. Degree days in Casablanca (2018, 2019, 2020) (Source: www.infoclimat.fr)
Consumption ratios: The variation of consumption in a building can be influenced by its surface, the number of occupants and by the climate when the building has an air conditioning system. In our case, we do not have the information of the number of occupants and the surface, therefore we will be satisfied to make the exercise with the meteorological data of Casablanca. Electricity consumption ratio: A linear regression analysis of the available data showed a possible correlation with the air-conditioning degree-days (Fig. 7, Fig. 8, Fig. 9). The correlation coefficients obtained for the years 2018, 2019 and 2020 are respectively R2 = 0.84 and 0.78 and 0.74. And the respective residual consumptions are 147 MWh, 117 MWh and 104 MWh.
Fig. 7. Electricity consumption VS DJU Air conditioning
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Fig. 8. Electricity consumption VS DJU Heating
Fig. 9. Electricity consumption VS DJU Air conditioning
Significant energy uses: The selection of significant uses is made for any use whose share of consumption exceeds 10%, hence the significant uses for the company are air conditioning, lighting, the Datacenter and office automation -Fig. 10.
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Fig. 10. Significant Uses
Mobility audit: The reduction of greenhouse gas emissions has become a key global objective. Recent scientific studies estimating the socio-economic costs of the effects of global warming reinforce concerns about environmental and energy constraints and emphasize the nature and modalities of the policies to be put in place [9]. Transport is now the leading sector in terms of greenhouse gas emissions and carbon dioxide emissions [10]. The mobility audit carried out for the company in question highlighted the numerous measures already implemented for the management of its truck fleet, which have led to a reduction in fuel consumption in recent years. Optimizing the fleet and experimenting with new modes of transport through pilot projects (electric cars, electric mopeds, etc.) is an asset for finding solutions to reduce the environmental impact of mobility. 4.2 Recommendations Various studies have shown that it is possible to reduce electricity costs without significantly reducing energy consumption when time-of-use electricity rates are applicable [11]. These strategies typically require little or no capital outlay and simply use optimized control strategies to prioritize the use of electrical energy outside of the more expensive periods of the day through time-shifting. In general, it is claimed that energy is consumed by buildings, even if the “building” is responsible for a very low energy consumption during its operational life, but much more during the design, construction and deconstruction phases. Indeed, most of the energy is consumed by the occupants, although they do not have direct contact with the energy carriers (fuel, gas, electricity), and more precisely by the equipment and goods that provide them with services; therefore, the users do not feel directly responsible for the energy consumption, which is consumed by the equipment and, more generally, by
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the buildings. It is interesting to note that energy consumption is measured in kW h/m2 year and not in kW h/person-year [12]. Thus, actions related to user behavior have also been implemented to ensure compliance with good energy use practices, including turning on lighting only when necessary turning off lighting when leaving the premises, using air conditioning only when necessary, appropriate adjustment of the air conditioning temperature (winter/summer), closing doors and windows when using air conditioning, limiting the use of table water in plastic bottles and favoring tap water. The choice of low consumption equipment is an important axis of the action plan. The optimization of water consumption through the use of drip systems and programming of watering time (avoid watering in full sun) as well as the choice of plants with low water demand. Other recommendations have been implemented to improve the building’s electrical energy performance: the generalization of the replacement of light points by LED lighting, the generalization of the choice of equipment with low power consumption, automatic control of lighting by presence detector, especially for sanitary facilities, adjustment of the automatic control of exterior lighting, and realization of programmed cut-off of lighting taking into account the working hours and functions of each room. The following areas for improvement have been identified: Improve the management of document archiving, generalization of the digital archiving of documents, implementation of a charter for the use of paper in the offices with the aim of Optimize pagination, use of appropriate fonts, prevent on-screen correction, minimizing the number of printouts, using electronic forms instead of paper forms, using double-sided functionality whenever possible, reuse paper waste, generalization of the monitoring of paper consumption by site and by function. In conclusion, the energy-water-waste-paper audit of administrative buildings also highlighted many actions already underway in buildings, including the deployment of low-energy LED lighting and the replacement of air conditioners with equipment that emits less greenhouse gas, the development of renewable energy production and the monitoring of electricity and water consumption. The deployment of digital technology to reduce paper consumption is also recommended. 4.3 Analysis of Public Procurement Responsible public procurement can be understood as a new concept of public purchasing [13], which could affirm the role of public actors in sustainable development. This initiative also aims to professionalize the “purchasing” function by developing skills and improving decision-making tools [14]. The combination of efficiency and sustainable development objectives is therefore at the heart of the new governance of purchasing [15]. The analysis carried out as part of this study shows that a corporate social responsibility policy is already in place for responsible purchasing management. The framework documents governing supplier contracts refer to environmental protection and almost all contracts include clauses on the social or environmental impacts of services. To go further, an audit of the most significant subcontractors was carried out in order to evaluate their performance in terms of respect for the environment. The audit was
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carried out on the basis of a grid integrating objective criteria linked to respect for the environment.
Fig. 11. Audit compagnies results
As shown in the Fig. 11, only 2 companies have a high level of global maturity. This study shows that public administrations have an important role to play in supporting and engaging subcontractors in energy efficiency initiatives. Beyond the contractual obligations, a work of accompaniment of these companies must be carried out. So it is very important to involve stakeholders as early as possible in the process to ensure wide dissemination of these principles.
5 Conclusion In this paper, a common framework has been developed for the evaluation of an energy management system. The PEA is based on provides a framework for improving the energy performance of organizations. This document provides a methodological framework for deploying the principles of the AEP. A Moroccan administration operating in the Casablanca area was chosen as a case study and, in particular, its buildings were used to assess energy performance and serve as an illustrative application to demonstrate the general validity of the method. The application of all the steps of the method revealed to the energy manager the necessary actions to be implemented. In addition, the monitoring and control of performance proved to be a fundamental step in gaining efficiency. In conclusion, the PEA axes deployed in a dynamic improvement logic gives convincing results and by acting with partners (subcontractors and suppliers) the Moroccan
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administration can act on the whole value chain and induce an important change in the Moroccan economic fabric. This commitment has enabled several ministries to make real savings in terms of resources. For example, some departments have been able to reduce their water consumption by 50% and produce 22% of their electricity needs from renewable energy. While others have been able to achieve 32% of their fleet of clean vehicles and achieve a rate of 1 waste of about 35% [16].
References 1. Idowu, S.O., Capaldi, N., Zu, L., Gupta, A.D.: Encyclopedia of Corporate Social Responsibility. Springer Berlin Heidelberg, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3642-28036-8 2. Ahmad, M., Ahmed, Z., Yang, X., Hussain, N., Sinha, A.: Financial development and environmental degradation: Do human capital and institutional quality make a difference?. Gondwana Research 105, 299-310 (2022). https://doi.org/10.1016/j.gr.2021.09.012 3. Bouyghrissi, S., Berjaoui, A., Khanniba, M.: The nexus between renewable energy consumption and economic growth in Morocco. Environ. Sci. Pollut. Res. 28(5), 5693–5703 (2020). https://doi.org/10.1007/s11356-020-10773-5 4. Choukri, K., Naddami, A., Hayani, S.: Renewable energy in emergent countries: lessons from energy transition in Morocco. Energy, Sustainability and Society 7(1), 1–11 (2017). https:// doi.org/10.1186/s13705-017-0131-2 5. Kingdom of Morocco Ministry of Energy Transition and Sustainable Development, « STRATÉGIE NATIONALE DE DÉVELOPPEMENT DURABLE (SNDD) 2030 » (2017) 6. M. of E. T. and S. D. Kingdom of Morocco Ministry of Energy Transition and Sustainable Development, « Pact of Exemplarity of the Administration » (2019) 7. Kingdom of Morocco Ministry of Energy Transition and Sustainable Development, « Methodological Guide for the exemplarity of the Administration in matters of Sustainable Development » (2019) 8. Boharb, A., et al.: Auditing and analysis of energy consumption of an industrial site in Morocco. Energy 101, 332-342 (2016). https://doi.org/10.1016/j.energy.2016.02.035 9. Fouillé, L., Broc, J.-S., Bourges, B., Bougnol, J., Mestayer, P. : La place des modèles de trafic dans les récentes modélisations des impacts environnementaux des transports. Importance de l’explicitation des méthodes et hypothèses. RTS 2012(03-04), 191-200 (2012). https://doi. org/10.1007/s13547-012-0037-5 10. Jia, S., Peng, H., Liu, S., Zhang, X.: Review of transportation and energy consumption related research. Journal of Transportation Systems Engineering and Information Technology 9(3), 6-16 (2009). https://doi.org/10.1016/S1570-6672(08)60061-6 11. Hu, S., Yan, D., Azar, E., Guo, F.: A systematic review of occupant behavior in building energy policy. Building and Environment 175, 106807 (2020). https://doi.org/10.1016/j.bui ldenv.2020.106807 12. Delgoshaei, P., Heidarinejad, M., Xu, K., Wentz, J.R., Delgoshaei, P., Srebric, J.: Impacts of building operational schedules and occupants on the lighting energy consumption patterns of an office space. Build. Simul. 10(4), 447–458 (2016). https://doi.org/10.1007/s12273-0160345-9 13. El Asri, N., Nouira, Y., Maaroufi, I., Marfak, A., Saleh, N., Mharzi, M.: The policy of energy management in public buildings procurements through the study of the process of delegated project management - Case of Morocco. Energy Policy 165, 112944 (2022). https://doi.org/ 10.1016/j.enpol.2022.112944
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14. Gayot, O. : De la Responsabilité sociale des organisations à l’achat public responsable : entre contraintes et performances. Compte rendu de journée d’étude. developpementdurable, 10(1) (2019). https://doi.org/10.4000/developpementdurable.13666 15. Cantillon, G.: L’achat public durable, un outil au service de l’Etat régulateur. Revue française d’administration publique 134(2), 335 (2010). https://doi.org/10.3917/rfap.134.0335 16. Diao, M.: Pacte de l’exemplarité de l’Administration: certains ministères sortent du lot. 23 mars 2021. [En ligne]. https://fnh.ma/article/developpement-durable/pacte-de-l-exemplaritede-l-administration-certains-ministeres-sortent-du-lot
Offline Parameter Identification of the Battery Equivalent Circuit Model for Electric Vehicles Using Particle Swarm Optimization Method Elmahdi Fadlaoui(B) , Hamza Hboub , and Noureddine Masaif Physic Department, Laboratory of Electronic Systems, Information Processing, Mechanic and Energetic Faculty of Science, Ibn Tofail University Kenitra, Kenitra, Morocco [email protected]
Abstract. The state of charge (SOC) estimation of lithium-ion batteries is considered a significant task, and there accuracy is related to the best parameters identification of the battery model. The equivalent circuit model (ECM) is used due to its clear physical meaning and simple mathematical expression, however, it remains a challenging issue to extract the best parameters of the model. In this paper, a particle swarm optimization method (PSO) is proposed to identify the parameters of the 1st order r´esistance-capacitance equivalent circuit model (ECM). Firstly, the parameter identification model based on the (PSO) method identifies the best parameters (R0 , R1 , C1 ) in each iteration using the objective function, which is the difference between the experiment and the estimated terminal voltage. This method can estimate these parameters using the metaheuristic search technique to find the best solution. The Urban Dynamometer Driving Schedule (UDDS) current profile is used as input in this method. The experiments show that the proposed method can estimate the parameters within a root mean square error RMSE = 4.62% and maximum absolute error MAE = 0.18v. comprehensive experimental results demonstrate the robustness of the proposed method, In addition, the simplification of the process that use laboratory experiments to tune the parameters. Keywords: equivalent circuit model (ECM) · particle swarm optimization · state of charge · electric vehicles
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Introduction
Due to high energy density, high power-to-weight ratio and high energy efficiency, lithium-ion batteries become the principal power source of electric vehicles. The importance of the battery management system (BMS) is in monitoring the battery state an ensure the safety of operation due to the degradation of c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 272–280, 2023. https://doi.org/10.1007/978-3-031-35245-4_25
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the battery because of the charging and discharging process. The state of charge (SOC), which is used to describe its remaining capacity during the operation charge-discharge cycle, is considered as a critical states in the battery management system (BMS); unfortunately, this state can not be measured directly. However, there are several methods that researchers have developed to estimate the SOC [1] using quantities that can be measured directly such as current and terminal voltage like: – Coulomb counting method (CCM): this method uses the integration of the charge/discharge currents of the battery Eq. (1). This approach is not suitable for online (SOC) estimate. – Open-circuit voltage method (OCVM): is developed through charging or discharging the battery using small current steps with small magnitude. However, this estimating approach remains inadequate for online estimation due to the strong reliance on OCV values and the long duration requirement to attain good equilibrium conditions [2]. – Machine learning method (ML): this approach sees the battery as a black box system, the estimation accuracy of this method depends on the quantity of data training [3,4]. A good estimate demands massive resources (CPU and Memory) to process a vast data collection, and this requires a large timeconsuming. – Model-based method (MBM): this method link a battery model with measured signals (Temperature, Current and Voltage) and an estimation algorithm in closed-loop to estimate SOC [5]. ECM uses circuit elements such as resistors and capacitors to simulate the battery’s behaviour. This approach is considered a promising online battery state estimation for electric vehicle applications. The circuit contains a series of n resistance and capacitance connected in parallel with an internal resistance (R0 ) to simulate the rapid change in a battery voltage of various time constants related to Li-ion battery charge transfer, diffusion and voltage source Uocv . The model gives better results with a high number of parallel branches. The n = 1 and n = 2 RC models are the most used in the literature for online SOC estimation thevenin’s electric model (n = 1) ignores dynamic behaviour during the charging and discharging process. For n = 2 is the widely popular model, and he is more accurate for SOC estimation compared to n = 1. The 2RC branches can match the data at the beginning of the transient. The open-circuit voltage Uocv , and the parameters R0 , R1 , C1 and C2 are all impacted by the temperature and SOC of the battery. Based on the operation state, parameter identification methods appropriate for battery ECM can be categorized into two types, online and offline. In the online estimation technique, estimate the parameters of the battery model during the simulation [6], but in the offline estimation, the input-output data are collected and then the model parameters are estimated. The offline parameter identification method is commonly applied for online estimation. The most widely used offline Parameter identification methods are curve fitting, Least square, Recursive Least Square algorithm, and Genetic algorithm [7].
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This study’s essential contribution is identifying the 1st Order Electrique circuit model parameters using the particle swarm optimization method (PSO), beginning with extracting the experimental terminal voltage from the battery using the UDDS current profile. The PSO search the best values of the parameters (R0 , R1 , C1 ) to minimize the error between the experimental and the optimized terminal voltage. The outline of the paper is as follows: The introduction is presented in Sect. 1. Section 2 describes the modelling progress of the first-order RC model and the particle swarm optimization method, and the parameter identification model. Application and simulation results in Sect. 3, and finally in Sect. 4 we describe the conclusions.
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This section provides an overview of the battery’s electrical equivalent circuit model (ECM) and the particle swarm optimization approach for determining the (ECM) parameters. Section 2.1 discusses the 1st order of the equivalent circuit battery model. Then Sect. 2.2 introduces the particle swarm optimization method to identify the parameters of the RC model. The offline parameter identification model is introduced in Sect. 2.3. 2.1
Equivalent Circuit Model (ECM)
In this paper, the model used to describe the dynamic behaviour of lithium-ion batteries is the equivalent circuit model of first-order RC shown in Fig. 1. This model has one resistor-capacitor branch. R0 represent internal ohmic resistance R1 , and C1 represent respectively electrochemical and electrochemical concentration polarization. UT is the terminal voltage, the voltage that we measure during the battery operation under the (UDDS) current profile Fig. 2. Uocv is an open-circuit voltage (OCV), the potential difference between the two electrodes when the battery is at equilibrium state. To accurately measure the OCV, the battery must be in the open-circuit condition for a long enough amount of time, which often takes several hours, that’s why we discharge/charge the battery with very low c-rate (C/15 in our case) this experiment take long period of time, usually many hours. the OCV-SOC relationsship Fig. 1 can be calculated using the integrate current output Eq. (1) where SOC0 is the initial state of charge at (t = 0), Q is the nominal capacity of the battery, and η is the coulombic efficiency. However, open-circuit voltage (OCV) can be considered as a function of the state of charge (SOC) [8]. 1 t ηI(τ )dτ (1) SOC = SOC0 − Q 0
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Fig. 1. 1st order electric circuit model battery
Fig. 2. UDDS input Current profile
The electrical behavior of the used ECM can be expressed as the state-space Eq. (2) (Fig. 3): U dU I1 = C1 dtp + Rp1 (2) UT = Uocv − Up − U0
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Fig. 3. SOC-OCV relationship
2.2
Particle Swarm Optimization (PSO)
The PSO algorithm is provided in 1995 by Kennedy and Eberhart for the first time [9]. The basic idea of this method is derived from the research behaviour of looking for the food of a bird swarm. The fastest technique to locate the best position is to search the area surrounding the bird closest to the food. In particle swarm optimization, each particle can update its position and velocity, the particle’s position represents the potential solution to the optimization problem and velocity determines the rate of change of the particle. In the iteration process, the particle swarm algorithm updates the particle parameters, including the particle velocity and position. The particles update their positions consistentlyn according to the to personal best (Pbest ) and global best (Gbest ) along the process. During the iteration ith , particles update their velocities v i,n+1 and positions xi,n+1 by the Eqs. (3) and (4) with P i and G represents respectively the personal best and the global best positions of the particles.
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v i,n+1 = v i,n + c1 r1 (P i − xi,n ) + c2 r2 (G − xi,n )
(3)
xi,n+1 = xi,n + v i,n+1
(4)
c1 and c2 represent acceleration constants, r1 and r2 are unifomaly distributed random numbers. 2.3
Parameter Identification Model
In this section, we describe the procedure of identifying the parameters (R0 , R1 , C1 ) of the ECM model based on a dynamic test data Fig. 4.
Fig. 4. Block diagram of parameters identification procedure
There are two main steps, first extracting the terminal voltage (UT ) from an experimental battery, second calculate the optimized terminal voltage based on the initial parameters, after that we calculate the error between the experimental and the optimized terminal voltage, based on this error the PSO estimate the new parameters, this step is repeated until we reach the maximum number of iteration.
3
Application and Simulation Results
This section reports the simulation result of the particle swarm optimization method and compared it with the experimental results to verify the accuracy of the proposed parameter identification method. The data used in this paper is collected from a lithium polymer battery (LiP oli) at 25 ◦ C, using the (UDDS) current profile, where the simulation period is 14000 s.
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0.0039 0.0086 3800
maximum
0.0079 0.0152 3900
Maximum iteration:100 and number of population: 20, the velocity is initialized at: v0 = 0, and c1 = c2 = 1.5, then the parameter bound is mentioned on the Table 1 based on the litterature and the author’s best knowledge [10]. According to the comparison between the experimental and the optimized shown in Fig. 5, we can see the (PSO) method can capture the dynamic of the battery, however, we can observe some errors appear over time, The Fig. 6 shows the error variation over time.
Fig. 5. Experimental and optimized Terminal voltage.
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Fig. 6. Error between experiment and optimized Terminal voltage
This error is due to the bound interval chosen to estimate the parameters, if the parameter value is out of the boundary interval, then we choose a large bound, in this case the probability to get accurate parameters increased, and also the influence of the RC Equivalent circuit model order, if we choose the n-order RC model (n = 2, 3...), the error between the true and the optimized terminal voltage decrease. The best parameters is illustrated on Table 2. Table 2. Best parameters Parameters R0 (Ω) R1 (Ω) C1 (F) Best Values 0.0059 0.0112 3870.14
To evaluate the parameter identification of the PSO method, we use the root mean square error (5), and the maximum absolute error (6). n (Optimizedi − Experimentali ) (5) RM SE = n i=1 1 |Optimizedi − Experimentali | n i=1 n
M AE =
(6)
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The error between the optimized and the experimental Terminal voltage showed that: RM SE = 0.0462 and M AE = 0.1832.
4
Conclusion
In this paper, the parameters (R0 , R1 , C1 ) of the 1st order Equivalent circuit model (ECM) model have been identified using the particle swarm optimization method (PSO). The parameter model identification can track the ECM parameters based on an objective function, that represent the difference between the true and the optimized terminal voltage. The effectiveness of the proposed method is evaluated using the root mean square error (RMSE) and the maximum absolute error (MAE) which is equal respectively 4.6% and 0.18v. In future work, the proposed method will be applied to the 2nd order ECM model in order to decrease the error, and also use different metaheuristic research in order to compare different methods.
References 1. Shrivastava, P., et al.: Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renew. Sustain. Energy Rev. 113, 109233 (2019) 2. Liu, K., Li, K., Peng, Q., Zhang, C.: A brief review on key technologies in the battery management system of electric vehicles. Front. Mech. Eng. 14(1), 47–64 (2018). https://doi.org/10.1007/s11465-018-0516-8 3. Song, X., Yang, F., Wang, D., Tsui, K.: Combined CNN-LSTM network for stateof-charge estimation of lithium-ion batteries. IEEE Access 7, 88894–88902 (2019). https://doi.org/10.1109/ACCESS.2019.2926517 4. Chemali, E., Kollmeyer, P.J., Preindl, M., Ahmed, R., Emadi, A.: Long short-term memory networks for accurate state-of-charge estimation of li-ion batteries. IEEE Trans. Ind. Electron. 65(8), 6730–6739 (2018). https://doi.org/10.1109/TIE.2017. 2787586 5. He, Z., Yang, Z., Cui, X., Li, E.: A method of state-of-charge estimation for EV power lithium-ion battery using a novel adaptive extended Kalman filter. IEEE Trans. Veh. Technol. 69(12), 14618–14630 (2020). https://doi.org/10.1109/TVT. 2020.3032201 6. Wang, C., et al.: Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive particle swarm optimization differential evolution algorithm to estimate state of charge. J. Energy Storage 44, 103244 (2021) 7. Xiong, R.: Battery Management Algorithm for Electric Vehicles. Springer, Singapore (2020) 8. Elmahdi, F., Ismail, L., Noureddine, M.: Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method. In: E3S Web of Conferences, vol. 234, p. 00097 (2021). https://doi.org/10.1051/E3SCONF/202123400097 9. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks (1995) 10. Ahmed, R., Rahimifard, S., Habibi, S.: Offline parameter identification and soc estimation for new and aged electric vehicles batteries. In: 2019 IEEE Transportation Electrification Conference and Expo (ITEC), p. 1–6. IEEE (2019)
Multi-horizon Short-Term Load Consumption Forecasting Using Deep Learning Models Ismael Jrhilifa1(B) , Hamid Ouadi1 , and Abdelilah Jilbab2 1 ERERA, ENSAM Rabat, Mohammed V University in Rabat, Rabat, Morocco
[email protected], [email protected]
2 E2SN, ENSAM Rabat, Mohammed V University in Rabat, Rabat, Morocco
[email protected]
Abstract. Energy consumption forecasting has known a big interest in the last few years due to its important role in the smart grid domain. It helps to manage and dispatch the smart grid energy sources. Furthermore, load consumption predictions and scheduling of the generation resources to satisfy the demand side enable minimizing the energy generation cost. This paper represents multihorizons electrical energy forecasting models developed using LSTM, BI-LSTM, unidimensional convolution neural network (1DCNN) and an individual household energy consumption dataset. In addition, a comparative study is exhibited between three forecasting horizons: two hours, four hours, and eight hours. The results show that LTSM and bi-LSTM-based prediction models’ mean squared error (MSE) increases by increasing the prediction horizon. In contrast, 1DCNN based prediction model’s MSE decreases by increasing the forecasting horizon. Keywords: Short-term energy forecasting · smart grid · smart buildings · deep learning · LSTM · bi-LSTM · unidimensional convolution neural network
1 Introduction The building sector has become the largest energy consumer worldwide due to population growth, increases in people’s comfort demands, and global climate change [1]. Additionally, a major component of the energy consumed in the buildings is wasted through over-utilization of energy appliances such as exhaust fans, and Heating, Ventilation, Air Conditioning (HVAC) systems, ineffective control over thermal comfort, and not optimizing the start-up time and sequencing of electrical equipment [2]. Therefore, it is essential to manage energy consumption in buildings, which could be done by creating smart buildings (SB) with installed wireless sensors networks, measuring devices, and various control strategies [3]. Efficient management of micro-grid systems requires the prediction of electricity consumption and the power production and remaining charge forecasting of storage devices [4]. Energy load forecasting is an essential part of the retail electricity side, enabling the electricity suppliers to make proper decisions for energy trading [5]. Generally, load forecasting refers to predicting the future load data © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 281–292, 2023. https://doi.org/10.1007/978-3-031-35245-4_26
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by systematically processing the past data with considering certain important characteristics, and the predicted results can satisfy certain accuracy requirements. Predicting the future load value is important to energy management with minimized energy wastage, including facilitating the grid operation rational arrangement and maintenance plan, saving fuel and coal, reducing costs, facilitating a reasonable power supply construction, and promoting electricity improvement. Electricity consumption is a stochastic process with a degree of regularity that occurs due to human behavioral responses to such factors as day-night cycles and weekend patterns [6]. Hence, the consumer-side electricity consumption forecasting is much more difficult. The micro-grid performs its power dispatching allocation strategy based on the predicted electricity consumption. The electricity consumption-forecasting task can be applied in residential, commercial, or industrial settings. In the literature, load-forecasting problems differ from one paper to another in terms of the used model, dataset, and forecasting horizon. This paper aims to present multi-horizon short-term load forecasting using different deep learning methods such as LSTM, CNN, and bi-LSTM. The outputs size arrives to 480 outputs and gives a comparative study between all of those models according to performance metrics, such as MSE, MAPE, and R2 _Score. The remainder of this paper is organized as follows. In Sect. 2, some related works will be cited. They used deep learning models will be presented in Sect. 3. In Sect. 4, the household energy consumption dataset is discussed. Coming to Sect. 5, the models implementations and results are shown. The conclusion is given in Sect. 6.
2 Related Works In the last few years, short-term load forecasting has become one of the most research interests in the smart-grid and smart buildings domains. Suppose the energy consumption is predicted with high accuracy despite its stochastic behavior. In that case, it will help in load balancing and managing the micro-grid in an optimized manner and with minimum costs. Next, some related works in the literature will be mentioned. In [7], Zhang et al. proposed a multiple support vector regression (SVR) model that forecasts half-hourly and daily energy utilization for nonresidential buildings. The drawbacks of this paper are the dataset was collected in a half-hour sample time, and it contains just 480 records furthermore, SVR is not suitable for large datasets, the decision model does not perform very well when data is noisy, the model performance shows a MAPE of 3,767% and the model output size is 48 points. In [8], Kong et al. proposed an LSTM memory-based framework for short-term energy forecasting at the residential level. They incorporated the appliances’ energy data from a Canadian household to illustrate the efficacy of their deep learning framework. Although minutely data were available, an aggregation of thirty minutes was utilized in their work. The limitation of this work is that the architecture of the models contains three hidden layers, 2 LSTM units, and 1 MLP, which make it a complicated model; also, the model output size is 48 points. They use the MAPE metric to evaluate their model; the model gave a MAPE of 21.99%, and it is too big. In [9], the authors developed data-driven models based on gradient boosting machines, random forests, support vector machines with radial basis function, and multiple linear regression. The used dataset is collected in a sample time of 10 min, and it contains
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19728 records. The limitations of this work are the used models are not suitable for large datasets, the model gives a MAPE of 13.43% for their model predictions using random forests, and the model output size is 144 outputs. The presented work in this paper differs from those previously cited above in terms of; sampling time, they used a from 10 minutes to one hour basis sample time; in contrast, the used time granularity in this work is one minute, which makes the load consumption forecasting with a high-resolution measurement (resolution of one minute). The models’ outputs arrive to 480 points. The used dataset comprises more than two million records. In addition, the previous works did not use data analysis methods such the features transformation and selection, which are based on algorithmic processes such as date transformation, and using principal component analysis (PCA), which helps to eliminate the collinearity problem between the features and therefore avoiding the overfitting problem. Furthermore, this paper compares all the used deep learning models (DL) in multiple forecasting horizons.
3 The Deep Learning Models Deep learning (DL) based models have become an emerging solution for load consumption forecasting thanks to their nonlinear estimation competencies that aid in offering a priced prediction than conventional statistical methods. But as with any method, DLs have their proper problems like the gradient vanishment and overfitting, to solve this issues, we can adjust the learning rate and regularization method like L2 regularization (Laplacien regularization), and minimization of Variance Inflation Factor (VIF) between the features. In this section, the utilized deep learning networks in this work will be presented. 3.1 Long Short Term Memory Networks (LSTM) The LSTMs are a type of Recurrent Networks typically developed to treat, analyze, and predict time series [10]. They forecast based on the input of the current time step and the output from the previous time steps. Furthermore, LSTMs have memory cells to accumulate steps over prediction sequences tasks such as energy predicting. The internal gates of LSTM enable control of the information passed through and overcome the vanishing gradient problem [11]. The LSTM cell using its construction (as depicted in Fig. 1), processes data in four following phases. The cell forgets the irrelevant data of the previous states, stores the relevant data of new data into its cell state, updates the relevant information from former and actual input into its interior cell states, and yields outputs as inputs for the next time step, the details are explained in [2]. 3.2 Bi-directional LSTM Networks (Bi-LSTM) The bi-LSTM network is a developed version of the LSTM network. The bi-LSTM treats the inputs in two paths. The forward path from the past inputs to future input, and the backward path, from future inputs to past inputs. The combination of hidden states
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Fig. 1. LSTM unit architecture
from the two paths preserves the information from the both paths across different hidden layers. The aggregate output of these layers passed to a single identical hidden layer. It helps bi-LSTM to keep the relevant data from the past and future inputs immediately [2]. Figure 2 below describes the architecture of the bi-LSTM network. The output of − → the forward path L is gotten utilizing inputs in the positive time sequence T ∈ [t-n, t], ← − where n is the past lag values. While the backward path L is obtained via the negative time sequence inputs T ∈ [t, t + m], with m is the future lag values.
Fig. 2. Bi-LSTM neural network architecture
3.3 The Convolution Neural Network (CNN) A unidirectional convolution neural network (1DCNN) is similar to a multi-layer perceptron network (MLP) but with its own data input instead of handcrafted features.
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The input data is processed through several trainable convolution layers for learning an appropriate representation. A (1DCNN) takes input vector X ∈ Rp and a filter W ∈ Rq , where q ≤ p. the convolution operation can be expressed as follow: T with i ∈ k Conv(X , w) = xi , xi+1, xi+k . wi , wi+1 , wi+k
(1)
Another layer that exists is the pooling layer; it involves chunking a vector into nonoverlapping equal-sized groups and then taking a summary statistic for each group. That further smooths out noise in local dynamics. Three common types of pool are max pooling, average or mean pooling, and min pooling. The third layer is the RELU layer; it takes the smoothed vector from applying a convolutional and pooling layer and then applies non-linearity to prepare it for the final output layer. Finally, the output layer receipts representation data from the previous layer and applies an activation function to a weighted sum of that representation to output data of the relevant form based on our choice of activation function [12].
Fig. 3. 1D Convolutional neural network architecture
4 Household Energy Consumption Dataset The utilized dataset in this present work is an individual household energy consumption, gathered in a house located in Sceaux (7km from Paris, France); it is available in the dataset archive of the University of California, Irvine [13]. The dataset contains 2075259 measurements. They are collected in 4 years with a sampling time of one minute from December 2006 to November 2010. Table 1 below lists all the used features and their statistics; the standard deviation, mean, maximum and minimum values of different features are tabulated.
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Features
Units
Min
Max
Mean
Std
Date
dd/mm/yyyy
Time
hh:mm:ss
Global Active Power (GAP)
kilowatt
0.076
11.122
1.091
1.057
Global current Intensity
Ampere
0.2
48.8
4.444
0.200
5 Forecasting Models Implementations and Results In this section, we discuss the developed DL models with the aim to forecast the electric power consumption in multi-horizon using different models. The applied DL algorithms are LSTM, unidimensional convolution neural network, and bi-LSTM. Before starting training the DL model, a few important operations must be done, helping the DL model to learn better, avoiding the overfitting problem, and having a model with good performance. Those operations are; data imputation in case there are some missing data in the dataset, like in our case, 1.25% of data are missing, studying the correlation between the features, studying multicollinearity studying to solve the overfitting problem, and finally, data normalization. All those steps will be discussed respectively in detail in the next subsections. A comparative study will be exhibited depending on the forecasting horizons and the DL models.
Fig. 4. Functional schematic
5.1 Data Imputation The most utilized approach to handle the missing data is the imputation, which replaces missing values with meaningful values. There are many ways for replacement values, such as mean imputation, median imputation, and mode imputation [14]. In this work, a mean imputation method was used. The formulation of this method is given by: xij =
xij nk
i:xi j∈Ck
(2)
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5.2 Correlation between Features After imputing the missing data, a new dataset was created from the collected one. The new features are: • The month number from 1 to 12 helps to distinguish the year months. • The day week number from 0 to 6 aid in differentiating between the weekdays (0: Monday to 6: Sunday). • The hour number is from 0 to 23 for distinguishing the day hours. • The minute number is from 0 to 59 for distinguishing the hour minutes. • The global active power (GAP). • The current global intensity. The data correlation was carried out with the goal of understanding the relationship between multiple variables and attributes in the dataset. Figure 4 below exhibits the correlation matrix between GAP and itself and GAP with the other features. The minutes were resampled into 10 min to see their correlation with GAP. As we notice in Fig. 4 above, the largest correlation value is between current Intensity GAP. The smallest one belongs to the minute number and 10 min number with GAP, and for that, they will be excluded by the PCA method.
Fig. 5. The correlation Matrix
5.3 Multicollinearity Studying Multicollinearity is the existence of linear relationships among the independent features. For example, if two variables are collinear, that means if one variable increases, then the other one increases and vice-versa. The multicollinearity reduces the precision of
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the estimated features, which weakens the statistical power of the forecasting model. We studied the Variance Inflation Factor (VIF) to check the multicollinearity. It must be equal to one between all the features. Table 2 lists the initial VIF value of each feature. Table 2. The initial VIFs of features Features
VIF factor
Month number
1.703696
Day number
2.683362
Hour number
3.155824
Minute number
2.729239
Global intensity
1193.864585
GAP
1190.556549
According to the VIF table above, the largest VIF values belong to current intensity and GAP. To eliminate this highest VIF. We applied the principal component analysis method (PCA) by calculating the Eigenvalues and the Eigenvectors after normalizing the dataset using a minimum-maximum scaler in a range between [–1,1]. The new dataset representation is formulated as follows: Final dataset = Scaled dataset. EigenvectorsT
(3)
After these operations, the new features VIFs are shown in Table 3 below. All the VIFs now are equal to one, which means there is no longer collinearity between the features. Table 3. The Final features of VIFs Features
VIF Factor
Month number
1
Day number
1
Hour number
1
Minute number
1
Global intensity
1
GAP
1
5.4 Models Configurations Table 4 highlights the models’ configuration, including layers types, layers numbers, and neurons numbers. These configurations were obtained by using the search grid
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method that helps to find the best configuration among many combinations of the hyperparameters. Table 4. Models configurations Model
Hidden layers
Neurons number
LSTM
2
256, 128
Bi-LSTM
1
128
1DCNN
1 conv + 2 dense
100,50
5.5 Results At this stage, the results of the different horizons predictions using the mentioned models above will be exhibited. Models’ performances will be compared using different metrics providing much information, such as Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and R2_score. The lag values of the past data were chosen in three hours. The number of epochs was decided in 10, and the batch size was determined in 16. The next table shows the results of the three used DL models for three forecasting horizons; two hours, four hours, and eight hours. They give a comparative study between the models; the results indicate except for the 1DCNN, the MSE increases if the forecasting horizon increases. This is due to the output size, which arrives in our case until 480 outputs represent the forecasted energy consumption in one minute. Therefore, according to Table 5 the best model is 1DCNN because its MSE is practically null. Table 5. DL energy consumption forecasting models performances comparison. Forecasting horizons 2h
4h
8h
Models
MSE
MAPE
R2_Score
MSE
MAPE
R2_Score
MSE
MAPE
R2_Score
LSTM
0.0350
0.1886
0.9438
0.0386
0.2901
0.9381
0.0494
0.23317
0.9209
B-LSTM
0.01734
0.1330
0.9722
0.0165
0.1354
0.9712
0.02173
0.1552
0.9652
1DCNN
0.000225
0.01406
0.9996
0.000136
0.01072
0.9997
0.0001078
0.00843
0.9998
Figure 6 on the left and Fig. 7 on the right represent the forecasted GAP by the three models compared with the true GAP for a period from 2009-06-06: 12:04:00 to 200906-06: 12:03:00 and from 2010-10-05: 14:44:00 to 2010-10-05: 15:43:00 respectively (Figs. 6, 7 and 8).
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Fig. 6. The forecasted GAP by the three models in summer
Fig. 7. The forecasted GAP by the three models in autumn
Fig. 8. DL models MAPE comparison
6 Conclusion This paper has proposed a comparative study of three deep learning models, LSTM, bi-LSTM, and 1DCNN, for four prediction horizons (two hours, four hours, and eight hours). The forecasted energy by the models is in a resolution of one minute. Furthermore, some methods were applied as preprocessing of dataset like data imputation to replace the missing data, data analysis methods such correlation matrix between features, studying collinearity between features and eliminating it by applying PCA, and normalizing the dataset. All those operations are applied to remedy the overfitting problem and ameliorate the training process. According to the discussed results, the MSE increases by increasing the forecasting horizons for LSTM and bi-LSTM models. On the other hand, the 1DCNN’s MSE behavior is completely different; it shows and decrement by increasing prediction horizons, and the 1DCNN model is the best one for this work.
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Acknowledgment. This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA), and the CNRST of Morocco (Alkhawarizmi/2020/39).
References 1. Liu, T., Chengliang, X., Chen, H., Li, Z.: Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy Build. (2019). https://doi.org/10.1016/ j.enbuild.2019.109675 2. Syed, D., Abu-Rub, H., Ghrayeb, A., Refaat, S.S.: Household-level energy forecasting in smart buildings using a novel hybrid deep learning model. IEEE Access 9, 33498–33511 (2021). https://doi.org/10.1109/ACCESS.2021.3061370 3. Jrhilifa, I., Ouadi, H., Jilbab, A.: Smart home’s wireless sensor networks lifetime optimizing using Q-learning. In: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, pp. 1–6 (2021). https://doi.org/10.1109/IECON48115.2021.9589460 4. Hadri, S., Naitmalek, Y., Najib, M., Bakhouya, M., Fakhri, Y., Elaroussi, M.: A comparative study of predictive approaches for load forecasting in smart buildings. Procedia Comput. Sci., 160, 173–180 (2019). ISSN 1877–0509. https://doi.org/10.1016/j.procs.2019.09.458 5. Yazici, I., Faruk Beyca, O., Delen, D.: Deep-learning-based short-term electricity load forecasting: a real case application. Eng. Appl. Artif. Intell. 109 (2022). https://doi.org/10.1016/ j.engappai.2021.104645 6. Ahajjam, M.A., Licea, D.B., Ghogho, M., Kobbane, A.: Short-term multi-horizon residential electric load forecasting using deep learning and signal decomposition methods. ArXiv, abs/2202.03264 (2022) 7. Zhang, F., Deb, C., Lee, S.E., Yang, J., Shah, K.W.: Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique. Energy Build. 126, 94–103 (2016). ISSN 0378-7788. https://doi.org/ 10.1016/j.enbuild.2016.05.028 8. Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y.: Short-term residential load forecasting based on resident behaviour learning. IEEE Trans. Power Syst. 33(1), 10871088 (2018). https://doi. org/10.1109/TPWRS.2017.2688178 9. Candanedo, L.M., Feldheim, V., Deramaix, D.: Data driven prediction models of energy use of appliances in a low-energy house. Energy Build. 140, 8197 (2017). https://doi.org/10.1016/ j.enbuild.2017.01.083 10. Liu, T., Liu, K., Fang, P., Zhao, J.: A hybrid model of AR and PNN method for building thermal load forecasting. In: Zhang, L., Song, X., Wu, Y. (eds.) AsiaSim/SCS AutumnSim -2016. CCIS, vol. 643, pp. 146–155. Springer, Singapore (2016). https://doi.org/10.1007/978981-10-2663-8_16 11. Zainab, A., Syed, D.: Deployment of deep learning models on resource decent devices for object detection. In: Proceedings of IEEE International Conference on Informatics IoT, Enabling Technologies (ICIoT), pp. 73–78, February 2020. https://doi.org/10.1109/ICIoT4 8696.2020.9089651 12. Fukuoka, R., et al.: Wind speed prediction model using LSTM and 1D-CNN. J. Sig. Process. 22(4), 207–210 (2018). Released on J-STAGE, 25 July 2018. Online ISSN 1880-1013, Print ISSN 1342-6230. https://doi.org/10.2299/jsp.22.207
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13. UCI Machine Learning Repository: Individual Household Electric Power Consumption Data Set. Accessed 22 Jan. 2021. archive.ics.uci.edu/ml/datasets/Individual + household+electric+power+consumption 14. Sessa, J., Syed, D.: Techniques to deal with missing data. In: Proceedings of 5th International Conference on Electronic Devices Systems and Applications, Sarawak, Malaysia, pp. 1–4, December 2016. https://doi.org/10.1109/ICEDSA.2016.7818486
Electric Motors and Control Strategies for Electric Vehicles: A Review Zineb Machhour(B) , Mhamed El Mrabet, Zineb Mekrini, and Mohammed Boulaala Laboratory of Informatics, Systems and Telecommunications, FSTT, Abdelmalek-Essaadi University , Tetouan, Morocco [email protected]
Abstract. Use of electricity to replace fossil fuels as a power source for transports has provided much attention in modern society. Electric vehicles have enormous potential for future transport communication, replacing current conventional vehicles.in this paper we will study electric motors and their control strategies for electric vehicle applications, on the one hand we will choose among the different types of motors and their controls, the most used in the design of EVs, and on the other hand we will give their principles, their advantages and disadvantages, and we will make a comparison between them to know the most efficient. Keywords: Electric vehicle · DC motor · Permanent magnet Brushless motor (PMBLM) · Permanent synchronous motor (PMSM) · Field oriented control (FOC) · Direct torque control (DTC) · Pulse width modulation (PWM)
1 Introduction Pure EVs are fully powered by an electrified propulsion system, while batteries and auxiliary power units power their electronic devices, as well as its main advantage which is high power output, that is why modern society is working on the development of electric vehicles so that their technologies and applications become widely available [1], as energy conservation and environmental protection are growing concerns around the world [2]. In this article, we will make a comparison of the different parts of the components of the electric car, of which the engine is the most important element of the propulsion system, the type used determines the type of converter required, the possible control types, we will describe the DC motor, permanent magnet Brushless motor (PMBM) and permanent magnet synchronous motor (PMSM) [3, 4]. So we will see as control algorithms, pulse width modulation (PWM), direct torque control (DTC) and field oriented control (FOC), the most suitable for the previously chosen types of motors [1]. We will give their principles, their advantages and disadvantages after we will make a comparison between them to know which is the most effective in EV applications (Fig. 1).
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 293–301, 2023. https://doi.org/10.1007/978-3-031-35245-4_27
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Fig. 1. Schema of a typical EV
2 Electric Motors The Fig. 2 describes a survey of different motor designs for application dictated by the performance of an existing electric vehicle drive, there are several types, there are brushed motors such as DC motors, and brushless motors such as synchronous motors, diagram below describes the different types of actuators used [6].
Fig. 2. Classification of electric motors for EVs
At the heart of EV drivetrain is the electrical motor, which converts electricity (generally from a battery) into mechanical energy to drive the wheels. The motor drives can be based on direct-current (DC) motors, Permanent magnet brushless motor (PMBM), switch reluctance motors (SRMs), permanent magnet synchronous motors (PMSM). 2.1 DC Motor Traditionally, DC motors have always played an important role in electric propulsion because their torque-speed characteristics are well suited to the need for traction and
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their speed regulations are simple. However, the DC motor has a commutator, so it needs regular maintenance [5]. The Table 1 shows the advantages and disadvantages of the DC motor. All the power involved in the electromechanical conversion is transferred to the rotor through stationary brushes which are in frictional contact with the copper segments of the commutator. It requires some maintenance and has a shorter lifespan. Electric vehicles under 10 kW use commutated DC motors primarily for simplicity of control [6]. Table 1. Advantages and disadvantages of DC motor. Strong points
Weak points
Easy field weakening for separately excited DC Presence of the collector brush system Simple control electronics requiring periodic maintenance Torque and power to weight Armature difficult to cool Construction cost, mechanically complex machine
2.2 Permanent Magnet Brushless Motor Permanent magnet brushless (PMBM) motors are very attractive for electric vehicles because they offer the advantages of higher efficiency, higher power density, lower operating cost, greater reliability and lower maintenance compared to DC motors, because it has no rotor winding and copper losses in the rotor. This motor has a short constant power range due to limited field weakening capability resulting from the presence of a PM field which can be weakened by a stator field. The constant power region is short. PM brushless motors are promising because they use PM to produce the magnetic field. The Table 2 shows the advantages and disadvantages of the brushless motor [7]. Table 2. Advantages and disadvantages of PMBL motor. Strong points
Weak points
High dynamic response High efficiency Long operating life Noiseless operation Higher speed ranges
The higher cost The cost of the control system Each brushless motor should be controlled by its own “controller” device
2.3 Synchronous Motor Permanent magnet synchronous motors (PMSMs) provide a competitive technology for EV traction drives owing to their high power density and high efficiency. Results show
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that PMSMs have the best performance in terms of system efficiency, power Density and power-speed range while their drawbacks are associated with limited reserve. The Table 3 show advantages and disadvantages of synchronous motor [1]. Table 3. Advantages and disadvantages of PMSM motor. Strong points
Weak points
It has excellent performance in terms of torque and power The inertia of the rotor is very low It is the machine technology that has the best performance in terms of torque and dynamics
Its price is still high Problem with the temperature resistance of the magnets Fairly complicated control electronics Fragility of the magnets and complexity of mounting the magnetic rotor Demagnetization of magnets
2.4 Comparison of Motors of EV In terms of efficiency, weight, and cost, a comparison of the motor drives has been made in the Table 4. Characteristics of motor drives have been switched from very high to low [7]. Table 4. Comparison of motors for EVs. DC Motor
Brushless motor
Synchronous motor
Power density
Low
Very high
Very high
Efficiency
Low
Very high
Very high
Controllability
Very high
High
Medium
Reliability
Medium
High
High
Maturity
Very high
High
Medium
Cost
High
Medium
Medium
We can conclude from the parameters that allow us to measure the performance of motors that can meet the advanced requirements of EV applications, that the brushless motor is the highest in terms of power density, efficiency, controllability, reliability and maturity compared to the competitors.
3 Control Strategies A general classification map of machine control strategies for electric vehicles is shown in Fig. 3 [1]. Conventional electronic control such as PID cannot meet the requirements of efficient EVs. Several control strategies have been developed recently.
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One only has to think of adaptive control by model reference, control with self-tuning parameters, variable structure control, use of the Kalman filter for speed estimation…etc. In general, the main electricity control strategy engine applied in electric vehicles is considered advanced technology and can be divided into three main types: Field Oriented Control (FOC), Direct Torque Control (DTC) and MPC [1]. All of these control strategies improve the dynamic behavior of vector control by compensating for variation in the physical parameters of the machine [8]. In this article, we will compare the algorithms of the most used control PWM, DTC and FOC,we will give their principles, their advantages and disadvantages, and then we will make a comparison between them to know the most compliant to meet the requirements of VE applications.
Fig. 3. Classification of control strategies of electric motors for EVs
3.1 Pulse Width Modulation (PWM) Pulse-width modulation (PWM) is a powerful technique for controlling analog circuits with a microcontroller’s digital outputs. The PWM is commonly used to control the speed of electric motors. This controller processes the electric motor as a digital system. This system is only allowed to run at low speed (DL) or high speed (DH). Speed regulation is achieved by alternating between low duty and high duty, which makes the concept of the controller extremely simple for design and production [9]. A PWM is basically a digital unipolar square wave signal where the duration of the ON time can be adjusted (or modulated) as desired. This way the power delivered to the load can be controlled from a microcontroller. The process of basic WPM is shown in Fig. 4 [1]. In the Table 5 we will present the strengths and weaknesses of the PWM controller. 3.2 Direct Torque Control (DTC) Direct torque control (DTC) replaces the decoupling in field-oriented control with bangbang control, which naturally fits the inherently discrete nature of switch-mode power inverters. DTC does not involve space vector modulation but utilizes a switching table
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Fig. 4. Control scheme diagrams of PWM [1]
Table 5. Advantages and disadvantages of PWM. Strong points
Weak points
Lowest switching frequency Easy to tune Fastest transient response Large current control bandwidth
High computation burden Sensitive to parameters Low robustness Sensitive to small- inductance motors
that consists of different voltage vectors. In addition, the rotor position sensing that is essential for FOC is not necessary for DTC to operate properly even if a speed control loop is included in the DTC scheme. Instead, the desired stator flux and torque are established through comparators and voltage commands [10]. In direct torque control it is possible to control directly the stator flux and the torque by selecting the appropriate inverter state. The total process of basic DTC is shown in Fig. 5.
Fig. 5. Control scheme diagrams of DTC [1]
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In the Table 6 we will present the strengths and weaknesses of the DTC controller. Table 6. Advantages and disadvantages of DTC. Strong points
Weak points
Low switching frequency Low computation burden Fast transient response Easy to tune
High THD All parameters required except resistance Modulation required and difficult to tune Normally combine with other strategies Small current control bandwidth
3.3 Field Oriented Control (FOC) Vector control, also called field-oriented control (FOC), is a variable-frequency drive (VFD) control method in which the stator currents of a three-phase AC or brushless DC electric motor are identified as two orthogonal components that can be visualized with a vector. One component defines the magnetic flux of the motor, the other the torque. The control system of the drive calculates the corresponding current component references from the flux and torque references given by the drive’s speed control. Typically proportional-integral (PI) controllers are used to keep the measured current components at their reference values. The pulse-width modulation of the variable-frequency drive defines the transistor switching according to the stator voltage references that are the output of the PI current controllers. Field oriented control (FOC) is an important control approach for brushless DC motors. It resembles sinusoidal commutation but adds a major mathematical twist [1]. The application of the FOC scheme can ensure the precise torque output of EVs [1]. As shown in Fig. 6.
Fig. 6. Control scheme diagrams of FOC [1]
In the Table 7 we will present the strengths and weaknesses of the FOC controller (Table 8).
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Strong points
Weak points
Fixed switching frequency Low computation burden Low THD Low torque ripple
High switching frequency All parameters required except resistance Modulation required and difficult to tune Slow transient response Small current control bandwidth
Table 8. Comparison of control strategies of motors for EVs. PWM
DTC
FOC
Transient response
Fastest
Fast
Slow
Switching frequency
Lowest
Low
High
Current control bandwidth
Large
Small
Small
3.4 Comparison of Control Algorithm The main differences between these three controllers are presented in the table these essentials parameters Transient response switching frequency, Current control bandwidth, allows us to compare between these control algorithms to be able to know which is the most adapted to the requirements of the motors applied to EVs.
4 Conclusion We can conclude in the application of controllers in EVs, DTC control schemes are discussed and can be widely applied in high- and medium power situations. The FOC controller can be applied to provide precise phase voltage. Nowadays, with the great development of the microprocessor, the PWM controller can be applied in electric motor controllers, with the complete utilization of the motor model, PWM provides great flexibility for controller design. Moreover, PWM is also getting increasing attention for EVs due to its wide speed range and fast dynamic response [1]. The electric motors are the most important element of the propulsion system, while the control strategies are the command of the brain of the EVs. This article has studied the types most used in the manufacture of electric vehicles and their control strategies. Their principles, characteristics, advantages and disadvantages are discussed. The objective of this article is to compare between these types to know the most powerful. we noticed that the brushless motor is preferable according to its qualities compared to the others, as well as PWM is also getting increasing attention for EVs due to its qualities compared to others.
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References 1. Chunhua, L., Chau, K.T., Christopher, H.T.L., Zaixin, S.: A critical review of advanced electric machines and control strategies electric vehicles. Proc. IEEE 109(6), 1004–1028 (2021). https://doi.org/10.1109/JPROC.2020.3041417 2. Emadi, A., Young, J.L., Kaushik, R.: Power electronics and motor drives in electric, hybrid electric, and plug-In hybrid electric vehicles. IEEE Trans. Industr. Electron. 55(6), 2237–2245 (2008). https://doi.org/10.1109/TIE.2008.922768 3. Chau, K.T., Chan, C.C., Chunhua, L.: Overview of permanent-magnet brushless drives for electric and hybrid electric vehicles. IEEE Trans. Indust. Elec. 55(6), 2246–2257 (2008). https://doi.org/10.1109/TIE.2008.918403 4. Yue, Z., Wenping, C., Sean, M., John, M.: Design and flux-weakening control of an interior permanent magnet synchronous motor for electric vehicles. IEEE Trans. Appl. Supercond. 26(7), 1–5 (2016). https://doi.org/10.1109/TASC.2016.2594863 5. Chan, C.C.: The state of the art of electric and hybrid vehicles. Proc. IEEE 90(2), 247–275 (2002). https://doi.org/10.1109/5.989873 6. Cheng, K.W.E.: Recent development on electric vehicles. In: 2009 3rd International Conference on Power Electronics Systems and Applications, pp. 1–5 7. Chau, K.T., Dong Zhang, J.Z., Jiang, C.L., Zhang, Y.: Design of a magnetic- geared outerrotor permanent-magnet brushless motor for electric vehicles. IEEE Trans. Magn. 43(6), 2504–2506 (2007). https://doi.org/10.1109/TMAG.2007.893714 8. Normand, L.: Electric propulsion system for proximity vehicle using an induction motor, pp. 1–147, December 2002 9. Anand, S., Nikola, M., Young-Joo, L., Mahesh, K., Emadi, A.: An FPGA-based novel digital PWM control scheme for BLDC motor drives. IEEE Trans. Indust. Elec. 56(8), 3040–3049 (2009). https://doi.org/10.1109/TIE.2009.2022067 10. Feng, N., Bingsen, W., Andrew, S.B., Kui, L., Elias, G.S.: Comparative evaluation of direct torque control strategies for permanent magnet synchronous machines. IEEE Trans. Power Elec. 31(2), 1408–1424 (2016). https://doi.org/10.1109/TPEL.2015.2421321
The Lighting Master Plan is a Lever for Efficient and Sustainable Management of Public Lighting in Moroccan Cities Youssef Kasseh(B) and Abdellatif Touzani Mohammadia School of Engineering, Mohammed V University, Rabat, Morocco [email protected]
Abstract. In Morocco, the public lighting network is often the first item of energy expenditure for municipalities. As part of the national energy efficiency strategy launched by His Majesty King Mohamed VI, several projects aim to accelerate the modernisation of public lighting in Morocco. Public lighting must be efficient, economical, and sustainable. The public lighting stock of urban municipalities at the national level counts more than 1.5 million light points with an estimated annual consumption of 1000 Gwh, recording an annual increase of 8% between 2016 and 2018. The objective of this document is to present the impact of the implementation of a Lighting Master Plan on the energy and environmental performance of public lighting. Based on best practice in energy efficiency, the Lighting Master Plan was implemented starting with a review of the street lighting network in the study area, leading to the design of the sustainable street lighting programme, including the financing model, and finally to the implementation of the action plan including training, awareness raising and performance monitoring. Three years after the implementation of the programme principles and guidelines, a clear improvement in energy performance has been observed: a gain of more than 4% in annual consumption and up to 40% reduction in some newly installed areas while improving lighting quality. Keywords: Public lighting · National energy efficiency strategy · Energy management system · ISO 50002 · Energy audit · Lighting Master Plan
1 Introduction The lighting strategy of cities is now a real urban issue, a well-lit city is a factor of attractiveness, safety for citizens and savings for communities. Since the 1980s, public lighting has been one of the tools used to enhance the value of cities and their heritage. The evolution of urban planning over time has led to an increase in the level of requirements in terms of safety, comfort [5] and environmental protection [2] Energy saving has become a central concern of decision makers and is an important step towards energy sustainability of cities [1]. Street lighting management must respond to the changing rhythms of modern life by balancing economic and ecological aspects. Urban communities must therefore define a management model with a sustainable lighting policy, the right light, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 302–315, 2023. https://doi.org/10.1007/978-3-031-35245-4_28
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where it is needed, when it is needed and at the best cost. There are a number of studies in the literature on energy savings in street lighting systems [8], but no comprehensive work on the impacts of implementing energy management systems in street lighting has been done. This work provides a comprehensive study of the assessment of the impact of implementing a lighting master plan on the energy performance of the street lighting network. The energy saving options studied in this paper include both strategies, technologies and budgeting of the street lighting system. The environmental considerations (emission reduction [3]) resulting from the adoption of energy saving options are also discussed. This study focuses on the Lighting Master Plan in a public lighting network of Greater Casablanca, the largest city and economic capital of Morocco with about 5 million inhabitants [4], and provides feedback that can guide decision makers in defining lighting energy policies and in taking effective energy efficiency measures. The main contributions of this paper are: • To present the methodology adopted to carry out the energy diagnosis according to ISO 50002: 2014. • To share the results of the multi-criteria analysis for the development of the lighting master plan. • To discuss the performance results achieved following the implementation of the lighting master plan.
2 Research Methodology The research methodology uses the classical energy management schemes in combination with the requirements of the ISO 50001 standard. A Lighting Master Plan is proposed on the basis of a diagnosis following the requirements of ISO 50002:2014 [9]. After the diagnostic stage, a design and implementation phase of a Lighting Master Plan is launched with the aim of reducing energy consumption costs, minimising environmental impacts, improving working conditions and satisfying citizens. This implementation affects organisational activities and technical procedures as well as behavioural patterns [7] in order to reduce the total energy consumption of Greater Casablanca. The diagram (Fig. 1) shows the steps followed in this research work.
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Fig. 1. Research methodology.
3 Definition of the Scope of the Study Morocco has a consistent public lighting network illustrated by an estimated 1.5 million light points installed in the urban environment in 2017. According to data collected by the Directorate General of Local Authorities (DGLA) [10], overall expenditure on the public lighting sector at national level amounted to approximately 1.8 billion Moroccan dirhams in 2018, a bill that weighs heavily on the economic and financial balances of local authorities, and in some cities represents more than 40% of the municipal budget [4]. Local authorities are experiencing increasing difficulties in settling arrears on electricity consumption for public lighting [11]. In order to define the perimeter of the study, an analysis of the number of light points and annual energy consumption by energy supplier in the urban environment of Morocco was carried out. The study covers the Greater Casablanca area, which is the economic hub of the Kingdom of Morocco. It presents a number of luminous points with approximately 129 142 luminous points (Inventory of 2017) with a consumption higher than 126 280 (MWh). The Table 1 below presents the technical data on the perimeter of the study.
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Table 1. Technical data on the scope of the study Data on the Greater Casablanca study area (Year 2017) Light points
129 142 points
Area
384 km2
Consumption 2017
126 280 MWh
Co2 emission
82 335 tons / year
Average power of a light point
200 W
Network length
4 669 km
Average height of light points
10 m
Operation
4 312 h/an
Average age of supports
19 years
Cost of consumption/inhabitant/year [6]
49,5 MAD (Moroccan Dirhams)
4 Context Analysis, Identification, and Validation of Needs The Greater Casablanca Development Plan aims to consolidate the economic positioning of the region to make it a true international financial hub. This plan is based on four strategic axes: improving the living environment, platform and mobility, economic excellence, and animation. The objective is to establish Casablanca as an attractive metropolis, pleasant to live in, in line with the needs and expectations of its inhabitants. It is in this context that the city has decided to draw up a Lighting Master Plan (LMP), the purpose of which is to set out the main guidelines for urban lighting. It will be a reference for the current and future management of public lighting. In the long term, the LMP [4] will lead to lighting plans by theme: enhancement of heritage, embellishment and animation of public squares, identification of structuring ways, safety of movement. The main objective of this first phase is to analyse and define the current context through meetings and surveys with stakeholders in order to identify and validate needs and expectations. Almost forty meetings were held. These meetings focused on concerns, needs, expectations and future projects that could influence the Light Master Plan study. The analysis of the data collected (Table 2) made it possible to identify the following main remarks and proposals, grouped by aspect. Table 2. Assessment of the need’s analysis. Aspects
Remarks and proposals raised
Security
› Improve user safety › Avoid modulation of lighting on roads at risk of accidents or attacks › Improve the level and quality of lighting in public spaces, squares, and parks (continued)
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Aspects
Remarks and proposals raised
Esthetics
› Improve the city’s night-time image › Better lighting, improving the quality and quantity of light › To have coherence between the lighting and the urban furniture › To have white light lighting compatible with the colour of Casablanca’s urban furniture
Socio-economic
› Reducing energy bills › Eliminate old and obsolete equipment › Upgrading the public lighting network › Ensure fair treatment between boroughs › Provide equipment that is suitable for vandalism and sabotage in certain high-risk areas › Provide high-tech, long-lasting equipment to save energy and reduce maintenance › Introduce and use renewable energy (e.g. solar) › Involve and inform stakeholders in the Lighting Master Plan › Provide the boroughs with the necessary competences to monitor the implementation of the Light Master Plan › Take into account the opinions of associations and citizens › Make the database available › Use the energy savings made to finance new investments
Historical and cultural › Enhance the value of built heritage (historical, tourist, etc.) › Integrate festive lighting, allowing flexibility in public lighting to create lighting effects during events Light urbanism
› Consider different areas, penetrations, industrial areas and commercial areas in the lighting treatment › When drawing up the Lighting Master Plan, take into account the Urban Travel Plan (PDU) and current or planned projects
Design criteria
› Take account of the vegetation in the lighting design (height and positioning of trees) › Adapt the height of masts according to the classification of roads and zones › Plan equipment that limits light pollution › Plan site visits by the lighting designer to review lighting concepts for specific buildings › Choose the technological solution with the least impact on the environment › Plan an intelligent public lighting network (remote management or connected network on a par with the city of Casablanca, which is considered a Smart City). › Give the Master Plan for Lighting Development a regulatory status
Categorising the comments raised by aspect allows us to see the main concerns of stakeholders regarding street lighting and to guide our recommendations in the lighting
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master plan. For example, socio-economic issues relating to equipment, technology and energy saving are central to the concerns raised. In the same way, expectations regarding safety and design criteria will be analysed and considered in the development of the Lighting Master Plan. The points mentioned on light urbanism direct our urban analysis.
5 Urban Analysis of the Street Lighting Network The objective of this stage is to establish a diagnosis of the existing conditions of public lighting. The aim is to assess the state of the equipment, the energy consumption and to list the potential for night-time enhancement of the city. The analysis focused on these aspects: Urban and architectural analysis, light analysis, analysis of the public lighting heritage, environmental and energy analysis. 5.1 Urban and Architectural Analysis In this phase the historical data of the city, the urban development plans and the changes after independence were reviewed. The analysis of the road network and the different functions of the ways, including those of the existing and future tramway lines, were listed according to their morphology. Residential, industrial, commercial, seaside and tourist areas and green zones have been identified. Maps illustrating the types of ways, zoning, public facilities, centralities and the location of 52 remarkable sites or buildings reflecting the architectural diversity were produced. The analysis of the road network reveals several types of roads mentioned: penetrating roads, structural roads, interconnection roads, main roads and local roads. The urban analysis identifies 7 types of zones in the city covering a number of centralities defined as primary, secondary and miniature. The centrality is identified with a central space that borders public facilities or public use, shopping, leisure and service areas. Centrality can also crystallize around a cultural facility, a historical monument, a sports complex, a business centre, etc. The public square, the esplanade, the green space, the urban furniture are important ingredients of the centrality. The analysis of the urban and architectural data leads to the conclusion that the lighting furniture and the lighting treatment could contribute to the distinction of the approaches to the city via the treatment of the penetrators and the differentiation of the urban areas and the centralities. The diversity and quality of architectural styles linked to the density of certain urban compositions offer a potential for enhancement to support night-time economy activities. This step of the study will identify the characteristics of the public lighting (colour of the light, level of illumination, type of lighting furniture, etc.) according to the type of areas and ways. 5.2 Light Analysis A light analysis was carried out on a sample of 357 km of roads of different morphologies and functions (penetrating, structuring, interconnecting, local). The data collected was compared and cross-referenced with data from the database of the Casablanca city
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network manager. The measured illuminance level was compared with the values recommended by the standard (NM EN 13201). Photos were taken to illustrate typical situations. The analysis will provide information on the following main elements for all categories of ways: The technologies used, Illumination and uniformity and Light pollution and visual comfort. Photometric surveys are carried out using the so-called “dynamic survey” or “on-board measurement” method, the principle of which is as follows: Technology: The vast majority of the ways in the sample are illuminated to at least 20 lx which means that the average illuminance is achieved [10] in the majority of situations (Table 3) it resents the three most used technologies in the public lighting stock. Table 3. Technologies used in the public lighting park of Casablanca Technology
Colour
Energy consumption
Average life span
HPS-high pressure sodium
Contrasting ochre yellow
High
24 000 h
MI - metal iodide
White hot or cold
Average
15 000 h
Led-light-emitting diodes
White hot or cold
Relative
60 000 h
Illumination, Uniformity, and Visual Comfort: The vast majority of the ways in the sample are illuminated to at least 20 lx which means that the average illumination is achieved in the majority of situations (Fig. 2). However, many of the ways have an illumination level above the standard. A small number of ways have a lower illumination level than the standard or are subject to breakdowns. Illumination level compared to the standard 5% 17% 45%
Illumination level in accordance with the standard Illumination level higher than the standard Illumination level below the standard
34%
Breakdown
Fig. 2. Illumination level compared to NM EN 13 201
The uniformity is quite good on all the ways. This can be explained by the height at which the sources are mounted on high masts. This configuration also poses a problem
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of light pollution [10] for narrow ways since the sources are close to habitats, which tends to cause visual discomfort. The Quality of Light: The main conclusion is that road lighting is dictated by the requirements of traffic conditions and pedestrian safety at intersections. Visual comfort and light for pedestrians and cyclists are generally absent from current design criteria for road and public space lighting. The recent introduction of lamp posts with pedestrian lanterns shows that awareness of visual comfort and the environment for pedestrians is increasing. 5.3 Analysis of Public Lighting Assets This analysis is based on information from the database provided in May 2017. From these data, it is possible to extract general information on the typology of the installations. In particular, the following data were studied: – – – – – –
Average age of luminaires and supports. Condition of luminaires and supports. Category of luminaires, sources and supports. Height of supports. This interpretation was done at two levels: Direct analysis of each element. Cross-referencing of information for a more relevant analysis of the data.
It was also carried out on several scales: At the overall city level, By municipality and by type of road. Condition of Luminaires and Supports: The average age of the luminaires is 15 years (Fig. 3) and the average age of the supports is 19 years. The condition of the luminaires and supports is largely in good condition, which is consistent with the average age found. This situation in Casablanca can be explained in two ways: A significant renovation of the luminaires in recent years. In fact, almost 25% of the luminaires have been renovated over the last 5 years (Priority Action Plan) and the urban development of the city with the creation of new districts. With nearly 130,000 light points, the amount of equipment to be upgraded remains very high and corresponds to the public lighting stock of a medium-sized city (over 40,000 light points). Status of the supports
Status of the lights
32% 31%
52%
54%
Good Medium Older
15%
16% Fig. 3. Distribution of luminaire status and supports
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Type of Luminaire: Distribution of assets by type (Fig. 4) of luminaire shows that the majority of luminaires are standard. 3% 0%
Luminaire type 1%
0% Terminal
13%
Decorative Projector Recessed Spot 83%
Standard Unknown
Fig. 4. Breakdown of assets by type of luminaire
Type of Source: The vast majority of the lamps are high pressure sodium (HPS) (Fig. 5) (almost 90%). We note in particular that the average power is high on discharge lamps and that the installation of LED luminaires allows considerable energy savings. Type of sources 1%
0%
1%
6%
Halogen IM LED SHP unknown
92% Fig. 5. Distribution of luminaires by type of source
The analysis of the heritage will make it possible to propose the orientations of the heritage policy with in particular the financial envelope for the upgrading of the public lighting stock, the type of materials to be favoured according to the zoning, etc. 5.4 Energy and Environmental Analysis This analysis is the result of an interpretation of several parameters, the data of which are taken from the database cross-checked with the field survey carried out on 357 km of ways. These parameters are:
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– Analysis of the installed power and consumption to define the reference indicators of the current situation and to understand the evolutions. – Distribution of installed power by type of way and by municipality, making it possible to highlight the location of energy-intensive equipment and the targets for energy savings. – Analysis of the luminous efficacy of the installations by cross-checking the heritage data with the results of the photometric surveys carried out, making it possible to assess the different current lighting situations of the ways. – Verification of the installations with a measurement plan integrating several parameters (phase balancing, verification with a thermal camera, cos ϕ, identification of supply outlets, status of public lighting panels, etc.) In summary, luminous efficacy consists of achieving the target illuminance by implementing the minimum electrical power. It is evaluated using a power flux density indicator “PD” which is measured in W/m2 *Lux. The analysis of these consumption data highlights the following significant elements: – The average power of the park remains high overall, at around 215 W, compared to the average power in France, which is 140 W, despite a downward trend in the average power installed in recent years. – The discrepancy between theoretical data and field surveys due to the lack of reliability of the public lighting asset management databases – Lack of identification of power supplies, which makes it difficult to evaluate consumption by electricity meters – The need to revise specifications and technical standards in order to integrate the energy efficiency dimension The analysis of overall energy efficiency was complemented by a sample analysis which revealed several trends: – Overall, more than half of the ways are energy balanced. – Penetrators are generally balanced with a slight under-lighting compared to the norm. – Energy-intensive households are mainly located on the service roads and main roads.
6 Recommendations for Energy Efficiency and Performance of the Public Lighting Network After the diagnosis and audit of public lighting of the city of Casablanca, the following recommendations are proposed: installation of 100% LED lights and systematic remote monitoring of the renewed installations which will allow; – Improvement and control of the functioning of the installations will allow a more accurate monitoring of energy consumption. – Remote management: to be located on important sites.
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– A remote management system is a bidirectional system that collects data from the field and sends it to a central station and possibly to an on-call system, and that allows commands to be transmitted to the field installations. – Reviewing public lighting specifications for the integration of new energy efficient equipment standards. The Casablanca lighting master plan adopts three standards (Table 4). Table 4. Standards adopted in the lighting master plan The standards adopted in the lighting master plan Penetrating Ways
Crossroads roundabouts and pavements
Service roads
Standard Type: Road Height: 10m Spacing: 40m to 60m Installation: Central or bilateral staggered with bracket Material colour: White or grey Quality: T° 3000K / CRI80 Quantity: 25 lx average depreciation Distinction: Linear coloured luminaires,
Standard Type: Road and pedestrian Height: 10m and 12m / 4m to 5m pedestrian Location: Central and dual carriageway Material colour: White and Grey Quality: T° 4000K / CRI80 Quantity: 25 lx To favour the installation of vertical lighting elements on roundabouts
Standard Type: Road Height: 6 m Spacing: 10 m Installation: Central or bilateral staggered with bracket Material colour: Grey shade Quality: T° 2700K / CRI80 Quantity: 25 lx average depreciation Distinction: Linear luminaires
7 Costing of the Implementation of the Lighting Master Plan for the City of Casablanca In order to carry out a costing of the implementation of the lighting master plan, the following methodology (Fig. 6) is adopted.
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Fig. 6. Methodology of the costing
The Table 5 presents the Scenario for the implementation of the Lighting Master Plan at the scale of Casablanca with two options. Table 5. Calculation of the energy gain by scenario Delegated management perimeter
Amount (MDH ET) *
Energy gain
Scenario 1 (Renewal of 33% of the network)
1 368
25%
Scenario 2 (Renewal of 50% of the network)
1 810
35%
* MDH: Million Moroccan Dirhams, ET: Excluding taxes
8 Multi-criteria Analysis for Renewal Prioritization In order to identify the prioritization of the equipment to be renewed (Fig. 7) (the priority sections - roads and architectural heritage) a multi-criteria analysis was developed to classify the heritage in three classes (dilapidated, average and good) this analysis is based on two components: Technical criteria (technology, operation, materials, age, height, illumination level, light color, compliance with SDAL standards…) and Environmental criteria (humidity at the waterfront).
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Investment needs by track type 5%
Service roads
25%
Interconnection routes Main roads
55%
Structuring roads Penetrating roads
8% 6%
Fig. 7. Investment needs by track type
9 Results and Discussion The implementation of several energy performance actions resulting from the recommendations of the lighting master plan has led to a 3.7% decrease in the consumption of the public lighting network in 2021 compared to 2020 (approximately 4.6 GWh decrease) with a 9% decrease in the consumption ratio per light point in 2021 compared to 2017 (Table 6). This decrease is due in part to the replacement of SHP/IM luminaires by LEDs and the maintenance and renewal of the installations (public lighting panels, astronomical clocks, balancing the phases). Table 6. Analysis of annual global performance indicators 2017
2018
2019
2020
2021
Total consumption MWH
126 280
126 289
126 402
125 888
121 217
Number of light points
129 142
129 211
131 693
132 300
134497
Availability rate
96.69%
96.30%
96.06%
96.5%
96.20%
Ratio kwh/light point
1 011
1 015
998
963
913
90387
87033
Emission CO2 (TCO2 ) * 90669 90675 90756 * The emission factor adopted in our study is 718 TCO / GWh 2
10 Conclusion Morocco’s energy efficiency strategy calls for a 13% [10] reduction in the electricity consumption of the public lighting network, which can only be achieved through good control of the parameters of the current public lighting network and good governance
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of future investments. The elaboration of master plans can be a lever to reach the set objective. The actions resulting from Casablanca’s lighting master plan made it possible to record a 3.7% drop in consumption between 2020 and 2021 despite the extension of the network. The energy gain on the public lighting network of the city of Greater Casablanca is potentially significant. An approach to support this energy saving policy has been put in place both for new projects by defining specifications with strong objectives, and for existing installations by deploying a renovation program targeting the most energy-intensive installations as a priority. The research and studies carried out within the framework of the implementation of the master plan of light development of the city of Casablanca, as well as the methodology followed, can serve as a model for other cities.
References 1. Aasen, M., Westskog, H., Korneliussen, K.: Energy performance contracts in the municipal sector in Norway: overcoming barriers to energy savings? Energ. Effi. 9(1), 171–185 (2015). https://doi.org/10.1007/s12053-015-9356-0 2. Bratt, C., Hallstedt, S., Robert, K.-H., Broman, G., Oldmark, J.: Assessment of Criteria Development for Public Procurement from a Strategic Sustainability Perspect. J. Clean. Prod. 52, 309e316 (2013) 3. Croci, E., Lucchitta, B., Molteni, T.: Low carbon urban strategies: an investigation of 124 European cities. Urban Clim 40, 101022 (2021). https://doi.org/10.1016/j.uclim.2021.101022 4. Morocco, H.C.P.: Kingdom of Legal population of the regions, provinces, prefectures, municipalities, arrondissements, and communes of the Kingdom of Morocco according to the results of the 2014 RGPH (2014) 5. Kuriyama, A.: List of Grid Emission Factor, Institute for Global Environmental Strategies (2017) 6. Ministry of Energy, Mines, Water and Environment: National Energy Strategy Horizon 2030 (2009) 7. Polzin, F., von Flotow, P., Nolden, C.: Modes of governance for municipal energy efficiency services – the case of LED street lighting in Germany. J Clean Prod 139, 133–145 (2016). https://doi.org/10.1016/j.jclepro.2016.07.100 8. Radulovic, D., Skok, S., Kirincic, V.: Energy efficiency public lighting management in the cities. Energy 36, 1908–1915 (2011). https://doi.org/10.1016/j.energy.2010.10.016 9. ISO 50002:2014 Audits énergétiques — Exigences et recommandations de mise en oeuvre (2014). https://www.iso.org/fr/standard/60088.html 10. Stratégue Nationale de l’Efficacité énergétique à l’horizon 2030.pdf 11. How the municipality manages public lighting, 10 November 2016. http://www.leseco.ma/ regions/51506-fes-comment-la-commune-gere-l-eclairage-public.html
High Gain Observer Design for PEM Fuel Cell State Estimation in Electric Vehicles Abdelaziz El Aoumari(B) and Hamid Ouadi ERERA, ENSAM Rabat, Mohammed V University in Rabat, Rabat, Morocco [email protected], [email protected]
Abstract. This work is devoted to the study and design of a state observer as a tool for the management of cost and durability issues of PEMFC (Proton Exchange Membrane Fuel Cells) in automotive applications. Indeed poor management of the air supply system usually leads to different degrees of oxygen starvation, which affect the durability and reliability of the fuel cell. Therefore, real-time monitoring of the internal states of the cathode side is imperative for feedback control to improve the performance of the air supply system and the net power. As a practical application, a nonlinear internal state observer that estimates the mass of oxygen and nitrogen inside the cathode of a PEMFC system and reconstructs the excess oxygen ratio using measurable variables, such as compressor angular velocity, manifold supply and return pressures, and load current. In this paper, a high gain observer (HGO) has been proposed whose main feature is the ease of its implementation while maintaining the desired favorable performance. The proposed observer gain is provided by an appropriate Riccati ordinary differential equation (ODE). The simulation results show the good performances and robustness of the proposed HG observer. Keywords: Proton exchange membrane fuel cell · Internal state observer · High gain observer · Oxygen excess ratio
1 Introduction Recent years, the electrification of vehicles has become an important solution to address the energy shortage and the protection of environmental pollution and greenhouse gas emissions. The development of new energy vehicles can have a significant and profound impact on the world’s automotive and energy industry as well as on social and economic development [1]. In order to provide energy for electric vehicle applications, electrochemical storage systems such as rechargeable batteries, supercapacitors and fuel cells play an important role for this reason. However, fuel cell vehicles have become one of the next generations of electric vehicles to be developed due to their advantages of fast hydrogenation, long range and no polluting emissions [2]. As for the different fuel cells, the proton exchange membrane fuel cell (PEMFC) system is a promising candidate for the future energy source for vehicular applications as its high efficiency, low operating temperature and relatively simple construction [3]. In order to address the critical objectives of fuel cell vehicles, the subcomponents of the fuel cell system must be controlled © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 316–330, 2023. https://doi.org/10.1007/978-3-031-35245-4_29
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manageably and in sequence, by high-quality controllers [4]. Existing research work demonstrates that the dynamic conditions of the vehicle can lead to huge fluctuations and variations in the internal states such as pressure and concentration inside the gas channel, gas diffusion layer and catalyst layer [5]. Insufficient reactant supply can lead to reactant depletion, hence the performance degradation the durability of the fuel cell deteriorates [6]. For this purpose. It is necessary to manage and control the concentration of the reagents at the expected level in order to avoid unexpected failures, so the variation and control of the excess oxygen ratio (OER) is the most certified method to avoid the lack of oxygen [7]. On the other hand, the determination of the OER is based on the mass of oxygen inside the cathode channel. However, this is a non-measurable variable, i.e. it cannot be measured directly by a sensor used in electric vehicles [8]. Furthermore, an excessive air supply leads to an exaggerated power loss of the air compressor, which leads to an excessive energy consumption and thus to a lowering of the efficiency of the PEMFC system. Therefore, an EDO-based control of the air subsystem can be designed to prohibit starvation and reduce power loss to improve system efficiency. Regarding the methods of monitoring the concentration of reactants there are two methods the first method is based on the technology of measuring the current density of printed circuit boards (PCB), which allows the local characteristics of the PEMFC to be obtained directly, and the distribution of the concentration of reactants can be evaluated [9]. The second is based on gas sampling analysis, in which capillary gas sampling tubes are inserted into the gas channel using a fuel cell with a perforated bipolar plate [10]. In this case and oxygen and nitrogen hydrogen partial pressure can be obtained at each sampling period using a mass spectrometer. However, the PEMFC system is of hermetic construction, so adapting the fuel cell structure to be suitable for these methods increases the complexity and cost of the cell, making it impossible to implement these techniques in automotive applications. Hence, the need to develop a virtual sensor called observer in order to reconstruct the states of the non-measurable systems, namely the mass of oxygen and nitrogen inside the cathode channel in order to determine the excess oxygen ratio (OER). Thus, subsequently the management of the air supply system, this sensor is based on the model of the fuel cell system plus measurable peripheral sensors. Such as compressor angular velocity, manifold supply and return pressures and charging current. In the literature concerning model-based observer the existing techniques for the estimation of the internal state of fuel cells can be classified into two categories: Kalman filter observers, Luenberger observers, sliding mode observers. The Kalman filter is a recursive filtering algorithm used to estimate the unknown state of a dynamic system in the sense of a minimum mean square error as a function of noise and measurement. For nonlinear systems, the extended Kalman filter (EKF) was applied by linearizing the system dynamics with the current mean value and covariance of the states. For fuel cell estimation, Pukrushpan [11] proposed an EKF estimator based on an eighth-order linear model to estimate the oxygen mass inside the cathode, and the observability of this observer is detected using different measurable variables. Nevertheless, linearization is not suitable for a complex higher order PEMFC system model. An alternative nonlinear estimation approach called unscented Kalman filter (UKF) is developed to estimate the
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internal states of the fuel cell, including oxygen-depleted mass flow rate [12], internal temperature [13], and nitrogen saturation inside the anode [14]. More recently, Hao Yuan [3] proposed an adaptive cubature Kalman filter (ACKF) algorithm. Adaptive fitting is implemented to match the covariances of process noise and measurement noise in real time. Thawornkuno [15] proposed a Luenberger observer for the water content inside the cell. The Luenberger observer is based on the water vapor pressure calculated from the mass flow equilibrium with the measured voltage and the liquid water content is derived from the calculated water pressure u and the membrane resistance. Nassif [16] also performs a comprehensive comparison between the Luenberger observer and the Kalman filter observer for the estimation of the internal gas pressure, and the results show that the Kalman filter observer converges faster than the Luenberger observer in terms of dynamic response speed. At the same time, the state reconstruction process does not take into account the influence of noise. The noise of the measured data must be filtered out before proceeding to the estimation of the state with the Luenberger observer. Concerning the sliding mode observer has attracted considerable attention because the design of the sliding mode convergence algorithm is independent of the object parameters and disturbances. Therefore, it guarantees good robustness and relatively high accuracy. In prophase, the first-order sliding mode observer (SMO) of the first order was usually chosen for the estimation of the internal state of the fuel cell [17]. However, there is usually a beat phenomenon at the output of the first-order SMO. Therefore, highorder sliding mode observers (HOSMs) for the estimation of the state of in fuel cells are attracting much attention due to their better performance in mitigating the hopping effect and maintaining robustness and accuracy compared to the conventional first-order SMO [8]. However, once the observer model is excessively complex, leads to highly nonlinear and state-dependent, it is still far from easy to compute the observer HOSM matrix that meets the full rank requirement. The computation of the observer EKF depends on the computation of the Jacobian matrix, which is also quite difficult to implement. Therefore, the design of estimation algorithms to observe the internal critical state must be easily implemented. To cope with these problems, in this paper an HGO observer has been proposed. The main feature of the HGO observer is the ease of its implementation and calibration while keeping the desired favorable performances. This article is organized as follows: Sect. 2 provides the general description of the issue. The nonlinear dynamic model of the PEMFC system used in our study and the processes for estimating internal states based on a high-gain observer are presented in Sect. 3. The Determination of the output stack voltage and the excess ratio of oxygen are described in Sect. 4. The implementation of the simulation and the validation of the results are described in Sect. 5. Finally, a summary and conclusion and a few lines of research for future work is presented in Sect. 6.
2 General Description of the Studied PEMFC Systems In order to facilitate subsystem management to extract full power and extend the life of the PEMFC system, real-time monitoring of the internal state is imperative for feedback control to maintain the internal states at expected levels. However, it is particularly
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difficult to directly measure internal states in the automotive environment due to the hermetic construction of the PEMFC. Therefore, it is necessary to use a model-based observer. In order to solve the above-mentioned problem, Fig. 1 represents the different blocks used where a high gain nonlinear observer (HGO) is proposed for the first time for the estimation of the two unmeasurable internal variables namely the mass of oxygen and nitrogen inside the cathode of a PEMFC system. These two variables are then used to calculate the excess oxygen ratio and the voltage generated by the cell. Again, these last variables are used in the power management block to manage a control signal needed to control the voltage supply to the compressor in order to keep the oxygen level within the desired levels.
Fig. 1. Schematic diagram of the fuel cell system used in this paper
3 High Gain Observer Design for Nonlinear Dynamic Model of PEMFC Based on the work of Pukrushpan [3, 18–20]. A simplified nonlinear model is proposed, this model contains. A model of the compressor, the air supply manifold, the return manifold and the stack. Some assumptions are used to derive a more accurate model of the PEMFC system. These assumptions are as follows: A1: The injector can adjust the anode pressure quickly, so the anode side model is ignored in this paper.
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A2: The air temperature and humidity at the fuel cell inlet are assumed constant. In reality, this is a real case where it is subject to very slow variations. Because of their slow response, a traditional controller cannot regulate these states. A3: The electrical model of the DC motor driving the compressor is neglected. This is because the motor winding impedance ratings (inductance plus resistance) have a small time constant. A fifth-order nonlinear dynamic model of the fuel cell system is obtained by integrating the component models in equations Proposed in the work [3, 18–20]., in which angular velocity of the air compressor ωcp , pressure of the supply manifold Psm , mass of oxygen inside the cathode mO2 , mass of nitrogen inside the cathode mN 2 , and pressure of the return manifold Prm , are state variables. The battery current Ist is considered as a disturbance, while the compressor supply voltage Vcm is considered as a control signal. x=
ωcp Psm Prm mO2 mN 2
U = [Vcm ]
T (1)
T y = ωcp , psm , prm
(2)
Moreover, the Equations from (1) to (5) can be rewritten by the following expressions
In order to avoid the singularity existing in Eqs. (1) to (7) and to put the model in the normalized form of a large gain observer given by [3] and [4] we multiply Eq. (3) by × 2.x1 and we propose to perform the following change of variables z1 = x12 , z2 = x2 , z3 = x3 , z4 = x4 , z5 = x5 , z6 =
1 x4 + x5 + e4
Then the equations become
With the coefficients ai , bi ; ci ; di , ei ; gi are defined as follows in Appendix 2.
(8)
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To design an observer for the PEMFC described in (9–14), we will consider the following class of MIMO nonlinear systems z˙ (t) = F(u(t), z(t) · z(t) + ϕ(u(t), z(t)) + B · ε(t) (15) y(t) = C · z(t) Where the state z(t) ∈ IRn, each Fk(u, x) is a nk × nk + 1 matrix which is triangular w.r.t. z, i.e. Fk(u, x) = Fk(u, z(1),…, z(k)), k = 1,…, q–φ(z(t), u(t)) is a nonlinear vector function that has a triangular structure w.r.t. x; u ∈ IRs denotes the system input, y ∈ IRp is the system output; ε: IR + → IRnq, is an unknown function which denotes the system uncertainties. In order to design the high gain observer, we have to write our system in a particular form proposed by J.L. Robles-Magdaleno [23] and M. Farza [24]. First let introduce the new following variables: ⎛
⎛ ⎞ ⎞ z1 z4 z1 = ⎝ z2 ⎠ and z2 = ⎝ z5 ⎠ z3 z6
(16)
One can easily verify that the original nonlinear system given in (29–34) can be rewritten as a function of the new state variables as follows: ⎛ •⎞ Z1 ϕ1(u,X1 ) 0 X1 0 F (3,3) 1(3,3) ⎝ ⎠= + + .Ist (17) . • 0(3,3) 0(3,3) B2 X2 ϕ2(u,X1 ,X2 ) Z2 ⎤ ⎡ ⎞ −e8 0 0 0 ⎥ ⎢0 ⎝ c13 c14 0 ⎠, = = = B2 F1 ⎦ϕ1 ⎣ 2 d2 d3 0 z6 .e8 ⎛ ⎞ √ √ β β+1 β+2 β 2 a1 + a2. z 2 + a3. z2 + a4 z1 + a5 z2 z1 + a6 z − a7 z2 − a8. z2 − a9. z2 − a10 z2 .z1 ⎜ ⎟ ⎜ −a z β+1 .√z − a z β √z + a .V .√z ⎟ 1 12 2 1 13 cm 1 ⎜ 11 2 ⎟ ⎜ ⎟ √ √ β β+1 β+2 2 ⎜ ⎟ c1 + c2 z2 + c3. z2 + c4 z1 + c5 z2 z1 + c6 z1 + c7. z2 + c8. z2 + c9. z2 ⎜ ⎟ ⎜ ⎟ ⎜ +c z β √z + c z β+1 .√z + c z β .z ⎟ 11 2 1 12 2 1 ⎝ 10 2 1 ⎠ −(d 1 + d8 ).z3 − d4 z35 − d5 z34 − d6 z33 − d7 z32 + d9
Where
⎛
⎛
⎞ −e1 z4 − e2 z5 + e3 z2 + z3 .z4 .z6 .k ca − z6 .z4 .e5 − z 4 .z5 .z6 .e6 − z42 .z6 .e7 − e9 ⎜ ⎟ ⎜ ⎟ 2 ⎜ −g 1 z5 − g 2 z4 + g 3 z2 + z3 .z5 .z6 .k ca − z6 .z5 .g5 − z 4 .z5 .z6 .g6 − z5 .z6 .g7 − g8 ⎟ ⎜ ⎟ ϕ2 = ⎜ ⎟ ⎜ z62 (z4 e1 + z5 g 1 ) + z62 (z4 g2 + z5 g 2 ) − z62 .z2 (e3 + g3 ) − z3 z63 .k ca (z4 + z5 ) ⎟ ⎝ ⎠ +z 36 (z4 .e5 + z5 g5 ) + z4 z5 z63 (e6 + g6 ) + z63 (z42 e7 + z52 g7 ) + z62 (e9 + g8 )
The observer design will be performed under the following assumptions. A1. The state z(t) and the control u(t) are bounded, i.e. z(t) ∈ Z and u(t) ∈ U where Z ⊂ IRn and U ⊂ IRs are compact sets. One sets zM = sup z . x∈X
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A2. Each φk (u, z), k = 1,…, q and each Fk (u, z), k = 1,…, q - 1, is Lipschitz on Z with respect to z uniformly in u, i.e. ∃Lϕ > 0/∀u ∈ U ; ∀(x, x) ∈ X × X one has φ (k) (u, z) − φ (k) (u, z) ≤ Lφ z − z, ∃LF > 0/∀u ∈ U ; ∀(z, z) ∈ Z × Z one has (k) F (u, z) − F (k) (u, z) ≤ LF z − z. A3. The unknown uncertainty ε is essentially bounded functions, i.e. ∃(δε ) ∈ I R+ / ε ∞ ess sup ε(t) ≤ δε . t≥0
Let us now recall the equations of a Standard High Gain Observer (SHGO) proposed in the above references [23, 24]. T z˙ˆ = F u(t), zˆ (t) zˆ (t) + ϕ u(t), zˆ (t) − θ −1 (18) θ P(t)C C zˆ (t) − y(t) Where ∧
Z (t) =
⎞ ∧ Z ⎝ 1⎠ ⎛
∧
Denotes the state estimate, θ is the setting parameter of the observer.
Z2 With
1 1 θ = diag In1 In2 · · · q−1 Inq θ θ
After the algebraic manipulation of Eq. (39), we obtain ⎤ ⎡ 1000 0 0 ⎢0 1 0 0 0 0 ⎥ ⎥ ⎢ ⎥ ⎢ ⎢0 0 1 0 0 0 ⎥ θ = ⎢ ⎥ ⎢ 0 0 0 θ1 0 0 ⎥ ⎥ ⎢ ⎣ 0 0 0 0 θ1 0 ⎦ 0 0 0 0 0 θ1 P(t) is a n × n symmetric matrix governed by the following Riccati ODE [23] . ∧ ∧ P (t) = θ (P(t) + F(u, z)P(t) + P(t)F T (u, z) − P(t)C T CP(t)) P(0) = P T (0) > 0
(19)
(20)
(21)
Recall that the convergence of the underlying observation error has been established under Assumptions A1 to A3.
4 Exploitation of the Estimated Variables 4.1 Determination of the Output Stack Voltage The voltage model is established according to Ref. [3, 24] and calculated as follow: Vstac = Ncell .Vcell = Ncell .(Enernst − Vact − Vohmic − Vconc )
(22)
Where Vcell represents the output voltage of PEMFC (V); Enernst denotes the reversible voltage defined by Nernst equation (V); Vact means the activation voltage loss (V); Vohmic
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is the ohmic voltage loss (V); and Vconc represents the concentration voltage drop (V), respectively. Furthermore, based on Nernst equation, Enernst is written as Enernst = 1.22 − 8.5 × e−3 .(T − 298.15) + 4.3085 × e−5 × T .[ln(PH 2 + 0.5PO2 )] (23) Where T represents the cell absolute Kelvin temperature (K); while PH 2 and PO2 mean the partial pressures of hydrogen and oxygen (Pa), respectively. In this model, it is assumed that pure hydrogen is supplied to the anode from a hydrogen tank. It is assumed that the hydrogen flow can be adapted instantaneously by a valve while maintaining a minimum pressure difference across the membrane. This was achieved by using a high-gain proportional controller to control the flow of hydrogen sorting that the anode pressure, Pan , follows the cathode pressure, Pca [3]. We therefore have PO2 =
RO2 .Tst .mO2 Vca
(24)
Moreover, the activation voltage loss Vact and the ohmic voltage loss is written as [3] Vact = θ1 + θ2 .Tst + θ3 .Tst .(Ist ) + θ4 .Tst . ln(CO2 )
(25)
Vohmic = (θ5 + θ6 .Tst + θ7 .Ist )Ist
(26)
Vconc = θ8 . exp(θ9 .Ist )
(27)
Where θi (i = 1,2, …, 9) means the semi-empirical coefficients, and Co2 means the oxygen concentration by [3]. Co2 = 1.97 × 10 − 7 × pO2 × exp(498/Tst )
(28)
4.2 Determination of the Excess Ratio of Oxygen A key parameter on the PEMFC system performance is the ratio between the inlet oxygen in the cathode side and the reacting oxygen in the cathode side (WO2,reacted ) of the fuel cell stack. This variable is known as oxygen excess ratio [21, 24] which control of the ratio avoids the starvation during a sudden load variation. The ratio is defined as in the following: λo2 =
WO2.in Oxygen supplied = Oxygen reacted WO2.reacted
(29)
The reaction oxygen flow rate can be measured or calculated efficiently it given by [3, 21, 22]. WO2.reacted =
Ncell .Mo2 .Ist 4.F
(30)
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However, the calculation of the input oxygen flow is a difficult task. Indeed, it depends on several variables its literary expression is given by the following equation yo2,in .Mo2 yo2,in .Mo2 + (1 − yo2,in ).MN 2 1
.
Wo2.reacted =
1+
Mv yo2,in .Mo2 +(1−yo2,in ).MN 2 . P
sm
φca,in .Psat,Tst
1−φatm .Psat,atm Patm
Ro2 .TsT .mo2 RN 2 .Tst .mN 2 Ksm Psm − − − Pv,ca Vca .Mo2 Vca .MN 2
(31)
5 Simulation Results In order to demonstrate the effectiveness and verify the accuracy of the high gain observer designed a simulation setup was constructed in Matlab/Simulink. Using the parameters given in Appendix 1. The simulation setup is shown in Fig. 2, where the block of constructed using the nonlinear state equations given in (29–34), while the high gain observer is constructed using Equation given in (38). As mentioned before, the high gain observer block used three measured state variables (z1 and z2, z3). Which are compressor angular velocity, manifold supply and return pressures respectively. The other state variables are estimated based on these three measurements.
Fig. 2. Simulation setup
During the simulations, the voltage of the DC motor driving the compressor is kept constant at 150 V. The system is subjected to different scenarios of sudden changes in load current. Which is shown in Fig. 3. All simulations are performed with a numerical solver considering a fixed step size of five us. The initial model and observer values are shown in Table 1.
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Fig. 3. The load current variation as disturbance
Table 1. Initial values of internal states in the system model and observers Starting points Real variable
Estimated variable
x1 = 7000rad /s
xˆ 1 = 1000rad /s
x2 = 0.8bar
xˆ 1 = 1.56bar
x3 = 1.21bar
xˆ 1 = 1.09bar
x4 = 1.5 × 10−3 kg
xˆ 4 = 5 × 10−4 kg
x5 = 0.008kg
xˆ 5 = 0.011kg
Figures 4, 5, 6, 7, 8 and 9 present the plots of each actual and estimated state variable for an observer parameter θ = 10. It can be seen that the proposed observer is perfectly capable of estimating the state of the PEMFC in a finite time. It is well noticed that the measured and estimated state variables used in the observer model are initialized with different values than the one embedded in the PEMFC block itself but they evolve faster to wait for desired real values. Then we notice that the estimated state variables correspond well to the measured ones what verifies the precision of the designed observer.
Fig. 4. The angular motor speed
Fig. 5. The angular motor speed
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Fig. 6. The oxygen mass at the cathode side
Fig. 7. The nitrogen mass at the cathode side
Fig. 8. The fuel cell stack
Fig. 9. The changes of the air excess ratio
6 Conclusion This paper proposes a non-linear internal state observer to estimate the mass of oxygen and nitrogen inside the cathode of a PEMFC system, with the aim of calculating and determining certain parameters necessary for the management and diagnosis of PEMFC systems. These parameters are essentially the excess oxygen ratio, the total internal resistance of the membrane and the voltage of the fuel cell. A dynamic model with lumped parameters was founded to describe the mass transport process on the cathode side. Based on this model, a high gain observer was developed. In this work, the gain of the proposed HGO is provided by an appropriate ordinary Riccati differential equation (ODE). The accuracy of the designed observer has been verified by simulation, showing that the estimated state variables match well with the measured ones. In addition, it should be noted that the designed observer makes the system less sensitive to noise, and could reduce the complexity and cost of a PEMFC system. In our opinion, there is still relatively little work on the estimation of internal fuel cell states. To enable further advances in the management of the PEMFC system, the internal state observer should be performed in the following aspects: (1) the design of estimation algorithms to observe the internal critical states should possess good robustness against complex perturbed conditions. (2) the observers should be verified on the basis of available real experimental data rather than off-line simulation data.
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Acknowledgement. This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco (Alkhawarizmi/2020/39).
Appendix 1 See (Table 2). Table 2. Physical parameters of the fuel cell system. Value of parameters of the PEMFC Parameter
Symbol
SI units
Value
Atmospheric pressure
Patm
Pa
101.325
Saturation pressure in ambient temperature
Psat,atm
Pa
3.1404 × 103
Saturation pressure in stack temperature
Psat,Tst
Pa
4.0943 × 104
Average ambient air relative humidity
φatm
-
0.5
Relative humidity in cathode inlet
des φca,in
-
1
Atmospheric temperature
Tatm
K
298.15
Temperature of the stack
Tst
K
353
Air-specific heat ratio
γ
-
1.4
Air density
Cp
J/kg/K
1004
Air gas constant
Ra
J/mol/K
286.9
Oxygen gas constant
RO2
J/mol/K
259.8
Nitrogen gas constant
RN 2
J/mol/K
296.8
Vapor gas constant
RV
J/mol/K
461.5
Molar mass of air
Ma
kg/mol
28.97 × 10–3
Molar mass of oxygen
MO2
kg/mol
30 × 10–3
Molar mass of nitrogen
MN 2
kg/mol
28 × 10–3
Molar mass of vapor
Mv
kg/mol
18.02 × 10–3
Maximum molar mass of vapor in cathode
Mv ,ca,max
kg/mol
0.002889
Faraday’s constant
F
C/mol
96487
N m/A
0.015
Parameters affecting modeling of the PEMFC Motor constant
Kt
(continued)
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Value of parameters of the PEMFC Parameter
Symbol
SI units
Value
Compressor rotational inertia
Jcp
Kgm2
0.03
Motor constant
Kv
V/(rad/sec)
0.0153
Compressor efficiency
ηcp
-
0.8
Compressor motor mechanical efficiency
ηcm
-
0.98
Number of cells in fuel cell stack
Ncell
-
75
cm2
340
Vsm
m3
0.02
Vca
m3
0.005
Return manifold volume
Vrm
m3
0.005
Supply manifold outlet orifice constant
Ksm
kg/sec/Pa
0.3629 × 10–5
Cathode outlet orifice constant
Krm
kg/sec/Pa
0.2177 × 10–5
Oxygen mole fraction at cathode inlet
yO2,in
-
0.21
mean the membrane thickness
l
m
0.0178
Motor constant
Rcm
ohm
0.82
Compressor rotational inertia
Jcp
g.m2
0.05
Characteristic fitting parameters of the air compressor
Polynomial coefficients of equations
B1 = 4.83 × 10−5 kg/sec B2 = -5.42 × 10–5 kg/sec B3 = 8.79 × 10−6 kg/sec B4 = 3.49 × 10–7 kg/sec/bar B5 = 3.55 × 10–13 kg/sec B6 = -4.11 × 10–10 kg/sec/bar
ζ1 = 0.001248-ζ2 = -0.001967-ζ3 = -0.001524 ζ4 = 0.002122-ζ5 = 0.02772-ζ6 = 0.07804
Fuel cell active area Supply manifold volume Single stack cathode volume
θ1 = 0.4883 -θ2 = −5. 9237 × 10−4 θ3 = 1.7546 × 10−4 -θ4 = −8.7752 × 10−5 θ5 = 0.4753-θ6 = −1.3655 × 10−3 -
θ7 = 6.8741 × 10−7 -θ8 = 1.1341 × 10−5 θ8 = 3.4749 × 10−3
Appendix 2
2·Bi ·Cp ·Tatm B C .T .ηcm .kv = 2. 6ηcpp.Jcpatm − kJt cp ηcp ·Jcp ; a6 .Rcm , ai(i=7,8,...,12) 2·B(i−6) ·Cp ·Tatm 2.kt .ηcm ; a13 = Jcp .Rcm . ηcp ·Jcp ·(Patm )β B .γ .R .Tatm.(η −1) B2 .γ .Ra .Tatm.(ηcp −1) γ .Ra .Pv,ca Tsm .ksm + 1 aVsm .ηcp cp ; c2 = c1 = Vsm Vsm .ηcp Bi .γ .Ra .Tatm.(ηcp −1) B(i−6) .γ .Ra .Tatm γ .Ra .ksm .Tsm ci(i=3,..,6) = ; ci(i=7,8,...,12) = V .η .(P )β ; c13 Vsm Vsm .ηcp . sm cp atm γ .Ra .Ro2 .Tst .Tsm .ksm γ .Ra .RN 2 .Tst .Tsm .ksm ; c = . 14 Vsm .Vca Vsm .Vca
ai(i=1,2,...,5)
=
= − =
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329
kca .Ra .RN 2 .Trm .Tst kca .Ra .Ro2 .Trm .Tst kca .Ra .Trm Vrm . ; d2 = Vrm .Mo2 .Vca ; d3 = Vrm .MN 2 .Vca ; d di(i=4,5,...,8) = kca .Ra .Trm. Pv,ca − ζ1 . ξ(10−i) ; d9 = Vrm . Yo2 .MO2 .ρ.ksm .Ro2. Tst .ρ.ksm .RN 2. Tst e1 = ; e2 = Yo2 .MO2Vca ; e3 = Yo2 .MO2 .ksm .ρ; e4 = Vca .Mo2 .MN 2 Pv,ca ·Vca ·Mv kca .RMN 2. Tst .Ro2. Tst g4 = Rv ·TsT ; e5 = g5 = Pv,ca .kca ; e6 = g6 = Vca .MMN 2 ; e7 = g7 = kca Vca .Mo2 ; .Mo2 e8 = Ncell4.F ; e9 = Yo2 .MO2 .ρ.Pv,ca .ksm ; ksm .ρ.(1−Yo2 )MN 2 .RN 2. Tst g1 = ; g2 = Vca .MN 2 ksm .ρ.(1−Yo2 )MN 2 .RO2. Tst ; g3 = ksm .ρ.(1 − Yo2 )MN 2 ; e9 = (1 − Yo2 ).ρ.Pv,ca .ksm .MN 2 ; Vca .MO2
d1 =
ρ=
1 YO2 .MO2 +(1−Yo2 ).MN 2 .
1+
ϕatm .Psat,Tatm Patm Mv .ϕatm .Psat,Tatm atm Ma .Patm
1−
.
References 1. Arabatzis, G., Kyriakopoulos, G., Tsialis, P.: Typology of regional units based on RES plants the case of Greece. Renew. Sustain. Energy Rev. 78, 1424–1434 (2017) 2. Andersson, M., Beale, S.B., Espinoza, M., Wu, Z., Lehnert, W.: A review of cell-scale multiphase flow modeling, including water management, in polymer electrolyte fuelcells. Appl Energy 180, 757–778 (2016) 3. Yuan, H., Dai, H., Wei, X., Ming, P.: A novel model-based internal state observer of a fuel cell system for electric vehicles using improved Kalman filter approach. Appl. Energy 268, 115009 (2020) 4. Daud, W., Rosli, R., Majlan, E., Hamid, S., Mohamed, R., Husaini, T.: PEM fuel cell system control: a review. Renew. Energy 113, 620–638 (2017) 5. Pei, P., Chen, H.: Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: a review. Appl. Energy 125, 60–75 (2014) 6. Yuan: Model-based observers for internal states estimation and control of proton exchange membrane fuel cell system: a review (2020) 7. Kim, B., Cha, D., Kim, Y.: The effects of air stoichiometry and air excess ratio on the transient response of a PEMFC under load change conditions. Appl. Energy 138, 143–149 (2015) 8. Deng, H., Li, Q., Chen, W., Zhang, G.: High-order sliding mode observer based OER control for PEM fuel cell air-feed system. IEEE Trans. Energy Convers. 33(1), 232–244 (2017) 9. Alaefour, I., Karimi, G., Jiao, K., Li, X.: Measurement of current distribution in a proton exchange membrane fuel cell with various flow arrangements–a parametric study. Appl. Energy 93, 80–89 (2012) 10. Yang, X.-G., Burke, N., Wang, C.-Y., Tajiri, K., Shinohara, K.: Simultaneous measurements of species and current distributions in a PEFC under low-humidity operation. J. Electrochem. Soc. 152(4), A759 (2005) 11. Pukrushpan, J.T., Stefanopoulou, A.G., Peng, H.: Control of fuel cell breathing. IEEE Control Syst. Mag. 24(2), 30–46 (2004) 12. Schultze, M., Horn, J.: State estimation with time delay and state feedback control of cathode exhaust gas mass flow for PEM fuel cell systems. In: 2013 European Control Conference (ECC), pp. 3560–3565. IEEE (2013) 13. Schultze, M., Horn, J.: Modeling, state estimation and nonlinear model predictive control of cathode exhaust gas mass flow for PEM fuel cells. Control. Eng. Pract. 49, 76–86 (2016) 14. Piffard, M., Gerard, M., Bideaux, E., Da Fonseca, R., Massioni, P.: Control by state observer of PEMFC anodic purges in dead-end operating mode. IFAC-PapersOnLine 48(15), 237–243 (2015)
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15. Kazmi, I., Bhatti, A.: Parameter estimation of proton exchange membrane fuel cell system using sliding mode observer. Int. J. Innovat. Comput. Informat. Control 8(7B), 5137–5148 (2012) 16. Xu, L., Hu, J., Cheng, S., Fang, C., Li, J., Ouyang, M., et al.: Nonlinear observation of internalstates of fuel cell cathode utilizing a high-order sliding-mode algorithm. J. PowerSources 356, 56–71 (2017) 17. Liu, J., Luo, W., Yang, X., Wu, L.: Robust model-based fault diagnosis for PEM fuel cell air-feed system. IEEE Trans. Ind. Electron 63(5), 3261–3270 (2016) 18. Sankar, K., Jana, A.K.: Nonlinear multivariable sliding mode control of a reversible PEM fuel cell integrated system. Energy Convers. Manage. 171, 541–565 (2018) 19. Pukrushpan, J.T., Stefanopoulou, A.G., Peng, H.: Control of Fuel Cell Power Systems: Principles, Modeling, Analysis and Feedback Design. Springer, London (2004) 20. Rakhtala, S.M., Noei, A.R., Ghaderi, R., Usai, E.: Design of finite-time high-order sliding mode state observer: a practical insight to PEM fuel cell system. J. Process. Control 24(1), 203–224 (2014) 21. Deng, H., Li, Q., Cui, Y., Zhu, Y., Chen, W.: Nonlinear controller design based on cascade adaptive sliding mode control for PEM fuel cell air supply systems. Int. J. Hydrogen Energy 44(35), 19357–19369 (2019) 22. Yang, B., et al.: A critical survey on proton exchange membrane fuel cell parameter estimation using meta-heuristic algorithms. J. Clean. Prod. 265, 121660 (2020) 23. Robles-Magdaleno, J., Rodríguez-Mata, A., Farza, M., M’saad, M.: A filtered high gain observer for a class of non uniformly observable systems–application to a phytoplanktonic growth model. J. Process Control 87, 68–78 (2020) 24. Farza, M., M’saad, M., Ménard, T., Ltaief, A., Maatoug, T.: Adaptive observer design for a class of nonlinear systems. application to speed sensorless induction motor. Automatica 90, 239–247 (2018)
Short-Term Electric Load Forecasting Model Based on SVR Technique Nada Mounir(B) and Hamid Ouadi ERERA, ENSAM Rabat, Mohammed V University in Rabat, Rabat, Morocco [email protected] Abstract. Nowadays the electrical consumption in the residential sector is becoming more and more energy intensive so it is important to use the energy consumption forecasting, which is one of the keys of the home energy management systems whose goal is to increase the occupant’s comfort but reducing the energy consumption. This work was based on a database that dates from 2011 until 2014 using load profiles of one, two and three days even a week before at the same time. The consumption depends on the outside temperature as a climatic factor. This paper explores one of the artificial intelligence algorithms, which is the Support Vector machine regression SVR. The model produced was evaluated using the Mean Square Error (MSE) and Mean Absolute Error (MAE) while comparing it to the results of a K nearest neighbor (KNN) model. The experiment was accompanied by the application of the model to predict electricity consumption for the next 24 h. Keywords: Short-term Load Forecasting · SVR · Load profiles · KNN · Electric load forecasting · Deep Learning
1 Introduction Electrical energy has become a global asset because of its use in all sectors but mainly in the building sector. For this reason, the home energy management system is becoming increasingly important. This management system offers benefits to both the building occupants and the electricity suppliers. It is a way to reduce energy consumption and most importantly, it reduces the electricity bill. Electricity management relies on the habits of the consumers, which will allow a better control of the consumption. Therefore, the power grid is becoming more and more intelligent by including demand response. As the efficiency of demand response techniques will improved by using consumption forecasts. The electrical load forecasting is the prediction of the future electrical consumption based on the past data. To estimate this future electrical load, inputs linked to an information system will be required, including weather data, building characteristics, types of loads used and usage schedules. The electrical load forecasting depends on size of dataset and the forecasting horizon. Artificial intelligence based methodology is most commonly used for shortterm load forecasting for one hour to a week with a certain degree of uncertainty which can be reduced by choosing the best parameters. This paper discusses the electrical short-term load forecasting in London using different methods like SVR and KNN and comparing their performance metrics. The main © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 331–342, 2023. https://doi.org/10.1007/978-3-031-35245-4_30
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innovation of the research presented in this paper is the use of 24 h, 48 h, 72 h and even one week lagged electrical load in addition of the same period lagged temperature. To show the performances of the model proposed four scenarios are developed, the first one to see the impact of the temperature of the day will be predicted, the second focuses on previous days temperature, the third one helps to know difference between adding the previous 7-day load and temperature and delete it. Furthermore, this paper is organized as follows. Section 2 focuses on passed studies. The description of the data set will be presented in Sect. 3. Coming to the Sect. 4, it introduces the methods used within this research. Section 5 focuses on the results obtained by the proposed method compared with the results obtained by the KNN.
2 Related Works In [1] the authors build load profiles for specific periods followed by a downward propagation through the LP to obtain lower-level predictions. It will allow the generation of forecasting for different periods, using the maximum load of a given period, then propagating it downward to obtain a lower level of forecasting. LP-based and AI-based forecasting gave different results (LP-based RF: MAPE = 3.86%, LP-based GBRT: MAPE = 3.94%, CNN: MAPE = 0.80%, SVR: MAPE = 0.91%), by comparing them the LP-based gave the worst results. Support Vector Regression (SVR) showed to be suitable for electrical load forecasting [2]. Improved the electricity demand forecasting accuracy by involving climate datasets and integrating the merits of the PSO algorithm with the SVR model. The dataset used contain so much information, the forecasting model must be developed by a performed laptop which is an inconvenient. The model performance shows a MAPE of 3.27% and it is too big for a hybrid model. In [3] Hyojoo Son and Changwan Kim provide a precise model for one-month-ahead forecast of electricity demand in the residential sector. Based on support vector regression and fuzzy-rough feature selection with particle swarm optimization algorithms, the proposed method automatically develops a forecasting model with variables that relate to the electricity demand series by ignoring variables that inevitably lead to forecasting errors. A data set covering the period from January 1991 to December 2012 and it is too much. The model gave a MAPE of 2.13% which is a big error. Moreover, [4] forecasted residential sector demand in energy using SVR. The performance model showed very good results by providing a large amount of weather and social variables alongside with load data.
3 Data Preparation Forecasting algorithms must develop to fit demand and anticipate future load values in order to avoid expansive production costs. While effective forecasting models are essential to the task. Although effective forecasting models are essential to the task, the characteristics used to feed these models are the most important aspect of the forecasting procedure. Therefore, an accurate domain study must perform to detect the different elements that influence consumption. 3.1 Database Study The database contains energy consumption records for a sample of 5567 London households that participated in the Low Carbon London project conducted by UK Power
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Networks between November 2011 and February 2014 [5]. The data from the communicating meters seems to be only associated with power consumption and I have added weather data for the London area. They resampled to a one-hour granularity for this study. Figure 1 illustrates daily power consumption, clearly showing the load gap between all hours of the day and evening hours.
Fig. 1. Electrical daily load
However, the power consumption seasonality appeared in Fig. 2. High temperatures in summer increases electricity demand, due to the mass usage of home appliances, especially air-conditioners. Furthermore, consumption decreases in spring and autumn, whilst in winter; the power consumption is moderately high. The observations below showed the effects of temperature on energy consumption. It shows how consumption varies depending on quarters of the year. The variations observed within days of the same week and months of the same season demonstrated that the inclusion of this information is essential for forecasting models.
Fig. 2. Electrical quarter load
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3.2 Features Selection The electrical load in London depends on several key factors. These factors influenced principally by temperature. The features in Table 1 were chosen as inputs for forecasting models. The forecasting of the future 24 h depends on: • • • • •
Electrical load of previous day at the same hour. Electrical load of the two previous days at the same hour. Electrical load of the three previous days at the same hour. Electrical load of one week before at the same hour. Temperature of these days and the temperature of the day forecasted.
Table 1. Inputs and outputs of forecasting model Inputs Electric loads
Temperature
E1 before one day (KWatt-Hour)
Temperature (°C)
E2 before two days (KWatt-Hour)
T1 before one day (°C)
E3 before three days (KWatt-Hour)
T2 before two days (°C)
E7 before one week (KWatt-Hour)
T3 before three days (°C) T7 before one week (°C)
Output Energy (KWatt-Hour)
Using correlation matrix help to get more information about features relationship in dataset. The Fig. 3 below shows that lag loads from previous days of the same hour are highly related to future loads. On the other hand, features related to temperature showed a very low correlation with electrical load but it minimizes errors (MAPE and RMSE).
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Fig. 3. Hourly load correlation heat map
3.3 Data Processing Before starting modeling with SVR, the data must be normalized over a range [6]. The normalization technique depends on the model to which the data is fed. For the SVR model, Min-Max normalization is used for data preprocessing. According to the Min-Max method [7], one can map the data linearly over the desired range [ymin , ymax ] for the transformed variable that is y can be transformed by the following equation: y = P(y) = U × y + V
(1)
Suppose that two extreme points, ymax and ymin , are the maximum and minimum values of the raw data respectively [7]. Then, two points, (ymax , ymax ) and (ymin , ymin ), are substituted into the above formula, we can solve the parameters U and V: ⎧ y −y ⎨ U = ymax −ymin max min (2) ⎩ V = y − ymax −ymin × y max max y −y max
min
After the implementation of the proposed forecasting model, the forecasted values can be reverted to the un-normalized data as follows: y − V y = P−1 y = U
(3)
In this paper, the range is set as [0, 1] (Fig. 4).
Training data
MinMax Scaler
SVR Training
Fig. 4. Data normalized
Trained SVR Model
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4 Prediction Approach Models of artificial intelligence are the most useful in predicting time series. They are more accurate and efficient. This document presents an SVR model compared with other contrast model as the KNN. 4.1 Data Description The data used for the experiment are the historical load data from 2011 to 2014, 80% of data used for training and 20% for test. The horizon of forecasting is one hour. The proposed model gave 24 outputs for 24 h (Fig. 5).
Lagged Electrical
Lagged Temperatures
Data preparation
Forecasting conDeep Learning models
sumption of the following 24 h
Fig. 5. Functional schematic
4.2 SVR Model Development Recently, SVR has become a popular and effective forecast model for forecasting electric load [7]. Chia et al. propose a SVR model to accurately forecast the load demand in advance for a solar energy application [8]. Based on SVR and fuzzy-rough feature selection with particle swarm optimization algorithms, Son and Kim propose a forecasting model to forecast the short-term electricity demand in residential sector [9]. By using the Phase Space Reconstruction procedure, Santamaría-Bonfil et al. present a hybrid model based on SVR to forecast the univariate wind speed time series [10]. YouLongYang et JinXingChe introduce the problem of incremental electric load forecasting under the SVR framework and develop a nested PSO algorithm to reduce the space search of the parameter selection for I-SVR [7]. The main idea of nonlinear regression of SVR is “kernel trick”, which maps the input patterns (W denotes the input space) into a higher-dimensional space F by a function φ: W to F. Then, a simple multiple linear regression can be performed in this higherdimensional feature space. For a more thorough coverage of it, we refer the readers to the excellent survey [10, 11]. Any function that satisfies Mercer’s condition can be used as Kernel function [9]. In this article, Gaussian kernel function was employed: 2 − xj − xi ) K(xj , xi ) = exp( 2 × δ2 where xj , xi
W, δ2 is the width parameter of the kernel function.
(4)
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To achieve the above goal, SVR considers the following linear regression function: f(x) = ωT x + b
(5)
where ω represents the weight vector; b represents the bias [7]. The coefficients ω and b can be solved by introducing the following optimization problem: n 1 T ω ω+C εi + ε∗i i=1 ω,b,ε,ε 2 ⎧ ⎨ yi − ωT xi + b ≤ ε + εi ωT xi + b − yi ≤ ε + εi* ⎩ εi , εi* ≥ 0 min ∗ =
(6)
(7)
where ε denotes the maximum value of tolerable error, εi and ε∗i is the distance between actual values and the corresponding boundary values of ε-tube, C>0 decides the trade-off of generalization ability and training error [7]. In this popular SVR model, there are three parameters (C, ε, δ) need to be selected at the same time, the estimates of the error are only vaguely specified functions with respect to these parameters and have many local maxima (or minima), so that the only applicable strategy which is a simple exhaustive grid search over the parameter space [13]. This grid is a tuning technique that attempts to compute the optimum values of hyperparameters. It is an exhaustive search that is performed on the specific parameter values of a model. The model is also known as an estimator. Grid search exercise can save time, effort and resources. In this paper, the best parameters are C=10, ε=0.0001 and δ=0.1. 4.3 KNN Model Development K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories [14]. It stores all the available data and classifies a new data point based on the similarity [14]. This means when new data appears then it can be easily classified into a well suite category by using K-NN algorithm [14]. It can be used for Regression as well as for Classification but mostly it is used for the Classification problems. K-NN is a non-parametric algorithm, which means it does not make any assumption on underlying data [14]. It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset [14]. KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data [14]. The value of K was chosen as three but in scenario three the value of K was 30.
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5 Results and Discussion This section will present the results of the forecasting based on SVR compared with a KNN model. 5.1 Strategy and Procedure of Simulation The lagged load demand for the same hour of each of the preceding 24 h, 48 h, 72 h and one week are considered into the model. Besides the lagged demand, lagged temperatures are embedded as input variables, the preceding 24 h, 48 h, 72 h, one week lagged temperature are involved in the model. To present the impact of each feature and choose the optimal mode of prediction, for the first time, it focused on the 24 h lagged temperature as an input of the model for the forecasting. Otherwise, to know the impact of the one, two, three and one week lagged temperatures of the previous days on the consumption, removing the features related to the consumption of the previous days will be important. Furthermore, the 7-day lag will have a high contribution because of the same day and same hour relating similar effects each day, to know his effect the feature of temperature and consumption will be removed. The model of SVR will be compared to KNN for each scenario (Table 2). Table 2. Features within Scenarios Scenarios
Features
Scenario 1 Temperature of the next 24 h Scenario 2 Temperature of the next day and the previous days Scenario 3 Load profile and temperature of previous days except the 7-day Scenario 4 All the features
5.2 Performance Evaluation To evaluate the performance of different predicting models, five metrics are used: Mean Absolute Error Percentage (MAPE), Root Mean Square Error (RMSE), Root-meansquare (RSquared) and Mean absolute error (MAE). N 100%
Ri −Fi
Ri
N i=0 N 1 RMSE = (Ri − Fi )2 N
MAPE =
i=1
(8)
(9)
Short-Term Electric Load Forecasting Model Based on SVR Technique
N (Fi − Ri )2 R2 = 1 − i=1 2 N i=1 Ri − Ri
1 N
MAE = R i − Fi
i=1 N
339
(10)
(11)
where N are the test data, Ri are the observed values and Fi are the forecasted values. 5.3 Evaluation of SVR Model SVR model based only on the temperature of the day to be predicted, good results were obtained (R2 R2 = 100%, RMSE = 0.03%, MAE = 0.00 and MAPE = 0.08%). Including the temperatures of the previous days will be beneficial as the errors are minimal (R2 = 100%, RMSE = 0.02%, MAE = 0.00 and MAPE = 0.06%). We can conclude from these results that the temperature will lead to more accurate and even more correct forecasts. Therefore, by adding more inputs to the model the results values will degrade until they cancel each other out. The electrical load and the temperature of one week before decreases the squared error in percentage (R2 = 100%, RMSE = 0.018%, MAE = 0.00 and MAPE = 0.052%). Adding more inputs to the model the metrics values will degrade until they cancel each other out. The forecasting load using several inputs, load profile of previous one, two, three and one week, SVR model obtained the best overall results percentage (R2 = 100%, RMSE = 0.018%, MAE = 0.00 et MAPE = 0.05%). Figure 6 shows the efficiency of the SVR algorithm alongside the KNN for predicting the 24 h. The prediction curve and the observation curve are the same. For the KNN model presented in Fig. 7, the prediction curve is noisy compared to the observation curve.
Fig. 6. SVR forecasted load and observed load
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Fig. 7. KNN forecasted load and observed load
5.4 SVR Prediction Model Performances Comparing to KNN The table above shows the results of the four scenarios using the two models (SVR and KNN). It gave a comparative study between these models. By comparing all these results, the MSE decreases when new lagged loads and temperatures are added to inputs. Table 3. The SVR model and KNN performances comparison Scenarios
SVR
KNN
R2
RMSE
MAE
MAPE %
R2
RMSE
MAE
MAPE %
Scenario 1
1
0.00
0.00
0.081
0.999
0.003
0.002
5.51
Scenario 2
1
0.00
0.00
0.058
0.958
0.021
0.016
5.251
Scenario 3
1
0.000
0.000
0.052
0.951
0.023
0.018
5.857
Scenario 4
1
0.00
0.00
0.05
0.920
0.029
0.023
7.324
Fig. 8. Deep learning models MAPE comparison
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According to Table 3 the best model is the SVR because it obtained the best results because the RMSE is null and the MAPE is practically null (Fig. 8). This is a novel solution to predict future based on load profile.
6 Conclusion This paper proposes an innovative electrical load prediction model using load profiles to generate accurate predictions. The model results are compared using several AI techniques. A reliable power system requires good analysis of energy consumption patterns. The paper investigates the energy consumption in London with the knowledge that the power demand is changing. Hourly power loads were correlated with the consumption patterns of the preceding days. Other factors such as days of the week, months and times of day had a direct contribution to energy demand. Temperature is not strongly correlated with energy demand but it decreases the errors of the model. However, the temperature profiles were taken into account in this paper. The main contribution of this paper was the use of load profiles from the days before and even a week before. These load profiles helped to understand consumer behaviors during the days before the future 24 h. Using this innovative method, the SVR-based model generated optimistic results (R2 = 1, RMSE = 0.00, MAE = 0.00 and MAPE = 0.05%) better than KNN. However, by adding new load profiles more than the 7-day as features, the forecasting algorithm will give the same results as if the new features have no influence. From the perspective of methodology, more variables that influence the load demand will be added to the forecasting model (occupancy, etc.) to improve the performances of the proposed model. Acknowledgement. This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco (Alkhawarizmi/2020/39).
References 1. Bendaoud, N.M.M., Farah, N., Ben Ahmed, S.: Applying load profiles propagation to machine learning based electrical energy forecasting. Electr. Power Syst. Res. 203, 107635 (2022) 2. Mohanad, S.A.-M., Yan, L., Ravinesh, C.D.: Particle swarm optimized support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset (2018) 3. Son, H., Kim, C.: Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resour. Conserv. Recycl. 123, 200–207 (2017). https://doi.org/ 10.1016/j.resconrec.2016.01.016 4. Lusis, P., Khalilpour, K.R., Andrew, L., Liebman, A.: Short-term residential load forecasting: impact of calendar effects and forecast granularity. Appl. Energy 205, 654–669 (2017). https:// doi.org/10.1016/j.apenergy.2017.07.114 5. Smart meters in London. https://www.kaggle.com/jeanmidev/smart-meters-in-london. Accessed 18 Apr 2022
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6. Che, J., Wang, J., Tang, Y.: Optimal training subset in a support vector regression electric load forecasting model. Appl. Soft Comput. 12(5), 1523–1531 (2012). https://doi.org/10.1016/j. asoc.2011.12.017 7. Yang, Y., Che, J., Li, Y., Zhao, Y., Zhu, S.: An incremental electric load forecasting model based on support vector regression. Energy 113, 796–808 (2016). https://doi.org/10.1016/j. energy.2016.07.092 8. Kavousi-Fard, A., Samet, H., Marzbani, F.: A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst. Appl. 41(13), 6047–6056 (2014). https://doi.org/10.1016/j.eswa.2014.03.053 9. Son, H., Kim, C.: Forecasting short-term electricity demand in residential sector based on support vector regression and fuzzy-rough feature selection with particle swarm optimization. Procedia Eng. 118, 1162–1168 (2015). https://doi.org/10.1016/j.proeng.2015.08.459 10. Santamaría-Bonfil, G., Reyes-Ballesteros, A., Gershenson, C.: Wind speed forecasting for wind farms: a method based on support vector regression. Renew. Energy 85, 790–809 (2016). https://doi.org/10.1016/j.renene.2015.07.004 11. The Nature of Statistical Learning Theory - Vladimir Vapnik - Google Livres. https://books. google.co.ma/books?hl=fr&lr=&id=sna9BaxVbj8C&oi=fnd&pg=PR7&ots=oqL7JVitba& sig=P-RL4IW7y46eKqo2skve06YqnA0&redir_esc=y#v=onepage&q&f=false. Accessed 18 Apr 2022 12. Coefficient de détermination. Wikipédia (18 June 2021). https://fr.wikipedia.org/w/index. php?title=Coefficient_de_d%C3%A9termination&oldid=183826304. Accessed 19 Apr 2022 13. Schölkopf, B., Smola, A.J., Bach, F.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002) 14. Chapelle, O.: Choosing multiple parameters for support vector machines. Mach. Learn. 46, 131–159 (2002) 15. Phi, M.: Illustrated Guide to LSTM’s and GRU’s: A step by step explanation. Medium (28 June 2020). https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-stepby-step-explanation-44e9eb85bf21. Accessed 18 Apr 2022 16. Coefficient de détermination. Wikipédia (2021) 17. JJ: MAE and RMSE — Which Metric is Better?. Human in a Machine World (23 March 2016). https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metricis-better-e60ac3bde13d, Accessed 19 Apr 2022
A Novel OPT-GBoost Approach for Predicting Direct Normal Irradiance Mohamed Khalifa Boutahir1(B) , Yousef Farhaoui1 , Mourade Azrour1 , Ahmed El Allaoui1 , and El Mahdi Boumait2 1 Engineering Science and Technology Laboratory, IDMS Team, Faculty of Sciences and
Techniques, Moulay Ismail University of Meknes, Errachidia, Morocco [email protected], [email protected], [email protected] 2 Smart Systems Laboratory, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco [email protected]
Abstract. An accurate estimation of a location’s solar potential is critical for solar system evaluation. Solar radiation variation and the absence of measuring stations at each site necessitate the development of credible prediction models. For accurate projections of solar plant production, a high forecast accuracy of direct normal irradiance is necessary, which has increased solar plant profitability. The OPT-GBoost model proposed in this paper uses an enhanced XGBoost classifier to estimate direct normal irradiance. We require extensive hyper-parameter tuning to design a more effective system that employs a classifier. As a result, we updated XGBoost’s hyper-values and trained the model with the new parameters obtained from the hyperparameter adjustment system OPTUNA. The validation of the model was conducted using the National Solar Radiation Database (NRSDB). We obtained superior results with our approach compared to other systems proposed in recent years by other authors. For example, using the coefficient of determination R2, we reached 99.96% in the NRSDB database. Keywords: Solar radiation · Machine learning · XGBoost · OPTUNA
1 Introduction Solar energy has attracted much attention in recent years as a critical source of renewable energy due to its usage in generating various forms of energy such as electricity and heat. Solar energy generation has become more efficient and cost-effective as solar systems and battery technology have advanced compared to traditional energy sources [1, 2]. However, forecasting the future of energy is inaccurate in the short term due to solar radiation variations. The output of the solar power plant drops from several of megawatts to nothing in a couple of minutes as a result of these adjustments, which could significantly impact the electrical grid’s reliability. Thus, an accurate solar energy prediction is the basis and critical technology for plant grid connection. Solar power predictions vary the geographical and temporal needs for various power system activities [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 343–350, 2023. https://doi.org/10.1007/978-3-031-35245-4_31
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In recent years, solar energy has drawn a lot of attention, especially in Morocco, where it is one of the main sources of energy [4]. With aspirations to make 42% of its energy renewable by 2020 and 52% by 2030, the nation has a high rate of solar radiation, which generates about 2000 mw of electricity [4]. Global solar irradiance is the most critical characteristic for solar system applications because it provides a clear picture of the installation and positioning of the various solar materials. Direct Normal Irradiance (DNI) is one of the solar irradiance metrics that power grid operators are interested in; these measurements directly impact the operation of a solar power plant. DNI is the amount of irradiation received by a perpendicularly aligned surface to the sun beam [5]. Unfortunately, solar stations are rare in many nations due to the high investment and maintenance costs, and data may be missing due to data transmission problems, sensor failures, or power outages. To address this issue and the shortage of DNI data, forecasting is proposed as a solution, even though solar radiation is a challenging parameter due to its high spatial and temporal variability and dependence on the geographical position of the other meteorological components [6]. Researchers in this subject have created various approaches for forecasting solar irradiance, and these methods differ in terms of the forecast’s geographical and temporal horizon. Rocha et al. [7] suggested two models for predicting DNI and GHI, the first using XGBoost and the second using a CNN-LSTM hybrid. The work made use of geostationary operational environmental satellite (GOES-16) pictures. The hybrid CNNLSTM model performs the best for DNI estimation, with a root mean square error of 238.22 W/m2 , followed by XGBoost with a root mean square error of 251.18 W/m2 . Zhu et al. [8] suggested a hybrid approach for estimating DNI. Their approach included a clear-sky and an error-correction model, improving Normalized Root Mean Squared Error (nRMSE) of between 28% and 70% over the standard clear-sky model used as a reference in the United States of America. Kumari et al. [9] created an ensemble model that uses ridge regression to combine ANN with XGBoost. They compared the model’s performance to SVR, random forest, EGF, and deep neural networks. In addition, ensemble models are more accurate and stable than individual ones. Azrour et al. [19] employ the K-means algorithm to determine if a caller is a spammer or not. Depending on the number of SPIT in the network, the experimental findings indicate that our suggested method can obtain a True Positive Rate between 83.3% and 99.23%. Waqas et al. [10] created an enhanced ensemble approach (DSE-XGB) that combines ANN and LSM (LSTM). The suggested DSE-XGB technique shows the best consistency and stability across numerous case studies, regardless of weather, and increases R2 by 10–12% over earlier models. Poh-Leng et al. [11] discussed the use of multiple regression (MR) and artificial neural network (ANN) models to estimate sun irradiance in Malaysia based on weather forecasting data. According to their findings, the ANN model can increase accuracy by 18.42% to the MR model in terms of root mean square error (RMSE). Azrour et al. [17] presents a predictive algorithm that can first predict the water quality class and subsequently the water quality index. The suggested approach is based on the four water characteristics of temperature, pH, turbidity, and coliforms. With a precision of 89.01% and an accuracy of 85.11%, the Artificial Neural Network produces the most
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highly effective technique to categorize the water quality, in comparison with SVM or Decision Tree algorithms. The literature study indicates that recent efforts have used machine learning to anticipate various energy system axes. However, we must improve the accuracy of our forecasting models if we are to construct flawless quality and intelligent energy systems. In our study, we suggest an improved method for predicting direct normal irradiance by combining the XGboost and Optuna. The structure of our paper will be as follows, in Sect. 2, we’ll discuss the data source used. The technique is covered in detail in Sect. 3, along with our workflow and the numerous models we used. Section 4 will examine the result and our findings, and Sect. 5 will provide the conclusion.
2 Data Source The National Solar Radiation Database (NSRDB) is a serially comprehensive compilation of hourly and half-hourly meteorological data and the three most often used solar radiation measurements: global horizontal, direct normal, and diffuse horizontal irradiance [12]. It applies to the United States of America and an increasing fraction of international locales. These data were obtained at many places and on a sufficient range of temporal and geographical scales to depict regional solar radiation conditions reliably. It is possible to view the quantity of solar energy available at a given moment for a particular place covered by the dataset and forecast the likely future availability of solar energy based on historical conditions [12]. In this study, we utilized the NRSDB data viewer to obtain data for the region of Errachidia, Morocco (31.932940, −4.423060) Over the Period 2017–2019 at a 30-min interval.
3 Methodology 3.1 Data Pre-processing As previously mentioned in the previous section, we used data from the NRSDB, which has 26280 data elements. The dataset was divided into two sections: training and test. 80% of data was utilized for training, while 20% was used for testing using the train_test_split function from the Sckit-Learn library. And then, we conduct some data cleaning, removing irrelevant columns to our model’s goal. 3.2 Feature Extraction One of the crucial tasks in a machine learning project is feature extraction. In our situation, obtaining the most potent features for DNI prediction is crucial. In our previous work [18], we used 4 different algorithms (Random Forest, XGBoost, Catboost, and LightGBM) to assess the impact of feature selection on the prediction of direct normal irradiance.
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In this study, we applied the XGBoost regressor to identify the most pertinent features for our model’s training, and the results are shown in Table 1. The results have been validated, as shown by the heat map in Fig. 1, which introduces the same top qualities associated with DNI. Table 1. The XGBoost regressor’s top features score for predicting DNI Feature name
Score
GHI
0.763299
DHI
0.157183
Clearsky DNI
0.0546874
Clearsky GHI
0.00932479
Clearsky DHI
0.00601276
Solar zenith angle
0.00413098
Fill Flag
0.00284847
Precipitable water
0.000792256
Dew point
0.000566956
Pressure
0.000376555
Temperature
0.000315679
Fig. 1. The data’s correlation map
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3.3 XGboost Academics and scientists worldwide are currently looking for the best machine learning algorithms for problem-solving. Then they observed that tree boosting algorithms have performed remarkably well in recent years and that the vast majority of academics are using them to improve results [13, 14]. XGBoost is a tree-boosting method that is also utilized in other applications on this side. And their performance and quickness are wellknown among researchers who work on data mining and machine learning challenges [13, 14]. Since the Gradient boosting method analyzes the dataset sequentially, the XGBoost model was built since the Gradient boosting algorithm generated the result at a very sluggish rate. As a result, it takes an extended period, which is inconvenient. XGBoost essentially enables or significantly boosts the model’s performance, primarily focusing on the model’s speed and performance. It possesses several characteristics that contribute to its speed and efficiency. Namely, it promotes parallelism by creating decision trees in parallel. It evaluates big or complicated models using distributed computing techniques. Additionally, it uses out-of-core computation to guarantee that vast and diverse datasets are examined and cache optimized to maximize hardware and resource utilization. These distinguishing characteristics convinced us to select XGBoost as the algorithm for our expert system [14]. 3.4 GridSearchCV The term “GridSearchCV” comprises two components: GridSearch and CV, which refer to grid search and cross-validation, respectively. GridSearch operates on the idea that parameters are modified within a given parameter range using a predetermined step size, and the learner is taught using the updated parameters. Each parameter combination corresponds to a model throughout the GridSearch algorithm’s operation. The parameter combination with the highest performance on the validation set is chosen to construct the optimal classification model. It is a training and comparing procedure. The fundamental concept of CV is to divide available data into two halves based on a specified ratio (generally 80:20). The majority of the data is in the training set, while the remainder is in the validation set. The classifier’s classification accuracy on the validation set will be used as the primary metric for evaluating the classifier’s performance [15]. GridSearchCV is used in this article to improve the parameters of the XGBoost algorithm. The parameters that require optimization in XGBoost are mostly ‘subsample’, ‘learning rate’, and ‘n estimators’. The optimal values for the parameters ‘subsample’, ‘learning rate’, and ‘n estimators’ are listed in Table 2. Table 2. The XGBoost classifier’s optimism value using GridSearch Parameter Subsample learning_rate n_estimators Best value 0,5
0,03
100
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3.5 Optuna: An Efficient Hyper-parameter Tuning Technique Without sufficient hyperparameter tuning, no machine learning model will perform better. Because hyperparameter tuning can affect the output of a machine learning model, it is a critical task while building a machine learning model. Individuals generate hyperparameters and barrier hyper-parameters due to training and evaluating a model. This cycle is repeated every few hours or days, resulting in time and energy being squandered. Here is where the OPTUNA has arrived. Optuna defines hyperparameter optimization as minimizing/maximizing the value of an objective function that accepts a collection of hyperparameters as input and returns its (validation) score. Optuna is built on the define-by-run principle; it creates the goal function step by step by interaction with the trail object. The search space will be dynamically formed throughout the goal function’s execution by the trail object’s operations [16].
Fig. 2. The hyper-parameters tuner concept
As seen in Fig. 2, the image summarizes the notion of hyperparameter tuners, in which the tuner is external to the model and tuning occurs before model training. The tuning procedure produces the ideal hyperparameter values, subsequently supplied into the model training stage.
4 Result and Discussion After pre-processing, cleaning, and visualizing the dataset and selecting the best features for predicting DNI, we evaluated our OPT-GBoost approach with two alternative models. The first model was created via a simple feat of the XGBoost algorithm, with no modifications to the hyperparameters. The second model used the GridSearchCV concept
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with XGboost to define and calculate the optimal combination of parameters values. Our approach model was by Optuna’s hyperparameter optimizer for the XGBoost algorithm. For tuning, we picked four hyperparameters: ‘learning rate’, ‘max depth’, ‘Reg_alpha’, and ‘Reg_lambda’. The best values of the four hyperparameters are: • • • •
Reg_alpha: 0.05494983902189819, Reg_lambda: 0.0824810253214559, Learning_rate: 0.9270761131134925, Max_depth: 8
Our findings from the three models we used are given in Table 3. The XGBoost with GridSearchCV technique has a lower accuracy rate than other algorithms. However, the OPT-GBoost approach’s accuracy has an R2 Score of 99.96%, an MSE of 607.563, and an MAE of 9.106. Compared to the XGBoost method, the new OPT-GBoost approach offers a decreased risk of overfitting and a 0.3% accuracy rate improvement. It may be determined that the OPT-GBoost approach outperforms other algorithms in estimating direct normal irradiance. Table 3. The various model scores and their R2 scores Model
R2 score
XGBoost
99,69%
XGBoost with GridSearchCV
98,62%
OPT-GBoost
99.96%
5 Conclusion The development of direct normal irradiance is a crucial precondition for energy systems studies. We assessed our suggested approach, which combines the XGBoost algorithm with Optuna as a hyperparameter optimization framework, using a dataset from the NRSDB. The studies in this paper show that the novel OPT-GBoost approach outperforms other algorithms (XGBoost and XGBoost with GridSearchCV). The result mentioned above has a lot of implications for Ensemble learning techniques used to estimate direct normal irradiance. Based on these findings, we anticipate that our approach will improve the consistency of future DNI forecasts. Other data sets should be examined in the future to determine if they can achieve the same level of precision.
References 1. Senatla, M., Bansal, R.: Review of planning methodologies used for determination of optimal generation capacity mix: the cases of high shares of PV and wind. IET Renew. Power Gener. 12(11), 1222–1233 (2018)
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2. Zhu, T., Zhou, H., Wei, H., Zhao, X., Zhang, K., Zhang, J.: Inter-hour direct normal irradiance forecast with multiple data types and time-series. J. Mod. Power Syst. Clean Energy 7(5), 1319–1327 (2019). https://doi.org/10.1007/s40565-019-0551-4 3. Yang, D., Quan, H., Disfani, V.R., et al.: Reconciling solar forecasts: geographical hierarchy. Sol. Energy 14, 276–286 (2017) 4. El Alani, O., Ghennioui, H., Ghennioui, A.: Short term solar irradiance forecasting using artificial neural network for a semi-arid climate in Morocco. In: 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–7 (2019). https://doi. org/10.1109/WINCOM47513.2019.8942412 5. Blanc, P., et al.: Direct normal irradiance related definitions and applications: the circumsolar issue. Sol. Energy 110, 561–577 (2014) 6. Sobri, S., Koohi-Kamali, S., Abd Rahim, N.: Solar photovoltaic generation forecasting methods: a review. Energy Convers. Manag. 156, 459–497 (2018) 7. Rocha, P.A., Santos, V.: Global Horizontal and Direct Normal Solar Irradiance Modeling by the Machine Learning Methods Xgboost and Deep Neural Networks with CNN-LSTM Layers: A Case Study Using the GOES-16 Satellite Imagery (2021). https://ssrn.com/abs tract=3957836, http://dx.doi.org/10.2139/ssrn.3957836 8. Zhu, T., Xie, L., Wei, H., Wang, H., Zhao, X., Zhang, K.: Clear-sky direct normal irradiance estimation based on adjustable inputs and error correction. J. Renew. Sustain. Energy 11(5), 056101 (2019) 9. Kumari, P., Toshniwal, D.: Extreme gradient boosting and deep neural network-based ensemble learning approach to forecast hourly solar irradiance. J. Clean Prod. 279, 123285 (2021). https://doi.org/10.1016/j.jclepro.2020.123285 10. Khan, W., Walker, S., Zeiler, W.: Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach. Energy 240, 122812 (2022). https:// doi.org/10.1016/j.energy.2021.122812. ISSN:0360-5442 11. Yew, P.-L., Yih, H.: Solar irradiance forecasting for Malaysia using multiple regression and artificial neural network. Def. S T Tech. Bull. 15(1), 83–90 (2022) 12. NSRDB: National Solar Radiation Database. https://nsrdb.nrel.gov 13. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August, pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785 14. Srinivas, P., Katarya, R.: hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost. Biomed. Signal Process. Control 73, 103456 (2022). https://doi.org/10.1016/j.bspc.2021.103456. ISSN 1746-8094 15. Wang, H., Sun, F.: Optimal sensor placement and fault diagnosis model of PV array of photovoltaic power stations based on XGBoost. In: IOP Conference Series: Earth and Environmental Science, vol. 661, 2020 2nd International Conference on Energy, Power, Environment and Computer Application, 22–23 November 2020, Beijing, China (2021) 16. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631 (2019) 17. Azrour, M., Mabrouki, J., Fattah, G., Guezzaz, A., Aziz, F.: Machine learning algorithms for efficient water quality prediction. Model. Earth Syst. Environ. 8(2), 2793–2801 (2021). https://doi.org/10.1007/s40808-021-01266-6 18. Boutahir, M.K., Farhaoui, Y., Azrour, M., Zeroual, I., El Allaoui, A.: Effect of feature selection on the prediction of direct normal irradiance. Big Data Min. Anal. 5(4), 309–317 (2022). https://doi.org/10.26599/BDMA.2022.9020003 19. Azrour, M., Farhaoui, Y., Ouanan, M., Guezzaz, A.: SPIT detection in telephony over IP using K-means algorithm. Procedia Comput. Sci. 148, 542–551 (2019). https://doi.org/10.1016/j. procs.2019.01.027
Evaluation of Smartness Level in Local Maritime Ports Ayoub El Idrissi1 , Abdelfatteh Haidine1(B) , Abdelmoula Ait-Allal2 , Abdelhak Aqqal1 , and Aziz Dahbi1 1 National School of Applied Sciences, University Chouaib Doukkali, El Jadida, Morocco
[email protected] 2 High Institute of Maritime Studies (ISEM), Casablanca, Morocco
Abstract. Maritime transports are playing an increasing crucial role in the worldwide trade. The maritime ports are the core elements of these transport network. Any malfunctions in the ports could dramatically affect the performance of the entire network. Thus, a certain level of smartness or intelligence is required. In the same paradigm of smart cities, smart maritime networks are envisaged to make an optimal use of different resources, such spaces, machines, quays, energy resources, containers, etc. In this paper, we analyze the smartness level of some Moroccan ports. This study has been initialized, because Morocco is playing an increasing role as gate between Europe and Africa, and also as a hub for the world maritime transport. Smart maritime port is seen as a special smart environment case, categorized between smart cities and smart buildings. Therefore, the measure of the metric of smartness level is derived from the smart readiness indicators from these two special smart environments. Such indicator should serve as an instrument for the different players to defend their interests. For example, a national government could impose a certain level of smartness, where the environment protection and energy efficiency have more weight, and check how far the port authority protect the national interests. A mathematical model is used to generate numerical results for some local port use cases. Keywords: Smart maritime ports · Internet-of-Things · energy efficiency · smart domains · smart applications · smartness level
1 Introduction One of the main aspects of the globalization is the dispatching and high connectivity between zones of row materials extractions, distributed productions sites and worldwide consumers and open markets. The logistic plays here a crucial role to connect the world. The maritime transports is one of the core elements for any normal logistic chain, where the maritime ports are the primary hubs in such complex network. A maritime port is a connection between the sea and the land which provides facilities for the ships docking to load and discharge passengers and cargo, as illustrated in Fig. 1. The ports have crucial impacts on the global economy as more than 80% of the world trade is transported by the sea, [1]. A port is a complex and dynamic environment including various activities © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 351–359, 2023. https://doi.org/10.1007/978-3-031-35245-4_32
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such as transportation, logistic, fishing, maintenance and rescue operations as well as protection of its environmental impacts.
Fig. 1. The maritime ports are playing a crucial role in connecting the entire logistic chain.
In this paper, we analyse the smartness level of some Moroccan ports. This study has been initialized, because Morocco is playing an increasing role as gate between Europe and Africa, and also as a hub for the world maritime transport. Smart maritime port is seen as a special smart environment case, categorized between smart cities and smart buildings. Therefore, the measure of the metric of smartness level is derived from the smart readiness indicators from these two special smart environments. Such indicator should serve as an instrument for the different players to defend their interests. For example, a national government could impose a certain level of smartness, where the environment protection and energy efficiency have more weight, and check how far the port authority protect the national interests. A mathematical model is used to generate numerical results for some local port use cases. The rest of the paper is structured as it follows: the second section gives an overview on smart maritime ports as well a model from the literature showing the main components to build a smart port. In this section we propose an extended models for smart ports based on smart port objectives, smart domains and smart domain applications. In the third section, a mathematical model for the measure of smartness level of a maritime port is discussed. This model is applied in the fourth section on use cases based on our local ports to generate some numerical results.
2 Definition, Objectives and Models of Smart Ports 2.1 Classification of Smart Environments One of the criteria to classify the smart items is the environmental (or spatial) characteristics. So, from the early concept of “smartness”, we talk about smart cities, smart homes, smart buildings, etc. The major idea of this work is to consider the maritime ports also as a smart environment, and to classify it besides other environments (cities, buildings, etc.), as depicted in Fig. 2. By classifying the smart ports environment between
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smart cities and smart building, we will try to overtake and adapt the models of “Smart Readiness” from these well and largely investigated concepts (i.e. smart cities and smart building). This adaptation we will give a model for “Smartness Level” for a maritime port.
Fig. 2. Classification of smart environments and the positon of the smart maritime port.
2.2 Smart Port Model and a Proposed Extension Nowadays, smart ports have to adopt (full-) automation of all operations in the port. Therefore, ships and containers have to show a level of intelligence to participate in this full automation. This is necessary to be cost efficient, reliable and secure. A model of smart ports showing main elements is illustrated in Fig. 3, [2]. In our work, we propose to extend this model. We propose first to determine the objectives to be achieved by smart ports, in other words: what are the objectives to be achieved through the implementation of a smart port. We determined three major objectives that are common for any port, which are: economic efficiency, energy efficiency and environmental impact. In the recent years, an ensemble of smart thing or smart environments have been developed, invested and (maybe also) implemented in the practice. Table 1 summarizes list of such smart domains (or things or environments), which could affect and help the achievement of the defined objectives of smart ports. The relation between the different smart domains and the affected objectives is depicted in Fig. 4. It is worth to underline here that each smart domain is composed of a set of smart domain applications to be implemented. For example, the smart domain “Smart grid” covers a large set of
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Fig. 3. Model showing the main elements to build a smart maritime port [2]
smart applications like automated smart metering infrastructure (for electricity, water, heat and gas distribution network), renewable energy (wind/solar/wave), smart lighting, demand/response, micro-grid, etc. Table 1. Smart domains that could affect and help the achievement of the defined objectives of smart ports Index
Smart domain part of Smart port environnement
SD.1
Energy/smart grid
SD.2
Smart building
SD.3
Container management
SD.4
Smart terminal operations
SD.5
Environment monitoring
SD.6
Site management
SD.7
e-Health/smart health
SD.8
Telecom/broadband access
SD.9
Waste management
SD.10
Intelligent traffic management
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Fig. 4. Thee mains objectives of smart maritime ports and their affecting smart domains.
3 Modeling the Metric of Smartness Level in Smart Maritime Ports For the calculation of the smartness level of a maritime port, we use the main objectives for smartness in maritime ports, as illustrated in Fig. 4. Thus, the deployed smart domains (and their corresponding smart domain applications) will affect each of these objectives. Generally, one smart domain may affect more than one objective. However, for a simplification in this phase of smartness level development, we will assume that each smart domain affects only one objective. Similar concept have been used for the smart buildings, [3, 4]. So, the smartness level (S L ) in smart maritime ports can be calculated as follows: 1 3 ω( O) Iz(O) (1) SL = z=1 z IMAX where: (O) (O) ωz is the weight allocated to the objective z, with 3z=1 ωz = 1. (O) Iz is the smartness indexed related to objective z, with: (SD/O) (SD/O) Iz(O) = αj,z ωj,z Ij,z
(2)
j{SD}
where:
The binary index: αj,z =
(SD/O)
ωj,z
1, if SD j affecting Objective z 0, otherwise
: Weight of SD j in influencing objective z, with
(SD/O) : Ij,z
(SD/O) j{SD} ωj,z
(3) = 1.
Smartness index of SD j while affecting objective z, with: (SD/O)
Ij,z where:
βi,j =
=
i{SDj}
(SDA/O) (SDA/O) I(i,j),z
βi,j ω(i,j),z
1, if application i of SD j is implemented 0, otherwise
(4)
(5)
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ω(i,j),z
: Weight of smart applications i of SD j in influencing objective z, with
(SDA/O) = 1. j{SD} ω(i,j),z (SDA/O) I(i,j),z : Smartness
index of smart applications i of SD j in influencing objective z.
4 Use Cases of Local Ports 4.1 Local Maritime Ports Morocco is a developing country which is playing geographical and political an increasing important role in the international business/trade and regional politic. Indeed, Morocco is the main gate between Europe and Africa, and is playing the role of hub world maritime transport. However, the local orts are facing different challenges, such as: strong environment/littoral pollution (especially for the industrial ports; [5, 6]), weak energy efficiency (with the absence of the renewable energies neither onshore/offshore wind parks nor solar parks), illegal immigrations, problem of parking for trucks, etc. The international maritime ports have seen that they must adopt more ICT solutions, intelligent systems, automation systems, etc. in order to overcome such challenges. Therefore, the concept of smart port with its different possible smart domains and their corresponding smart domain applications are the key for the evolution of any port towards “the port of the future”. So, in this work, we analyse which smart applications are currently implemented in our local ports, in order to be able to compare their smartness level. An example of the collected data is depicted in Fig. 5 for smart domains: smart grid/Energy and smart environment monitoring. It is worth to underline there is a very few official information that are publicly available. SO, the used data is collected just through discussion with some experts and technical stuff, who have heard or seen such implementation in the ports. This means, we cannot guarantee that this data is hundred percent correct. Therefore, we use the name of the ports anonymized, by just using an index for each ports (from #1 to #5) instead of using the proper name of the ports.
SD.1.1 SD.1.2 SD.1.3 SD.1.4 SD.1.5 SD.1.6
Smart Domain Applications (SDA) Port#1 Micro-grid 0 Renewable energy: onshore, offshore, etc 0 Smart metering (Electricity /Water/Gas/He 0 Smart lighting of port areas 0 PV panels on roofs 0 Charging station for e-Mobility&e-engines 0
Port#2 0 0 0 0 0 0
Port#3 0 0 0 0 0 0
Port#4 Port#5 0 0 0 0 0 1 0 1 0 0 0 1
Smart Domain Applications (SDA)
Port#1
Port#2
Port#3
Port#4 Port#5
SD.5.1 SD.5.2 SD.5.3 SD.5.4 SD.5.5 SD.5.6
Water quality/ pollution Air quality/ pollution Littoral monitoring Acoustic/Noise pollution monitoring Forest/bush fire detection/protection Oil/ chemical leaks on water and port yards
0 0 0 0 0 1
0 0 0 0 0 1
0 0 0 0 0 1
SD.1: Energy/ Smart Grid
Smart Domain
SD.5: Environnement Monitoring
Smart Domain
0 0 0 0 0 1
1 1 1 0 0 1
Fig. 5. Each smart domain (SD) includes a set of smart domain applications (SDAs), with examples for smart grid and smart environment monitoring, (where “1” means applications is implemented and “0” means SDA not implemented).
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4.2 Evaluation of Smartness Level in Local Ports
0.5 0.4 0.3 0.2 0.1 0 #1
#2
#3
#2
t#3
#4
#5
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
#1
#4
#5
Fig. 6. Smartness scores for “Environment Impact” (top) and “Economic Efficiency” (bottom).
The achieved scores for the five considered local ports are depicted first for each one of the three objectives separately and then we consider the overall score in Fig. 7. For the objective “Environmental Impact” in Fig. 6, we see that four ports have a similar score. This is normal, because if consider the input data from Fig. 5, we see that there is weak (or absolutely absent) monitoring of the impact of the port activities on the environment (sea, littoral or air quality). But, we can see that exactly one port is taking the environment monitoring and protection as considered objective in the practice. A different level of achievement can be observed for the objective of “Economic Efficiency”. This is a little bit strange, because from the first day of the port this is oriented on economic objectives. Only recently, ports agencies and government made pressure on port operators to make the economic efficiency conditioned by environment protection. In this second objective, the port number five is the leader. Similar to the first objective (i.e. environmental impact), the ports have the same level of smartness in the energy efficiency, except for ports #5 that is very far leader. However, in spite of this leading position of port #5 in the energy efficiency, according to our input data this port is far from goal to be optimal in dealing with the energy efficiency problematic. Indeed port #5 is not adopting renewable energy as active source for energy, knowing that renewable energy is crucial in the energy efficiency. The behavior observed in the separate objectives is summarized again in the
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0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 #1
#2
#3
#4
#5
#1
#2
#3
#4
#5
2 1.5 1 0.5 0
Fig. 7. The smartness scores for the ports according to objective 2 “Energy Efficiency” (top) and the overall scores “Smartness level” (bottom).
overall score (smartness level) in Fig. 7 (top), where the leading position of port five is underlined. In the future, and in order to have more concise conclusions about the smartness level of our local ports, we need set “smart port reference” in order define how far are our local ports from the international standard of smart ports. This will help the regulatory agencies to find out which smart applications should enforced, in order to reach the wished threshold in each of the objective axes; namely the environmental impact, the economic efficiency and the energy efficiency. This will be done by focusing on and analyzing the regional/Mediterranean leading smart maritime ports (e.g. like ports from Barcelona, Valencia, Genova, etc.).
5 Conclusions The smart ports is concept that has been proposed to overcome the challenges facing any ports depending on its type of activity, eco-system, etc. In this work, we propose first to define the objectives to be achieved through the setting and deployment of smart port concept/roadmap. Then, we can define the smart domain that should help to achieve these objective. From these objectives, we propose to use “Smartness level” to measure
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how the implemented applications in the practice will help to achieve the set objectives. This model is applied to local use case, in order to check the state-of-the-art of our ports and to check far are smart port objectives (environmental impact, economic efficiency and energy efficiency) are achieved in these ports. As next step we will develop a standard model of smart port, with its corresponding smartness level. This will be taken as reference in order define how far are our local ports from the international standard of smart ports. This will help the regulatory agencies to find out which smart applications should enforced, in order to reach the wished threshold in each of the objective axes; namely the environmental impact, the economic efficiency and the energy efficiency. This will be done by focusing on and analyzing the regional/Mediterranean leading smart maritime ports (e.g. like ports from Barcelona, Valencia, Genova, etc.).
References 1. Rajabi, A., Khodadad Saryazdi, A., Belfkih, A., Duvallet, C.: Towards smart port: an application of AIS data. In: Conference Paper, Smart City Conference (June 2018). https://doi.org/10.1109/ HPCC/SmartCity/DSS.2018.00234 2. Douaioui, K., Fri, M., Mabrouki, C., Semma, E.A.: Smart port: design and perspectives. In: 2018 4th International Conference on Logistics Operations Management (GOL), pp. 1–6 (2018). https://doi.org/10.1109/GOL.2018.8378099 3. VITO - Flemish Institute for Technological Research NV: Smart Readiness Indicator for Buildings: SRI technical support studies. https://smartreadinessindicator.eu/. Accessed 24 Jan 2022 4. Lit, iu, A.V., Seppänen, O., Pantelis, S., Hogeling, J.: The smart readiness indicator for buildings: current status and next steps. REHVA Journal (Federation of European Heating, Ventilation and Air Conditioning Associations - REHVA), pp. 28–33 (June 2021). https://www.rehva.eu/rehvajournal/chapter/the-smart-readiness-indicator-for-buildings-current-status-and-next-steps 5. L’ECONOMISTE: Pollution au dioxyde de soufre: Trois centrales marocaines dans la liste. Par L’Economiste| Le 22/08/2019 (in French). https://www.leconomiste.com/flash-infos/pol lution-au-dioxyde-de-soufre-trois-centrales-marocaines-dans-la-liste 6. Rouhi, A., Sif, J., Chemaa, A.: Evaluation de la pollution métallique du littoral de la ville d’El Jadida (Maroc): utilisation de l’annélide Arenicola grubii comme indicateur biologique. Bull. de l’Institut Scientifique, Rabat, section Sciences de la Vie 34(2), 163–171 (2012)
The Dynamic Impact of Renewable Energy Consumption on CO2 Emissions: The Case of Morocco Soufiane Bouyghrissi1(B) , Salwa Bajja2 , Maha Khanniba3 , Hassan Radoine2 , and Jerome Chenal4 1
2
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Higher School of Technology, Cadi Ayyad University, Marrakesh, Morocco [email protected] School of Architecture Planing and Design SAP+D, Mohammed VI Polytechnic University, UM6P, Ben Guerir, Morocco [email protected] National School of Business and Management, Hassan II University, Casablanca, Morocco 4 Ecole polytechnique f´ed´erale de Lausanne, EPFL, Lausanne, Switzerland
Abstract. In Morocco as well as globally, the energy sector is one of the major drivers of climate change. Additionally, Morocco is also extremely dependent on fossil fuels import which is jeopardizing the country’s present and future energy security. Actually, the country imported 95.6% of its energy demand. Further, energy consumption in the Northern African country has risen due to economic growth, population rise and increase in per capita energy consumption. In order to ensure the development of a more sustainable, environmentally responsible and overall more liveable planet, we need to radically transform our energy sector and pave our way towards a cleaner and more just future powered by 100% Renewable Energy. To achieve this transformation, policy makers play an important role. In this context, this study explores the link between environmental degradation, economic growth, renewable energy consumption (REC) and non-renewable energy consumption (NREC) in Morocco over the period between 1990 and 2015. This would be analysed by adopting the Autoregressive Distributed Lag (ARDL) approach and the Granger causality test. The results show that REC reduces CO2 emissions. It is also revealed that there is a unidirectional causality running from CO2 emissions to REC. Keywords: Renewable energy consumption · environmental degradation · economic growth · non-renewable energy consumption Morocco
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Introduction
The most important problems of our country are combating climate change and reducing poverty and inequality. As we know, they are interconnected. We will c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 360–367, 2023. https://doi.org/10.1007/978-3-031-35245-4_33
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never succeed in the latter if we do not succeed in fighting climate change. In Morocco, people are facing another challenge: the demand for energy is rising in the Northern African countries, in particular the demand for electric power which drives this development includes economic growth, advancing industrialisation, greater prosperity, and a growing population. At the same time, Morocco possesses virtually no fossil resources of its own and therefore relies on imports for 95% of its fuel supply. These energy imports negatively affect Morocco’s national budget. Forecasted demand will cause Morocco’s greenhouse gas emissions (GHG), currently still low, to rise considerably. In Morocco as well as globally, the energy sector is one of the major drivers of climate change. To ensure development in a liveable planet for current and future generations, we need to transform the energy sector to 100% renewable energy. Converting our energy system is more than just replacing fossil fuels with sun and wind etc. as new energy sources. Our dependence upon fossil resources has built a centralized system that lacks diversity and security, excludes people’s participation, menaces the health of our Moroccan citizens, the stability of the earth’s climate, and also menaces the future generations of clean air, clean water, and energy independence. We organise the paper as follows. A literature review is presented in Sect. 2 where we explore the environmental degradation, economic growth, renewable energy consumption, non-renewable energy consumption and CO2 emissions nexus in the extant literature. Section 3 follows with a presentation of the methodology involving the ARDL approach and the Granger causality test and it analyses the empirical results and the paper is brought to a conclusion with policy recommendations in Sect. 4.
2
Literature Review
Theoretically and empirically, there is strong consensus among scientists and researchers regarding the crucial role that RE plays in reducing CO2 emissions [1–6]. They recommend the use of RE as an alternative to fossil fuels to mitigate climate change, protect the environment and reduce GHG emissions. In very recent work, [7] studied the link between RE and environmental degradation in top-10 polluted countries from 1990 to 2017. They concluded that there is a bidirectional causality found between renewable energy utilization and environmental degradation. Furthermore, [8] investigated the linkage between REC and CO2 emissions from 1970 to 2012 in Pakistan. Their results show that REC have a significant impact on CO2 emissions in Pakistan. Moreover, [9] validate the results of [8] for Pakistan and they demonstrated that the reduction in GHG emissions is due to a 1.086% increase in RE. Despite these series of studies finding a positive relationship between RE and CO2 emissions, other studies find no relationship between these two variables [10,11]. In this context, [12] found that RE has not reached a level where it can make a significant contribution to reducing CO2 emissions. Similarly, [13] estimates that renewable energies, although they have a negative effect on CO2 emissions and an increasing effect in the long term, remain a better alternative to conventional energies.
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Variables
ADF Intercept
PP Intercept and trend Intercept
Intercept and trend
LCO2
−1.30
−2.2737
−0.7469
−2.9447
LNRENC
−1.8067
−3.3694
−1.8067
−3.2450
LRENC
−2.2578
−2.4663
−2.2578
−2.4663
LGDP
−1.0756
−2.4259
−1.2046
−2.4601
ΔLCO2
−9.8292*** −1.2759
−9.0706*** −10.7678***
ΔLNRENC −4.8266*** −4.8230***
−5.6303*** −5.5918***
ΔLNRENC −5.6136*** −5.4752***
−5.8144*** −5.6579***
ΔLGDP −5.6297*** −5.6307*** −5.7843*** −5.7440*** Notes: Δ denotes the first difference operator. *** p-value < 1%; ** p-value < 5%; * p- value < 10%.
3
Model Presentation
In this paper, we seek to examine the link between CO2 emissions, renewable and non-renewable energy consumption and economic growth in Morocco using an ARDL (Autoregressive distributed lag model) developed by [14] and annual data between 1990 and 2015. The variables are gathered and obtained from the World Bank [15] and the Energy Information Administration [16]. Thus, our model can be written in the following linear logarithmic form: LCO2t = β0 + β1 LRECt + β2 LGDPt + β3 LN RECt + εt
(1)
Where: CO2 : is natural log of CO2 emissions defined in Million metric tons LGDP: is natural log of real gross domestic product (GDP) per capita LREC: is natural log of renewable energy consumption per capita LNREC: is natural log of non renewable energy consumption per capita εt : error term.
4 4.1
Results and Discussions ARDL Cointegration Tests
The results of Table 1 indicate that all the series are nonstationary at level but stationary at first difference and also show that all the variables are integrated of order 1. We used the tests of stationarity of Augmented Dickey-Fuller (ADF) and of Philips Perron (PP). To verify the stationary of variables, we apply Augmented Dickey-Fuller (ADF). The results outlined in Table 1 and indicate that all variables stationary at first difference, also show that all the variables are integrated of order 1 I(1). In addition, to determine the optimal number lag (Table 2), we used Schwarz Criterion (SC) criteria.
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Table 2. Lag length criteria Lag LogL
LR
FPE
AIC
SC
HQ −7.528214
0
87.32439 NA
6.03e−09
−7.574945
−7.376573
1
144.6305 88.56398∗ 1.46e−10
−11.33005
−10.33819∗ −11.09639
2
165.0504 24.13264
1.16e−10∗ −11.73186
−9.946514
3 184.0247 15.52442 1.46e−10 −12.00225∗ −9.423419 ∗ the optimal lag chosen by each criterion.
−11.31128 −11.39475∗
Table 3. Short-run analysis Dependent variable: LCO2 Coefficient Std. Error t-Statistic Prob.*
Variable ΔLNRENC
2.148795 0.481409
4.463555 0.0006
−0.014863 0.024561
−0.605157 0.5555
1.133202 0.268655
4.218051 0.0010
CointEq (ECM)(−1) −0.922750 0.202018
−4.567671 0.0005
ΔLRENC ΔLGDP R-squared Adjusted R-squared
0.990391 0.984477
Table 4. Long-run analysis Variable LNRENC
Coefficient Std. Error t-Statistic Prob 4.348457 0.706561
6.154394 0.0000
−0.016108 0.024966
−0.645182 0.5300
LGDP
1.228071 0.066520
18.461673 0.0000
C
1.661996 1.645108
1.010265 0.3295
LRENC
In Tables 3 and 4, we show the short term and long term ARDL estimation. In the short-run, GDP and NREC are positively attached to the CO2 emissions. The results indicate that the increase in GDP and NREC by 1% will enhance the CO2 emissions by 1.13% and 2.14%, respectively. In the short term, a rise in GDP and NREC by 1% will increase the CO2 emissions by 1.228% and 4.348%, respectively. Furthermore, an increase in REC by 1% reduces the CO2 emissions by 0.0148% and 0.0161% in the short run and the long run, respectively. Similar findings exist in literature for Pakistan and China [9,17]. Other studies have found that no relationship between economic growth and increase urbanization [11,18]. These results indicate that the transition to the RE in Morocco start to give their positive effects on environment, also the negative impact of NREC on CO2 emissions is explained by the country’s energy dependence on fossil energy imports. As a result, Morocco has increased its investments in RE over the last decade to reach an aim of 52% by 2030.
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Signif. I(0) I(1)
F-statistic
6.488748 10%
2.45 3.52
k
5
2.86 4.01 3.74 5.06
5% 1%
Table 5 indicates the results for cointegration using the ARDL approach. The F-statistics of 6.4887 is higher than the upper and lower bound critical value at the 1%, 5% and 10% significance. This result suggests the existence of cointegration between the variables. Table 6. Granger causality test Dependent variable Short-run ΔLCO2 ΔLREC ΔLNREC ΔLGDP ΔLCO2
–
6.315**
0.771
Long-run ECT (−1)
10.107*** −0.292
ΔLREC
0.253
–
0.079
11.606*** 6.456***
ΔLNREC
2.059
9.154**
–
11.504*** 0.010
ΔLGDP 1.291 1.769 0.179 – 0.537 *** Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
4.2
Causality and Parameter Stability
In the short run, Table 6 shows that there is a unidirectional causality going from CO2 and NREC to REC, also from NREC, REC, and CO2 to GDP. In terms of the long-run causality, we find that the lag error terms (ECTt−1 ) for GDP and REC have the expected negative signs. Thus, there is unidirectional causalities running from CO2 emissions to REC, NREC and GDP and there is bidirectional causality between REC and NREC, also between GDP and NREC. Moreover, Figs. 1 and 2 show that the results of the cumulative sum of recursive residuals (COSUM) and the COSUM of square (COSUMs) tests. The both diagrams are situated inside the critical bounds at 5% significance level, which indicate the stability of the ARDL parameters in our study.
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Fig. 1. CUSUM
Fig. 2. CUSUM of square
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Conclusion and Policy Implications
The depletion of conventional energies and the environmental problems linked to their use are the main reasons for all countries in the world to look for alternative and clean technologies that emit less GHG. Hence the idea of the transition to RE emerges. The latter plays an important role in achieving sustainable development with its triple objectives, namely economic efficiency, ecological balance and social equity, because the use of this type of energy reflects positively on the three pillars that constitute it. In this context, our study, examines the link between environmental degradation, economic growth, renewable and nonrenewable energy consumption in Morocco over the period between 1990 and 2015. This study applies the Autoregressive Distributed Lag (ARDL) approach and the Granger causality test. Furthermore, the study finds positive impacts of REC on CO2 emissions, as a rise in REC by 1% reduces the CO2 emissions by 0.0148% and 0.0161% in the short run and the long run, respectively. Furthermore, we saw in this study that the RE in Morocco will have a positive effect on the environment in the short and long term. Moreover, these results indicate that Morocco is continuing its efforts in terms of mitigation of GHG emissions and adaptation to their impact. Moreover, this study could be useful for Moroccan decision-makers to identify the necessary measures and develop effective policies for the deployment of RE. The development of RE is one of the pillars of Morocco’s energy transition strategy. In order to achieve the ambitious objectives set for 2030, i.e. 52% of RE in final energy consumption, the deployment of RE in Morocco requires a strong involvement of local communities. As Morocco seeks to reduce its energy dependency and develop a green economy over the next decade, it must increase its investments in RE as it has a large stock of natural and energy resources. Thus, it must implement environmental policies to reduce carbon emissions in order to protect the environment for future generations.
References 1. Dincer, I.: Renewable energy and sustainable development: a crucial review. Renew. Sustain. Energy Rev. 4(2), 157–175 (2000) 2. Economou, A.: Renewable energy resources and sustainable development in Mykonos (Greece). Renew. Sustain. Energy Rev. 14(5), 1496–1501 (2010) 3. Khanniba, M., Bouyghrissi, S., Lahmouchi, M.: Renewable electricity production, economic growth and CO2 emissions: the Moroccan experience. In: 2020 5th International Conference on Renewable Energies for Developing Countries (REDEC), pp. 1–6 (2020). https://doi.org/10.1109/REDEC49234.2020.9163828 4. Bouyghrissi, S., Murshed, M., Jindal, A., et al.: The importance of facilitating renewable energy transition for abating CO2 emissions in Morocco. Environ. Sci. Pollut. Res. 29, 20752–20767 (2022) 5. Jebli, M.B., Youssef, S.B.: The role of renewable energy and agriculture in reducing CO2 emissions: evidence for North Africa countries. Ecol. Ind. 74, 295–301 (2017)
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6. Mendonca, A.K.S., et al.: Hierarchical modeling of the 50 largest economies to verify the impact of GDP, population and renewable energy generation in CO2 emissions. Sustain. Prod. Consum. 22, 58–67 (2020) 7. Sharif, A., Mishra, S., Sinha, A., Jiao, Z., Shahbaz, M., Afshan, S.: The renewable energy consumption-environmental degradation nexus in top-10 polluted countries: fresh insights from quantileon- quantile regression approach. Renew. Energy 150, 670–690 (2020) 8. Danish, B.Z., Wang, B., Wang, Z.: Role of renewable energy and non-renewable energy consumption on EKC: evidence from Pakistan. J. Clean. Prod. 156, 855– 864 (2017) 9. Khan, M.T.I., Ali, Q., Ashfaq, M.: The nexus between greenhouse gas emission, electricity production, renewable energy and agriculture in Pakistan. Renew. Energy 118, 437–451 (2018) 10. Lin, B., Moubarak, M.: Renewable energy consumption - economic growth nexus for China. Renew. Sustain. Energy Rev. 40, 111–118 (2014) 11. Cherni, A., Jouini, S.E.: An ARDL approach to the CO2 emissions, renewable energy and economic growth nexus: Tunisian evidence. Int. J. Hydrogen Energy 42, 29056–29066 (2017) 12. Menyah, K., Wolde-Rufael, Y.: Energy consumption, pollutant emissions and economic growth in South Africa. Energy Econ. 32(6), 1374–1382 (2010) 13. Zoundi, Z.: CO2 emissions, renewable energy and the environmental Kuznets curve, a panel cointegration approach. Renew. Sustain. Energy Rev. 72, 1067–1075 (2017) 14. Pesaran, M.H., Shin, Y., Smith, R.J.: Bounds testing approaches to the analysis of level relationships. J. Appl. Econ. 16, 289–326 (2001) 15. World Bank. http://data.worldbank.org/products/wdi 16. Energy information administration. https://www.eia.gov 17. Kangyin, D., Renjin, S., Hongdian, J., Xiangang, Z.: CO2 emissions, economic growth, and the environmental Kuznets curve in China: what roles can nuclear energy and renewable energy play? J. Clean. Prod. 196, 51–63 (2018) 18. Pata, U.K.: Renewable energy consumption, urbanization, financial development, income and CO2 emissions in Turkey: Testing EKC hypothesis with structural breaks. J. Clean. Prod. 187, 770–779 (2018)
Energy Demand Management in a Residential Building Using Multi-objective Optimization Algorithms Saad Gheouany(B) , Hamid Ouadi, and Saida El Bakali ERERA, ENSAM Mohammed V University, Rabat, Morocco [email protected]
Abstract. Demand Side Management (DSM) is one of the most important functions of a smart building that allows customers to make informed decisions about their energy consumption and remodulate their load profile in order to reduce overall operational cost and carbon emission levels. This paper presents a demand side management strategy based on shifting load from peak hours where electricity prices are high to off-peak hours where electricity prices are low. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the electricity bill and the peaks related to high energy consumption (PAR), while maintaining the user’s comfort. The optimization problem proposed in this paper is formulated mathematically as a multi-objective optimization problem that involves constraints and consumer preferences. A bioinspired meta-heuristic algorithm “Black widow optimization” based on the Pareto front has been developed to solve this optimization problem and manage the trade-off between conflicting objectives. Simulations were performed based on a smart home equipped with multiple appliances and a time-of-use (TOU) pricing scheme that encourages customers to use energy during off-peak hours. The algorithms were implemented on Matlab R2021a , and the results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Keywords: Multi-objective optimization · Demand side management Meta-heuristic algorithm · Smart building · Day-ahead scheduling
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The increasing demand for electricity in traditional buildings is inevitably leading to higher unit electricity prices in many countries as well as carbon emissions due to inefficiency in energy management. Today’s smart buildings are equipped with energy management systems that increase energy efficiency, provide indoor comfort for occupants and manage energy use. The variation in the pricing tariffs depends on the peak load because of high power demand in a particular Supported by organization x. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 368–377, 2023. https://doi.org/10.1007/978-3-031-35245-4_34
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time period called the on-peak hour period [1]. This information on tariffs helps to realize an optimal planning of electrical appliances in a day “j” for the day “j+1”. The most widely used technique in the literature is the technique of shifting the energy demand of some appliances from on-peak to off-peak hours in order to simultaneously reduce the electricity bill and stress on the main grid for continuous power supply to the consumer. Load management is a multi-objective problem with multiple trade offs regarding different target objectives. This optimization problem has been widely studied in the literature in various ways. Authors have used a single-objective function in [2,3] which consists of minimizing the electricity bill and load peaks by the weighted sum method. The goodness of the solution depends on the choice of the weight factors, each choice giving a single solution of the Pareto front. If multiple solutions are desired, the problem must be solved several times with different weight combinations [4], which is more expensive than using Paretobased optimization from the beginning [4]. In addition The genetic algorithm (GA) used in [2,3,5] solves a multi-objective optimization problem but it has a convergence problem, i.e., it requires a maximum number of iterations and a huge search space [1]. In [6] the electricity bill and peaks were minimized based on day-ahead Scheduling, but comfort was ignored. As compared to the previously mentioned works [2,3,5,6], in this work, we propose a multi-objective constraint-based optimization model to minimize three conflicting objectives, which are electricity bill, peak load demand, and waiting time by respecting a load shifting step so as not to leave the consumer waiting and to keep a satisfactory customer comfort. This problem is resolved using an energy consumption management strategy based on a bio-inspired meta-heuristic algorithm (Black Widow Optimization), that allows us to define the appliances that are expected to operate during a given day and to determine the optimal start times of these appliances. Thus the optimization algorithm adopted in this problem has good performance to find the global optima with good precision level and fast convergence [7]. Moreover to solve the optimization problem, we propose to use an automated decision making strategy based on the distance of non-dominant solutions (Pareto-optimal solutions) to the “optimal point”. The simulation was performed on the basis of a smart home with 15 different appliances and using real TOU tariffs. The results showed a 45.07% reduction in the electricity bill and a 54.64% reduction in the PAR while respecting the user’s comfort. The rest of the paper is organized as follows. Section 2 presents the mathematical formalization of this problem, namely the constraints and objectives related to this optimization problem. In Sect. 3 we develop the proposed solution to the optimization problem as well as a description of the BWO meta-heuristic algorithm used in this work. The results and discussions are illustrated and verified using Matlab software and presented in Sect. 4. And finally the paper is concluded in Sect. 5.
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Proposed Energy Management Strategy
This section provides an overall description of the proposed energy management system model as shown in Fig. 1. The proposed energy management system shifts the load from on-peak to off-peak hours due to the high demand for electricity during on-peak hours. Furthermore, the 24-hour period is divided into on-peak and off-peak hours according to the electricity tariffs provided. The proposed model enables the scheduling of the operation of the appliances on day “d” for day “d+1” i.e. for the next day. The daily scheduling is completed at the beginning of the day and requires input parameters such as the electricity price, the predicted and unscheduled load profile and the power rating of each appliance. The energy management system model provides the scheduled load profile as output (see Fig. 1). In addition, the user will be invited to list time-shifting appliances such as washing machines that can be time-shifted, and non-shifting or fixed appliances that have fixed work times such as the refrigerator and lamps. The optimization problem is a multi-objective problem, with each objective having the same importance. These objectives are: reducing the electricity bill, minimizing the peak load or PAR and the waiting time while respecting certain constraints.
Fig. 1. Overview of the multi-objective optimization model.
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Resolution of the Multi-objective Optimization Problem
Optimizing multi-objective problems (MOP) involves more than one objective function that should be optimized simultaneously [8]. In (1) we express the mathematical form of the considers objectives functions. In this work we will focus on a meta-heuristic optimization algorithm and the Pareto front which consists in keeping a set of the best solutions in an archive and update it per iteration [8]. In this method, the best solutions are defined as non-dominant solutions or Pareto optimal solutions. A possible solution is obtained by assigning values to the decision variables defined in (1), here the decision variables are:
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the ON or OFF state and the operating time of an appliance which directly influence the defined objective functions. ⎧ ⎨ Ocost (∅) (1) min(F (∅)) = min OP AR (∅) ⎩ Owait (∅) T where ∅t = [∅1t , ∅2t , ∅3t , ..., ∅m t ] is the vector of decision variables, and m is the total number of variables.
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Electricity Bill Minimization
The first objective to be minimized is the electricity bill of the day which is represented mathematically in (2): T otal Ocost = min(Ecost )
(2)
The minimization of electricity consumption is directly proportional to the reduction in electricity bill, therefore, the decision variables defined in (1) are T otal can be used for electricity bill minimization. Formally, The electricity bill Ecost written as: 24 M hour Ecost = (C hour × AppdP rate × ∅d ) (3) hour=1 d=1
represents the electricity price per hour. AppdP rate represents the power C rating in KWh of an appliance and ∅d is the ON/OFF status of an appliance d. The On-peak hour is defined as H on if the electricity price C hour during the hour is higher than the average of the electricity price list mean(C hour ) and Off-peak hour is defined as H of f if C hour is less than or equal to the average of the electricity price list. The electricity tariff used in this work is shown in Fig. 2, in which it can be observed that the on-peak hours start from 17:00 h until 22:59 h and the off-peak hours are from 23:00 h until 16:59 h. on if C hour > mean(C hour ) H (4) of f H if C hour ≤ mean(C hour ) hour
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Peak to Average Ratio Minimization
The stability of the grid cannot be ignored because giving preferences to consumers could lead peak generations. The objective function for minimizing the PAR is defined in (5). (5) OP AR = min(P AR) The PAR is calculated directly from (6): P AR =
1 ( 24
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hour where Escheduled load is the scheduled load per hour and is defined in (7).
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Appliances’s Operational Waiting Time Minimization
Waiting Time is a target to be minimized to satisfy the consumer. Therefore, it is worth mentioning that if a consumer wants to reduce his electricity bill, he has to pay a certain cost in terms of waiting time. The objective function of waiting time is expressed in (8): Owait = min(AppdW t )
(8)
where AppdW t is the Waiting Time calculated according to (9): ∅ AppdW t = |t∅ s−i − tuns−i | d
d
(9)
where t∅ s−i is the On instant “i” of the appliance d of the scheduled profile and d
t∅ uns−i is the On instant “i” of the appliance d of the unscheduled profile. d
2.5
Summary of Constraints
The constraints related to the multi-objective optimization problem are summahour rized in (10–12). first, the total scheduled load Escheduled load of the day must be hour the same as the total unscheduled load Eunscheduled load , i.e., each device must fulfill its allocated operating time (10). Second, the maximum peak in the scheduled load profile must be strictly lower than the maximum peak in the unscheduled load
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profile (11). The last constraint is that the operational waiting time of each appliance AppdW t must not exceed a maximum value AppdW tmax (12).
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hour hour sum(Escheduled load ) = sum(Eunscheduled load )
(10)
hour hour max(Escheduled ) < max(Eunscheduled ) load load
(11)
AppdW t ≤ AppdW tmax
(12)
Proposed Black Widow Optimization Algorithm
The proposed BWO algorithm represented in the diagram below (see Fig. 3) is chosen for the several advantages it presents, firstly it is an algorithm adapted to the nonlinear optimization problem, secondly it is able to escape from local optima problem and keep the balance between the exploitation and exploration phases of the search space and therefore has a higher convergence rate compared to other meta-heuristic algorithms [7]. The BWO algorithm follows four basic steps, Initial Population generation, Procreate, Cannibalism and Mutation [7].
4
Simulation Results and Discussion
In this section, we discuss the simulation results to evaluate the effectiveness of the proposed multi-objective management system. The algorithm has been implemented on MATLAB and the simulation results represent the scheduled load profile with a step refined over 24 h, the electricity bill of the day, the PAR and the Appliances’s Operational waiting time. We also study the effect of the waiting time or the instant of operation of an appliance on the electricity bill and the PAR. In this simulation, we are interested in a TOU (Time of use) pricing which represents a high price during on-peak hours and a lower price during offpeak hours. The proposed optimization algorithm will be used for the scheduling of a set of appliances (15 appliances) of a house with 4 occupants. The power and operating times of our set of appliances are taken from [9]. The results of the simulations describe the scheduled energy consumption profile from a predicted and unscheduled consumption profile. At the end several schedules are generated with different waiting time to see its impact on the other objectives, moreover the pareto front is used to select the optimal schedule which is represented in the following results. In Fig. 4 it can be seen that the unscheduled consumption profile represents very high consumption during on-peak hours as well as very high peaks which involve very high costs. Figure 5 shows the scheduled load profile and it can be seen that the consumption is reduced during the on-peak hours. The total consumption of the whole day is the same before and after the optimization and which represents a total consumption of 86.93 KWh, and we can validate that the constraint defined in (10) is well verified. It can also be seen that the fixed
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Fig. 3. The structure of the BWO optimization algorithm.
appliances have not been shifted and that the peaks are also reduced. The Fig. 6 shows the electricity bill over 24 h that corresponds to the unscheduled load profile and the scheduled load profile. The last values represent the electricity bill for the total day and we observe a value of 4983.4 cent before the scheduling and a value of 2743.4 cent after the scheduling which represents a reduction of 45.07% of electricity bill. The PAR of the day is represented in Fig. 7 with a value of 11.62 before the scheduling and a value of 5.27 after the scheduling and which represents a reduction of 54.64% of the peaks. Also the second constraint
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defined in (11) is respected. The average waiting time of each appliance is also shown in Fig. 8. The maximum value chosen in this simulation is 3 h and we notice that this value is not exceeded, and the third constraint defined in (12) is well respected. The last appliances are fixed and their operating times remain the same.
Fig. 4. The unscheduled energy consumption profile over 24 h.
Fig. 5. The scheduled energy consumption profile over 24 h.
5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 2
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Fig. 8. The average waiting time of all appliances in the building.
The results show that the proposed energy management system is able to simultaneously reduce the electricity bill and PAR during the day-ahead scheduling. Furthermore, there is a trade-off between the different performance parameters, namely comfort (waiting time), electricity bill and PAR. The pareto front was deployed as a solution to this multi-objective problem to facilitate the selection of a solution based on the customer’s preferences. In addition, our proposed algorithm respects the defined time step and load demand to maximize user comfort. Based on these results, the performance of the optimization technique is tested for several random load profile inputs and the results of the algorithm respects the defined constraints and is efficient and could reduce the electricity bill and PAR.
5
Conclusion and Perspectives
This paper proposes a model of a multi-objective based energy management system for the daily scheduling of appliances. The load shifting strategy is implemented to achieve the desired objectives. This study considers a single smart home consisting of multiple appliances. Each appliance in the house is scheduled using the optimization technique based on BWO meta-heuristic algorithm and pareto front. The proposed technique helps to find the optimal schedule of each appliance with respect to the system constraints. The optimization problem is a multi-objective problem, these objectives are conflicting of which we have: reduction of electricity bill, waiting time and minimization of PAR. This is eventually implemented via solutions based on the Pareto front which is obtained from the search space containing several solutions. Each solution represents a daily schedule according to the variation of the appliances’s operational waiting time which affects the electricity bill and the PAR. Furthermore, to verify the performance of the algorithm, the simulation results are evaluated using the real TOU electricity prices. The results show that daily scheduling is effective and has
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reduced the electricity bill by 45.07%, and the PAR by 54.64%. By considering the consumer’s comfort level in terms of waiting time. Further investigations are envisaged for the study and realization of a HEMS model for the optimization of energy consumption in an intelligent building integrating renewable resources such as photovoltaic system and wind turbine, and energy storage systems and also connected to the main electricity grid, it is a global optimization with several objectives and constraints. Thus, case studies and real world implementation are desirable to test the performance of the developed algorithms. Acknowledgement. This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco (Alkhawarizmi/2020/39).
References 1. Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., Ilahi, M.: Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE Access 6, 19509–19529 (2018). https://doi.org/10.1109/ACCESS.2018.2791546 2. Alsallout, A., Tutkun, N.: Low cost and reliable energy management in smart residential homes using the ga based constrained optimization. Front. Energy Syst. Power Eng. 2(2), 16–25 (2020) 3. Tutkun, N., Burgio, A., Jasinski, M., Leonowicz, Z., Jasinska, E.: Intelligent scheduling of smart home appliances based on demand response considering the cost and peak-to-average ratio in residential homes. Energies 14, 8510 (2021). https://doi. org/10.3390/en14248510 4. Bre, F., Fachinotti, V.D.: A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings. Energy Build. 154, 283–294 (2017). https://doi.org/10.1016/j.enbuild.2017.08.002 5. Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012). https://doi.org/10.1109/TSG.2012.2195686 6. Shakouri, G.H., Kazemi, A.: Multi-objective cost-load optimization for demand side management of a residential area in smart grids. Sustainable Cities Soc. 32, 171–180 (2017). https://doi.org/10.1016/j.scs.2017.03.018 7. Hayyolalam, V., Kazem, P., Asghar, A.: Black Widow Optimization Algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2019). https://doi.org/10.1016/j.engappai. 2019.103249 8. Sharifi, M., Akbarifard, S., Qaderi, K., Madadi, M.R.: A new optimization algorithm to solve multi-objective problems. Sci. Rep. 11, 20326 (2021). https://doi.org/10. 1038/s41598-021-99617-x 9. How much electricity does my appliance use from fans to slow cookers. https://www. nimblefins.co.uk/how-much-electricity-does-appliance-use. Accessed 6 Oct 2019
A Novel Approach of Hotspot Detection in PV Plant M. Limam El Hairach1(B) , Insaf Bellamine1 , Amal Tmiri2 , and Khalid Zine Dine3 1 LAROSERI, Faculty of Sciences, Chouaïb Doukkali University, 24000 El Jadida, Morocco
[email protected]
2 ENSAM, Mohammed V University in Rabat, Rabat, Morocco 3 Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco
Abstract. In recent years, the world has witnessed a massive increase in solar power plants. Solar plants depend on the sunlight that is converted into a direct current (DC) by photovoltaic (PV) modules. To ensure better efficiency, the condition of the PV modules must be maintained, since they can experience various faults. Providing methods for quick and accurate detection and diagnosis of PV systems defects becomes necessary. Two of the most used methods are infrared thermal imaging and data analysis of production data. This paper presents a novel approach to detecting hotspots in PV modules. Measured data selected under adequate conditions are processed by using a squared distance metric (SDM). The method was developed and validated using field measurements from a Noor PV Laayoune plant. Keywords: Hotspot detection · Solar plant · String performance · Module degradation
1 Introduction Annual additions to global renewable electricity capacity are expected to average around 305 GW per year between 2021 and 2026. Solar PV alone accounts for almost 60% of all renewable capacity additions. In PV plant, a PV module generates DC electricity from sunlight which is fed through the inverter to convert it into AC electricity. It has a high probability of being able to perform adequately for 25 years under typical operating conditions. Recent studies have reported degradation rates of approximately 0.6–0.7% a year [1]. PV modules can experience various faults. Almost 33% of fault is the hotspot. The hotspot increases the temperature of the cell where it appears, it prevents the PV module from producing normally. To increase the voltage, 20 PV modules are connected in series, if one hotspot occurs in one PV module, it will impact the production of the rest 39 modules, finding these cells and replacing them before a serious event occurs has become increasingly important. In Sect. 2, we introduce some definitions of SOA, its concepts and its characteristics. The proposed approach is presented in Sect. 3. Section 4 discusses an evaluation in terms of energy efficiency. Section 6 concludes the paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 378–386, 2023. https://doi.org/10.1007/978-3-031-35245-4_35
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2 Related Work Some studies quantify the energy loss due to hotspots, in [2] the performance of a string of 20 modules with 3 bypass diodes on average reduced by 2% per module substring containing thermal anomalies. In the beginning, detection of hotspots was possible by observing the I-V characteristic of a PV array or by measuring its open-circuit voltage, and that was very difficult, which advises the use of infrared imaging equipment for this purpose. The thermal imaging method become the most useful method for the detection of hotspots [5], the technique relies on stable weather conditions and high irradiation. The operator observes the PV module one by one with an infrared camera to see the occurrence of hotspots on the PV. Nevertheless, this method is limited by the big number of modules which is counted in hundreds of thousands. Using drones equipped with infrared cameras become very useful for a big number of modules, but drones are flown at certain times and weather conditions [6]. Data analysis involves as a powerful tool. In the literature, there are many examples of research on data based performance analysis. There are many suggested metrics that can be used for this purpose, including the performance ratio (PR) [7], the closely related temperature-corrected PR (PRSTC), and the weighted relative energy error [8]. There are also many examples in the literature where machine learning (ML) models have been employed for performance analysis, e.g. in [9]. Both, thermal imaging and machine learning models are mixed in some approaches. In [10] an approach developed a TensorFlow model to obtain a classification and localization of hotspots generated in Photovoltaic modules by classification of thermographic images. 2.1 Hotspot Causes The faults that can create hotspots can be divided into two parts: physical problems in the module or faults caused by environmental factors [3]. The physical problem that occurs in the PV module can be in the form of encapsulation material damage, delamination, cracking, interconnection failure, corrosion, bypass diode failure, mismatch fault, and arc fault. Physical faults in PV modules can trigger a short circuit, such as ground fault, phase fault, and open circuit fault. The main environmental factors are shading and soiling, the shadow of an object that is located around the PV can create hotspots. The second main factor is the soiling cumulated due to dust, snow, and any other areal particles. Keeping PV modules cleaned is essential for a long lifetime. Many studies discussed the energy loss due to soiling [4]. 2.2 Description of PV Systems The DC capacity of the plant is 65 MWh, with 66 central inverters. Each inverter has one MPPT, and about 7 strings monitoring box (SMB), each SMB collects 14 or 15 strings connected in parallel, with 20 modules in each string (Fig. 1). The modules have 72 cells and a peak capacity of 320 W. All the modules have three bypass diodes, defining three module substrings of 24 cells connected in series. The string is the smallest unit measured for each minute. In Laayoune PV plant, a supervisory control and data
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Fig. 1. Single diagram of SMB
acquisition (SCADA) system provides a remote monitoring and control, as well as an acquisition and processing of the real-time data. Laayoune PV plant has an East-West 1-axis tracking system, the tracking range is [130°, 230°]. The stow position (Safety position) is 180° (horizontal). When the wind speed is equal to or greater than 16m/s, trackers go automatically to the safety position. From time to time some trackers have some electrical or mechanical issues so the operation maintenance (O&M) operators turn them to the safety position (stow position) to protect them from any aggressive wind. Differences among strings Current refer to many causes, such as misalignment of trackers, the motion of clouds, shading, damaged connectors or fuses, or to a hotspot. To avoid false results, our approach uses data recorded when the tracker angle is (180°) to prevent the deviation caused by trackers under maintenance work. A clear sky is required for an accurate result. A review of seventy clear sky irradiance models was discussed in [11].
Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 3 Inverter 3 Inverter 3 Inverter 3 Inverter 3 Inverter 3 Inverter 3
SMB1 SMB2 SMB3 SMB4 SMB5 SMB6 SMB7 SMB1 SMB2 SMB3 SMB4 SMB5 SMB6 SMB7 SMB1 SMB2 SMB3 SMB4 SMB5 SMB6 SMB7
String 1 14.86 14.63 14.61 14.85 15.19 14.79 14.78 15.13 14.94 14.90 15.02 14.96 14.84 14.62 15.62 15.87 15.52 15.34 15.54 15.24 15.01
String 2 15.15 15.25 14.61 14.62 15.23 14.66 14.70 15.04 15.11 14.71 14.69 14.13 15.02 14.50 15.54 15.68 15.28 15.15 15.60 15.26 16.01
String 3 15.27 15.46 14.50 15.14 14.74 14.64 14.64 15.08 15.26 14.68 14.40 14.00 15.07 14.89 15.62 15.80 15.41 15.34 15.69 15.16 13.64
String 4 14.95 14.89 14.74 15.41 14.90 14.70 14.69 15.13 15.29 14.92 14.29 14.27 14.76 14.51 15.47 15.89 15.54 15.65 15.81 15.19 13.70
String 5 14.85 14.93 14.59 14.90 14.75 14.39 14.86 15.23 15.25 15.05 14.65 13.74 14.70 14.53 15.29 15.71 15.76 15.80 15.48 14.64 15.09
String 6 14.80 14.90 14.57 14.77 14.67 14.50 14.98 15.10 15.32 14.95 14.52 14.09 14.68 14.68 15.10 15.71 15.79 15.68 15.20 15.12 14.86
String 7 14.89 14.79 14.41 14.72 14.63 14.57 14.81 15.07 15.26 15.05 14.64 14.70 14.48 14.51 14.88 15.81 15.73 15.18 14.87 14.81 15.06
String 8 15.06 14.90 15.12 14.69 14.68 14.63 14.61 15.11 15.50 15.01 14.90 14.81 14.60 14.76 14.96 15.79 15.78 15.21 14.97 15.37 14.94
String 9 String 10 String 11 String 12 String 13 String 14 15.07 15.21 15.09 15.21 15.02 15.06 14.86 14.93 14.77 14.51 14.32 14.79 15.26 15.35 15.03 14.11 14.33 14.52 14.60 14.87 15.06 15.30 14.99 14.69 14.79 14.71 14.55 14.79 15.22 15.10 14.40 14.84 14.76 14.89 14.62 14.88 14.76 7.60 14.63 14.61 14.86 14.90 15.09 15.13 15.20 15.40 15.22 15.25 15.01 15.02 15.10 15.25 15.56 15.38 14.94 14.93 14.83 15.00 15.07 15.28 14.94 15.05 15.09 14.98 14.48 14.50 14.85 14.73 14.53 14.35 14.44 14.26 14.98 15.10 14.65 14.67 14.60 14.64 14.41 14.70 14.77 14.97 15.03 14.89 15.09 15.58 15.60 15.61 15.58 15.10 15.84 15.71 15.86 15.87 15.86 15.21 15.72 16.04 15.72 15.25 15.81 15.89 15.37 15.50 15.36 15.55 15.45 15.79 15.30 15.32 15.29 15.58 15.70 15.63 15.29 15.31 7.79 15.38 15.52 15.50 14.96 15.05 14.99 15.14 15.06 15.03
Fig. 2. The average of current at 180° (990 W/m2 )
A Novel Approach of Hotspot Detection in PV Plant Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 1 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 2 Inverter 3 Inverter 3 Inverter 3 Inverter 3 Inverter 3 Inverter 3 Inverter 3
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String 1 String 2 String 3 String 4 String 5 String 6 String 7 String 8 String 9 String 10 String 11 String 12 String 13 String 14 0.04 0.00 0.00 0.01 0.04 0.07 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.13 0.31 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.24 0.07 0.01 0.00 0.06 0.00 0.00 0.00 0.01 0.02 0.03 0.07 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.01 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.02 0.01 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00 50.87 0.01 0.02 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.06 0.02 0.00 0.00 0.00 0.00 0.05 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.07 0.15 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.03 0.00 0.07 0.16 0.02 0.43 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.04 0.00 0.03 0.03 0.00 0.03 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.16 0.39 0.30 0.17 0.00 0.00 0.00 0.00 0.16 0.00 0.02 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.36 0.04 0.20 0.10 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.00 0.00 0.00 0.07 0.01 0.00 0.00 0.00 0.06 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.41 0.30 0.04 0.04 0.05 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.37 0.02 0.19 0.00 0.00 0.00 55.53 0.00 0.00 0.00 0.00 0.00 1.91 1.75 0.00 0.03 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00
Fig. 3. Color scale of SDM
3 Proposed Approach 3.1 Performance Analysis When the Current value becomes null, that refers to a damaged “Y” connector. And if it equals half of the average of string values, that refers to a damaged fuse. These anomalies can be detected visually on the SCADA screen, but when we have thermal anomalies, the string still produces energy but less than the nominal. The visual detection of these anomalies is not easy for the Scada Operator. The comparison is based on comparing each string current with the average of the 14 strings, but the average cannot be a reference if one string value equals zero. In our approach, we compare strings current versus the median of strings, since it is not impacted but the case of 0. The performance analysis is carried out in three steps: 1) Time series data from the plant are acquired. These data are filtered to remove corrupted data. All measurements that are corrupted by communication loss during logging are removed. We compute the average of strings’ current (Fig. 2). 2) We compare the average of each string with the median of SMB strings; we filter values greater than the median and we compute the squared distance between the lowest strings and the median. 3) We apply a color scale (Fig. 3), to identify the greater values which refer to the lowest production strings. In Table 1 we determine the range of each color, as well as the possible causes of abnormal strings. The metric formula defined as: σ (s, m) = (s − m)2 If s < m σ (s, m) = 0 else
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Where s is the string value and m is the median value of strings, the square function will reduce the difference if it is less than 1 and increase it if it is greater, which allows filtering of the lowest production strings (Figs. 4 and 5).
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Color
σ
Light green
Equals zero
Good string
Dark green
Greater than 0 and less than 0.9
Misalignment, soiling Hot junction box
Orange
Greater than 0.9 and less than 20
Hotspot or damaged PV module Active diode
Red
Interpretation and possible causes
Damaged fuse or connector
Fig. 4. Active diode
Fig. 5. Broken PV module
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3.2 Experimental Results The distribution of the SDM (Fig. 6) leads to a classification of the output. The probability of having hotspots in strings where the SDM is greater than 0.9 is certain, even values between 0.5 and 0.9 can refer to hotspots, but in general, most inspections show that the cause is shading, soiling, or hot junction box.
Fig. 6. SDM distribution
We classify the results into 4 patterns with 4 colors: light green, dark green, orange, and red. The infrared thermal images of the orange cells show that these strings contain hotspots (Fig. 9) or broken PV modules (Fig. 7). The red ones refer to damaged DC fuse or damaged “Y” connectors or refer to corrupted data. The dark green refers to many causes, such as soiling (Fig. 10), misalignment of trackers, shading (Fig. 7), Hot
Fig. 7. Shading over a part of a string
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junction box due to poor connection (Fig. 8), and others. A clear sky and stable weather are required for a significant result. The clear sky is the main condition, mostly the total absence of clouds rarely happens, thus selecting data at 180° is not necessary, if at a partition of a day sky is clear, we can use data at that time if the wind speed is low.
Fig. 8. Hot junction box
Fig. 9. Hotspot
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Fig. 10. Cumulated soiling on the corner of modules
4 Conclusion The paper presents a metric for the detection of hotspots in solar PV arrays. The current hotspot fault main detection methods utilize thermal imaging, manually or using drones. Data analysis of production data became more useful for that purpose since the scale of the solar plants becomes greater than before. In addition, manual thermal imaging requires time and employees. Besides, areal thermal imaging can not be continuously available due to weather conditions, thus, the data analysis of production data became more performant. The experimental results show that the proposed metric can detect hotspots in the PV plant with high efficiency. That metric can be added to the SCADA system for future solar plants for real-time thermal anomaly detection.
References 1. Kim, J., et al.: A review of the degradation of photovoltaic modules for life expectancy. Energies 14(14), 4278 (2021). https://doi.org/10.3390/en14144278 2. Skomedal, Å.F., et al.: How much power is lost in a hotspot? A case study quantifying the effect of thermal anomalies in two utility scale PV power plants. Sol. Energy 211, 1255–1262 (2020). https://doi.org/10.1016/j.solener.2020.10.065 3. Pramana, P.A.A., Dalimi, R.: Large scale photovoltaic (PV) farm hotspot detection using fish eye lens. In: 2020 IEEE Student Conference on Research and Development (SCOReD), pp. 505–509. IEEE (2020). https://doi.org/10.1109/SCOReD50371.2020.9251016 4. Simal Pérez, N., Alonso-Montesinos, J., Batlles, F.J.: Estimation of soiling losses from an experimental photovoltaic plant using artificial intelligence techniques. Appl. Sci. 11(4), 1516 (2021). https://doi.org/10.3390/app11041516
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5. Jadin, M.S.b., Safian, S.F.A., Ghazali, K.H., Ven, T.L., Shah, A.S.M.: Hotspot detection in photovoltaic array using thermal imaging method. In: Md. Zain, Z., Sulaiman, M.H., Mohamed, A.I., Bakar, M.S., Ramli, M.S. (eds.) Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering. Lecture Notes in Electrical Engineering, vol. 842. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-8690-0_10 6. de Oliveira, A.K.V., Aghaei, M., Rüther, R.: Automatic inspection of photovoltaic power plants using aerial infrared thermography: a review. Energies 15(6), 2055 (2022). https://doi. org/10.3390/en15062055 7. Guerriero, P., et al.: Mismatch based diagnosis of PV fields relying on monitored string currents. Int. J. Photoenergy 2017, 1–10 (2017). https://doi.org/10.1155/2017/2834685 8. Bizzarri, F., et al.: Monitoring performance and efficiency of photovoltaic parks. Renew. Energy 78, 314–321 (2015). https://doi.org/10.1016/j.renene.2015.01.002 9. Rodrigues, S., Ramos, H.G., Morgado-Dias, F.: Machine learning PV system performance analyzer. Prog. Photovoltaics Res. Appl. 26(8), 675–687 (2018). https://doi.org/10.1002/pip. 3060 10. Sandeep, B., et al.: Monitoring of PV modules and hotspot detection using TensorFlow. In: 2022 International Conference on Electronics and Renewable Systems (ICEARS), pp. 155– 160. IEEE (2022). Doi: https://doi.org/10.1109/ICEARS53579.2022.9752346 11. Antonanzas-Torres, F., et al.: Clear sky solar irradiance models: a review of seventy models. Renew. Sustain. Energy Rev. 107, 374–387 (2019). https://doi.org/10.1016/j.rser.2019.02.032
Categorizing Data Imperfections for Object Matching in Wastewater Networks Using Belief Theory Omar Et-targuy1(B) , Yassine Belghaddar1,2,3,4 , Ahlame Begdouri1 , Nan´ee Chahinian2 , Abderrahmane Seriai3 , and Carole Delenne2,4 1
2
LSIA, Univ. Sidi Mohamed Ben Abdellah, Fez, Morocco [email protected] HSM, Univ. Montpellier, CNRS, IRD, Montpellier, France 3 Berger-Levrault, P´erols, France 4 Inria Lemon, CRISAM - Inria, Sophia Antipolis, France
Abstract. Nowadays, data on wastewater networks covering the same geographical territory are available from different sources. The fusion of multi-source spatial data provides a new and richer dataset that can serve several purposes such as quality improvement, decision making, or delivery of new services. It has given rise to several research works focused on the visualization, analysis, and fusion of spatial databases. However, the original data is often imperfect: imprecise, uncertain, vague, incomplete, etc. Therefore, it is essential to use formalisms allowing the modeling of imperfections and to propose adapted fusion mechanisms. In this work, we aim to handle data imperfections in a generic way. We first propose a categorization, according to several dimensions, of data imperfections encountered when fusing multi-source spatial data. We then propose to model these imperfections according to the formalism of the belief theory. We consider our conducted experiments that allowed us to match nodes and edges in the different cases of data imperfection, as promising. Keywords: Wastewater networks theory
1
· Imperfection · Fusion · Belief
Introduction
Big data treatment is a main challenge in the field of information systems. Nowadays, and with the adoption of new technologies such as Internet, smartphones, connected objects and GPS, the data related to a domain are now available from several sources with huge masses. However, this quantitative explosion has given rise to new problems related to its processing and exploitation. Wastewater network data is available from several sources (private managers’ databases, high resolution images, open data, pdf documents). The fusion of multi-source wastewater network data allows to create a new and richer data c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 387–398, 2023. https://doi.org/10.1007/978-3-031-35245-4_36
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set that can help in decision making inter alia. However, most of the time, this information is imperfect which doesn’t help treating it in an homogeneous manner. Data imperfection, in the field of artificial intelligence, is processed according to the three aspects: imprecision, uncertainty and incompleteness [7,9, 10]. It is then essential to use formalisms that allow to model these imperfections and propose adapted fusion mechanisms. The formalism of the belief theory unifies all uncertainty theories, allows to represent knowledge in a relatively natural way and enables the modeling of various forms of imperfection. This is why we have essentially turned to this formalism. This paper is organised as follows, the first section is devoted to the research context where we focus on the main mathematical methods of belief theory and the related works. In the second section, we propose our categorization of the forms of imperfection related to wastewater networks. In section three, we expose the application of the belief theory for wastewater object matching. Section four is devoted to the experiments and results obtained for each category of imperfection. Finally, section five concludes the paper and presents future perspectives and improvements.
2 2.1
Research Context Concepts of the Belief Theory
The belief theory is also known in the literature as Dempster-Shafer theory and more particularly as evidence theory [7]. The strength of belief functions in modeling uncertain knowledge was first demonstrated by Shafer [13]. Indeed, it allows to represent imperfect data in a more natural way than with probabilities. An extension of this theory is proposed through the Transferable Beliefs Model TBM [15], which is characterized by its fundamentally non-probabilistic character. This approach separates the relation between belief representation and decision making. In this section, we briefly describe the basic concepts of the transferable belief model. Frame of Discernment. The frame of discernment or frame of interest noted Ω, indicates the whole set of the possible answers (Hi ) to a problem (Hypotheses): Ω = {H1 , . . . , HK }
(1)
From the frame of discernment Ω, we consider the derived set 2Ω , including the 2k subsets A ⊆ Ω: 2Ω = {A, A ⊆ Ω} = {{H1 } , {H2 } , . . . , {HK } , {H1 ∪ H2 } , . . . , Ω}
(2)
where 2Ω contains the different Ω hypotheses, but also all the possible disjunctions of these hypotheses. This set allows to define the set of quantities used by the theory of belief functions to evaluate the truth of a proposition.
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Basic Belief Assignment. Given a question Q to be answered and a frame of discernment containing all of the possible solutions to this question Ω = {H1 , . . . , HK }. A function m, called basic belief assignment or bba, also called masse, is defined from 2Ω to values in the interval [0, 1], with as constraint: m(A) = 1 (3) A⊆Ω
The amount m(A) represents the belief that A, as element of 2Ω , contains the answer to the question Q. A set A such that m(A) > 0 is called a focal element. The mass function represents an imperfect knowledge on Ω. Combination. The combination phase allows to synthesize, in the form of a single belief function, all the knowledge coming from several functions. The objective of this step is the fusion of the complementarity and redundancy of the knowledge of different sources in order to obtain a more general knowledge, in the form of a more precise and reinforced belief function. The fundamental combination method, presented in the TBM, is the conjunctive combination rule. For two functions of masses m1 and m2 , this rule is defined by: m1 (B)m2 (C) ∀A ⊆ Ω (4) m1 ⊕2 (A) = m1 ⊕ m2 (A) = B∩C=A
The mass function m corresponds then to the synthesis of the knowledge of m1 and m2 related to A. This conjunctive combination rule allows to verify several properties like associativity, commutativity and has a neutral element which is the empty mass function. One of the particularities of this rule is to generate a non-normalized mass function m(∅) > 0. As this condition is not possible in a closed world, a normalization phase is necessary. The conjunctive combination normalization is called the orthogonal combination rule or the Dempster combination rule, it is also the first rule presented by the theory and is written as follows: m1⊕2 (A) = m1 ⊕ m2 (A) =
1 1−κ
m1 (B)m2 (C)
∀A ⊆ Ω, A = ∅
(5)
B∩C=A
where κ = m1⊕2 (∅) =
m1 (B)m2 (C).
(6)
B∩C=∅
κ is the conflict mass which takes values between 0 and 1. In general, it reflects the degree of contradiction between the combined sources. For κ = 1, S1 and S2 are entirely considered as conflicting and the sources are note fused. On the contrary, if κ = 0, the sources are in perfect agreement.
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Reliability of Sources. When a confidence knowledge on a source S that has given a mass function m is available, it is possible to consider this metaknowledge in order to perform a weakening operation. Let α ∈ [0, 1], a weakening coefficient, the weakened mass function is obtained by: αm( A) = (1 − α)m( A), ∀A ⊂ Ω (7) αm(Ω) = (1 − α)m(Ω) + α The Pignistic Probability. The decision phase is based on the pignistic distribution [14] noted BetP and obtained from the mass function m. It is also called pignistic probability for the set of probabilities on the singletons it generates. The transfer of the mass function m into a pignistic probability function BetP expressed on Ω, is characterized for any Hk ∈ Ω by: BetP (Hk ) =
m(A) 1 1 − m(∅) |A|
(8)
A∈Hk
After this transformation, it is impossible to find the initial mass function m. Indeed, a mass function is only linked to a single pignistic probability but a pignistic probability can be obtained from an unlimited number of mass functions. 2.2
Related Works
The belief theory is widely used in various areas. In the field of multiple criteria decision making [3], the approach consists of constructing a belief set for each criteria to be studied. The different sets of beliefs are then combined by Dempster’s combination rule to allow the decision making. The most important works of belief theory in the area of data analysis are those of T.Denoeux [4–6,18]. Various problems are discussed, including regression, form recognition and classification. This theory allows in this context to treat noisy and imprecise data. In the area of data fusion, the most important benefit results from the combination step, which generally allows to reduce the uncertainty on a prediction by using the redundancy and complementarity of the information. 2.3
The Belief Theory in Matching Road Networks
Object matching is the identification of corresponding objects in different data sources [16,17]. The word ‘objects’ refers to points, lines or polygons. Similarity measures are used to obtain some degree of comparison between instances [11]. They represent the criteria on which a match is based [8]. The most intuitive similarity measure is the one based on position, such that we assume that two objects are homologous, when they are close in terms of distance. The most used is the euclidean distance [2,12,17]. The proposed approach by [10] is concerned with the matching of road networks (linear data) and reliefs (point data) using the belief theory. We find
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this approach relevant to our case since its mass modeling allows for quantification: the complete knowledge, the incomplete knowledge and the ignorance. The matching process uses two databases BD1 (reference data base) and BD2 (comparison data base), every object obj in BD1 is examined with every object in BD2 to find its homologous. The first step corresponds to the selection of candidates, which consists in finding, for each object in BD1, potential homologous objects in BD2, called matching candidates and noted Ci,(i=1,2,...,N ) . Then, every of the matching candidates is analyzed to determine the correspondence relation. The initialization of the belief masses (second step of the process) consists, for every matching measure, in pronouncing on every of the candidates by assigning a belief to the assumptions defined for each candidate. The matching measures can be for example the Euclidean distance, the toponym-based distance or the orientation. Then, and after fusing the matching measures for each candidate (third step of the process), the fusing of the candidates is performed to have a global view of the beliefs assigned to all candidates (fourth step of the process). Finally, the decision step consists in selecting the best candidate.
3 3.1
Data Imperfections in a Wastewater Network Database Wastewater Network Graph
In a wastewater network database, the nodes actually represent structures, equipment, and repairs (manholes, gullies, etc.), and the edges illustrate pipes (collection, transportation, etc.) as shown in Fig. 1.
Fig. 1. Node-Edge representation of a wastewater network
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Categorization of Imperfections in a Wastewater Networks
Imperfections in data fusion are considered according to three aspects: i) imprecision, which concerns a difficulty in declaring information, ii) incompleteness, which corresponds to the absence of information and iii) incertitude, which refers to the veracity of an information: the information can be precise and complete but false [7,10]. With the aim of handling these imperfections in a generic way, we propose to deepen this analysis and categorize them according to the following dimensions, relevant for the wastewater network data: – Nature of the object target of the matching: node or edge. – Cardinality: i) when the object is a node, it represents the number of candidates selected for an object in the comparison database that can be matched (i.e. inside the buffer around the object in the reference database), and ii) the number of candidates, among the selected candidates, that represent the real correspondent, when the object is an edge. The possible cardinalities are: 1:1 and 1:n for both nodes and edges. – Offset: characterizes the shifting between the reference and the comparison objects, distance for the nodes and angle for the edges. For the edges, When the angle is equal to, or near 0, the offset is uniform. Otherwise, the offset is non-uniform. Our categorization of wastewater networks data imperfections is illustrated in Fig. 2. Examples of each category are shown in Figs. 3, 4 and 5.
Fig. 2. Imperfection categorization of wastewater network data
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(a) Single candidate
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(b) Multiple candidates
Fig. 3. Node offset
(a) Cardinality 1:1
(b) Cardinality 1:n
Fig. 4. Uniform offset of an edge
(a) Cardinality 1:1
(b) Cardinality 1:n
Fig. 5. Non-uniform offset of an edge
4
The Belief Theory in Wastewater Object Matching
Our objective is the fusion of two wastewater network databases, gathered from different sources: a reference database (to be completed) and a comparison database. Inspired by the work of [10], we define our general frame of discernment as Θ = {C1 , C2 , .., Ci , .., CN , NA}, where N is the number of candidates, Ci is the confirmation that the homologous of the reference object is the candidate i and NA is the hypothesis that there is no match in the comparison database.
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The initialization of the belief masses is performed based on the model proposed by Appriou [1], where each source focuses on one hypothesis of the frame of discernment. In other words, the source of information analyzes a given hypothesis Ci , and pronounces in favor of it (Ci ), against it (¬Ci ) or does not pronounce on it (Θ). Assignment of values to the masses functions corresponds to the semantic modeling of knowledge related to the similarity measures we adopt. Table 1, shows the similarity measures we used per object type. Table 1. The measures used for each object type Measures of similarity Euclidean distance Hausdorff distance Angle Length Node x Edge
x
x
x
The choice of these similarity measures is relevant to the nature of our considered types of objects. Indeed, the Euclidean distance is used to measure the offset between nodes. For the edges, we use the length and the Hausdorff distance that measures the distance between two subsets of a metric space. We use the angle to measure the deviation between edges. We have introduced several thresholds in order to transfer these similarity measures properly into masses. We give in Fig. 6 the example of masses functions for the Angle measure. The same reasoning apply for the other measures. Each edge is characterized by two endpoints. To calculate the angle between the reference edge and the candidate edge, we use the scalar product between two vectors: −−→ −−→ AB · CD = AB ∗ BC ∗ cos(θ) (9) The direction of the edges is not important in our case. But the formula 9 of the scalar product takes into account the directions (the values of cos(Θ) are between −1 and 1). We propose to transform the negative values into positive values to ignore the directions and then, after this step, the values of cos(Θ) are between 0 and 1. This means that the final values of the angles belong to the interval − π2 , π2 .
Fig. 6. Representation of knowledge for the Angle measure
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If cos(Θ) is between S2 and 1, we assume that the two edges are almost on each other, so the masses are distributed as follows: m(Ci ) = 1, ¬m(Ci ) = 0 and m(Θ) = 0. Between S1 and S2, when the angle between the reference object and the comparison object is smaller, the probability that the comparison object is the homologous object is higher. The ignorance is important when the angle is in the neighborhood of S1, i.e. when the angle between the reference object and the comparison object is neither large enough to conclude with certainty that it is not him, nor small enough to conclude that it is the true homologue.
5
Experiments and Results
In this section, we present our experiments and results obtained for some categories of imperfections. The data set we have used are: 1. Montpellier M´editerran´ee M´etropole (3M): The official source of the wastewater networks of the city of Monpellier (south of France) containing nodes and edges. We use it as a reference database, the objective is then to complete it. All red objects in Figs. 3, 4 and 5 represent the nodes and edges of this database. 2. Experimental database Ex-DB : This is an experimental nodes and edges database that we have created to be able to manipulate all data imperfections use cases. All blue objects in Figs. 3, 4 and 5 represent the nodes and edges of this database. We detail hereafter the results related to the “Uniform offset of cardinality 1:1 ” imperfection illustrated in Fig. 4a. We used a distance buffer equal to 10 m to select the candidates of edge number 898. Therefore, the frame for discernment is defined as follows: Θ = {603, 318, NA} (10) Figure 7 shows the sets of masses for each candidate attributed for the measures Angle, Hausdorff distance and Length.
(a) Angle
(b) Hausdorff distance
(c) Length
Fig. 7. Initialisation of masses for each measure
After combining the measures (see Fig. 8), we can affirm with 99% certainty that the homologous of edge 898 is 603. Furthermore, we notice that the candidate number 318 is not the homologous with 85% of certainty and a conflict of 0.12%.
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Fig. 8. Set of masses after the combination of measures
After the fusion of candidates with normalisation of masses using the Dempster operator, we notice on Fig. 9 that the hypothesis related to candidate 603 is clearly further away compared to the other hypotheses, with a belief mass equal to 99.2%. When the calculation of the pignistic probability is completed (see Fig. 10), the 603 hypothesis is chosen, since the pignistic probability has reached a maximum value, P(603) = 0.0996. Therefore, reference edge number 898 is matched to candidate edge number 603.
Fig. 9. Fusion of the candidates with normalization
Fig. 10. Pignistic probability for each hypothesis
We conducted the same experiments for the six categories of imperfection. The results are summarized in Table 2.
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Table 2. Results for each category. The highest values are in bold. Imperfection category
Reference object
Frame of discernment Θ = {H1 , . . . , HK , NA}
Pignistic probability H1 H2 H3 H4
H5
H6
H7
Selected hypothesis
Node offset with a single candidate Fig. 3a
Node 648
{270, NA}
0.589 0.411
–
–
–
–
–
Node 270
Node Offset with Node 495 multiple candidates Fig. 3b
{205, 505, NA}
0.659 0.108
0.233 –
–
–
–
Node 205
Uniform offset Card 1:1 Fig. 4a
Edge 898
{318, 603, NA}
0
0.996 0.04
–
–
–
Edge 603
Non-uniform offset Card 1:1 Fig. 5a
Edge 99
{893, 894, 895, NA}
0.007
0.984 0.005 0.004 –
–
–
Edge 894
Uniform offset Card 1:n Fig. 4b
Edge 897
{171, 310, 318, 371, 603, 605, NA}
0.191
0.191
0.191 0.006 0.004
0.004 0.414 NA
Non-uniform offset Card 1:n Fig. 5b
Edge 1100
{553, 1201, 1202, 1203, NA}
0.003
0.157
0.137 0.161 0.544 –
6
–
–
NA
Conclusions
In the context of geographic database fusion, we addressed the problem of fusing imperfect spatial data of wastewater networks. For this purpose, our contribution concerns, firstly, the categorization of the different forms of imperfections related to the nodes and edges of the wastewater networks, which allowed us to handle each category distinctly. Secondly, the application of the theory of belief in the fusion process. The results allowed us to match the reference objects in most cases. Our perspective is to further improve the results in the case of matching the edges.
References 1. Appriou, A.: Probabilities and unknowns in multisensor data fusion (Probabilites et incertitude en fusion de donnees multi-senseurs). In: Revue Scientifique et Technique de la Defense, 1st Quarter, pp. 27–40 (1991) 2. Beeri, C., et al.: Object fusion in geographic information systems. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 816–827 (2004) 3. Beynon, M., Curry, B., Morgan, P.: The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling. Omega 28(1), 37–50 (2000) 4. Denoeux, T.: A neural network classifier based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Humans 30(2), 131–150 (2000) 5. Denœux, T., Masson, M., Dubuisson, B.: Advanced pattern recognition techniques for system monitoring and diagnosis: a survey. J. europ´een des syst`emes automatis´es 31, 1509–1540 (1997) 6. Denœux, T., Zouhal, L.M.: Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets Syst. 122(3), 409–424 (2001) 7. Lefevre, E., Colot, O., Vannoorenberghe, P.: Belief function combination and conflict management. Inf. Fusion 3(2), 149–162 (2002) 8. Li, L., Goodchild, M.F.: Automatically and accurately matching objects in geospatial datasets. Adv. Geo-Spat. Inf. Sci 10, 71–79 (2012)
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9. Olteanu-Raimond, A.-M., Mustiere, S., Ruas, A.: Knowledge formalization for vector data matching using belief theory. J. Spatial Inf. Sci. 10, 21–46 (2015) 10. Olteanu, A.-M.: Fusion de connaissances imparfaites pour l’appariement de donn´ees g´eographiques: proposition d’une approche s’ appuyant sur la th´eorie des fonctions de croyance. Ph.D. thesis. Universit´e Paris-Est (2008) 11. Rosen, B., Saalfeld, A.: Match criteria for automatic alignment. In: Proceedings of 7th International Symposium on Computer-Assisted Cartography (Auto-Carto 7), pp. 1–20 (1985) 12. Samal, A., Seth, S., Cueto, K.: A feature-based approach to conation of geospatial sources. Int. J. Geogr. Inf. Sci. 18(5), 459–489 (2004) 13. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976) 14. Smets, P.: Decision making in the TBM: the necessity of the pignistic transformation. Int. J. Approx. Reasoning 38(2), 133–147 (2005) 15. Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66(2), 191–234 (1994) 16. Tong, X., Shi, W., Deng, S.: A probability-based multi-measure feature matching method in map conation. Int. J. Remote Sens. 30(20), 5453–5472 (2009) 17. Volz, S.: An iterative approach for matching multiple representations of street data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 36(Part 2/W40), 101–110 (2006) 18. Zouhal, L.M., Denoeux, T.: An evidence-theoretic k-NN rule with parameter optimization. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 28(2), 263–271 (1998)
Modeling and Simulation of Piezo-Composite Energy Harvesting from Beam Subjected to Moving Load Yassin Belkourchia1(B) , Nada Tassi1 , and Lahcen Azrar1,2 1
Research Center STIS, M2CS, ENSAM Rabat, Mohammed V University in Rabat, Rabat, Morocco {yassin.belkourchia,nada.tassi}@um5s.net.ma, [email protected] 2 Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
Abstract. In this paper, a mathematical modeling of energy harvesting obtained from the dynamic response of a beam with an homogenized piezoelectric composite patch subjected to a moving load is investigated. A micromechanical model is used to predict the piezoelectric composites with optimized homogenized properties to be used in the considered energy harvester. The differential quadratic method (DQM) coupled with an implicit time scheme are elaborated for the spatial and time discretizations to numerically solve the resulting partial differential equations. The generated electric power from the homogeneous piezocomposite patches is evaluated based on the root mean square (RMS). The numerical results demonstrate the effect of piezo-composite material on the RMS estimated. Keywords: Energy harvesting · Piezo-composite patch · Quadratic Differential Method · Micromechanical methods · Effective behavior
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Introduction
Energy harvesting is a new way to exploit energy by taking into consideration the global warming and air pollution espacially vibration-based energy harvesting. One of the technologies used to extract the new energy generation and harvesting is those obtained from the coupled between a beam and piezoelectric patch under an excitation either static or dynamic. Due to its unique characteristics, piezoelectric materials have received much attention to study firstly active vibration of beams. Beam on foundation under a moving loads has been historically studied by several authors [1,12]. In the same side, Lorenzo et al. [6] derived analytical solutions for the response of an infinite Euler-Bernoulli beam land on a viscoelastic foundation. An experimental study is done by [7] to validate the analytical formulation and generate electrical energy from base excitation. N. Tassi and L. Azrar—Contributed equally to this work. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 399–407, 2023. https://doi.org/10.1007/978-3-031-35245-4_37
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Based on these studies, many works has been used the dynamic response of beam to extract the energy. Wang et al. [17] analyzed the piezoelectric energy harvester by exploring the longitudinal motions of water particles in sea waves. The fluid-structure interaction of the water flow and the structure displacement is studied by Belkourchia et al. [3,4] using the Stokes equation for the flow and the Euler-Bernoulli beam theory for the structure motion based on differential quadratic method (DQM). Another source of vibration or excitement that used to extract the energy harvesting is interrogated electrodes attached to a beam. Vatanabe et al. [16] proposed a methodology to design functionally graded piezocomposite materials. In [9] a mathematical model of the dynamic behavior of active-fiber composites (AFC) and the harvested voltage are proposed. To calculate the homogenized parameters of a multi-phase piezocomposite using analytical and numerical homogenization techniques various approaches were proposed [13]. Lenglet et al. [11] proposed a numerical homogenization technique based on finite element method and on Mori-Tanaka approach for a specific case of inclusion. For several shape and type of inclusion, a combination of micromechanical methods based on Eshelby inclusion problem are examined in [8,14,15]. In this work, a numerical simulation based on the DQM coupling with an implicit scheme for energy harvesting is elaborated. Micro-mechanical methods based on the Mori-Tanaka approach (MT) and Self-consistent (SC) are used to predict homogenized properties of piezo-composite with improved electromechanical properties. The numerical results obtained are compared and discussed for various inclusion’s volume fraction and orientation.
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Homogenization of Piezoelectric Composite Materials
Considering an homogeneous piezoelectric infinite domain V with an inclusions that called RVE (Relative Volume Elementary). In the stationary theory of linear piezoelectricity the following relations are obtained [8] Cijkl elij εkl σij = (1) Di eikl −κil −El where σij and εkl are the stress and the strain respectively. Di , El , Cijkl , elij , and κil are the electric displacement, electric field, the elastic moduli, the piezoelectric coefficients, and the dielectric constants, respectively. In order to make easy the manipulation of these equations, the condensed expression are used. ΣiJ = EiJKl ZKl
(2)
where the lower case subscripts assume the range of 1–3, while the capital subscripts take the range of 1–4. By using Green’s function techniques, integral equation and equivalent inclusion’s principal of Eshelby the localization tensors are derived and used to obtain the effective behavior of the electro-elastic composite. This methodology is based
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on the averaging physical property (f = given as follow [8,14]:
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1 f dV ). The effective behavior is V V
f ΣiJ = Eef iJKl Z Kl
(3)
where Σ and Z is considered to be the macroscopic evolution stress and strain field, respectively, applied over the REV V . Based on the micro-mechanical methods the linear effective electroelastic tensor of a two phase composite is given by [8,14] f M I I M I (4) Eef ijkl = Eijkl + f (Eijkl − Eijkl )Aklmn where AI is the fourth order localization tensor of the inclusion, that take into account the volume fractions (f I ) of constituents as well as the shape of the inclusion. This tensor could be approximated using different micromechanical approach. In this work, the Mori-Tanaka approach is considered with the investigation of the inclusion orientation effect.
3
Harvesting Model
For modeling purpose, a beam with piezoelectric composite patch using a homogeneous piezoelectric material with a uniform electrical field aligned in the x3 direction are used. For energy harvesting a cantilever beam with a several piezocomposite patches is considered subjected to a moving load as shown in Fig. 1.
Fig. 1. Sketch of Euler-Bernoulli beam with homogenized piezo-composite patches.
The length of the beam is noted by L, h and hp are thicknesses of the beam and the homogenized piezocomposite patch, respectively. In this study, the thickness hp is neglected compared to that of the beam (h). A moving force is applied on the cantilever beam from the left-hand side with constant speed v, so that the load defined as: f (x, t) = f0 δ(x − vt) where x is the position on the beam (0 ≤ x ≤ L), δ is the Dirac delta function to model a point force, and f0 is a constant force.
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The theory of Euler-Bernoulli beam is used in the modeling of the cantilever motion. Thus, the governing equation of the considered beam is given by: EI
∂ 2 w(x, t) ∂ 4 w(x, t) + (ρA) = f (x, t) 4 ∂x ∂t2
(5)
where EI, A and ρ are the rigidity properties, cross section area and the material density of the cantilever beam, respectively. It is assumed that the homogenized piezo-composite patch is attached tightly on the surface of the cantilever. After obtained the displacement w, the generated charge Qig and voltage Vgi on the surface of the homogenized piezo-composite can be calculated using the following expressions [10]: Qig (t)
eef f b(h + hp ) × = − 31 2
Vgi (t) = −
f eef 31 (h + hp )
2CVef f
×
∂w(x, t) ∂w(x, t) |x=ai +lp − |x=ai ∂x ∂x
∂w(x, t) ∂w(x, t) |x=ai +lp − |x=lp ∂x ∂x
(6)
(7)
where 1 ≤ i ≤ m, and m is the number of the homogenized piezo-composite f patches mounted on the face of beam. eef 31 is the effective piezoelectric coefficient obtained by the micromechanical approach and lp is the associated length. CVef f is the homogeneous electrical capacity of the piezo-composite patches; and CVef f is the effective electrical capacity per unit width of the piezo-composite patches given by: C ef f CVef f = V b The obtained displacement and the homogenized piezo-composite patches are used to generate electric charge and voltage based on the presented methodological approach of the dynamic behaviors of piezoelectric beams. The aim of this work is to used the homogenized piezo-composite patches with the EulerBernoulli beam under a moving load for energy harvesting. For this purpose the root mean square (RMS) of the total generated electric power is investigated. However, the RMS generated electric power under a periodic moving load with a period T is written as: 1 T rms [pe (t)]2 dt (8) pe = T 0 in which pe (t) is the general electric power generated of all homogenized piezocomposite patches where are bonded on the Euler-Bernoulli beam, at time t (0 < t < T ) and can be written as: pe (t) =
m dQig (t) i=1
dt
Vgi (t)
(9)
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The interval [0, T ] is discretized into j time sections with a small discretization Δt, then the summation given in Eq. 9 is given as:
j Δt rms ([pe (ti )]2 + [pe (ti−1 )]2 ) (10) pe = 2(T − Δt) i=2 3.1
DQM Implementation
To solve Eq. (5) the DQM is used. The consists of considering n-grid points, noted by x1 , x2 , ..., xN , in the x-direction and applying the quadrature rule to Eq. (5), the following discrete system is obtained EI
N
(4)
Aij w(xj , t) + ρA
j=1
∂ 2 w(xi , t) = f (xi , t) ∂t2
(11)
This system of equation can be transformed to a discretized matrix form as follow [2] EI[A](4) {W (t)} + ρA[I]{W¨(t)} = {F (t)} (12) where [I] is an identity matrix of size N × N and [A](4) is the matrix of DQM weighting coefficient of the 4th -order derivative, which can be given explicitly by the following form ⎤ ⎡ (4) (4) (4) A11 A12 . . . A1N ⎢ (4) (4) (4) ⎥ ⎢ A21 A22 . . . A2N ⎥ (13) [A](4) = ⎢ .. . . . ⎥ ⎥ ⎢ .. . .. ⎦ ⎣ . . (4)
(4)
(4)
AN 1 AN 2 . . . AN N Moreover, the vectors are given by {W (t)} = [w(x1 , t) w(x2 , t) ... w(xN , t)]T ¨ (t)} = [w(x ¨ 2 , t) ... w(x ¨ N , t)]T {W ¨ 1 , t) w(x
(14)
{F (t)} = [f (x1 , t) f (x2 , t) ... f (xN , t)]
(16)
T
(15)
The boundary conditions of the beam are considered with Eq. (12), more details of implementation of boundary conditions of the beam can be found in [5]. The time-dependent resulted discrete system of equation is solved based on implicit time scheme.
4
Result and Discussion
Based on the mathematical model and the numerical simulation developed above, the generated charge and voltage from the homogenized piezo-composite patches as well as the RMS of the generated electric power can be estimated using the homogeneous technical and the DQM coupled with the implicit time scheme.
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Micromechanical Results
Considering the piezocomposite formed by a transversely isotropic material (PZT-5A) inclusion embedded into an isotropic epoxy matrix that called by active fiber composite (AFC) with half axes a, b and c. The global coordinate system related to the matrix are (x1 , x2 , x3 ) and the third half axe c of inclusion is respected with the polling direction x3 . The inclusion orientation effect is analyzed by considering the three Euler angles ω, β, and γ. The direction cosine matrix is given as follow ⎡
⎤ cos ω cos β cos γ − sin β sin γ cosω sin β cos γ + cosβ sin γ − sin ω cos γ− Ψ = ⎣ − cos ω cos β sin γ − sin β cos γ − cos ω sin β sin γ + cos β cos γ sin ω sin γ ⎦ sin ω cos β sin ω sin β cosω
(17) in which the global coordinates are related to the local ones based on the following formulation. (18) ξ = Ψξ where ξ represent the vector in the (x1 , x2 , x3 ) global coordinates system and ξ represent the vector in (x1 , x2 , x3 ) coordinates system linked to the inclusion. After performing the micromechanical method, the results are obtained and presented, the properties associated to the considering composite are given in Table 1 where κ0 = 8.85 10−12 (C 2 /N m2 ) is the permittivity of free space. Table 1. Electroelastic material properties Promerties C11 (GPa) C12 (GPa) C13 (GPa) C33 (GPa) e31 (C/m2 ) e33 (C/m2 ) e15 (C/m2 ) κ11 /κ0 κ33 /κ0 P ZT − C3 148
77.4
79.2
135.5
-1.21
11.03
9.48
338
BaT iO3
77
78
162
-4.4
18.6
11.6
1265.5 1423.7
166
338
f ef f 2 ef f Fig. 2. Effective piezoelectric coefficient eef 31 (a) and (e31 ) /κ33 parameter for different orientation angles using MT approach.
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f In Fig. 2 (a), the effective piezoelectric coefficient eef 31 is presented as a function of volume fraction of a fiber inclusion using MT approach for different orienf ef f tation angle β. Figure 2 (b) illustrates the evolution of the coefficient eef 31 /κ33 by varying the volume fraction as well as the orientation angle of the fiber inclusion. It can be seen that these coefficients depend strongly on the inclusion’s orientation. Moreover, for β = 45◦ or β = 60◦ the evolution have a significant electrelastic effect that will be retained for the rest of this paper. Noting here that the internal equivalent capacitance noted by CVef f is f b expressed in case of homogenized piezo-composite as: CVef f = κef 33 d hp , where d is the distance between the electrodes and b is the width of the homogeneous piezo-composite patch.
4.2
Application
In this application subsection, we study the effectiveness of the micro-mechanic structure harvester. In particular, we study the force applied to the beam and the effective properties of the piezo-composite patches on the generated charge, voltage and the RMS of the power harvesting. To do so, we consider a clumped-free beam with a length (L) of 1 m, width b = 0.2 m, thickness h = 0.1 m, the Young’s modulus E = 2.068 × 1011 P a, mass density ρ = 10686.9 kg/m3 , subjected to a f0 moving force, and the geometrical of the homogeneous piezo-composite patch are taken as: lp = 0.2 m and hp = 0.001 m. The effective properties of the homogeneous piezo-composite patches used in this application are considered for a volume fraction f I = 50% in case of β = 60◦ and f I = 80% in case of β = 45◦ . Figure 3 illustrates the effect of the force applied to the beam on the voltage and generated charge respectively created by one homogeneous piezo-composite
Fig. 3. Time history of (a) the produced voltage and (b) the generated charge by one piezoelectric patch for different f0 and v = 10 m/s.
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Fig. 4. RMS of the electric power versus the number of pure piezoelectric and piezocomposite patches.
patch. It can be seen from this Figure that the maximum voltage and generated charge increases by increasing the forces. Figure 4 presents the effect of the patches material and its numbers on the RMS of the generated electric power. According to the presented results, the RMS increases by increasing the number of piezoelectric and piezo-composite patches. Otherwise, the RMS obtained by using the homogenization method is more significant compared to the one obtained using only a pure piezoelectric patches.
5
Conclusion
A methodological approach is investigated for energy harvesting from homogeneous piezocomposite patches attached to a clamped-free beams based on the Euler-Bernoulli beam theory subjected to a moving load. The homogenization problem is solved using the Mori-Tanaka micro-mechanical approach to predict the effective properties of the piezo-composite patches and the DQM method coupled with the implicit scheme to analyze the dynamic response of the harvester. The presented micro-mechanical model used to derive the overall behavior of the piezo-composite patches. The voltage and generated charge created by the homogeneous piezo-composite patches are extracted. The RMS of the electric power is examined for a pure piezoelectric and an homogeneous piezo-composite patches with various inclusion’s volume fraction and orientation. The obtained numerical results show a strong effect of the piezo-composite behavior patches on the energy harvesting as well as the number of patches. Acknowledgments. The authors would like to acknowledge the financial support of the CNRST and the Moroccan Ministry of Higher Education and Scientific Research with the project PPR2/06/2016.
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References 1. Basu, D., Kameswara Rao, N.: Analytical solutions for Euler-Bernoulli beam on visco-elastic foundation subjected to moving load. Int. J. Numer. Anal. Meth. Geomech. 37(8), 945–960 (2013) 2. Belkourchia, Y., Azrar, L.: A new numerical procedure for vibration analysis of beam under impulse and multiharmonics piezoelectric actuators. J. Appl. Math. 2020, 7391848 (2020) 3. Belkourchia, Y., Bakhti, H., Azrar, L.: Numerical simulation of FSI model for energy harvesting from ocean waves and beams with piezoelectric material. In: 2018 6th International Renewable and Sustainable Energy Conference (IRSEC), pp. 1–5. IEEE (2018) 4. Belkourchia, Y., Bakhti, H., Azrar, L.: Optimization approach for piezoelectric energy harvesting from ocean waves and beams. In: 2019 5th International Conference on Optimization and Applications (ICOA), pp. 1–5. IEEE (2019) 5. Bert, C.W., Malik, M.: The differential quadrature method for irregular domains and application to plate vibration. Int. J. Mech. Sci. 38(6), 589–606 (1996) 6. Di Lorenzo, S., Di Paola, M., Failla, G., Pirrotta, A.: On the moving load problem in Euler-Bernoulli uniform beams with viscoelastic supports and joints. Acta Mech. 228(3), 805–821 (2017) 7. Erturk, A., Inman, D.J.: An experimentally validated bimorph cantilever model for piezoelectric energy harvesting from base excitations. Smart Mater. Struct. 18(2), 025009 (2009) 8. Fakri, N., Azrar, L., El Bakkali, L.: Electroelastic behavior modeling of piezoelectric composite materials containing spatially oriented reinforcements. Int. J. Solids Struct. 40(2), 361–384 (2003) 9. Jemai, A., Najar, F., Chafra, M., Ounaies, Z.: Mathematical modeling of an activefiber composite energy harvester with interdigitated electrodes. Shock Vib. 2014 (2014) 10. Lee, C.K., Moon, F.C.: Modal sensors/actuators (1990) 11. Lenglet, E., Hladky-Hennion, A.C., Debus, J.C.: Numerical homogenization techniques applied to piezoelectric composites. J. Acoust. Soc. Am. 113(2), 826–833 (2003) 12. Ludwig, K.: Deformation of rail elastically supported of infinite length by loads moving at a constant horizontal velocity. In: Proceedings of 5th International Congress on Applied Mechanics, pp. 650–655 (1938) 13. Odegard, G.M.: Constitutive modeling of piezoelectric polymer composites. Acta Mater. 52(18), 5315–5330 (2004) 14. Tassi, N., Bakkali, A., Fakri, N., Azrar, L., Aljinaidi, A.: Well conditioned mathematical modeling for homogenization of thermo-electro-mechanical behaviors of piezoelectric composites. Appl. Math. Model. 99, 276–293 (2021) 15. Tassi, N., Bakkali, A., Fakri, N., Azrar, L., Aljinaidi, A.: Mathematical modeling of fully coupled reinforced magneto-electro-thermo-mechanical effective properties based on conditioned micromechanics. Compos. Struct. 280, 114896 (2022) 16. Vatanabe, S., Paulino, G., Silva, E.: Design of functionally graded piezocomposites using topology optimization and homogenization-toward effective energy harvesting materials. Comput. Methods Appl. Mech. Eng. 266, 205–218 (2013) 17. Xie, X., Wang, Q., Wu, N.: Potential of a piezoelectric energy harvester from sea waves. J. Sound Vib. 333(5), 1421–1429 (2014)
Solar Radiation Forecasting Using Artificial Intelligence Techniques for Energy Management System Saida El Bakali(B) , Hamid Ouadi, and Saad Gheouany ERERA, ENSAM, Mohammed V University, Rabat, Morocco [email protected]
Abstract. The purpose of this paper is to highlight solar radiation forecasts with high accuracy and reliability over a 24-h horizon in order to deduct a prediction of the energy production of a photovoltaic (PV) system integrated in a positive energy building. This prediction will feed algorithms for optimal energy management within the building. Our study will focus attention on the most widely used and effective techniques for solar radiation prediction, namely: MLP-based neural networks and the popular ensemble learning technique under the name “STACKING”. The aim is to predict hourly solar radiation for time horizons ranging from h + 1 to h + 24 using current and past values of daily solar radiation, temperature and humidity. At this point, different natures of these input parameters have been tested. In addition, the most appropriate forecasting technique that gives the best results was selected. In our simulations, 3 years of available data (2019–2022) were used for the city of Rabat. Python was used to develop 24-h GHI prediction models. To evaluate the performance of our prediction models, two evaluation scales were selected, namely: nRMSE (normalized root mean square error), nMAE (normalized mean absolute error). The good results and the best evidence are the results of the scales; Also obtained nRMSE = 2.384% and nMAE = 1.556%. The outputs obtained show that in order to acquire good results, it is necessary to take into account the input parameters that have an effect on the accuracy, the optimal selection of the number of past values as well as the optimal selection of the prediction technique. Keywords: Solar radiation forecasting · Multi-layer perceptron · Ensemble learning · Stacking · Global horizontal irradiation · Weather data · Management of energy sources
1 Introduction Optimal management of hybrid generation sources requires a large amount of data captured by the manager. So, among the data that will be used, namely: solar radiation, wind speed and many others to enhance the mass of data collected. This light will allow to have an efficient management of the means of production in order to optimize the management of the energy sources applied to the building. The final interest is in the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 408–421, 2023. https://doi.org/10.1007/978-3-031-35245-4_38
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increase of reliability and decision support. This work was done specifically in this context. It is therefore necessary to develop a model for forecasting weather conditions. In this paper, the application of artificial neural networks (ANN) based on MLP and ensemble learning technique has been implemented in the field of renewable energy and more precisely in the framework of smart grids. The goal is to predict solar radiation over a 24-h horizon using data associated with the city of Rabat. The choice of input parameters was made on the basis of the results of a correlation matrix involving present data on humidity, temperature and solar irradiation. In recent years, several studies have been developed to predict solar radiation using machine learning techniques and climate parameters. In this sense, the five most recent articles have been processed. In [3], the authors adopted three artificial intelligence methods, including intelligent persistence, artificial neural networks, and random forests. These three prediction methods compared the three components of solar radiation at the site of Odeillo, France. The authors predicted hourly solar radiation for time horizons ranging from h + 1 to h + 6. The authors conducted a seasonal study which showed that the prediction of solar radiation in spring and fall is less reliable than in winter and summer. In this article, the forecast horizon stops at 6 am, which means that if we go further into the horizon, the margin of error will increase. However, in our paper, the solar radiation was predicted over a 24-h period. In [9], the authors studied the effects of meteorological parameters on the mean daily solar radiation (DASR) in the city of Dohuk, Iraq. The authors used artificial neural networks (ANNs) based on multilayer perceptual techniques (MLP-FF) to predict the daily average solar radiation. In this paper, the authors project only the average daily solar radiation. While in our work, the solar radiation was predicted over a 24-h period. In [4], the authors applied a multilayer visualization of the global horizontal radiation (GHI) prediction of a warm semi-arid climate in Benguerir, Morocco. In this previous work, the solar radiation forecast results for cloudy days were poor. However, in our simulations, the model was tested for two days of each season to find better results for all seasons. In [2], the authors proposed an ensemble learning approach. Specifically, the “Stacking” method to improve the one-day prediction of solar radiation intensity. They used SVR and RNN as base learners and MLP as meta-learner. In [8], the authors proposed an ensemble learning approach based on deep neural network technology to predict the hourly time series of global solar radiation associated with Marrakech, Morocco. The authors used a seven-year data set. In [2] and [8], which deal with the topic of GHI prediction using the “Stacking” technique, the authors used many inputs, which makes the learning complex and this leads to inaccuracies and deterioration of the quality of the results. In contrast, in our paper, a study is made on the input parameters based on the correlation matrix to make the right choice of parameters. A comparative study of solar irradiance prediction results covering two main axes: – Effect of the choices of input parameters on the prediction results. – Effect of the choice of the algorithm used. Different studies have been conducted with two different techniques. First, start with scenarios using MLP technology to plot the effect of input parameters on accuracy. At this point, the importance of using past values of input parameters and their impact
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on accuracy are clarified. Second, our simulations were tested over 8 days (2 days per season) to demonstrate the reliability of our model. This paper is organized as follows. In the second section, the general structure of the forecasting system, it includes the empirical data and their pre-processing, as well as the algorithms used and the statistical performance indicators used. In the third section, the approved configurations. In the fourth section, the results are discussed. In the fifth section, a conclusion of our work and some perspectives.
2 Proposed Prediction System Structure In this section, the general structure of a 24-h solar radiation forecasting system is explained for better management of power generation sources. To give a clear idea of the structure of our system, more details on each part of the above figure are discussed. This structure consists of several steps: data collection, pre-processing and data processing (Fig. 1).
Fig. 1. A comprehensive view of the proposed residential building forecasting system model.
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2.1 Collect the Data Since the simulation would apply to a building in the city of Rabat in Morocco, the data collected is available for this city to give meaning to our work. Therefore, data are collected hourly from January 1, 2019 through March 31, 2022. These are weather conditions that severely affect the production of energy sources. As a result, these hourly data were obtained during the same period, including solar radiation, temperature, humidity, wind speed and direction via the Soda Pro site. 2.2 Pre-processing The assessment of data quality is essential to obtain effective and applicable predictive models. To achieve this, the following elements were considered, as shown in the figure below: data structure, data purification, selection of associated attributes, data segmentation and data transformation (Fig. 2).
Fig. 2. Elements for Assessing Data Quality in Developing Predictive Models: Data preprocessing.
2.2.1 Input Parameter Structure In this paper, five 24-h solar radiation forecasting scenarios were implemented to improve energy resource management. Before going deeper, an overview of the input parameter structure was done for the first scenario, it is the same for the other scenarios except for the modification of the input parameters (Table 1).
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Table 1. An Overview of Input Parameter Structure in Five 24-Hour Solar Radiation Forecasting Scenarios for Improved Energy Resource Management. Day
The GHI of the present day
The GHI of the day before
day1
GHI(0)j1
GHI(1)j1
.
……
dayn
……
GHI(0)j0
GHI(1)j0
…… GHI(24)j0
……
…… GHI(24)j1 …… ……
……
……
…… ……
……
…… ……
……
……
…… ……
2.2.2 Selection of Relevant Attributes The selection of weather variables as input variables is tedious. Thus, a correlation sensitivity analysis is performed for the data to investigate the most effective variables [1]. The result of the correlation matrix is shown in the figure (Fig. 3).
Fig. 3. Examining the correlation Between Solar Radiation,Temperature, Humidity, and Wind Speed: A Correlation Matrix Analysis.
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The relationships between the most relevant variables remaining the existing relationship between GHI and humidity (r = 0.75), GHI and temperature (r = 0.56). As a result, a positive correlation between GHI and humidity then they are strongly correlated, as well as a negative correlation between GHI and temperature. 2.2.3 Division of the Data The data sets are then divided into training (70%) and test (30%) sets. It is important to perform this division before applying the machine learning algorithms. Afterwards, the result of this algorithm is validated with test data. This gives the assurance that the algorithm can really visualize real data. 2.2.4 Data Standardization A normalization process is applied before feeding the models with the input features to avoid inaccurate predictions due to data size, all input and output variables are normalized to [−1, 1] according to the formula: y = −1 +
x − xmin ×2 xmax − xmin
(1)
where x is the input data, xmin and xmax are the minimum and maximum values in the x data set. 2.3 Processing the Data At this level, the results of the data analysis advocated above were used to provide a prediction model based on two techniques, namely: multi-layer perceptron (MLP) and stacking to predict solar radiation at the 24-h horizon. To this end, the main conclusions of the previous analysis were applied to justify the choice of the neural network architecture to be implemented: the number and nature of input and output data, the number of hidden layers. Thus, the study was divided into five scenarios, and the final result was drawn for each scenario. The first step is to identify the selected algorithms and then reveal a structured approach to apply them to the 24-h solar radiation prediction. Finally, the quality of the prediction will be evaluated by applying different performance indicators. 2.3.1 Multilayer Perceptron (MLP) Structure The structure of an MLP consists of three types of layers: an input layer, one or more hidden layers and an output layer. Once the type of neural network has been chosen, it is necessary to consider its modeling. This one will be brought under two different angles: an architectural modeling and an algorithmic modeling [7]. Architectural modeling aims to fix the architecture of the RNA by adjusting the number of layers and neurons that compose them [7]. Whereas, to determine the number of hidden layers and the number of neurons in the hidden layers, it is necessary to compare the performance of different architectures by successive trials. Once the architecture has been set up, the identification of the network from a learning algorithm follows, which modifies the synaptic
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weights of each neuron in order to minimize an error criterion on the output such as minimizing the difference between the obtained and desired outputs. In our case, the gradient backpropagation algorithm has been selected. The latter is used on large volumes of data, especially for solar radiation forecasting applications. For this reason, its use in this thesis has been privileged over other learning algorithms. Before applying the back-propagation algorithm, it is necessary to define some parameters such as the choice of the activation function, the initialization of the synaptic weights and the constitution of the database. At this stage, The scenarios already mentioned in the figure above. Where, G(ti )j: The GHI of the present day G(ti )j − n: The GHI of the days before T(ti )j: The temperature of the present day T(ti )j − n: The temperature of the days before RH (ti )j: The humidity of the present day RH (ti )j − n: The humidity of the days before i is varies from 0 to 23 n: Past values (Fig. 4).
Fig. 4. Modeling an MLP for Solar Radiation Forecasting: Architectural and Algorithmic Considerations.
This figure illustrates the concept of studying the influence of input parameters on the accuracy of solar radiation prediction. In the first step, a model with only the solar radiation history was developed. In the second, adding solar radiation history and
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temperature. In the third, adding solar radiation history and humidity. In the fourth, the history of solar radiation, temperature and humidity was compiled. The scenarios are illustrated in the following table (Table 2): Table 2. Scenarios for Evaluating the Impact of Input Parameters on Solar Radiation Prediction Accuracy. Scenarios
Switch status
Scenario 1
1, 2: ON/3, 4, 5, 6: OFF
Scenario 2
1, 2, 3, 4: ON/5, 6: OFF
Scenario 3
1, 2, 5, 6: ON/3, 4: OFF
Scenario 4
2, 3, 4, 5, 6: ON
2.3.2 Stacking Technique Structure The use of ensemble methods is necessary when one wants to take a step forward in obtaining better prediction results. Ensemble methods, such as stacking, are designed to improve predictive accuracy by mixing predictions from multiple machine learning models. STACK is a method that aims to improve predictive accuracy, integrating multiple sub-models and operating in layers [5]. In this scenario 5, all switches are closed. 2.3.3 Performance Indicators The calculation of the error consists of determining the difference between the actual data and the predicted data. It is essential to think about evaluating the quality of the result obtained. Among the many indicators that measure forecast accuracy, the most common are nRMSE and nMAE. – Normalized Mean Absolute Error: nMAE is an indicator that is calculated by dividing the MAE by the mean value of the measured data, given by the formula: 1 n 1 − GHI GHI i i i=1 n n (2) nMAE = 1 n i=1 GHI i n
where, n is the number of input’s/output’s samples, GHI i is the measured solar radiation, GHI i is the forecasted solar radiation,
– Normalized Root Mean Square Error: The nRMSE is an indicator that is calculated by dividing the RMSE by the mean value of the measured data, being given by the
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formula: nRMSE =
1 n
n
2 i=1 (GHI i − GHI i ) 1 n i=1 GHI i n
(3)
The performance of the model is defined by the nRMSE range as follows [6]: Perfect if: nRMSE < 10% Good if: 10% < nRMSE < 20% Correct if: 20% < nRMSE < 30% Poor if: nRMSE > 30%
3 Configurations 3.1 Introduction In this section, the definition of the ANN architecture and the stacking technique are defined, involving a careful choice of the number of layers and neurons in each layer. The proposed network type consists of three layers: an input layer, a hidden layer and an output layer. The choice of a single hidden layer is based on research results presented in the literature [10]. The latter recommends the use of a single layer for an application dedicated to solar radiation prediction. The work then boils down to identifying an appropriate number of neurons in the different layers of the network, which allows the most efficient prediction approach to be implemented. The predictive model developed is intended to predict in real time the solar radiation at the scale of the city of Rabat at a horizon of 24 h. 3.2 MLP Architecture The construction of an MLP predictor consists of “training” the network with known input/output data and then “testing” the resulting model with different data. The best MLP architecture is obtained with a hidden layer containing 900 neurons and batch size contains 150 as well as the number of iterations is about 1,000. For the application, a single multilayer network is adopted. Considering the application to the solar radiation forecast from t + 1 h to t + 24 h, the output layer contains 24 neurons corresponding to the daily solar radiation. The inputs considered are based on three types of data defined from the temporal and statistical analysis above, including temporal parameters, meteorological parameters and input data history. 3.3 Stacking Architecture In the proposed work we deployed, a stacking ensemble technique consisting of a homogeneous group of core learners was designed and validated. The MLP models were organized and trained in parallel mode with the same previous setup to forecast the one-day
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solar radiation. The same type of neural network was designed to combine the outputs of the basic learners of the stack with a hidden layer contains 500 neurons. All individual models were designed and trained using a subset of the scaled dataset having records from 01/01/2019 to 04/08/2021. In total, a set of two models was obtained. Each of the individual models, and thus the ensemble model, is deployed to make a 1-day forecast of solar radiation intensity. Thus, each of these models receives the same input parameters, namely: temperature, humidity and GHI as shown in the figure above. The desired target is the GHI value of the next day.
4 Results and Discussion Five scenarios were applied in this work, in which the performance of the 24-h solar radiation forecast was examined. Then, a performance comparison is performed for each scenario to positively evaluate the effects of input parameters on solar radiation. Finally, the performance of two learning techniques (1) MLP technique and (2) stackbased learning technique were determined to evaluate the impact of the choice of the algorithm used. In order to have a better prediction, the choice of selection of the similar day is important. In this study, it was kept 01/02/2022. To analyze the performance of the forecasts, a comparison was made of the results of the three scenarios through statistical indicators, as shown in the following table: Table 3. Forecast result using different parameters for 01/02/2022. Past values (n)
Scenario 1: GHI
Scenario 2: GHI-Temperature
Scenario 3: GHI-Humidity
nRMSE (%)
nMAE (%)
nRMSE (%)
nMAE (%)
nRMSE (%)
nMAE (%)
1
9.783
5.792
8.023
4.382
7.612
4.714
2
9.193
5.622
7.716
4.934
7.361
4.288
3
8.839
5.066
5.817
3.733
3.994
2.623
All scenarios are intended to predict solar radiation over a 24-h period at n variable with n is the past value, the difference between them being the input parameters. For the first scenario, the GHI history was used as input parameters. For the second scenario, add temperature to this parameter. And for the third scenario, the GHI and humidity history were used. The objective of all these scenarios is to exploit the influence of the input parameters on the accuracy of the prediction. From Table 3, the observation shows that the humidity positively influences the prediction accuracy and good results were obtained for nRMSE = 3.994% and nMAE = 2.623%. And if we want to correlate the data used available for Rabat and these results, these are logical results because Rabat is a humid environment. Therefore, humidity plays a very important role in the prediction of solar radiation.
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For the rest, the results of the selected performance indicators for scenarios 4 and 5 were compared with n equal to 3 and using historical GHI, temperature and humidity as input parameters for two days of each season, as shown in the following table: Table 4. Forecast result using two techniques (MLP and Stacking). Day
MLP nRMSE (%)
STACKING nMAE (%)
nRMSE (%)
nMAE (%)
12/03/2022
9.728
6.380
4.224
2.969
19/03/2022
10.606
6.953
5.161
3.590
11/08/2021
15.993
8.504
3.294
2.171
22/08/2021
21.088
14.360
5.469
3.375
16/11/2021
38.283
21.119
2.384
1.556
21/11/2021
29.584
17.389
5.049
2.491
25/12/2021
29.326
18.209
3.872
2.160
30/12/2021
15.005
9.092
6.650
4.269
At this stage, the comparison of performance indicators using the MLP technique and the stacking technique was considered. This table summarizes the results obtained for two days in each season. For example, the results of the metrics for the day 16/11/2021 via the MLP method: nRMSE = 38.283%, nMAE = 21.119%, and via the stacking method: nRMSE = 2.384%, nMAE = 1.556%, this shows that the values of the metrics drop dramatically. Moreover, for the spring season, an average decrease in nRMSE of 53.95% and nMAE of 50.95% was obtained. In fact, good results were obtained using the stacking method for 24-h solar radiation prediction. Thus, the stacking method outperforms the MLP predictor. In the interpretation phase, our results are presented in graphical form to make it readable. In the first phase, 12/30/2021 which had poor results was selected based on stacking (see Table 4) compared to other days, but satisfactory results compared to MLP technique, such as nRMSE = 6.650% and nMAE = 4.269%. Figure 5 represents a comparison of the actual and predicted GHI values in a single day. This figure shows that the actual and predicted GHI values match throughout the day with only a slight deviation at daylight.
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Fig. 5. Hourly GHI measured and predicted using the stacking technique for 30/12/2021.
The regression plot for the measured and predicted values of the test data is shown in Fig. 6. If the linear correlation coefficient R = 1, it means that there is an exact linear relationship between measured and predicted values [11]. In our case, the value of R is greater than 0.90 which means that there is a good agreement between the measured and predicted values.
Fig. 6. Regression plot for testing dataset for Stacking.
In the second phase, Three graphs are presented showing the actual measured values of GHI, the predicted values via MLP and the predicted values via the stacking technique. The adequate choice was the day 11/16/2021 which had good results via stacking technology and poor results via MLP technology to present it in graphical form (see Fig. 7). Upon first reading the graph, it is easy to notice the huge discrepancy between the actual GHI and the MLP-based expected GHI such as nRMSE = 38.283% and nMAE = 21.119%. On the other hand, the graphical comparison results of the actual and stacking predicted GHIs showed no differentiation observed by the naked eye during the day such as nRMSE = 2.384% and nMAE = 1.556%.
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Fig. 7. Hourly GHI measured and predicted using the MLP and stacking technique for 11/16/2021.
5 Conclusion and Perspectives The study revealed that solar irradiance prediction is an essential step to optimize the management of energy production sources. This paper presents a study to predict global solar irradiance over a 24-h horizon using data associated with the city of Rabat. The results of our solar radiation prediction study allow us to conclude the accuracy and reliability of our model and decision making. Overall, the effect of the input parameters and the effect of the choice of the algorithm used in solar radiation prediction were positively evaluated. This paper has allowed us to perform a comparative study of solar irradiance prediction results by covering two main dimensions. First, the effect of the choice of input parameters on the prediction results. Second, the impact of the algorithm selection. The results show that the ensemble learning technique performs well compared to the MLP technique with a reduction rate in the evaluation metrics: nRMSE = 6.227% and nMAE = 7.367%. Moreover, the results showed that humidity plays a very important role in increasing the accuracy, with nRMSE = 3.994% and nMAE = 2.623%. The ensemble learning technique developed in this work will undoubtedly be a useful tool in future research to predict and model the building load profile as well as for the prediction of wind speed and temperature. In addition, the path of future research will be to use the results of our work to develop optimization algorithms for the management of generation sources in the building. Acknowledgement. This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco (Alkhawarizmi/2020/39).
References 1. Abuella, M., Chowdhury, B.: Solar power forecasting using artificial neural networks. In: 2015 North American Power Symposium (NAPS), Charlotte, NC, USA, pp. 1–5. IEEE (2015)
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2. Al-Hajj, R., Assi, A., Fouad, M.M.: Forecasting solar radiation strength using machine learning ensemble. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), Paris, pp. 184–188. IEEE (2018) 3. Benali, L., Notton, G., Fouilloy, A., Voyant, C., Dizene, R.: Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew. Energy 132, 871–884 (2019). https://doi.org/10.1016/ j.renene.2018.08.044 4. El Alani, O., Ghennioui, H., Ghennioui, A.: Short term solar irradiance forecasting using artificial neural network for a semi-arid climate in Morocco. In: 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco, pp. 1–7. IEEE (2019) 5. Fraccanabbia, N., da Silva, R.G., Henrique Dal Molin Ribeiro, M., Moreno, S.R., dos Santos Coelho, L., Mariani, V.C.: Solar power forecasting based on ensemble learning methods. In: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, pp. 1–7. IEEE (2020) 6. Guermoui, M., Rabehi, A., Gairaa, K., Benkaciali, S.: Support vector regression methodology for estimating global solar radiation in Algeria. Eur. Phys. J. Plus 133(1), 1–9 (2018). https:// doi.org/10.1140/epjp/i2018-11845-y 7. Imane, I.: Short-term load forecasting and home energy management. Nantes (2020) 8. Jallal, M.A., Yassini, A.E., Chabaa, S., Zeroual, A., Ibnyaich, S.: Ensemble learning algorithm-based artificial neural network for predicting solar radiation data. In: 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain, pp. 526–531. IEEE (2020) 9. Mahdi, B.H., Yousif, K.M., Dosky, L.M.S.: Using artificial neural networks to predict solar radiation for Duhok City, Iraq. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), Duhok, Iraq, pp. 1–6. IEEE (2020) 10. Osman, Z.H., Awad, M.L., Mahmoud, T.K.: Neural network based approach for short-term load forecasting. In: 2009 IEEE/PES Power Systems Conference and Exposition, Seattle, WA, USA, pp. 1–8. IEEE (2009) 11. Premalatha, N., Valan Arasu, A.: Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. J. Appl. Res. Technol. 14, 206–214 (2016). https://doi.org/10.1016/j.jart.2016.05.001
Towards Machine Learning Applications for Computational Fluid Dynamics Modeling in Chemical Engineering Safae Elmisaoui1,2(B) , Sanae Elmisaoui1,2,3 , Lhachmi Khamar4 , and Hasnae Zerouaoui1 1
MSDA, Mohammed VI Polytechnic University, Ben Guerir, Morocco [email protected] 2 Mohammed V University, Rabat, Morocco safae [email protected] 3 LRGP, CNRS-ENSIC, Universit´e de Lorraine, Nancy, France 4 LIPIM, Sultan Moulay Slimane University, Khouribga, Morocco
Abstract. Computational Fluid Dynamics (CFD) simulation of multiphase industrial flows is a significant research concern for studying the performance and efficiency of chemical processes. Within the last years, CFD had growth interest from researchers with the significant increase in computational resources capacities and high-performance calculations. The trend toward focusing on machine learning (ML) techniques to solve complex industrial problems is observed. In contrast, ML has found encouraging and promising applications in that research field by offering a wealth of techniques to extract unreachable knowledge from data that can improve the chemical processes understanding and allows efficient optimization and intensification. This study aims to present the different uses of ML in the chemical processes’ field, particularly the CFD modeling, and highlight the main and variate use of ML for complex geometries design and mesh optimization. We have paid particular attention to the ML trend in turbulence modeling to generate data-driven physical models from CFD simulations at different fidelity levels. Then, the extended application of ML to accelerate CFD calculations, and the development of surrogate models are provided. This work reveals that the application of ML to chemical engineering is a promising way for developing this research area. Keywords: Machine learning · Computational Fluid Dynamics Chemical engineering · Modeling
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Introduction
Chemical engineering (ChemEng) is a research field that devotes considerable efforts to understand and predict the outcomes of chemical processes. In 130 years ago, mathematical modeling was the primary tool for dealing with the complexity of physical and chemical phenomena that occur in the process being investigated [1,2]. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 422–434, 2023. https://doi.org/10.1007/978-3-031-35245-4_39
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The inter-disciplinarity of Computational Fluid Dynamics (CFD) modeling approach allows a widespread application of the numerical tool, to unpack the multiphysics behind it, involving mathematics, fluid mechanics, and computer sciences. CFD proves its effectiveness in the analysis and prediction of flow behaviour, the study of heat and mass transfer in the process, and the analysis of hydrodynamics [3,4]. For decades, CFD numerical modeling has enabled remarkable progress on a wide range level, particularly in design optimization, enhancing transfer mechanisms, efficient control and sustainability, while obtaining a considerable imprint on ChemEng [5]. However, many challenges still persist in CFD modeling of chemical processes. The CFD model complexity strongly depends to the physical phenomena considered [6]. For Three dimensional CFD models with real dimensions, and with a complex geometry presenting some structural details, the chemical engineer suffers to reach the convergence of the numerical model, and the resolution requires robust hardware resources, in addition to the costly computational time. Furthermore, the post-processing of the simulations generates a massive amount of data to be carefully analyzed in order to extract useful conclusions. Today, Artificial Intelligence (AI), particularly ML founds a large applicability in chemical processes modeling using CFD approach, which attracts many researchers to confront the main problems in this field and improve the predictions’ reliability. There are numerous interactions between CFD and ML models on different levels: – Data driven models from the database generated by post-processing for process optimization. – Clustering of ML algorithms for computational time optimization by speeding up the solution time of numerical schemes and determining the optimal grid generation [7]. – Enhancement of the turbulence closure models for critical areas of the simulation domain. – Improvement of hydrodynamic analysis and flow pattern recognition for physical informed prediction models [8]. With these many pillars of ML strength, their use to solve CFD models’ problems in ChemEng applications becomes the trending research field to dig into. In the present work, we aim to highlight the most interesting application that integrates ML in the CFD field through modeling and simulation approaches.
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Machine Learning for CFD Models Optimization
Nowadays, CFD-based optimization is used as an umbrella term for all types of optimization algorithms by using ML techniques. This new combination between the use of ML and optimization algorithms enhances the reliability of CFD simulations, by reducing the computational time and the cost of the required resources [9]. Generally, there are three main categories of optimization algorithms as described below [10]:
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1. Meta models based algorithms [11,12], 2. Gradient-based algorithms [13,14], 3. Stochastic algorithms [15,16]. Each type have proven its efficiency application depending on the size, the design’s structure and shape. In this section we deeply highlight the essential pre-processing stage in the CFD models development: The Computer Aided Design (CAD) of the simulation domain, and the mesh grid optimization. 2.1
Computer Aided Design Optimization
ML shows its high potential in CAD Optimization for smarter chemical processes components; by developing new designs with respect to the manufacturing process requirements and goals. The CAD Engineering research ears deals with multiple objectives and constraints that are leading to Multi-Objective Optimization discipline (MOO) [17]. CAD conception assisted by ML techniques provides a real benefit for chemical engineers to overcome commercial license costs, long simulation running time, and speed up the CFD model building [18,19]. One of the large applications of ML in CAD optimization is ML coupled to Genetic Algorithm approach (ML-GA). However, To prepare the optimal geometry design, a large number of CFD simulations should be carry out to assess the design sensitivity to each physical and structural parameter considered. This sensitivity analysis is based on the Design of Experiments (DoE) approach, allowing a significant tests’ proposals to achieve the convergence of the experiments and numerical simulation towards an efficient geometry design [20]. The ML-GA approach is based on a surrogate modelling techniques to realize a CFD simulation calculations of physical parameters by means of a ML model previously developed, hence the CFD simulation results served as the objective function. Relevant results have been found in recent application studies [20,21], more details will be given in Sect. 2.3. 2.2
Mesh Optimization
The mesh generation and numerical schemes have a crucial influence on the CFD simulation accuracy. Indeed, their control and mastering enable to avoid the numerical non-convergence, and computation issues. To overcome these problems, a based ML mesh optimization method is used by means of the mesh adaptation techniques. This method is based on mesh fitting, while respecting the flow specifications [15]. There are several methods of mesh optimization, and their reliability depends on the number of mesh nodes and their distributions. The following figure (Fig. 1) summarizes the most interesting ones [15].
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Fig. 1. Schematic diagram of the mesh optimization techniques
Each of the techniques has its own characteristics. In fact, the p-adaptivity technique is based on increasing the order of the numerical format while keeping the same number of initially generated meshes. The principle of this method is to increase the accuracy without changing the number of mesh nodes. However, the h-adaptivity technique relies on increasing the number of meshes in order to reduce the computational error, and to have a well-refined mesh in the regions where the error is large enough. Unfortunately, this technique is limited by the degradation in computing effectiveness as the number of mesh nodes increases, and consequently the calculation costs. A third technique is developed to overcome the limitations of the p-adaptivity and h-adaptivity techniques. Indeed, the r-adaptivity is based on an approach of adjusting the nodes of the mesh by distribution in the regions whose resolution of the flow field is complex, while keeping the same initial number of nodes. This principle allows to enhance the reliability of the CFD numerical simulations results, without increasing their computational costs as developed by Wilson [22]. It is known as the Moving Mesh Method (MMM) [15], and it consists in creating adaptive functions according to a variational approach. In CFD, variational mesh adaptation is of a great importance, as the construction of adaptive functional verifies the key mesh requirements, including alignment, smoothness, and adaptability. Indeed, this approach involves using a functional to transform the coordinates of the mesh nodes in order to generate an adaptive mesh that meets the flow field specifications. Tingfan Wu et al. [15] proposed an efficient mesh optimization method by introducing ML methods. It is based on combining the variational mesh adaptation approach with ML regression algorithms. It consists of moving the mesh nodes following the variational approach to relate the flow field to the initially generated mesh nodes. Thus, the mesh function is solved iteratively to update the mesh while respecting the flow field characteristics. The objective was to use regression models to overcome the problems of non-linearity and complexity
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of the input/output relationship in CFD models. The main models used are: Neural Networks and Gaussian Processes. Wu et al. method relies on solving regression problems in each mesh node to predict an appropriate numerical solution. ML allows to have a model that can provide reliable predictions and ready to be exploited for giving new distributions of the mesh nodes. Thus, the technique of coupling the variational approach and regression models is very promising in this area of CFD, as it does not involve the phenomenological equations describing the flow, but the flow field is predicted based on the meshes obtained from regression models [15]. 2.3
Surrogate Modeling Approach
In addition to the optimization of the CAD and the mesh construction, the surrogate modeling approach has brought the attention of researchers in chemical applications of CFD models. Indeed, sensitivity analysis, dynamic optimization, and uncertainty quantification are the areas that rely on the construction of complex models. Thus, surrogate models come into play in order to facilitate the models’ exploitation, as well as their applicability thereafter. Generally, the development of regression models is a key step in the process of building a surrogate model, based on experimental data from previously conducted tests, in order to carry out reliable parametric identification [23]. The simulation of CFD models is unfortunately limited by its costly computation time. Hence, regression algorithms have been used to reduce the number of simulations while keeping a good reliability of the simulation outputs, as well as performing good interpolation values for the construction of the surrogate model [23]. Simpson et al. [24] listed the most commonly used techniques for generating samples in parameter space while ensuring a regular and diverse distribution. Indeed, the experimental designs can be designed by: Hammersley sequence sampling, Latin hypercubes, uniform designs, and orthogonal arrays. Homogeneous filling of the parameter space is a necessary condition that needs to be validated to ensure a better distribution of input features. Furthermore, the accuracy of the surrogate model construction approach is highly dependent to the number of the generated sampling points, which directly impact the time consumption. Thus, ML comes in with several approaches to solve regression problems where samples are not easily obtained. Among these approaches: – Transfer Learning (TL): This is an approach for training deep learning models, and consists in building models based pre-trained deep learning models on an information-rich database such as ImageNet, and using just a few labeled data. This method is widely used for unstructured data for the objectives of audio and image recognition, and also for object detection. It allows to have a model that does not need to be trained from scratch by loading the weights of the pre-trained models, which considerably reduces the required computation time [25].
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– Semi-Supervised Learning (SSL): Which is a method that relies on exploiting unlabeled data in an unsupervised, constraint-guided algorithm for certain examples. It is also considered as a supervised learning with additional data. It is therefore a combination of supervised and unsupervised learning established with the goal of producing correct predictions [26]. – Active Learning (AL): Usually called “Parameter Tuning”; it allows to choose the most informative parameters, while checking a mathematical criterion in order to have an optimal sampling method. Indeed, this strategy simplifies the selection of the parameters in the grid, and uses a grid search to determine the optimal simulation in terms of necessary computation time. It relies on the exploitation of previous results via an underlying structure in the parametric search to be performed. Consequently, this method is based on the grid search which risks exploding while the increase of the dimension [27]. Regression algorithms are often used extensively in active learning. G. Gon¸calves et al. [28]; compared five sampling methods (random sampling, greedy sampling on the inputs, greedy sampling on the output, greedy sampling on both inputs and output, and variational sampling) with five regression algorithms (linear regression, gaussian process regression, random forest regression, support vector regression, and multilayer perceptron based neural network) to construct surrogate models based on CFD simulations, by means of industrial flow cases. Their study reveals that the use of the greedy sampling on both inputs and output strategy give the most stable results, and the additional sampling points are not required. Thus, they found that the regression technique based on Gaussian processes is sufficiently efficient to give reliable predictions.
Fig. 2. Workflow diagram for constructing surrogate models for CFD simulations [28]
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Keeping in mind that the main objective of surrogate models development is reducing the computational time, as well as producing reliable predictions based on CFD simulations. The adopted methodology is based on the application of a generic algorithm verifying the sampling condition to select a set of parameters. The scenarios of the simulations are organized in a grid, according to the possible conditions. Firstly, initial results are obtained using initial conditions of simulations. Thus, these results are exploited to produce a first version of the substitution model. Then, as illustrated in Fig. 2, the model is evaluated at distinct conditions, by performing different simulations. Thus, after a predefined number of simulations, and comparing between the results, wich allows to distinguish between these methods and to classify them according to their reliability and robustness. It is important to mention that a stopping criterion is essential to implement the comparison, to measure the deviation between the predictions and the results of the CFD simulations, in order to evaluate the quality of the constructed substitution model.
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Machine Learning for Turbulence Modeling
In the development of CFD models, the modeling of turbulence is a difficult step due to unsolved problems in physics and mathematics. Fluid flows are governed by the Navier-Stokes equations, which are partial differential equations (PDEs), to model the conservation of mass, and momentum in the system. These equations exhibit a non-linearity due to the chaotic and capricious eddies that influence the flow behavior [29]. A few years ago, turbulence modeling has reached a level of maturity allowing a good understanding of fluid mechanics. Despite all the efforts made, turbulence modeling is not sufficiently developed to be able to keep up with the technological revolution reached not only in the field of advanced CFD, but also in the field of scientific computation, especially the high performance of computers [20,30]. Recent advances in ML for engineering applications open the doors to many possibilities for improving the efficiency, flexibility, and accuracy of turbulence modeling. This use of ML techniques remains a hot area of research, particularly in the field of chemical engineering [31]. In this section, we will discuss many applications of ML to improve turbulence modeling in CFD models. There are three main turbulence models: RANS (Reynolds Averaged Navier Stokes) model, LES (Large Eddy Simulation) model, and DNS (Direct Numerical Simulation) model. RANS is the most used to simulate chemical industrial devices, due to its ability to give a robust hydrodynamics predictions with significant and acceptable computational costs, but Reynolds stress closures in RANS
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Fig. 3. ML applications in CFD: turbulence modeling and ROMs (With permission of publisher [7])
model are the weakest points behind many discrepancies in near wall turbulent flows modeling. Figure 3 illustrates the active research area of ML applications in CFD, and highlights the special attention that RANS models have from both: simulation acceleration and physical understanding research fields. ML algorithms have been used to identify discrepancies of Reynolds stress terms in RANS model for high fidelity simulations [32,33]. 3.1
RANS Models
The principal use of ML in RANS models is to keep the same classical turbulence models and apply ML algorithm to analyse the large amount of data generated by CFD simulations for further optimization, and to ensure high fidelity prediction of the results. In general, the supervised learning method is adopted, and a training dataset is used, which aims to minimize prediction error by Bayesian inversion of the RANS model. These datasets are used for training, validating, and testing the machine learning models. Neural Network algorithms are the most used to optimize the model parameters and to enhance its accuracy [34].
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Because of possible accuracy limits introduced by the RANS approach’s underlying assumptions and the generation of closure models, its prediction power is less trustworthy. Although formal uncertainty quantification approaches have recently made it possible to understand RANS predictions in probabilistic terms while describing the relevant confidence levels, direct quantification of the errors induced by closure models is still difficult in general. In order to calibrate the closures and try to increase the precision of the generated calculations, experimental observations have been employed often [35]. A more thorough merging of data and models is attained by statistical inference techniques, and improving predictions [36]. On the other hand, ML algorithms are utilized to optimize the turbulence approach. However, the model is trained using the pertinent produced datasets. The physics informed neural network (PINN) was initially developed by Raissi et al. (2017) [37] to tackle inverse problems and partial differential equations (PDE) and recently raises with Maruyama et al. [38]. With its significant innovation demonstrating that the PINN can forecast the variables based on physical laws, PINN updated the conventional form of the loss function and was integrated with physical models. Tartakovsky et al. [39] created the constitutive equations for the decay flow using PINN. They showed that PINN performs well when handling inverse problems. 3.2
DNS Turbulence Models
Direct numerical simulation (DNS) is a high-fidelity CFD approach to model the turbulence in flows, were the governing Navier-Stokes equations are discretized and integrated in time with sufficient degrees of freedom to resolve all flow structures. With fluctuations structures in a variety of sizes and energies, the turbulent flows clearly present multi-scale natures. In order to avoid introducing numerical aberrations that might alter the underlying physics, this complexity requires refined mesh grids and high performance computing techniques. The most detailed depiction of the flow field among CFD techniques may be obtained with a great configuration and setting of the DNS turbulence model. However, the extremely high computational costs that come with using thin computational meshes to resolve the lowest scales and capture a maximal amount of data from the flow rises with Reynolds number [40]. Machine learning offers a novel approach to assist in the construction of more accurate turbulence models by relying on a suite of high-fidelity datasets in order to construct a more accurate closure term formulation. As an illustration, a wellresolved DNS simulation offers a field of mean-flow values at each grid cell, such as velocity and pressure gradients, as well as turbulent characteristics, such as the Reynolds stress tensor. The evolution of turbulence as a function of mean quantities and their gradients is described for each grid point [41,42]. Therefore, a vast corpus of information on the correlation between meanflow and turbulent variables may be obtained from a set of DNS computations, supplemented with information from LES and experimental data. As a result, a turbulence model can preprocess this vast amount of data using machine learning
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to improve the source term. The DNS Reynolds stress tensor would be pointwise projected into a turbulent eddy viscosity. Using the difference between convection and diffusion, the source might then be calculated [43]. 3.3
LES Turbulence Models
LES investigations are analogous to another CFD modeling approach that is documented in the literature. These models incorporate SubGrid-Scale models (SGS), which simulate the behavior of the smallest eddies in the flow, in an order to limit the excessive grid needs observed in DNS investigations. Although LES models use less computing resources than DNS, they really need a high grid resolution to capture the majority of the turbulent eddies [44]. As a result, the problems identified in DNS research are only partially resolved. Since LES models build spatial filters for the turbulent fluctuations and only make an effort to describe those interactions that were missed by the coarser grid, they vary mathematically from the RANS equations. However, given the fast increase in computer power and the use of machine learning techniques to enhance SGS models, the applications of LES models may expand significantly in the future. ML has also been used to develop SGS models for turbulent flows modeling with LES approach. Many research studies [45] employed artificial neural network with on local convolutional filters to predict the mapping between the flow in a coarse mesh grid and the closure terms. Others have chosen a multilayer perceptron (MLP) approach to predict the SGS model in an LES using numerical dataset for the model training [46]. Novati et al. [47] proposed an innovative approach Multi-agent reinforcementlearning (RL) in an intriguing modern method to LES modeling in order to approximate the unresolved subgrid-scale physics. The findings for isotropic turbulence are reported [47], and this unsupervised technique demonstrates excellent generalization features across grid sizes and flow conditions.
Conclusions In this contribution, an overview on ML applications in the chemical engineering fields, especially in the modeling and simulation of chemical devices using CFD approach was provided. The ML techniques used proved their reliability and performance in accelerating time required for CFD simulations, even for enhancing turbulence models accuracy, and improving reduced-order models effectiveness. This modelling approach based ML techniques is able to efficiently capture the behaviour of the flow field with high precision and a notable reducing computational time, demonstrating the reliability of the ML applications approach in chemical engineering modeling using CFD. Upcoming works will focus on the establishment of surrogate models for the development of reliable CFD models needed for the optimization in some chemical processes.
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Numerical Prediction of Effect of Hardening Laws on Springback and Blank Thickness Distribution During Cylindrical Deep Drawing Process Sara Bendrhir1,4(B) , Kenza Bouchaala3,4 , Lahcen Azrar1,2,4 , Farah Abdoun1,4 , and Elhachmi Essadiqi3,4 1 Research Center STIS, M2CS ENSAM, Mohammed V University, Rabat, Morocco
[email protected]
2 Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University,
Jeddah, Saudi Arabia 3 LERMA LAB, Ecole Aérospatial Université, Paris, France 4 Internationale de Rabat, Rocade Rabat-Salé, 11100 Sala El Jadida, Morocco
Abstract. Spring back is one of the hardest challenges in sheet metal forming, it is the undesirable shape change that occurs during unloading after deep drawing operation due to the occurrence of elastic recovery of the part. To investigate this behavior, a numerical model based on finite element is elaborated by using the ring–splitting method based on different hardening laws coupled with Von Mises and Hill’s criterion allowing to investigate the influence of the material model on the spring back prediction. This research is carried out to study the influence of hardening laws on numerical prediction of spring back and to investigate the critical impact of numerical parameters on the accuracy and reliability of results. Keywords: Aluminum alloys · Deep drawing · Finite element · Split-ring · Springback · Hardening laws
1 Introduction It seems to be an ever-increasing demand in light weighting requirements and safety performance in automobile and aeronautic industries. Aluminum alloys are widely used in the automotive industry thanks to its low density, high specific strength, good corrosion resistance and exceptional stiffness yet Aluminum alloys are more prone to wrinkling and springback than mild steels [1]. However, the utilization of drawn metal sheets is limited by the springback defect which occurs after opening the tools of a forming process, due to the stored elastic energy. This elastic recovery is one of the challenges in the metal forming industry. Because the higher material strengths and thinner designs tend to increase the amount of springback. Moreover the optimization is based on empirical trial-and-error methods. Choi et al. [2] studied the reduction of empirical trial-and-error methods by suggesting suitable modifications to the forming process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 435–447, 2023. https://doi.org/10.1007/978-3-031-35245-4_40
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Springback defect is one of the highly undesirable defects during the unloading part of deep drawing process Thus the springback prediction with numerical simulation is limited by the accuracy and suitability of numerical parameters. For this reason, an accurate prediction of spring back by FE simulations has become an important research topic in automotive industries. Finite element (FE) simulations are an essential tool for the prediction and better understanding of the defects of deep drawing process. Bouchaala et al. investigated the effect of several parameters on the precision of FE simulation included geometrical and material parameters, such as friction [3] and contact [4], Asgari et al. [5] studied the influence of mechanical properties while Carden et al. [6] investigated the type of constitutive equation, boundary condition, nature and size of element [8]. The effects of the yield criteria were studied by [7], Slota et al. [8] who investigated the effects of yield functions (isotropic and orthotropic) on the springback prediction accuracy of aluminium alloys. They concluded that the orthotropic yield is the well suited in predicting springback. Seo et al. [9] carried out a study on the evaluation of the effect of constitutive equations on the springback prediction accuracy. Using two yield functions, Hill48 and Yld2000, in combination with the Yoshida-Uemori hardening it was found that it is critical to choose the right yield function to get an accurate prediction of springback. Jung et al. [10] studied the anisotropic hardening behavior and the springback of AHSS steels. They showed that the isotropic hardening model is sufficient for predicting the springback of formed parts made of aluminum alloy. Moreover, the degradation of Young´s modulus is not present for aluminum alloys which are precipitation hardened, and that the degradation is not as significant as in AHSS steels (max. 2% degradation of Young´s modulus for aluminium alloys). To characterize springback of cylindrical cup the Demeri test [11], Laurent et al. [12] is used. The opening gap obtained at the end of the cutting process, where a ring is cutted from the wall of a drawn cylindrical cup, and split after to measure and quantify the value of the springback which is caused by the release of internal stresses induced during the forming process. In this paper a finite element model is established to investigate the influence of hardening Laws ability to predict the springback behavior of cylindrical cup of aluminum alloy AA5052-O, namely Swift and Hollomon coupled with anisotropic yield criteria and the Von mises by evaluation of split ring test. Predictions based on various hardening laws are obtained and the influence of springback on thickness distribution is discussed.
2 Methodology 2.1 Modelling Approach A deep drawing process of a cylindrical cup using ABAQUS Explicit FEA has been simulated, where a punch presses against a 1 mm thick circular blank inserted between die and blank holder. The numerical analysis of the deep-drawing process was performed by using only one quarter of the numerical model to reduce the computational time as shown in Fig. 1.
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Fig. 1. Numerical model of deep drawing process
The geometrical parameters of the model are presented in Table 1 and the material is modeled as an elastoplastic material these parameters are presented in Table 2. Table 1. Geometrical parameters (mm) Blank size radius
Blank thickness
Punch radium
Punch nose radius
Die radium
Die shoulder radius
Radial clearance
112
1
56
4
57.5
2
1.15
Table 2. Physical parameters Young’s modulus (E) Gpa
Poisson’s ratio
Tangent modulus (Et) Gpa
Density kg/m3
Yield stress MPa
Blank holder force
Friction coefficient
206
0.3
0.5
7800
167
0.1
18
2.2 Yield Criteria and Hardening Law 2.2.1 Yield Criteria The mathematical description for yielding under complex stresses is a challenge. It is well known that the constitutive model has a strong influence on the accuracy of the results. Many attempts have been made to describe mathematically the behavior of the materials during forming processes. Plastic anisotropy caught the attention of many researchers and it is observed in the literature that the Hill 48 criterion is widely used to model these materials. The Hill’s 1948 yield function (Hill, 1950) is described as: f (σ ) =
F(σ22 − σ33 )2 + G(σ33 − σ11 )2 + H (σ11 − σ22 )2 + 2Lσ23 2 + 2M σ31 2 + 2N σ12 2
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where f is the yield function; F, G, H, L, M and N are constants of the anisotropy of the material given by: + 12 − 12 ; R22 R33 R11 1 1 1 1 ; G= 2 + − R33 2 R11 2 R22 2 H = 21 1 2 + 1 2 − 1 2 ; F = 21
1
L=
2
R11
R22
R33
3 ; 2R23 2
M =
3 ; 2R13 2
N=
3 ; 2R12 2
These coefficients by yield stress parameters R11 R22 R33 R12 R13 R23 can be calculated as follows: R11 = R13 = R23 = 1 R22 =
ry (rx + 1) , R33 = rx ry + 1
ry (rx + 1) ; R12 = ry + rx
3r y (rx + 1) (2r45 + 1)(ry + rx )
where ry rx r45 are the Lankford parameters. The anisotropic parameters of Hill’s quadratic yield criterion are chosen as shown in Table 3 and the applied stress that describes the applied hardening law is presented in Fig. 2. Table 3. Anisotropic parameters of Hill’s 48 R11
R22
R33
R12
R13
R23
1
1.0402
24897
1.07895
1
1
True stress (Mpa)
600 400 200 0 0
0.1
0.2 0.3 True Plastic Strain
0.4
0.5
Fig. 2. True stress vs true strain
2.3 Hardening Law The hardening law describes the evolution of yielding surface, we present differtent isotropic hardening laws: Hollomon and swift coupled with isotropic plastic criteria (von mises) and anisotropic (Hill 48).the materials parameters are shown in Table 4.
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• Hollomon: σ = Kεn • Swift: σ = k(εp + ε0 )n • Ludwick: σ = σe + Kεp n
Table 4. Laws parameters of AA5052-O Hollomon
Swift
Ludwick
K
n
K
n
ε0
σe
K
n
449.5
0.335
422
0.297
0.006
62.03
367
0.409
2.4 Finite Element Model In the present study, the tooling set is modelled as discrete rigid bodies and were meshed as R3D4 elements while the blank sheet metal is modeled as deformable with a planar shell base and discretized to 11283 linear quadrilateral S4R and triangular elements of type S3R. The meshing techniques have big effect on CPU time and the accuracy of obtaining results. Therefore, in order to reduce time of calculation, different meshing techniques were used and mesh refinement at the punch corner and the die corner was performed to get convergence and better results. For the ring splitting process the blank is partitioned at the beginning of the numerical process. After removing the blank from the drawing tools, ring was cut by using the model change.the sheet is partitioned in 4 areas (Fig. 3) and (Fig. 4) the meshing of the tool set. Splitting the ring was performed by removing the boundary condition of symmetry at one end of the ring and then spring back was calculated by letting the part relax. Actually the contact in metal forming process plays a major role in the exactitude of obtained results should be dealt with care. Therefore, 4 to 5 elements per radii are suggested to help speeding up the simulation and getting accurate results.
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Fig. 3. Partition of the deformable sheet
Fig. 4. The meshing of rigid parts
3 Results and Discussion 3.1 Validation of the Model In order to validate our model a comparison between the thickness distribution of the present numerical model and the experimental results [14] was done.
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The followed procedure to have this comparison was illustrated in Fig. 5. Eight nodes were taken from the final drawn cup in order to see the impact of sheet forming process on thickness variation in the blank.
Fig. 5. Location of thickness measurements
Figure 6 presents the final drawn cup and highlight the non uniformity of thickness distribution in the cylindrical cup at the end of the deep drawing process and that’s due to the anisotropy and the nonlinearity of the material’s properties, strong in some direction and weak in others.
Fig. 6. The thickness distribution in the cylindrical drawn cup
The measured thicknesses at the 8 points of the present work and the results from the experiment of [13] are presented in Table 5. It is demonstrated that the results of our numerical simulation was so close with a difference of only 0.9% on average. After validating our model the spring back phenomenon will be investigated on Aluminum AA5052-O. After validating the model and getting accurate results numerically we can investigate further the effect of the parameters of interest, such as hardening laws and study the spring back of the cylindrical cup. 3.2 3D Springback Prediction The thickness variation after the deep drawing process is shown in Fig. 7. This variation is due to residual stresses induced in deep drawn cup as shown in Fig. 8. It’s is observed
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S. Bendrhir et al. Table 5. Thickness distribution in the present work and literature Present study
Experimental results
Difference in percentage
1
1.11604
1.132
1.6%
2
1.03881
1.032
0.7%
3
9.20E−01
0.888
3.2%
4
9.00E−01
0.83
7.0%
5
8.56E−01
0.823
3.3%
6
8.70E−01
0.871
0.1%
7
9.50E−01
0.966
1.6%
8
9.40E−01
0.979
3.9%
The average
0.94885625
0.940125
0.9%
that large residual stress is induced during the stretch–bending process which lead to significant spring back while relaxation phase. Figure 7 shows that the thickness distribution of the cup change in the spring back after relaxation of the cup, and Fig. 8 demonstrated that the stress will be localized at the upper part of the cup.
Fig. 7. The thickness distribution before and the after spring-back with anisotropy material model
Fig. 8. The stress distribution before and the after spring-back with anisotropy material model
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3.3 The Spring Back of the Aluminum 5052-O
Swift
Ludwick
Hollomon
Performing the split-ring on AA5052O after the deep drawing process, we split a ring from the wall of the cup and the induced residual stresses after release during the stretchbending process are computed. Due to the symmetry the numerical prediction of the spring back is modeled with the quarter of the model. The Fig. 9 the residual stresses are shown, It’s observed that the material exhibit different behaviors after total relaxation. The final shape of the ring and the initial state are illustrated in Fig. 10, swift law is over estimating the spring back more than the hollomon’s law moreover in Fig. 11 the stress distribution is clearly observed where the stress with swift law has reached the 128 MPa while with hollomon 192 MPa. The Fig. 12 Shows that the residual stresses in the deep drawn ring differ slightly after unloading step and the stresses after relaxation are approximately two times lower than the stresses at the end of deep drawing step (Table 6).
Fig. 9. Stresses after spring back with different Hardening models
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(a)
(b)
Fig. 10. The layout of the ring after spring back with Hardening models (a) Swift (b) Hollomon
Table 6. Residual radius Hollomon
Swift
ludwick
Residual radius (mm)
8.29635e−02
1.22942e+00
7.82233e−02
% of springback
0.16%
2.3%
0.15%
After spring-back
Swift
Ludwick
Hollomon
Before Spring back
Fig. 11. Stresses after spring back with different Hardening models
The numerical prediction of the spring back of the 3 models is slightly different but acceptable. The hollomon and ludwick hardnening laws shows little difference while swift estimates the springback of 2.3%.
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Stress relaxation
Stress (Mpa)
200 180 160 140 120 100 80 60 40 20
hollomon 1E-09
swift
Ludwick
1E-08 0.00000010.000001 0.00001 0.0001
0.001
0.01
0.1
1
Time Fig. 12. Residual stress in deep drawn cup
We can conclude that the studied hardening law models are able to model and describe reliably the behavior of the sheet metal during spring back in deep drawing. Table 7. Thickness mesearement before and aftre springback of AA5052-O Ludwick
hollomon
swift
1.17538
1.17113
−0.00425
1.18524
1.20072
0.01548
1.17814
1.17651
1.15198
1.10487
−0.04711
1.10424
1.13787
0.03363
1.10928
1.10941
0.00013
1.01159
9.50E−01
−0.061528
9.66E−01
9.66E−01
−0.000384
9.87E−01
9.46E−01
−0.040857
8.68E−01
9.12E−01
0.044035
8.80E−01
8.78E−01
−0.002115
9.08E−01
8.94E−01
−0.014483
9.00E−01
9.66E−01
0.066047
9.54E−01
9.58E−01
0.003952
9.64E−01
9.66E−01
0.00175
8.82E−01
9.22E−01
0.040029
8.75E−01
8.78E−01
0.003028
9.29E−01
9.32E−01
0.002836
9.42E−01
9.59E−01
0.016802
9.35E−01
9.40E−01
0.004821
9.56E−01
9.59E−01
0.002758
9.44E−01
9.60E−01
0.015698
9.37E−01
9.41E−01
0.003868
9.57E−01
9.60E−01
0.00316
0.069723
the average difference
0.06228
the average diffrence
the average difference
−0.00163
0.046336
Table 7 shows the influence of hardening laws on numerical prediction of thickness distribution ludwick and hollomon laws have similar average while swift have and a difference average of 0.04 that means the difference in thickness after spring back is lower with swift.
4 Conclusion This study adopt a numerical approach based on finite element method to evaluate the spring back of deep drawn cup and investigate the effect of hardening law on result prediction.
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The developed model predicts the spring back, and thinning of the blank as affected by materials models, the critical zones can be defined easily. The following conclusions can be drawn: • A deep drawn cup presents significant residual stresses that result in large springback value relaxation of the cup which increase the inaccuracy of the results. • the comparison of springback predictions using different hardening laws models, showed that the hardening law has an effect on the spring back.and the need to choose carefully the flow rule. • the results showed that ludwick and hollomon can simulate accurately enough but swift is more able to simulate accurately the spring effect in deep drawing. Furthermore the effect of hardening laws on thickness distribution was investigated. It was found that swift is able to predict the thickness accurately before and after springback.
References 1. Mahabunphachai, S., Koç, M.: Investigations on forming of aluminum 5052 and 6061 sheet alloys at warm temperatures. Mater. Amp Des. 31(5), 2422–2434 (1980–2015) 2. Choi, J., Lee, J., Bae, G., Barlat, F., Lee, M.-G.: Evaluation of springback for DP980 S rail using anisotropic hardening models. JOM 68(7), 1850–1857 (2016). https://doi.org/10.1007/ s11837-016-1924-z 3. Bouchaâla, K., Faqir, M., Ghanameh, M.F., Mada, M., Essadiqi, E.H.: Investigation of friction behavior of aa2090 al-li alloy in cylindrical deep drawing sheet metal using finite element. Int. J. Mech. Prod. Eng. Res. Dev. 4. Bouchaâla, K., Ghanameh, M.F., Faqir, M., Mada, M., Essadiqi, E.: Evaluation of the effect of contact and friction on deep drawing formability analysis for lightweight aluminum lithium alloy using cylindrical cup. Procedia Manuf. 46, 623–629 (2020). https://doi.org/10.1016/j. promfg.2020.03.089 5. Asgari, S.A., Pereira, M., Rolfe, B.F., Dingle, M., Hodgson, P.D.: Statistical analysis of finite element modeling in sheet metal forming and springback analysis. J. Mater. Process. Technol. 203(1), 129–136 (2008). https://doi.org/10.1016/j.jmatprotec.2007.09.073 6. Carden, W.D., Geng, L.M., Matlock, D.K., Wagoner, R.H.: Measurement of springback. Int. J. Mech. Sci. 44(1), 79–101 (2002). https://doi.org/10.1016/S0020-7403(01)00082-0 7. Lee, M.-G., Kim, D., Kim, C., Wenner, M.L., Chung, K.: Spring-back evaluation of automotive sheets based on isotropic–kinematic hardening laws and non-quadratic anisotropic yield functions, part III: applications (2005). https://doi.org/10.1016/J.IJPLAS.2004.05.014 8. Slota, J., Šiser, M., Dvorák, M.: Experimental and numerical analysis of springback behavior of aluminum alloys. Strength Mater. 49(4), 565–574 (2017). https://doi.org/10.1007/s11223017-9900-6 9. Seo, K., Kim, J.-H., Lee, H., Kim, J., Kim, B.-M.: Effect of constitutive equations on prediction accuracy for springback in cold stamping of TRIP1180 (2017). https://doi.org/10.20944/PRE PRINTS201703.0044.V1 10. Jung, J., Jun, S., Lee, H., Kim, B.-M., Lee, M.-G., Kim, J.: Anisotropic hardening behaviour and springback of advanced high-strength steels (2017). https://doi.org/10.3390/MET711 0480
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11. Demeri, M.Y., Lou, M., Saran, M.J.: A benchmark test for springback simulation in sheet metal forming. SAE Technical Paper (2000). https://doi.org/10.4271/2000-01-2657 12. Laurent, H., Grèze, R., Manach, P.Y., Thuillier, S.: Influence of constitutive model in springback prediction using the split-ring test. Int. J. Mech. Sci. 51(3), 233–245 (2009). https://doi. org/10.1016/j.ijmecsci.2008.12.010 13. Colgan, M., Monaghan, J.: Deep drawing process: analysis and experiment. J. Mater. Process. Technol. 132, 35–41 (2003). https://doi.org/10.1016/S0924-0136(02)00253-4
Comparison of MPPT Algorithms for Grid Connected PV System Mohamed Bahri1(B) , Mohamed Talea1 , Hicham Bahri1 , and Mohamed Aboulfatah2 1 Information Processing Laboratory, Faculty of Science Ben M’Sik, Hassan II University of
Casablanca, Casablanca, Morocco [email protected] 2 Mathematics, Informatics, Engineering Science Laboratory, Physics Department, Faculty of Science and Technologies, University Hassan I, Settat, Morocco
Abstract. Different techniques of MPPT (Maximum Power Point Tracking) algorithms have been proposed in the literature to track the MPP and benefit from the high power efficiency of the PV (Photovoltaic) system. Moreover, the recent advances managed to transfer this power into the grid with high efficiency by optimizing the MPPT algorithms and safety by securing the synchronization and lowering the THD (Total Harmonic Distortions). This paper presents a comparative study between AI (Artificial Intelligence), metaheuristic and classical MPPT algorithms for grid-connected PV systems to determine the best rapid and efficient algorithm that can work under abrupt variation of irradiance and weather conditions and more importantly. This is the first paper to our knowledge to investigate the influence of these MPPT algorithms over the THD when put under the same system parameters and weather conditions. The simulation was done in MATLAB/SIMULINK. Results show that ANN algorithms performed better than all other algorithms in response time with 0.0063 s, an efficiency of 99.8%, practically no oscillations around MPP, perfect synchronization and a very low THD ratio of 1.36%. Keywords: Photovoltaic system · MPPT · ANN · FL · IC · Grid connected · Total Harmonic Distortion
1 Introduction Due to the demographic increase in population, and the big deficit in global energy, countries raced to find the best and most abundant substitute for fuel and fossil-based energy. In fact, photovoltaic PV is considered the first choice for a clean energy source nowadays. It replaces the polluted sources and gives a safe and reliable alternative [1, 2]. However, PVs major problem is the low conversion ratio of only 20% and the penetration problems regarding the distribution system [3]. For this reason, extracting maximum power point is of the utmost importance to benefit from the maximum power generated under various weather conditions.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 448–461, 2023. https://doi.org/10.1007/978-3-031-35245-4_41
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However, other varying parameters influence the PV system’s extracted power, especially the P-V curve’s nonlinearity [4]. Various types of PV systems with MPPT algorithms could be implemented. Standalone PV systems [5], hybrid PV system with batteries and grid-connected [6], and finally, Grid-connected type with DC-DC converter is chosen due to their advantages compared to autonomous and hybrid systems, which are power limited because of the batteries that also increases the costs of the system and need supervising. Furthermore, grid-connected PV systems improve cost reduction and energy [7]. Numerous studies in the literature intend to choose the best MPPT algorithm [8], that can track the MPP with good stability, low tracking speed and high accuracy, that works under various weather conditions. Indeed, the P&O (perturb and observe) and IC (Incremental Conductance) [9], are well known in the literature for their good accuracy in tracking MPP. However, they suffer from a low tracking speed and high oscillations in the transient regime. The nonlinear algorithms like SM (Sliding Mode) [10], manage to reduce the tracking time, but still with high oscillations around MPP, the AI (Artificial Intelligence) like FL (Fuzzy Logic) presents good enough tracking speed but still high oscillations [11] while the ANN (Artificial Neural Network) have even higher performance in tracking speed and accuracy with no oscillations. On the other hand, new bio-inspired MPPT algorithms like GWO (Grey Wolf Optimization) [12] have been introduced in literature with good enough performances. However, they still suffer from low tracking speed and high oscillations. The study focuses on highlighting the differences in the performances of these algorithms under the same system parameters and weather conditions. In addition, the impact of these MPPT algorithms over the THD ratio is studied. Indeed the THD is important when considering injecting the PV power into the grid [13], because it calculates the signal distortions. The more distortions in the signal, the more heat it generates because of the harmonics contained in the signal, which present a significant danger to the system. We kept the same control strategy for the inverter and compared different MPPT algorithms for the Boost converter. Furthermore, a classical control [14, 15] is applied to control the inverter, regulate the boost voltage and inject a sinusoidal synced current into the grid.. Results showed that the ANN-based MPPT performed very well in all aspects with the best tracking speed, best efficiency, lowest oscillations and the lowest THD ratio.
2 Description and Model of the System The complete suggested system is shown in Fig. 3 and consists of a PV generator that generates a maximum power of 103 KW. Vpv and Ipv are the PV voltage and current that are the input of the Boost converter with, Cin = 0.0077 F and Cdc = 3227 × 10−6 F are, respectively the input and the output capacitors of the boost converter with R = 7 × 10–3 , L = 4 × 10–4 H is the boost converter inductor. For final part, the inverter is PWM-controlled to deliver a synchronous power to the grid. The inductor filter filters out harmonic distortions and respects interconnection rules between the voltage sources, where Lf = 11 × 10–4 H and r = 0.06 (Table 1).
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Description
Specifications
Maximum power point(MPP)
200.14 W
Open circuit voltage (VOC)
32.9 V
Short circuit current (ISC)
8.21 A
Maximum power point voltage (VMP)
26.3 V
Maximum power point current (IMP)
7.61 A
The considered the equations can represent balanced three phase grid: Va = V cos(ωt) 2π Vb = V cos ωt − 3 2π Vc = V cos ωt + 3
(1) (2) (3)
V as the maximum voltage of three phase grid, and ω its pulsation. The three phase inverter current and the grid are connected as shown in (4) as follows: ⎛ dia ⎞ ⎝
dt dib dt dic dt
⎛
⎞⎛ ⎞ − RL 0 0 Ua − Va ⎠ = ⎝ 0 − R 0 ⎠⎝ Ub − Vb ⎠ L 0 0 − RL Uc − Vc
(4)
where (ia, ib, ic) and (Ua, Ub, Uc) are, respectively the inverter output currents and voltages. Equation 4 is presented in three phase frame, we apply a dq transformation to obtain the equation in synchronous rotating dq reference as shown in (5): T Id Iq = T (θ ) · [Ia Ib Ic]T
(5)
T Vd Vq = T (θ ) · [Va Vb Vc]T
(6)
T Cd Cq = T (θ ) · [Ca Cb Cc]T
(7)
With
T=
2 3
cos θ + 2π cos(θ ) cos θ − 2π 3 3
2π − sin(θ ) − sin θ − 2π 3 − sin θ + 3
(8)
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where T represents the transformation matrix from abc frame√ to dq frame, θ = ωt is the grid angular frequency, t is the time variable. The coefficient 23 is used to preserve the same power magnitude. did 1 −R ω Vdc Ud 1 Vd id dt = + (9) − diq iq L −ω −R L Vq L Uq dt where Vd and Vq are the grid voltage in dq axis, (id, iq) and (Ud, Uq) are respectively the output current and voltage of the inverter in dq axis. Using (Eq. 5) the expression of the inverter voltage in dq axis is given by: 1 Ldi + Rid − ωLiq + Vd (10) Ud = Vdc dt 1 Ldiq Uq = + Riq + ωLid + Vq (11) Vdc dt
3 Control Strategy The Problem of PV systems is to maintain constant maximum power at the output, MPPT algorithms can do this with different performances. The other issue of this work is the regulation of the DC voltage and control of the three phase inverter to perfectly synchronize the grid with the inverter current. 3.1 MPPT Controller In this paper, we compared five major MPPT algorithms representing different techniques. First the classical IC, the ANN, FL and the GWO algorithm were simulated. • Incremental Conductance MPPT This algorithm is well known in literature with its high accuracy under different changing conditions. The output voltage and current are directly measured to calculate dI ). This later equals zero at the MPP, positive on the left side the conductance (C = dV and negative on the right side. The flowchart of this algorithm is given in Fig. 1. • Fuzzy Logic MPPT The Fuzzy Logic-based MPPT algorithm easily finds the PV system’s MPP. Furthermore, this AI algorithm does not need any information about the modelling of the system. The inputs consist of the error E and CE is the error variation. Equations (12) and (13) describes E(k) and CE(k). E(k) =
P(k) − P(k − 1) dP = dV V (k) − V (k − 1)
CE(k) = E(k) − E(k − 1)
(12) (13)
where dP and dV are respectively the change in PV power and voltage, and are equal to the difference between previous value P(k − 1), Vk − 1) and instant value P(k),
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Fig. 1. Flowchart of the IC
V(k). If dP and dV are both positive or both negative to reach the MPP the reference voltage which is the output of the fuzzy logic algorithm should be increased, and when dP and dV have different sign the reference voltage should be decreased to reach MPP the rules of this algorithm are shown in Table 2. Table 2. Fuzzy Logic rules E/CE
PB
PM
PS
ZE
NS
NM
NB
PB
ZE
ZE
ZE
NB
NB
NB
NB
PM
ZE
ZE
ZE
NM
NM
NM
NM
PS
ZE
ZE
ZE
NS
NS
NM
NM
ZE
NS
NS
ZE
ZE
ZE
PS
PS
NS
PM
PM
PS
NS
ZE
PS
ZE
NM
PM
PM
PM
PB
ZE
ZE
NS
NB
PB
PM
PM
PB
ZE
ZE
ZE
• Artificial Neural Network MPPT The proposed ANN based MPPT algorithm [16] generates the reference voltage needed to reach MPP. The structure of the ANN as in Fig. 2 was generated using MATLAB neural network toolbox and consists of one input layer with one input
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from an irradiance sensor, one hidden layer with ten neurons and one output layer representing the reference voltage. Data created for this ANN consists of 1200 inputs representing various changes in weather conditions. The algorithm is then trained with the levenberg-marquardt algorithm.
Fig. 2. Flowchart of the ANN
• Grey Wolf optimization MPPT From the wild wolfs that live and hunt in groups with a specific optimized technique, a metaheuristic algorithm was inspired for optimization problems [17] that are difficult to resolve by standard methods. The pack of wolfs consists of a leader α and also a decision maker, β and δ assist α in decision making and ω are the followers employed for replicating the leadership pyramid. The following equations can represent the attacking scheme. → → (14) xp(t) − − xp(t) e = c · − → x(t + 1) = − xp(t) − a · e
(15)
→ where t is the current iteration, a, e and c are the vectors coefficient, − xp(t) is the prey position vector and x the position of the grey wolf. Where, − → a = 2 · b · r1 − b (16) − → c = r2
(17) − → − → The components of b decreases linearly from 2 to 0. r1, r2 are random vectors between [0, 1]. The following steps explain how to use this algorithm to track MPP. First initialize the position of the wolves in fixed positions, between 10% and 90% of the duty cycle then maximize the PV output power ‘Ppv’ at each position of the wolfs, and evaluate the output power: Ppv = V.I
(18)
The next step is to adjust the grey wolf position as follows: Di (k + 1) = Di (k) − a · e
(19)
where D is current grey wolf, k indicates which iterations and i the current grey wolves, a , e are vectors coefficient. Then repeat the process until convergence of the wolves is met at the MPP. Finally, after finding the MPP, the P&O track and lock the MPP.
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3.2 Inverter Controller A classic controller was chosen to control the three-phase inverter due to its simplicity and successfully regulating the output boost voltage and delivering a good sinusoidal current to the grid. Equations (10) and (11) indicate the coupling of dq axis control. The next section presents the proposed control system’s simulation results and performance analyses under MATLAB/SIMULINK. The inverter control law ensures the DC voltage regulation and imposes the synchronization between the inverter current and grid voltage. In addition, this control law was deliberately the same for all MPPT algorithms simulated in this work, and that is to see only the impact of these MPPT algorithms over the THD ratio of the inverter current that will be injected into the grid. The stability of this control method is satisfied with the use of a PI regulator, as shown in Eqs. (20) and (21) represent the controller output signal. Ki 1 Kp + (20) Cd = (idref − id ) − ωLiq + Vd ] Vdc s Ki 1 Kp + Cq = (21) (iqref − iq) + ωLid + Vq] Vdc s
Fig. 3. Control strategy of the proposed system
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4 Simulation Results and Discussion In this study, different algorithms representing AI, metaheuristic and classical MPPT algorithms for a three-phase grid-connected PV system were simulated under the same system parameters in MATLAB/SIMULINK for a duration of 1 s. The simulated scenarios of irradiance are given in Fig. 4 and Table 3, which consider abrupt variations in irradiance with a constant temperature. Results presented in Fig. 5, show that all simulated algorithms successfully tracked the MPP under abrupt irradiance variation. Figure 6 shows the performances of each algorithm in response time, efficiency and oscillations. Figure 6 a shows the excellent performance of the ANN compared to all others in response time under 1000 w/m2 . Figure 6b and Fig. 7b demonstrate the superiority of the ANN in efficiency and low oscillations in both scenarios of 1000 w/m2 and 400 w/m2 . Indeed the ANN MPPT was very efficient by 99.8%, the tracking time was excellent with a recorded 0.0063 s, and oscillations around MPP were negligible with only 40 W and 10 W respectively for 1000 w/m2 and 400 w/m2 . The THD was very low, only 1.36%. Figure 8 demonstrates that the output boost converter voltage, is well regulated around the reference value of 600 V with notable oscillations for the IC, GWO and SM, which were negligible for the ANN. For the second part, Fig. 9 shows that the inverter current is successfully synchronized with the grid voltage for all four algorithms, with very low harmonic distortions as seen in Fig. 10, with 1.36% recorded for the ANN algorithm, 2.79% and 6.54% as the highest THD recorded for the IC algorithm in both scenarios. The FL and IC recorded 2.64% and 2.79% respectively and finally the GWO scored 2.74% under 1000 w/m2 . The successful decrease in THD for the ANN is also due because it delivers a steady power independently of other varying parameters. This outstanding performance ranked the ANN MPPT algorithm in the first place in this comparison. More importantly, this study proves that the choice of the MPPT algorithm to control the DC-DC converter is quiet important because it affects the THD ratio, which means the number of harmonics transmitted from the PV power to the grid. Table 3. Scenarios selected for the study Irradiance W/m2
Duration (s)
VMPP (V)
IMPP (A)
MPP (W)
1000
0–0.2
290.8
345.1
1.003 × 105
600
0.2–0.4
293.2
207.9
6.097 × 104
400
0.4–0.6
292.6
138.7
4.059 × 104
200
0.6–0.8
287.1
69.55
1.997 × 104
1000
0.8–1
290.8
345.1
1.003 × 105
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M. Bahri et al. 4
10
ANN MPPT
5
1.003x10
1000w/m2
10
IC MPPT
GWO MPPT
600w/m2
10
400w/m2 200w/m2
8
6.097x10
4
4.059x10
4
PV Power (W)
6
4 1.997x10
4
5
0
2
0 0
50
100
150
200
250
3500
300
0.2
400
0.4
Fig. 4. Irradiance scenarios chosen ANN MPPT
10
12
FL MPPT
0.6
0.8
Time (s)
PV Voltage (V)
IC MPPT
Fig. 5. PV power under varying irradiance ANN MPPT
GWO MPPT
4
44
10
10.05
FL MPPT
IC MPPT
GWO MPPT
4
8
PV Power (W)
10
PV Power (W)
PV Power (W)
FL MPPT
4
ANN response time:0.0063s 10
6
4
FL response time:0.008s
10
4 5
2 0
0
0
0
0.005
0.05
0.1 Time (s)
(a)
0.01
0.015
0.15
10
9.95
0.16
0.165
0.17
0.175
0.18
Time (s)
(b)
Fig. 6. (a) Zoomed view of the PV power under 1000 w/m2 , (b) PV Power oscillations under 1000 w/m2
Results shown in Table 4, clearly demonstrate that the use of different MPPT algorithms to control the boost converter directly impacts the THD ratio even when put under the same system parameters and with the same control law of the inverter. The ANN algorithm has the lowest THD ratio in scenarios of high irradiance 1000 w/m2 or low irradiance of 400 w/m2 , while the other techniques have higher THD with the worst THD for the IC under both 1000 w/m2 and 400 w/m2 . We can also observe that the THD tends to increase with lower values of PV Power, which makes the MPPT algorithm very important to avoid a distorted signal injected to the grid. The MPPT is correctly tracked, the DC voltage is well regulated, and the injected current is successfully synchronized with the grid. Thus, the ANN algorithm and the decoupled control method are efficient, rapid and perform very well under any conditions. In addition, the use of ANN based MPPT algorithm, influences the system to reduce the THD ratio, which represents an immense improvement in one of the most important parameters in grid connected topologies which is to reduce harmonics.
1
Comparison of MPPT Algorithms for Grid Connected PV System ANN MPPT
FL MPPT
IC MPPT
GWO MPPT
ANN MPPT
44
10
10
FL MPPT
IC MPPT
GWO MPPT
4
4
2
Oscillations
Oscillations
1.5
10
2
3
4
PV Power (W)
PV Power (W)
4
457
1.995
1
1.99
0.5
1.985
4
4.06
2
4.05 4.04
1
4.03 4.02
1.98 0.38
0 0.2
10
4.07
0.22
0.385
0.39
0.24
0.395
0.26
0.4
0.28
0 0.4
0.3
0.48
0.42
0.485
0.49
0.44
0.495
0.46
0.5
0.48
0.5
Time (s)
Time (s)
(a)
(b)
Fig. 7. (a) Zoomed view of the PV power under 400 w/m2 , (b) zoomed view on PV Power under 400 w/m2
1200
750 ANN
FL
IC
ANN
GWO
1000
DC Voltage (V)
800
DC Voltage (V)
FL
IC
GWO
700
600 400 200 0 0
0.2
0.4
0.6
Time (s)
(a)
0.8
1
650
600
550
500 0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Time (s)
(b)
Fig. 8. (a) Regulated DC Voltage around 600 V, (b) Zoomed view of DC Voltage under 1000 w/m2
5 Conclusion This paper Compared different MPPT algorithms, the AI based algorithms like ANN and FL, the metaheuristic GWO and the classical IC. In addition, it is the first paper to study the impact of these different MPPT algorithms over the THD for grid connected PV system when put under the same parameters with same control of the inverter by a decoupled control method. The aim of this comparative study is to determine the best MPPT algorithm that can correctly track the MPP under abrupt variations of irradiance which simulate real weather conditions, and to observe the influence of these algorithms over the THD ratio. The simulated results proved the superiority of the ANN MPPT algorithm in response time, efficiency, oscillations around MPP and also in THD ratio over the other MPPT algorithms. The future works will focus on a hardware comparison between these MPPT algorithms and there implementation.
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M. Bahri et al. 400
1G 2G 3G
Grid Voltage V)
Inverter Current (A) and
200
1ANN 2ANN 3ANN 1FL
0
2FL 3FL 1IC 2IC
-200
3IC 1GWO 2GWO 3GWO
-400 0.9
0.905
0.91
0.915
0.92
Time (s) (a)
220
200
180
160 0.903
0.904
0.905
0.906
0.907
(b)
Fig. 9. (a) Inverter Current and Grid Voltage, (b) Zoomed view on phase 1 of Inverter Current
Comparison of MPPT Algorithms for Grid Connected PV System FFT analysis
10
2
10
0
Fundamental (50Hz) = 196.2 , THD= 1.36%
Mag (% of Fundamental)
Mag (% of Fundamental)
FFT analysis
10
-2
0
200
400
600
800
10
2
10
0
Fundamental (50Hz) = 194.4 , THD= 2.64%
0
1000
200
400
(a) 2
10
0
800
1000
FFT analysis
Fundamental (50Hz) = 206.6 , THD= 2.79%
Mag (% of Fundamental)
10
600
(b)
FFT analysis
Mag (% of Fundamental)
459
0
200
400
600
800
10
2
10
0
10
Fundamental (50Hz) = 198.8 , THD= 2.74%
-2
0
1000
200
(c)
400
600
800
1000
(d)
Fig. 10. THD analysis of inverter Current under 1000 W/m2 for: (a) ANN, (b) FL, (c) IC, (d) GWO Table 4. Results comparison of the simulated MPPT algorithms Comparison results of the simulated algorithms Algorithm Irradiance Theoretical Obtained Efficiency Response THD Oscillations case MPP (W) MPP (W) (%) Time (s) (%) (W) (w/m2 ) ANN
1000
1.003 × 105
1.001 × 105
99.80
0.0063
1.36
40
FL
1000
1.003 × 105
9.93 × 105
99.35
0.008
2.64
200
IC
1000
1.003 × 105
9.97 × 104
99.41
0.1
2.79
400
GWO
1000
1.003 × 105
1.0035 × 99.73 105
0.05
2.74
650
ANN
400
4.059 × 104
4.0535 × 99.86 104
0.0005
3.38
10
FL
400
4.059 × 104
4.05 × 104
99.77
0.0016
3.79
100
IC
400
4.059 × 104
4.005 × 104
98.66
0.05
6.54
960 (continued)
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Comparison results of the simulated algorithms Algorithm Irradiance Theoretical Obtained Efficiency Response THD Oscillations case MPP (W) MPP (W) (%) Time (s) (%) (W) (w/m2 ) GWO
400
QLFL [18]
1000
4.059 × 104
4.046 × 104
99.67
0.056
5.58
400
98.1
97.02
98.1
0.07
–
–
Conflicts of Interest. The authors declare no conflict of interest.
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Identification of the Parameters of the Lithium-Ion Battery Used in Electric Vehicles for the SOC Estimation Nasri Elmehdi1(B) , Jarou Tarik1 , Salma Benchikh1 , and Nabiha Saadi2 1 Advanced Systems Engineering Laboratory, The National School of Applied Sciences, Ibn
Tofail University, Kenitra, Morocco {elmehdi.nasri1,tarik.jarou}@uit.ac.ma 2 LPRI, SIA TEAM, EMSI, Tanger, Morocco [email protected]
Abstract. With the development of new energy vehicle technology, lithiumion batteries are an important component of energy storage systems used in various applications such as electric vehicles. Especially since this type of battery is the most used in the electric vehicle industry. The battery management systems used to control the state of the battery have been widely researched. The accuracy of the battery state is heavily reliant on the battery model parameters precision, and the accuracy of the estimate technique is directly proportional to the model used to characterize the batteries parameters. We apply a piecewise linear approximation with variable coefficients to express the intrinsically nonlinear link between the open-circuit voltage (VOC) and the battery’s state of charge (SOC), while using a resistance–capacitance (RC)-equivalent circuit to simulate the battery dynamics. Both data from a simulated battery model and experimental data were used, to validate the moving window least squares parameter-identification algorithm. Keywords: Parameters identification · SOC estimation · ECM · Lithium-Ion Batteries · BMS · Electric vehicles
1 Introduction Electric cars have arisen as a necessary and more popular alternative in the twenty-first century. As environmental pollution and the petroleum energy problem have become more significant challenges [1]. The battery, as an energy storage source, is critical to the development of electric cars (EVs). Because of its numerous benefits, including as high energy density, quick charging and discharging, and safety, the lithium-ion battery is recognized as the most promising green battery, and is prefered by most new-energy vehicles [2]. Lithium-ion batteries offer a higher energy density, cheaper prices, lower self-discharge rates, and longer life cycles, and thus have been the subject of the bulk of recent BMS (Battery Management System) investigations [3–4]. Estimating state of charge (SOC) is significant in BMS-related research, since it offers information about a battery’s remaining capacity. Accurate SOC estimate may help © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 462–472, 2023. https://doi.org/10.1007/978-3-031-35245-4_42
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avoid overcharging and discharging. As well as providing a greater variation tolerance for other controls like charge/discharge current regulation. A BMS in a battery application, for example, takes observable data such as current, voltage, power, and battery temperature, and compares it to anticipated values from the appropriate model, allowing the BMS to identify battery defects [5, 6]. The equivalent circuit model ECMs are used to research electrical behavior and characteristics during operation. As opposed to electrochemical models, which investigate the battery’s inner reaction process. The model’s complexity is proportional to its accuracy, hence an ECM with higher accuracy must also have a larger complexity. The Combined model, the resistor-capacitor (RC) model, and the Thevenin model are some of the most often utilized ECMs. The RC or Thevenin models are frequently used in SOC estimate studies [7–8]. The rest of this paper is laid out as follows. Section 2 introduces the RC-thevenin battery model ECM. Section 3 present a SOC Estimation system design based on an state observer & Filter and Finally, The identification and optimization of model parameters necessary for SOC estimation using the least square technique and the SOC-OCV Curve are presented in Sect. 4. Finishing this project and concluding our study with research conclusions.
2 Battery Model and Parameter Identification In terms of battery model research, the battery model that will be developed is consistent with the battery’s exterior properties. The battery’s internal chemical reaction is a complicated non-linear process. The battery becomes polarized when the charging and discharging currents vary, i.e., the voltage across the battery does not exhibit pure resistance characteristics but varies nonlinearly. The resistance of the charge and discharge current passing through the battery increases when the battery is polarized [9]. Electrochemical models [10, 11], neural network models [12], and comparable circuit models [13], among others, are currently the most often used battery models. Electrochemical models are first-principle models that can define exterior properties as well as simulate the distribution and variations of interior characteristics that have a definite physical significance. Their intricate partial differential equations and electrochemical parameters, on the other hand, need a large amount of computing work. As a result, they are not yet suitable to BMS SOC estimate. From a data-driven approach, neural network models imitate the battery. They seek for potential correlations between battery monitoring data and predicted parameters based on a vast amount of battery operating data, without needing to consider internal battery properties. These models, on the other hand, need improved data quality and larger data quantities. Equivalent circuit models depict the exterior properties of the battery using circuit components such as resistors, capacitors, and a constant voltage supply. In the BMS, they have been commonly employed. The Rint model, Thevenin model, PNGV model, and 2nd order RC equivalent circuit model are now the most extensively utilized equivalent circuit models. Ref [13] evaluated the accuracy and stability of SOC estimation under multiple analogous models and found that Thevenin’s model performed well in both categories. As a result, the equivalent circuit model of Thevenin is used in this research, as illustrated in Fig. 1. Where
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Uoc denotes the battery’s OCV, R0 denotes the ohmic internal resistance, Ut denotes the terminal voltage, I denote the current, Up denotes the bias voltage, Cp denotes the bias capacitance, and Rp is the bias resistance. The equation for the battery’s continuous state space is given by Kirchhoff’s law: ⎧ Up dUp ⎪ ⎪ ⎪ ⎨ Cp dt + Rp = i (1) ⎪ Uoc = Ut + iR0 + Up ⎪ ⎪ ⎩ Uo = iR0
Fig. 1. Thevenin Model.
In general, SOC is defined as the ratio of the current remaining capacity to the maximum capacity of the battery. The continuous-time SOC equation can be obtained by coulomb counting, Eq. (2): η t SOC(t) = SOC 0 − I dt (2) Qn t0 Thevenin’s model consists of an ideal voltage source, an ohmic resistance Ro, and an RC network to represent the open circuit voltage (OCV, UOC). The resistance and polarization capacity are represented by Rp and Cp, respectively. The terminal voltage is Ut , while the current is I, (assumed positive for charge, negative for discharge). The polarization voltage on the RC network is described as up. A Laplace transform is utilized to create a state space equation for the Thevenin ECM, which is represented by Eq. (3): (3)
Pour le modèle d’espace d’état établi, les paramètres à identifier sont CP , RP , et R0 ainsi que Uoc . For the established state space model, the parameters to be identified are CP , RP , and R0 as well as Uoc .
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3 SOC Estimation System Design Because of the battery properties are more quantifiable, they must be estimated for an accurate online observation of the SOC. In this case, we estimated the OCV online using the OCV-SOC curve implemented in Matlab and the lithium battery experimental data. This parameter is still useful in SOC estimation. We then used the experimental data to perform a linear regression to estimate the other parameters online using the least square algorithm implemented in Matlab. This estimation will serve as the foundation for our next research project, which will focus on developing a state of charge estimation method for a lithium battery used in electric vehicles. To build the state space expression of the system, the state space model is based on the battery model. And the battery SOC is utilized as one of the state variables, after which the battery SOC is evaluated by the filter or observer [14]. The fundamental concept is to link the battery SOC to current, voltage, temperature, and other measured factors. The difference between the anticipated value of the terminal voltage generated by the model and the actual sampled value of the terminal voltage can be reduced by using these quantifiable variables as input to the model. It is possible to acquire the terminal voltage. Finally, the filter or observer obtains the current value of the battery SOC. As illustrated in Fig. 2. The present study of the state-space battery model’s SOC estimate approach, focuses on three aspects: • Research on the structure of the equivalent circuit model of the battery, • Research on the method of identifying the parameters of the battery model, • Research on the battery SOC estimation observer.
Fig. 2. Structure diagram of SOC estimation method based on state space battery model.
Some adaptive filtering methods and state observers were alleged to have been utilized to estimate the battery SOC, including Kalman filters (KF) [15],and their nonlinear application forms [16]. Levenberg observers [17]. H-infini filters [18]. Proportionalintegral observers (PIO), and sliding mode observers (SMO) [17]. The reference [19] present a review of SOC and the comparative studies between the different algorithms.
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4 Simulations and Results Discussion The internal parameters of the developed battery model, including U oc ., R0 , Rp, C P ., are identified in this section. Because the amount of embedding in the active material determines the SOC of a lithium-ion battery, an open circuit voltage may be used to estimate the SOC after the battery has rested sufficiently to achieve equilibrium [20]. This is a straightforward procedure with good precision. The fundamental disadvantage of the OCV approach is that it takes a long time to establish a steady state [21]. The amount of time takes to transition from the operational state to the steady state is determined by the status of the SOC, the temperature, and other factors. To attain the steady state at low temperatures, for example, a considerable resting time is required. At low temperatures, for example, C/LiFePO4 equilibrium takes more than two hours to attain. As a result, the approach can only be used while cars are parked rather than driven [22]. The Methods for estimating the SoC typically require characterization of the OCV curve (most commonly through a polynomial or a look-up table). As they either use a direct inversion method of the OCV curve (if the application allows measurement of the cell’s steady-state voltage), or methods based on a cell model [23]. By taking voltage measurements to determine the cell’s SoC, we can define the following relationship, Eq. (4): SOC = f −1 (OCV )
(4)
The OCV-SOC functionality is represented by a controlled source circuit, which connects the SoC to the OCV through a voltage-controlled source. The VCO is a standard instrument for assisting other approaches. In [24], for example, a batch discharge approach for constructing the OCV-SoC characterization is utilized to determine the internal resistance of the model battery and estimates the SoC using an ECM and an extended Kalman observer. It’s also utilized to figure out a cell’s ECM properties in [25, 26]. The experiment, however, is carried out at a constant temperature. The temperature of the battery pack in electric vehicles (EVs) and hybrid electric vehicles (HEVs) is supposed to be adequately managed by the temperature management system. As a result, the influence of temperature on the OCV is not taken into account in this study. We used data from a Panasonic 18650PF Li-ion Battery in this investigation, which is available on the Mendeley data plateform [27, 28]. • OCV identification: The relationship between VCO and SOC has been developed to find OCV values between two data points, using nth since the fitted OCV should be as close as possible to the experimental OCV and the fitting performance is better with a higher order polynomial function [30, 31]. The 7th order of the polynomial function is used, by the application of Matlab “curve fitting”, the mathematical relationship between SOC and Uoc is identified such relationship is necessary to estimate the state of charge: i=7 ai,k SOC ik (5) SOC(OCV ) = i=0
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SOC(OCV ) = k0 + k1 ∗ SOC + k2 ∗ SOC 2 + +k3 ∗ SOC 3 + k4 ∗ SOC 4 + k5 ∗ SOC 5 + k6 ∗ SOC 6 + k7 ∗ SOC 7 + k8 ∗ SOC 8
(6)
SOC(OCV ) = 2.766 + 14.23 ∗ SOC − 108.4 ∗ SOC 2 + +435 ∗ SOC 3 − 958.4 ∗ SOC 4 + 1171 ∗ SOC 5 − 741.7 ∗ SOC 6 + 190.1 ∗ SOC 7
(7)
The non-linear variation curve of VOC with respect to SOC is given in Fig. 3, based on the Matlab “curve fitting” function:
Fig. 3. Uoc and SOC curve of the lithium battery
• Identification of R0 In the case of R0, it can also be determined by using the above battery discharge data. Discharge the battery with constant current (i = I) in Fig. 3 and then rest for a long time. When the current i changes from I to 0, the terminal voltage has a step change in u because of the Ohmic resistance R0 . R0 can be obtained by Eq. (8): u (8) R0 = I In the case of R0 , it can also be determined by using the above battery discharge data. The value of R0 was calculated corresponding to the SOC of each period, by dividing the Difference determined value of the voltage drop by the discharge current, where voltage drop is the difference between the no load and loaded voltages. The variation of R0 according to the SOC is given in Fig. 4, which fits into Eq. (9). R0 = 0.00573 − 0.03427 ∗ SOC + 0.1455 ∗ SOC 2 − 0.32647 ∗ SOC 3
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Fig. 4. Uoc and SOC curve of the lithium battery.
+ 0.41465 ∗ SOC 4 − 0.28992 ∗ SOC 5 + 0.09353 ∗ SOC 6 + 0.00634 ∗ SOC 7 • Identification of Rp et Cp In rest period, terminal voltage Ut can be mesured and recorded. According to Eq. (1) can be deduced as: (10) That is the terminal voltage Ut can be expressed by the following exponential function: f (t) = b1 − b2 e−b3 t
(11)
By fitting a nonlinear exponential function to the acquired voltage data, the values of b1 , b2 , and b3 in Eq. (4) may be calculated. In this post, the function nlinfit in MATLAB is computed (R2016b, Mathworks, NATIK MA USA). Which fits data to functions using the nonlinear least squares technique [46]. Equation (12) (13) may be used to get model parameters Rp and Cp by comparing Eqs. (10) and (11). b2 I
(12)
1 b3 Rp
(13)
Rp = Cp =
The obtained functions of Rp and Cp according to the SOC are shown in Eqs. (12) and (13), respectively. Figure 5 describes the variation of Rp and Cp according to the
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SOC by Eq. (14) (15): Rp = 0.01513 − 0.18008 ∗ SOC + 1.05147 ∗ SOC 2 − 3.27616 ∗ SOC 3 + 5.79793 ∗ SOC 4 − 5.81819 ∗ SOC 5 + 3.08032 ∗ SOC 6 − 0.66827 ∗ SOC 7 (14) (15)
Fig. 5. Rp and Cp Curve according to SOC: (a) Rp; and (b) Cp
In this paper, we proceeded to a modelling of the lithium battery based on thevenin model, which is the most used in SOC estimation cases, followed by a parameter identification based on the OCV-SOC curve, and the least square algorithm implemented on Matlab using the nlinfit curve function. The next research aims at completing the current model with the most suitable observer or filter for real-time state of charge estimation, and the simulation in time of the model including the three fundamental elements for the estimation of the state of charge of a lithium battery: battery model, parameter identification algorithm and a filter or state observer for real-time estimation of the SOC of the battery. Several recent researches have proceeded the SOC estimation. By fixing the battery parameters in order to simplify the estimation algorithm, and the computation time constraints [32, 33]. The improvement brought by this paper resides in the fact of ensuring an online update of the battery parameters which ensures a robustness of the state-ofcharge estimation.
5 Conclusion In order to estimate the SOC of the battery, the RC-equivalent circuit is used to model the dynamics of the battery. All the parameters are subject to change and need to be
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identified with a proper frequency during the SOC estimation. Most of the observer based SOC estimation algorithms proposed so far design. The state observer based on a model with fixed parameters, that are obtained from offline identification, in our case we proceed an online parameters estimation based on the OCV-SOC curve and the least square algorithm implemented in MATLAB software in order to estimate the parameters. The main purpose of this paper is the parameters identification required, to the design of the state observer or the filter associated to the state of charge estimation of lithium battery. Following this online identification, several filters or observers can be applied to estimate the state of charge of a lithium battery. Namely mathematical optimization methods, artificial intelligence methods, or even adaptive observers. And this is what constitutes the heart of our next research aimed at comparing and choosing the most efficient method to apply.
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Voltage Profile Improvement of IEEE14 Bus System Using SVC and STATCOM Ismail Moufid(B) , Zineb En-nay, Soukaina Naciri, Hassan EL Moussaoui, Tijani Lamhamdi, and Hassane El Markhi Intelligent Systems, Geo-Resources and Renewable Energies Laboratory (ISGREL), FST Fez, Sidi Mohamed Ben Abdelah University, Fez, Morocco [email protected]
Abstract. The present work investigates the FACTS devices role to improve the voltage profile of loads. Our analysis used a static synchronous compensator STATCOM and Static Var Compensator SVC device over an IEEE 14 bus system. Firstly, a description of STATCOM and SVC is presented. Then we studied the voltage profile of the IEEE14 bus system with and without STATCOM. The simulations results were done using NEPLAN Software. We compared all the obtained data to the IEEE14 bus original power flow, and the results demonstrate that including STATCOM into the examined system is essential for improving voltage profiles. Keywords: STATCOM · SVC · IEEE-14 bus · NEPLAN · Voltage Profile
1 Introduction Nowadays, the energy demand maintains on growing. The increase of load demand in the distribution network creates a fundamental challenge for the distribution network administrator in phases of studying the management of the distribution network to satisfy the need, besides these challenges, the necessity to increase energy sources. Therefore, new systems are introduced to manage such obstacles and improve the power grid’s performance. Flexible Alternative Current Transmission Systems FACTS devices are potential keys to the aforementioned issues. The integration of FACTS controllers in the power systems like STATCOM “static synchronous compensator” and SVC “Static Var Compensator” play a significant role in enhancing the power transfer capability and improving the system stability due to their agility and adaptability [1]. In addition, converter-based FACTS controllers can autonomously control the reactive and active power flow in the electrical power system [2]. The static synchronous compensator STATCOM is one of the FACTS devices that is used to compensate for the reactive power, improve the voltage profile, increase the power factor, and minimize the power losses of the system [3]. The integration of a battery energy storage system (BESS) into the DC side of the converter makes it feasible for a STATCOM to give active power supported by the network [4]. The STATCOM also has the capability for voltage regulation for grid voltage at the common coupling point (PCC) with injecting or absorption a certain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 473–479, 2023. https://doi.org/10.1007/978-3-031-35245-4_43
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quantity of reactive power by using energy storage into voltage source converters (VSC) [5]. STATCOM can also control the voltage magnitude and regulate the phase angle in a very compressed time and, therefore, can improve the system performances [6]. Static Var Compensator (SVC) gives a compelling responsive pay for voltage profile during potential occurrences, which would make some way or another push down the voltage for a colossal. This device utilizes electronic ability to control power and voltage on the force framework [7]. The SVC can also be used to control the voltage stability for a significant disturbance when voltage droop or open circuit [8] affects the network. This paper proposes the use of SVC and STATCOM to improve the distribution network’s voltage profile. The paper is organized as follows: The first section presents the description of svc and STATCOM as a fact device. The second part is divided into three parts. In the first part, the simulation is done before using any fact device. In the second part, the SVC was installed in our system, and in the third part, the simulation is done using STATCOM. Finally, some significant conclusions are outlined.
2 FACTS Devices 2.1 Static Synchronous Compensator STATCOM To respond to the large dynamic performance of the network, STATCOM has become one of the most powerful devices for reactive power compensation [9]. STATCOM is dominant new generation devices for FACTS, applied to control the voltage through reactive power compensation by both either injecting or absorbing the reactive power in a given network. The STATCOM is shunt joined at the bus of the power network to give steady-state voltage regulation and improve the short-term transient voltage stability [10]. 2.2 Static Var Compensator SVC SVC employs a thyristor for switching purposes that do not turn off capability. SVC is a shunt-connected device that serves as a static VAR generator or absorber. The voltage profile level is managed by exchanging capacitive or inductive current delivered by SVC. By changing the firing angle of the thyristor, we can change the equivalent value of the shunt admittance which appears across the line to which SVC is connected. The operating principle and characteristics of thyristors realize SVC variable reactive susceptance. For steady-state analysis, we can model this SVC configuration along similar lines. The model considers the firing angle of the TCR as a state variable. It is identical to a power source that produces leading reactive power when the SVC operates within its operating limits.
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3 Simulation and Discussion 3.1 Simulation of the Base Case The simulation scenario is applied to the IEEE 14 Bus System using NEPAN software to examine the performance of our suggested method, as shown in Fig. 1. The voltage profile of the buses is shown in Table 1 and Fig. 1 as a result of the load flow simulationof NEPLAN software. The data demonstrate that the four buses (1, 6, 8, and 12) exceeded the assumed 95% to 105% limit. As a result, STATCOM must be implemented to reduce the number of buses that exceed the limit.
Fig. 1. The IEEE 14 bus system simulation using NEPLAN software “base case”
3.2 Simulation with SVC We used the NEPLAN software to simulate the IEEE14 bus system illustrated in Fig. 1. We placed the SVC in BUS 8 because it is the bus bar with the heist voltage “108.67” to improve the optimum voltage profile of our system. The ieee14bus system with the SVC placed in bus bar 8 is illustrated in Fig. 2, and the table of the voltage profile of the system is presented in Table 2.
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U (kV)
U (%)
BUS_1
73,14
106
BUS_10
14,226
103,09
BUS_11
14,444
104,66
BUS_12
14,536
105,33
BUS_13
14,446
104,68
BUS_14
14,076
102
BUS_2
72,105
104,5
BUS_3
69,69
101
BUS_4
69,803
101,16
BUS_5
70,09
101,58
BUS_6
14,766
107
BUS_7
14,46
104,78
BUS_8
19,561
108,67
BUS_9
14,238
103,17
Fig. 2. The IEEE 14 bus system simulation with SVC connected to bus 8
We placed the SVC in bus bar 8, as shown in Fig. 2. In this case, the bus voltage profile is presented from the load flow simulation, as shown in Table 2. It was noticed that only too bus bars (1, 6) are out of limit, compared to the base case we can Remarque
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Table 2. Voltage profile for IEEE14 bus system with SVC connected to bus 8 Node Name
U(kV)
U (%)
BUS_11
14,21
102,97
BUS_12
14,342
103,93
BUS_13
14,243
103,21
BUS_6
14,583
105,68
BUS_4
69,118
100,17
BUS_5
69,532
100,77
BUS_1
73,14
106
BUS_3
69,69
101
BUS_2
71,91
104,22
BUS_8
18,209
101,16
BUS_7
13,985
101,34
BUS_14
13,814
100,1
BUS_9
13,938
101
BUS_10
13,945
101,05
that with using SVC in our ieee14 bus system we reduced the number of bus bar that is out of the limit from 4 to 2. Therefore, we can improve the voltage profile by integrating SVC into our system. 3.3 Simulation with STATCOM In this part, the STATCOM is connected to bus bar 8, as shown in Fig. 3, and the bus voltage profile is presented from the simulated load flow in Table 3. This position of the STATCOM provides a significant improvement of the voltage profile since only the bus 1 “slack bus” presents the over-limit value. 3.4 Discussion From the simulation done using the load flow of NEPLAN software, we can Remarque that in the first case “without SVC & STATCOM,” we have four buses (bus6, bus8, bus1 &bus12) out of limit, so to reduce the number of buses bur that is out of limit we introduced the SVC in the ieee14 bus system. Figure 2. And the simulated load flow in Table 2 show that the number of bus bars out of limit decreased from four to two buses. In the second case, the STATCOM is introduced to the ieee14 bus system, as shown in Fig. 3, and the bus voltage profile is presented from the simulated load flow in Table 3. This introduction of the STATCOM provides a significant improvement of the voltage profile since only the bus 1 “slack bus” presents the over-limit value. Finally, we can conclude that by introducing the fact device, especially the STATCOM, we can improve the quality of our system.
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Fig. 3. The IEEE 14 bus system simulation with STATCOM connected to bus 8
Table 3. Voltage profile for IEEE14 bus system with STATCOM connected to bus 8 Node Name
U(kV)
U(%)
BUS_11
14,038
101,72
BUS_12
14,2
102,9
BUS_13
14,093
102,12
BUS_6
14,449
104,7
BUS_4
68,679
99,53
BUS_5
69,131
100,19
BUS_1
73,14
106
BUS_3
69,69
101
BUS_2
71,737
103,97
BUS_8
18,151
100,84
BUS_7
13,846
100,34
BUS_14
13,622
98,71
BUS_9
13,721
99,43
BUS_10
13,74
99,57
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4 Conclusion This paper studied the usefulness of using fact devices, STATCOM and SVC,we concentrated on their impact on the voltage profile of the IEEE14 bus power system. NEPLAN software was used to simulate the system. The results show the effectiveness of STATCOM to improve the voltage profile of the IEEE14bus when it is connected to bus bars number 8. We conclude that the integration of STATCOM in our IEEE14bus system gives a result more important than SVC in terms of the voltage profile.
References 1. Foad, H.G., et al. : Review of FACTS technologies and applications for power quality in smart grids with renewable energy systems. Renew. Sustain. Energy Rev. 82 (2018), https://doi.org/ 10.1016/j.rser.2017.09.062 2. Naderi, E., Pourakbari-Kasmaei, M., Cerna, F.V., et al.: A novel hybrid self-adaptive heuristic algorithm to handle single-and multi-objective optimal power flow problems. Int. J. Electric. Power Energy Syst. 125, 106492 (2021). doi.org/https://doi.org/10.1016/j.ijepes.2020.106492 3. Moufid, I., Moussaoui, H.E.L., Lamhamdi, T., et al. : Distribution network reconfiguration for power loss minimization using soft open point. In: 2020 Global Congress on Electrical Engineering (GC-ElecEng), pp. 38–42. IEEE (2020). https://doi.org/10.23919/GC-ElecEn g48342.2020.9286283 4. Marco, S., et al.: A comprehensive review of the integration of battery energy storage systems into distribution networks. IEEE Open J. Indust. Electron. Soc. 1, 46–65 (2020). https://doi. org/10.1109/OJIES.2020.2981832 5. Ismail, M., Hassane, E., Hassan, E.L.M., et al. : Power losses minimization in distribution system using soft open point. In: 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–5. IEEE (2020). https:// doi.org/10.1109/IRASET48871.2020.9092002 6. Diab, A.A.Z., Ebraheem, T., Aljendy, R., Sultan, H.M., Ali, Z.M.: Optimal design and control of MMC STATCOM for improving power quality indicators. Appl. Sci. 10(7), 2490 (2020). https://doi.org/10.3390/app10072490 7. Farhad, K.M.: Power Losses Minimization in Distribution System Using Soft Open Point Namdari, and Esmaeel Rokrok. “Traveling wave-based protection for SVC connected transmission lines using game theory. Int. J. Electric. Power Energy Syst. 123 (2020). https://doi. org/10.1016/j.ijepes.2020.106276 8. Benmahdjoub, M.A., Mezouar, A., Boumediene, L., Saidi, Y.: Embedded electrical network advance controlling based on SVC device and automatic voltage regulator. J. Control Autom. Electric. Syst. 31(1), 189–206 (2019). https://doi.org/10.1007/s40313-019-00550-2 9. Arya, S.R., Singh, B., Chandra, A., Al-Haddad, K.: Power factor correction and zero voltage regulation in distribution system using DSTATCOM, pp. 1-6 (2012). https://doi.org/10.1109/ PEDES.2012.6484388 10. Wei, Q., Venayagamoorthy, G.K., Harley, R.G.: Real-time implementation of a STATCOM on a wind farm equipped with doubly fed induction generators. IEEE Trans. Indust. Appl. 45.1, 98–107 (2009). https://doi.org/10.1109/IAS.2006.256657
Assessing the Impact of Digitalization on the Energy Transition Kawtar Agouzzal(B) and Ahmed Abbou Department of Electrical Engineering, Mohammed V University of Rabat Mohammadia School of Engineers, Rabat, Morocco [email protected]
Abstract. Digitization offers opportunities for innovation of the energy transition and reduction of operating costs. To achieve this, autonomous systems will need to interact with their real-world environment, commonly referred to as a “digital twin”. Keyword: MBSE · CPPS · Digital Twin · Synergies · Digitization · Energy saving
1 Introduction At every level of product design, manufacture, and production, digitization boosts efficiency across all industries, particularly those that use significant amounts of energy. Energy is needed to operate digital technologies and the new services they enable. And to the degree that digitization increases energy use efficiency, it may also motivate industry to utilize more of it, particularly when it comes to procedures that result in sizable cost reductions at little or no net cost. Manufacturing companies now have the chance to reach a completely new level of productivity because to the growing digitization of every stage of the production process. Starting with the modularity of product design and manufacturing modules, this results in increased production system engineering efficiency. The production system can respond to unanticipated occurrences with intelligence and effectiveness thanks to autonomy without the need for supervisory reconfiguration. The digital twin, a notion where information created at each step of the product lifecycle is made accessible to succeeding phases in a seamless manner, is the means to accomplish all of these objectives. This paper’s goal is to assess the fast rise in complexity of ensuring the autonomous system behaves appropriately during production in order to meet the intended production goal. Only by heavily utilizing model-based simulation, not only during design and planning but also during other stages of the life cycle for things like diagnostics and operations optimization, can this objective be reasonably fulfilled. This essay emphasizes the significance of the following four factors that will affect manufacturing’s future: Connectivity, Digital Twinning, Autonomy, and Modularity. This paper focuses on the importance of the four aspects that determine the future of manufacturing: Modularity - Connectivity - Autonomy - Digital Twinning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 480–487, 2023. https://doi.org/10.1007/978-3-031-35245-4_44
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2 Preparation Approach of the Simulation by the Concept of Digital Twin Today, simulation serves as the foundation for design decisions, validation, and testing of both individual systems and entire systems. The idea of using "twins" is a very old one. It goes back to NASA’s Apollo program, when at least two identical spacecraft were created to enable mission-specific simulations of the spacecraft’s environment. The twin was the name of one of the vehicles that was still on earth. During the lead-up to the trip, the twin was heavily utilized for training. In order to aid the astronauts in orbit in life-or-death circumstances, it was utilized to simulate alternatives on an Earth model during the flight mission. The available flight data were used to duplicate the flight conditions as exactly as possible. The life of a comparable flying twin is replicated by a digital twin, which is an integrated multiphysics, multiscale simulation of a vehicle or system that makes use of the best physical models, sensor updates, fleet history, etc. The next major thing in simulation will be to include it into the fundamental functioning of the product or system at later stages of the life cycle, for instance by delivering it prior to the actual product or by supporting the operation with simulation-based assistance (Figs. 1 and 2).
Fig. 1. Notion of a digital twin in a product lifecycle
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Fig. 2. The next development in simulation technology is the digital twin.
3 Methodology The digital twin is a very dynamic idea that gets more complex over the course of its life. The digital twin, which is delivered concurrently with the product or perhaps earlier, is built around the MBSE. It serves as the framework for the support systems while they are in use. These software solutions assist operators by making simulation-based forecasts and computing control and service decisions when used in conjunction with intelligent data techniques. Over the course of the system’s or product’s life, the models automatically change. Autonomous systems must have as much knowledge as feasible about the system’s general condition, the goods to be produced, the geometry and capabilities of the parts and tools to be employed, and their own configuration and capabilities. All of this historical information will be gathered via the idea of the digital twin and made available to the autonomous systems that are currently carrying out certain industrial procedures. Consequently, the digital twin depicts the entire status of the process and environment at any given time. By using this data and the models provided by the digital twin, the autonomous system will be able to predict the outcomes of its actions in a particular situation and adjust its course of action in response to changes in the products it produces, the volume of its output, and the automatic management of exceptions and errors without the need for manual supervision or reconfiguration (Fig. 3).
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Fig. 3. Difference between systems that are automated (b) and autonomous (a).
4 Modeling Autonomous Systems 4.1 Cyber Physical Production System (CPPS) Imagine a system of four cyber-physical manufacturing units, such as a robotic loading/unloading station with a buffer carousel, a CNC drilling machine, a CNC milling machine, and a transport system (Fig. 5). Every production unit keeps a digital doppelganger of itself that includes details like the unit ID, skills and capabilities, configuration, present statuses, and pallets it now has. Each pallet maintains a local copy of the digital twin of the component it transports (Fig. 4). All the production-related data for this CPPS is contained in the digital twin, including the number of parts in the pallet, number of parts in the pallet, and number of parts in the pallet: – Room ID and room type – The priority and production order number – A list of talents and equipment needed for each stage of the operation, together with information on the production flow such as the NC program number.
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The place and state right now Program Files (NC) Production background (e.g. which operation was performed on which machine) Bulk data, such as the NC program file, is saved in the contact memory, whereas frequently utilized information, such as part identification, is stored on the RFID.
Fig. 4. A pallet with local memory as an example
Fig. 5. Cyber-Physical Production System Model
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4.2 Model of Digital Twin Synchronization Consider the following illustration of the movement of pieces through this CPPS: – At the loading/unloading station, a part is taken out of a tray and connected to a pallet. – The tray memory is accessed to read the digital copy of the part, which is then written to the pallet memory. – The raw portion is first carried to the buffer on a pallet. – The pallet is moved to the milling machine’s input branch when it is ready. – The part’s digital twin is updated following milling. The next procedure is then awaited in the output segment. Note: The pallet can be moved to the carousel’s buffer to clear space so as not to obstruct the machine. – The pallet is brought in for processing as soon as the drill is ready. – The pallet is delivered to the buffer once the part has been drilled, and its digital twin is updated. – The part is transported from the buffer to the load/unload station, where it is taken off the pallet and put in a tray. After that, the part’s digital twin is moved from the pallet memory to the tray memory (Fig. 6).
Fig. 6. Model of part flow through the CPPS
4.3 Model of Autonomy in the Flow of Part The milling machine examines the digital twin of the component and decides that drilling is the subsequent operation. He issues an invitation to all machines to volunteer to perform
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the drilling (i.e. to the drilling machine as well as to himself since he also has the drilling skill). It compares all returning bids according to predetermined criteria (such as cost vs. time): – Case A: The milling machine sends a transport order to have the part delivered to it, and the drilling machine receives the order (this should be the typical case by design). – Case b: Because the drilling machine, for instance, has a backlog of work, the milling machine receives the order and begins to drill the part. – Case c: The milling machine sends a transit order for the part to be buffered when no equipment is able to bid (for example, the drilling machine is down and the milling machine does not have the tools). 4.4 Model of Autonomy in Handling Breakdowns Assuming that the milling machine in the CPPS example has the capacity to stamp the pallets in addition to milling and drilling. Think about the failure of a carousel buffer as an example (Fig. 7). Since the finished part in the drill has nowhere to go, this would often result in a standstill in the part flow. The transport system temporarily substitutes the milling machine, which has the ability to buffer, for the carousel buffer. To make place for the semi-finished parts to be drilled, the finished drill bits can now be sent to the milling machine’s infeed leg for buffering. By doing this, the buffer carousel would not need to be fixed until all the pieces had been machined by the CPPS. The delivery of raw parts is resumed, the finished parts in the milling machine are removed, and production resumes as soon as the buffer carousel is functioning once again.
Fig. 7. Self-adaptability example
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5 Conclusion The degree of technological use and digitization varies greatly between member states, regions, and corporate size. In this case, a thorough knowledge management of the production system’s capabilities and state-of-the-art is necessary. The production system’s collection, storage, and processing of all available (sensor) data as well as operational factors like order quantity primarily address the first topic. All desirable quantities, however, are not directly measurable, and making predictions about future behavior based solely on operating data is challenging, particularly when taking into account a flexible production system. On the other hand, combining real-world data with design simulation models can produce accurate predictions based on real-world data. In order to support operators and planners during routine operations as well as for maintenance and service using simulation-based forecasts, this enables the usage of simulation support systems. This process is made possible by the digital twin concept since all models and data are accessible in a unified and well-aligned environment. There are significant synergies between the climate and digital transitions that the industry is currently through, including the potential for energy savings and decarbonization. However, the energy business could also be disrupted by digitalization. New regulations, however, based on a thorough comprehension of the energy and digital sectors, would minimize these negative effects and emphasize the positive ones.
References 1. Forschungsunion, A.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0, Final report of the INDUSTRUE 4.0 Working Group (2013) 2. George, A.B.: Autonomous Robots. MIT Press (2005) 3. Shafto, M., Conroy, C.M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., Wang, L.: DRAFT Modeling Simulation Information Technology & Processing Roadmap Technology Area 11 (2010) 4. Siemens: A worldwide production language. Siemens Indust. J. (2014). siemens.com/industry 5. Tenorth, M., Beetz, M.: A knowledge processing infrastructure for cognition-enabled robots. Int. J. Robot. Res. (2013) 6. Wahlster, W.: Digital product memory: embedded system keep a diary, In Harting tec News 15 (2007) 7. Zühlke, D.: Smart factory - from vision to reality in factory technologies. In: Proceedings of the 17th IFAC World Congress (plenary paper), Seoul (2008)
Application of Controlled DC-Chopper to Improve the Dc-Link Voltage During a Fault Grid Zineb En-nay(B) , Ismail Moufid, Hassan El Moussaoui, Tijani Lamhamdi, and Hassane El Markhi Intelligent Systems, Geo-Resources and Renewable Energies Laboratory, Sidi Mohamed Ben Abdelah University, FST, Fez, Morocco [email protected]
Abstract. Recently, the Doubly Fed Induction Generator (DFIG) is becoming more important for power production and is widely used due to its advantages. However, it’s susceptible and fragile to any grid faults, especially its components, which are costly. This paper proposes a novel DC-Link circuit protection control approach using fuzzy logic to enhance the fault ride-through (FRT) capability of DFIG based wind turbines. The main goal of this work is to improve the performance of the traditional DC-Chopper, in which the IGBT switch is controlled by a comparator between the DC-Link voltage and the threshold voltage and replace it with a Fuzzy logic controller for DC-Chopper. The technique is evaluated for a DFIG under three-phase to the ground (3LG) and two-line (2L). Keywords: DC-Link capacitor · Fuzzy logic · DC-Chopper.
1 Introduction Recently, electricity has been generated from renewable resources, notably wind energy, widely integrated into the grid to avoid significant environmental problems [1]. Wind energy has attracted a lot of attention from many researchers. Among various wind power generators, the doubly-fed induction generator (DFIG) based wind turbine is becoming the most demanding and dominant energy conversion system due to its multiple advantages. The DFIG is connected directly to the grid through the stator windings and indirectly through the rotor windings via two power electronics converters, known as the rotor side and grid side converter. The RSC is used to control the active and reactive powers in order to capture the maximum power. GSC is used to regulate the voltage of the DC-Link capacitor and control the active and reactive powers delivered. However, DFIG-WT is vulnerable to grid voltage disturbances. During fault incident and deep voltage dip, there is an uncontrollable rotor current, which leads to a high transient overcurrent in the rotor side, and that causes a rapid and severe increase in Z. Ennay—Intelligent Systems, Geo-resources and Renewable Energies laboratory (ISGREL) Sidi Mohamed Ben Abdelah University, FST Fez, Morocco. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 488–496, 2023. https://doi.org/10.1007/978-3-031-35245-4_45
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DC-Link voltage [2]. Both the rotor overcurrent and DC-Link overvoltage can damage wind turbines, especially the converters and DC-link capacitor. Therefore, the behavior of wind turbines has been carefully analyzed in terms of transient stability when a fault occurs. The ability to ensure the safe operation of DFIG during grid faults is stated as the Fault Ride-Through (FRT) capability of the wind turbine, which is proposed in code grid requirements [3]. Several approaches in FRT were improved and studied during the fault condition. In this regard, the passive method, which consists in installing protective devices to the DFIG system such as a parallel crowbar with rotor side to restrain the overcurrent and protect the RSC [4], a series voltage compensation (SVC) [5], and a static synchronous compensator (STATCOM) [6] are discussed in many types of research. The DFIG system should incorporate DC-Link protection to ensure effective FRT capability, avoiding the damage of the power converters and DC-Link capacitor, which are expensive to replace. A DC-Link is connected in parallel with the DC-link between the power converters to limit the DC-Link voltage to an acceptable value. Conventionally, the IGBT switch should be closed when the DC-Link voltage exceeds the threshold value [7]. But this method cannot control the dissipating of the extra power charged into the DC-Link capacitor efficiently. The contribution of this paper is to improve the operation of the conventional DC-Chopper. Thus, an intelligent approach based on the fuzzy logic controller (FLC) is proposed for DC-Chopper. The new strategy will enhance the transient stability of the DC-Link. Furthermore, the FLC has the capability to work in variable ranges during the grid fault, which provides an optimal response and an effective performance. The controlled DC chopper’s effectiveness and robustness are evaluated through simulation using MATLAB/Simulink by subjecting different types of grid faults.
2 Proposed Protection of DC-Link Using Fuzzy Logic Controller 2.1 DC-Link Protection with DC-Chopper The GSC output power of the DFIG decreases during grid disturbance. Thus, an excess voltage appears in DC- Link circuit of the power converters of DFIG. When a fault occurs, the voltage between the RSC and GSC increases suddenly due to the extra energy. If a chopper resistor is inserted parallel with the DC-Link circuit, as represented in Fig. 1, the IGBT switch is in series with the chopper resistor. Normally, the IGBT switch is controlled with a comparator of the DC-Link voltage in the traditional method. Otherwise, when the value of DC-Link voltage Vdc exceeds the predefined Vth Vdc > Vth, the switch is ON state, then the transient overcurrent pass through the Rchopper and limits the overvoltage in the DC-link capacitor [8, 9]. In addition, different values of DC-chopper resistances are evaluated in different scenarios of a grid fault. Unlike the conventionnel configurations of the DC-chopper, the controlled DC-chopper based on fuzzy logic can strengthen the voltage transient stability in the DC-Link circuit. The next section describes the design of the proposed approach FLC.
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Fig. 1. DC-Chopper protection for DC-Link circuit
2.2 Design of Fuzzy Logic Controller Fuzzy methods have been applied successfully in control in several fields, and else fuzzy logic controller provides superior performance in controlling. The overall structure of the proposed FLC is shown in Fig. 2, which presents a fuzzy inference system implemented for fuzzification, a fuzzy rule, and defuzzification. This subsection describes the operation of the fuzzy logic process and uses one input and two outputs:
Fig. 2. Structure of FLC
• First step: Fuzzification The goal of the fuzzification process is to convert deterministic input variable Vdc into linguistic variables by defining member functions for VDC. For the fuzzifier program, it is required to determine the range of fuzzy variables associated with the crisp inputs. Thus, the input of fuzzy sets is defined as 3 triangular membership functions (MFs). In this case, the fuzzy sets of DC-link voltage input are referred to the rules base as “SV” = Small value, “NV” = Normal value, “BV” = Big value, on the other hand, the shape membership of the outputs are constant, is defined as "NF" = No-Fault, and "FO" = occurrence fault, which have two values 0 to indicate no fault and 1 meant that there is fault. The input of the FLC is as follows: Vdc = DC-Link voltage. The figure below presents FLC input and output for DC-chopper (Fig. 3):
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Fig. 3. Input and output of fuzzy logic controller.
• Second Step: Rules bases After the inputs are fuzzified the controller continues to make decisions. Therefore, the rules bases are designed to map an input to output using “IF...THEN...” condition. The IF part describes the situation for which the input is designed, the THEN part represents the response of this situation. The rules are determined based on the knowledge of the behavior system with and without fault, as is depicted in Table 1. The control algorithm has 3 rules, and the inference mechanism used is the Takagi and Sugeno method.
Table 1. Set of FLC rules Rules
IF Input (Vdc) is
THEN the Output is
1
SV
FO
2
NV
NF
3
BV
FO
• Last Step: Defuzzification As a final phase, the output of the fuzzy logic controller is composed of two values of the DC-Chopper switches, which are zero and one. The defuzzification method of this process is the ‘Wtaver’ Weighted average.
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3 Simulation Analysis and Discussion To evaluate the operation of the controlled DC chopper using fuzzy logic, the simulation is carried out in the MATLAB/Simulink platform by applying two different faults at the terminal of the DFIG. Triple-phase-to-ground (3LG) and two-phase (2L) fault are the most common and severe fault in power systems. Simulation was carried out for both 3LG and 1LL faults. Simulations analyses two cases of faults as illustrated in Fig. 4.a and Fig. 4.b for fault duration 300 ms, and two values of DC-Chopper resistances to validate the effectiveness of the proposed approach. During the faults the GSC is unable to transmit the power from RSC to the grid, therefore the extra energy charges the DCLink capacitor, and the value of voltage increase to 1650 V and to 1375 V for 3LG and 2L respectively. These values can damage the DC-Link capacitor. The DFIG based wind turbine was operating under a wind speed of 15 m/sec and the DC-Link voltage set at 1150 V at steady-state condition.
Fig. 4. a. The DC-Link voltage of DFIG during 3LG. b. DC-Link voltage of DFIG during 2L
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Fig. 4. (continued)
The figures below illustrate the DC-link voltage during 3LG and 2L and show the effect of controlled DC-chopper with two values of Rchopper 0.15 and 0.45. In Figs. 5.a, 5.b it can observed that the approach efficiently limits the overvoltage in the DC-Link circuit during 3LG fault, and the dangerous variations of voltage exceeds the safety limits. The present values are decreased to 1170 V, 1237 V. The Figs. 6.a and 6.b show the enhanced response of the controlled obtained for Vdc in reducing the overvoltage created during the faults period; Thus, the present value is lowered to 1170 V, and 1179 V. On the other hand, the peak of the dangerous oscillations is significantly lowered during and after the recovery, preventing the DC-Link circuit from damage. The simulation outcomes give the better response for small value that is 0.15 Ohms of DC-chopper resistance.
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Fig. 5. a. DCDC-Link voltage of DFIG during 3LG with resistance value 0.15 Ohms. b. DCDCLink voltage of DFIG during 3LG with resistance value 0.45 Ohms
Fig. 5. (continued)
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Fig. 6. a. DCDC-Link voltage of DFIG during 2L with resistance value 0.15 Ohms. b. DCDC-Link voltage of DFIG during 2L with resistance value 0.45 Ohms
Fig. 6. (continued)
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4 Conclusion To enhance the transient stability of the DC-Link circuit, a novel protection approach based on the fuzzy logic controller is proposed in this paper to verify that different scenarios of grid faults (3LG) and (2L) are considered taking into account the value of DC-Chopper resistance. The simulation results in MATLAB/ Simulink show that the proposed technique has a promising potential for limiting the overvoltage in the DC-link capacitor in two cases of grid faults. In addition, the simulation outcome gives better results for small valued DC chopper resistor, that is, for 0.15 Ohms.
References 1. Hussain Baloch, M., Ishak, D., Tahir Chaudary, S., et al.: Wind power integration: an experimental investigation for powering local communities. Energies 12(4), 621 (2019) 2. Din, Z., Zhang, J., Zhu, Y., et al.: Impact of grid impedance on LVRT performance of DFIG system with rotor crowbar technology. IEEE Access 7, 127999–128008 (2019) 3. Naderi, S.B., et al.: Optimum resistive type fault current limiter: an efficient solution to achieve maximum fault ride-through capability of fixed-speed wind turbines during symmetrical and asymmetrical grid faults. IEEE Trans. Ind. Appl. 53(1), 538–548 (2016) 4. Wen, H., Cai, S.: Modeling and LVRT analysis of DFIG wind power system. In: 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, pp. 1–5 (2015) 5. Zhang, S., Tseng, K.J., Nguyen, T.D.: Advanced control of series voltage compensation to enhance wind turbine ride through. IEEE Trans. Power Electron. 27(2), 763–772 (2011) 6. Nguyen, T.H., Lee, D.C.: Advanced fault ride-through technique for PMSG wind turbine systems using line-side converter as STATCOM. IEEE Trans. Industr. Electron. 60(7), 2842– 2850 (2012) 7. Pannell, G., et al.: Evaluation of the performance of a DC-link brake chopper as a DFIG low-voltage fault-ride-through device. IEEE Trans. Energy Convers. 28(3), 535–542 (2013) 8. Okedu, Kenneth E., Muyeen, S.M., Takahashi, R., et al.: Wind farms fault ride through using DFIG with new protection scheme. IEEE Trans. Sustain. Energy 3(2), 242–254 (2012) 9. Rini Ann Jerin, A., Kaliannan, P., Subramaniam, U., Shawky, El., Moursi, M., et al.: Review on FRT solutions for improving transient stability in DFIG-WTs. IET Renew. Power Gen. 12(15), 1786–1799 (2018)
Machine Learning Based Predictive Maintenance of Pharmaceutical Industry Equipment Fatima-ezzahraa Ben-Bouazza1,2(B) , Oumaima Manchadi1 , Zineb El Otmani Dehbi1 , Wajih Rhalem4 , and Hassan Ghazal1,3 1
3
Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco [email protected], [email protected] 2 LaMSN, La Maison des Sciences Num´eriques, Saint-Denis, France National Center for Scientific and Technical Research (CNRST), Rabat, Morocco 4 Laboratory of Electronic and Biomedical Engineering (E2SN), National High School of Arts and Professions (ENSAM), Mohammed V University in Rabat, Rabat, Morocco
Abstract. Medical equipment represents the core of the medical field. Therefore, it is essential to maintain it in good working order to guarantee maximum availability and minimum breakdowns. With all the advances in digital healthcare technologies, such as Artificial Intelligence (AI) and Machine Learning (ML), the future of healthcare is evolving and is being shaped step by step. These advancements are the main drivers of implementing new maintenance strategies that enhance the performance of the equipment. This paper discusses a Predictive Maintenance (PdM) technique for identifying equipment defects and repairing essential equipment that exhibits a variety of frequent failure modes. The selected equipment for this study is a compact particle accelerator also known as the Cyclotron from a pharmaceutical laboratory in Casablanca, Morocco specialized in radiopharmaceuticals production for diagnostic use. The Cyclotron was selected due to its frequent failures and deteriorating performance and efficiency. The dominant and most frequent failure mode occurs in the target system, representing one of the equipment’s nine central systems. This review focuses on implementing an AI and MLbased system for the predictive maintenance of the Cyclotron. Extensive experiments were conducted to analyze the proposed approaches.
Keywords: Machine learning maintenance · Cyclotron
1
· Medical equipment · Predictive
Introduction
The healthcare market is constantly striving to improve the operational efficiency of health systems, control risks, and ensure diagnosis accuracy through cuttingedge technology [19]. As a result, the amount of medical equipment required to treat patients has doubled over the last decade. The increasing number of pieces c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 497–514, 2023. https://doi.org/10.1007/978-3-031-35245-4_46
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of equipment contributes significantly to rising healthcare expenses, contradicting the emphasis on making healthcare inexpensive. These devices are affected by degradations and failures caused by environmental and operational conditions generating downtimes [10]. Any unplanned downtime can have serious potential consequences involving patient safety and the delivery of patient care. Due to the complexity and criticality of equipment, an effective maintenance management plan becomes critical, even more so when budgets are restricted and a balance between maintenance costs and service level is required. Maintenance is the collection of all technical, administrative, and managerial actions taken throughout the life of an item to preserve or restore it to a state where it can perform the required function. Maintenance management approaches can be classified into three broad categories as illustrated in Fig. 1.
Fig. 1. Maintenance strategies.
– Corrective maintenance (CM) [23]: considered the simplest maintenance strategy, and often referred to as reactive maintenance or run to failure maintenance, this type of maintenance entails intervening following a breakdown. The device is permitted to function indefinitely. – Preventive maintenance (PM) [2]: consists of inspecting and maintaining equipment while it is functioning to minimize the likelihood of a breakdown. Preventive maintenance can be scheduled in advance or depending on usage (e.g., every 100 km). While this strategy helps reduce failures, unnecessary inspections and unforeseen failures continue to occur, increasing maintenance expenses. – Predictive maintenance (PdM) [17]: this strategy is based on predicting the future health state of a machine using condition monitoring data. This method seeks to forecast when, where, and which components may fail. As can be concluded, it is more efficient to implement a PdM approach since, contrary to CM and PM approaches, PdM is only applicable when the need for maintenance actions arises (see Fig. 2. Moreover, a PdM strategy increases availability and prolongs the equipment’s life because it allows continuous monitoring of the equipment and prevents unnecessary maintenance activities, thus minimizing the downtime and reducing the number of fatal breakdowns. It consequently leads to a higher level of safety. Although it is expensive to implement a PdM
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system, it is a beneficial long-term business case since a PdM strategy reduces the number of spare parts in stock and the overall storage size while maintaining a proper maintenance procedure [14].
Fig. 2. PdM purposes.
Over the last few decades, many research efforts have been conducted to develop different models for predictive maintenance, especially in the industrial field [3]. The most traditional approach is the knowledge-based model based on determining the similarity between an observed event and a database of failures previously defined and then deducing future faults or life span from the previous events. The emerging models are the Data-driven models performed by learning the behavior of physical assets based on the acquired operation data. In this approach, knowledge about machine states is extracted internally from equipment operation data instead of externally from experts. The Data-driven models are classified into: Machine learning techniques that are considered powerful and effective solutions for PdM, such as Artificial Neural Network (ANN) [24], decision tree (DT) [15], Support Vector Machine (SVM) [5], and k-Nearest Neighbors (k-NN) [13], etc. Statistical techniques that represent regression methods based on linear or algorithmic functions, some commonly used methods in this approach are Stochastic filtering [12], Particle filters [8], etc. And Deep learning techniques [18] gaining a high ability in feature learning and fault prediction using multilayer nonlinear transformations such as Auto-Encoder (AE) and Convolutional Neural Network (CNN) and other models widely used in PdM. The concept of PdM has existed for years in the industrial, particularly in the environment of Industry 4.0 [25], agricultural and aeronautical fields. Only recently has this concept surfaced in the biomedical field. The rest of the paper is structured as follows: The study case and its objectives are described in the “Study Case” section. The proposed approach is presented and demonstrated using the case study in the “Proposed approach” followed by “Experiment results and discussion” section, where the evaluation metrics employed for choosing the appropriate classification method are defined, and
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the study results are presented and discussed, and finally a conclusion and future work in the last section.
2 2.1
Study Case: The Cyclotron Context and Objectives
In our case study, we have selected to work on the cyclotron. The cyclotron is a particle accelerator that produces the fluorine 18 by irradiating Oxygen 18, a naturally occurring stable oxygen isotope, with a proton H +. Fluorine 18 is the most often used radioactive tracer in the positron emission tomography (PET). PET is a functional imaging type that uses radioactive compounds called radiotracers by injecting them intravenously to visualize and quantify changes in metabolic processes and other physiological functions. The cyclotron is an expensive and complex piece of equipment. This particle accelerator is a critical equipment with a high risk of failure, any variation of its parameters can indicate the start of a malfunctioning state. Therefore, it requires continuous control since its breakdowns can generate damages and detrimental consequences on the equipment’s performance and its remaining useful life. This study aims to prevent the cyclotron from fatal failures and to perform better monitoring of its condition. Thus we opted to follow a predictive maintenance strategy for the Cyclotron based on data simulation and advanced machine learning methods [22]. This approach will improve equipment performance by increasing maintenance effectiveness. We conducted a Failure Mode and Effects Analysis (FMEA), also known as a Failure Mode, Effects, and Criticality Analysis (FMECA), on the selected equipment to undertake this investigation. This study identifies and treats probable failure causes in advance of their occurrence [9]. This analysis will enable us to establish which machine systems are the most vital and malfunctioning in our scenario. 2.2
Cyclotron: Overview
The cyclotron is a particle accelerator designed in the early 1930s s by Ernest Orlando Lawrence and Milton Stanley Livingston of the University of California, Berkeley 3. It is the most commonly used device for accelerating particles to sufficient energies to initiate the required nuclear reactions. E. Lawrence had the brilliant idea of bending the circle of particles in a linac and accelerating them repeatedly using the same electrode arrangement. This concept underpins all modern cyclotrons and has resulted in the cyclotron becoming the most extensively utilized particle accelerator type (Fig. 3). This particle accelerator uses the combined action of an electric field and a magnetic field to confine and accelerate particles in a restricted space to deliver a beam of high-energized hydrogen particles [21]. The device consists of two halfcylinder-shaped cavities called the Dees, separated by a small gap. A charged particle emitting device (Ion source) is located near the center. The ensemble
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Fig. 3. Cyclotron
is submitted to a high vacuum. The hydrogen particle source is located in the center of the acceleration chamber. After being ionized by an ion exchanger, the particles are accelerated by a high-frequency electric field E, which is present only in the space between the dees. This electric field accelerates the particles, receiving a “quantum” of energy at each passage, which increases their speed. At the same time, they are also submitted to an electromagnetic field B applied perpendicularly to the velocity V , which bends their path to describe a circular trajectory of radius as follow: R=
m×V (q ∗ B)
The particle then repass in the acceleration area. They then follow a spiral path, until they reach the edge of the machine. The cyclotron allows the creation of virtual orbits because of the electromagnetic field. The ion moves from one virtual orbital to another so that the energy is equal to 16.5 Mev. It is known that E = m × v 2 with v = d/t, if the distance between the cyclotron center and the virtual orbitals increases over time, then the velocity also increases, this causes the energy to increase since the mass of the proton (H + ) remains constant. The accelerated H − ions reach adequate energy from the last trajectory and are ejected towards a carbon foil (activated carbon filter); during the passage through the foil, the negative ions lose their electrons, thus converting the incoming negative ions to positive ions. These accelerated protons are then guided and focused on the target to allow the bombardment. We put a small quantity of 18 O enriched water (H218 O) in this target. Oxygen is an element close to fluorine in the periodic table. The oxygen 18 nuclei are bombarded with protons (H + ) accelerated by the cyclotron; a nuclear reaction will take place and transform them into fluorine 18 by the reaction [1] (p, n)(see Fig. 4: All the operations taking place in the cyclotron are automated. The control system directs the beam to the target and irradiates it with the required current.
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Fig. 4. Bombardment reaction by
18
O oxygen.
The interlock system allows for managing malfunctions and operational errors to protect the personnel and the equipment. The cyclotron is composed of nine central systems, which comprise sub-systems, each destined to perform a primary technical function that enables it to operate correctly. The systems of the equipment are the following: – Magnet system: This system provides a magnetic field that keeps the accelerated particles in controlled orbits. The magnetic system consists of the magnet, the coils that produce and increase the magnetic field, the magnet power supply (PSMC) responsible for delivering the necessary current to the magnetic system, and the water cooling circuits or water connections that provide cooling for the magnet. The cooling water is monitored by a flow meter. – Radio Frequency system (RF system): This system produces the accelerating voltage of the cyclotron, which is a high voltage oscillating at a high frequency. The RF system consists of 2 hollow D-shaped metal electrodes (Dees) and an RF generator that powers them inside the vacuum chamber, allowing the acceleration of the particles. The cyclotron control system automatically controls the operation. This system also permits the extraction of ions from the ion source. – Ion Source system: generates the negative ions H- that need to be accelerated. The ion source is located in the center of the cyclotron. This system consists of the ion source that produces the H- ions, the ion source power supply (PSARC) that delivers a constant current to the ion source, and the gas handling system that supplies the H2 gas to the ion source while keeping its flow rate stable. – Extraction system: This system is responsible for converting accelerated H- ions into protons using the extraction foil technique (thin carbon foil) and directs the particle beam to the selected output port. – Diagnostic system: This system monitors the beam current at different positions in the cyclotron and the target system to control the beam from the ion source to the target. – Vacuum system: It allows the vacuum chamber to be evacuated to the required pressure level to let the particles be accelerated with minimum beam
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losses. The vacuum also provides isolation for high voltage RF fields. This system includes the cyclotron vacuum chamber, pumps, valves, and gauges. – Target system: When the particle beam has passed the selected extraction foil, it will hit the corresponding target at the Cyclotron beam exit. The beam enters the target chamber through a double-foil assembly which is a part of the target. This bombardment will result in a nuclear reaction. – Cooling water system: The accelerator consumes electrical energy that dissipates as heat. This system allows evacuating an important part of the heat coming from different subsystems. – Accelerator Control system: Its purpose is to control the various processes of the accelerator under the Master System command.
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Proposed Approach
Since the beginning of the cyclotron’s life cycle and throughout its operation, its maintenance has been based on preventive and corrective maintenance strategies. This study aims to optimize the performance of the equipment and its reliability, improve the quality of its maintenance, and increase its life span. Therefore, the proposed approach for transforming the cyclotron mere maintenance strategy to a PdM strategy consists of the following steps as illustrated in Fig. 5:
Fig. 5. Steps of the proposed approach.
3.1
Develop FMECA for the Cyclotron
As previously mentioned, the cyclotron is composed of several different systems, each of them having a specific technical function for whichf it is intended. In
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Fig. 6. Functional decomposition table headers.
our study, we will focus on one of these systems. The selected unit will be the unit with the highest failure rate. Therefore, this step consists of performing a Failure Mode, Effects, and Criticality Analysis (FMEA) on the Cyclotron. The FMECA is a predictive analysis technique that estimates the risks of failure and the consequences on the proper functioning of the equipment and engages the necessary corrective actions. Its main objective is to obtain maximum availability. This method is based on a multi-step process. Like other approaches, we start with a preparation phase that involves collecting the information needed to conduct the study and creating a working group. The different phases are as follows: – Functional decomposition analysis: The Cyclotron decomposition step is necessary because it is essential to have a perfect knowledge of the functions of each machine to analyze the risks of malfunctioning. To this end, we will divide the Cyclotron down to the level of its sub-systems. To determine the elements to be identified as well as their functions, we will exploit the technical documentation available on the system, allowing us to divide the Cyclotron into functional units, which will, in turn, be decomposed into sub-systems while determining the technical function of each component of the machine. The results of the analysis are organized in a table with the headers illustrated in Fig. 6. – Failures and effects identification: This step consists of identifying all the potential failure modes, determining the effects of each failure mode, and searching for their most probable causes. To achieve this task, we rely on the functional decomposition analysis.
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Failure modes: It is how a system can break down and fail. In other words, the equipment becomes unfit to perform its function. A failure mode is relative to the function of each element. Causes of failure: It represents the initial event that can lead to the malfunction or failure of the machine. Three types of causes can lead to failure: Internal causes to the equipment - External causes due to the environment, to the operation. - External causes due to the workforce Failure Effect: The failure effect is the consequence that the machine and the user will endure. It depends on the mode-cause of the defect. Example: Production shutdown, equipment deterioration. Severity determination (G): The severity G represents the gravity of the failure effects in terms of productivity losses (production shutdown, quality defect), maintenance costs, safety, and environment. G is rated on a scale of 0 to 4, where 0 is the lowest severity, and 4 is the highest. The occurrence frequency determination (F): The occurrence frequency F relates to the frequency of failure occurrence. This frequency expresses the combined probability of failure mode occurrence by the failure cause occurrence. F is rated on a scale of 0 to 4, where 0 represents the probability that a failure occurrence is practically impossible, and 4 is the certainty of a failure appearing. The failure detection mode determination (D): Detection mode D refers to how a user is likely to detect the occurrence of a failure. Detectability is an essential element. Not being able to predict a failure will cause the system to have a greater risk of shutdown. D is rated on a scale of 0 to 4, where 0 indicates the presence of sensors capable of determining the start of a failure, and 4 indicates that the malfunction is undetectable or that its location requires in-depth expertise (Figs. 7, 9 and 10). The criticality evaluation (C): Criticality is a quantitative evaluation of the risk measured from the combination of the three factors mentioned above: – The frequency of the occurrence of the mode-cause pair. – The severity of the effect. – The possibility of using methods of detection. Calculated by the formula: C = G × F × D , it aims to evaluate the level of risk associated with equipment functionality, allowing us to decide the action to be taken. We have classified the criticality into four levels: Level A: Negligible criticality Level B: Medium criticality Level C: High criticality Level D: Very high criticality Figure 6 sums up the steps of the study.
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By summing up the criticalities of the failures related to the same system, we obtain the total criticality of each one presented in Table 1.
Fig. 7. FMECA steps.
Table 1. Systems hierarchization according to their total criticality. Systems
Total criticality
RF system Target system Vacuum system Magnet system Extraction system Cooling water system Ion source system Diagnostic system Accelerator Control system
233,00 113,00 98,00 88,00 84,00 61,00 42,00 36,00 24,00
According to this hierarchy of the total criticality of each system, we conclude that the most critical system is the RF system, followed by the target system. During our analysis, we noticed that the effects of the failures occurring in the RF system lead to considerable damage to the cyclotron functioning. However, the frequency of their appearance is very infrequent, close to a few minor failures per year. The target system is considered the most failing system, having a failure frequency that reaches 1 to 3 breakdowns per week, leading to a production shutdown. Therefore, the system upon which we will conduct our study is the Target system. 3.2
Feature for Fault Detection
During this step, we will determine the indicators of the target system that enable us to detect faults that precede the occurrence of the failures identified in the previous step. The FMECA allows us to understand the physics of failure modes. Consequently, it will narrow down the parameters to be collected. In our
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Fig. 8. The target subsystems.
case, we selected the features based on the engineer’s expertise and experience with the cyclotron and its systems. Figure 8 illustrates the target system and its four principal units which are: – Target Flange: Responsible for the mounting and fixation of the target on the cyclotron. No failure has been registered in this unit. – Target: The target unit comprises different subsystems, each fulfilling a specific function. The front flange guides the target to the proper position on the cyclotron. A Helium cooling flange that contains the helium responsible for the foils cooling. The target chamber is a chamber containing the enriched product to be irradiated. Finally, we have the connection plate fixed to the target through the rear flange. This plate allows us to supply the target system with the necessary liquids and gas. The target unit has a high failure rate and requires feature monitoring to detect errors and avoid breakdowns. The selected features of this unit are the heat load of the cooling chamber and the helium cooling flange, the helium cooling flange pressure, the liquid volume within the target, the target leakage control, and the acceptable leakage rate. – Target Panel: This unit consists of pipes responsible for the supply of cooling water from the cooling water system to the target, and pipes for cooling helium that supplies the helium received from the helium cooling system for foils cooling. The system is also composed of liquid target fillers (LTF), considered a liquid target filling system that allows an automated filling of the liquid targets. This subsystem has a high risk of failure. The selected features to detect errors are the LTF flow rate, the target filling duration, and the pressure of the pipes for cooling water and the pipes for cooling helium. – Helium cooling system: The purpose of this system is to cool the foils of each target. The selected features are the helium cooling system pressure and flow rate.
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The Isotope production duration feature is also selected to allow us to determine the operating state of the target system. Table 2 represents all the selected features, each with its limit values. If the value of an indicator exceeds its limit value, this indicates an error that may lead to system failure. Table 2. Target system selected features Features
Critical values
TL of the cooling chamber Q = m × Cp × ΔT
CT < 480W
TL of the Helium Cooling flange Q = m × Cp × ΔT CT < 30W P of the Helium Cooling flange P × V = α
0.3M P a < P < 0.5M P a
Target Volume P × V = α
0.3ml < V < 1.2ml
LTF debit D = V / t
1ml/min < D < 5ml/min
LTF time
T < 30s
Pipe P for Cooling helium
0.3M P a < P < 0.5M P a
Pipe P for cooling water
P < 0.1M P a
P of the helium cooling system
0.3M P a < P < 0.5M pa
Isotope manufacturing time
60min < T < 120min
Target Leakage Control
4bars < P < 5bars
Acceptable leak rate
τ < 10psi/h
Flow rate of the helium cooling system
3IN/s < D < 3IN/s
TL: Thermal load Q = m × Cp × ΔT where Q is the heat load or TL and m is the mass flow rate (g/s) Cp is specific heat of the system ( J/(kg ◦ C)) ΔT is the temperature variation (K ou C) P is the pression P × V = α ⇔ P = α/V where α : Constant V : Volume D : Output D=V/t 3.3
Data Collection
The approach followed, in this case, is a data-driven approach. Therefore, this step consists of collecting the data. Once the needed features to determine equipment health are identified and selected, data covering both the defective and healthy conditions is collected. In our case, this data collection is integrated into the equipment. Therefore, there is no need for additional hardware or processing. The data we were able to collect was not sufficient to properly train our machine
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learning model and effectively predict defects, so we resorted to artificially manufactured data, “synthetic data.” This synthetic data is created algorithmically. After generating the data, the next task is data preprocessing to transform the raw data into a more meaningful, efficient, and understandable format. It must be mentioned that this entails archiving the data collected. There are numerous storage techniques (cloud, local, etc.) that vary according to the user’s preference and infrastructure. Data storage is not addressed in this framework.
4 4.1
Experiment Results and Discussion Evaluation Metrics
Various predictive models based on machine learning like decision trees, support vector machines support vector machine (SVM), logistic regression can be employed to analyze and classify the data into distinct categories. The data collected is split into two categories: training and test. A predictive model is constructed using the training set while the model is evaluated using the test data set. Employing assessment metrics is to assess and quantify the machine learning model’s quality. It is required to examine our model using a variety of assessment measures to do this evaluation. Indeed, a model may perform well on one measure of an evaluation metric but poorly on another. Evaluation metrics are critical for ensuring that our model operates effectively and appropriately. The data used in our study is imbalanced. An imbalanced data set signifies having more instances of one class than the other. In our case, it implies that there are more instances of data covering the healthy state of the machine than data covering the faulty conditions. The concern about training the model with an imbalanced dataset is that the model will be skewed towards the majority class only, which is a challenge when we are only interested in predicting the minority class. Consequently, the classification is inadequate. Therefore, we will use different model evaluation metrics that consider the data distribution. The metrics used in this study to evaluate the model are as follows: Precision: The accuracy of a positive prediction performed by the model. It is the ratio between the True Positives and all the Positives. The precision value is always comprised between 0 and 1 : TP: True Positives FP: False Positives P recision =
TP TP + FP
Recall/Sensitivity: Represents the measure of our model properly identifying True Positives. TP Recall = TP + FN F1 score: The F1 score is a metric for evaluating the performance of classification models with two or more classes (two classes in our case). It is especially
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used for problems using imbalanced data. The F1 score summarizes the precision and recalls values into a single metric. Mathematically, the F1 score is the harmonic mean of precision and recall, expressed by the following equation: F 1score =
2 1 precision
+
1 recall
=
2 ∗ (precision ∗ recall) precision + recall
ROC curve: Or a receiver operating characteristic, is a metric for the performance of a binary classifier. The ROC metric is often illustrated as a curve representing the True Positive rate and the false positive rate. ROC curve is a metric for the performance of a binary classifier. The ROC metric is often illustrated as a curve representing the True Positive rate and the false positive rate. Figure 9 shows that a perfect classifier is characterized by a TPR of 1 and a FPR of 0. While a No-skill classifier has as many TP as FP and realistic curve made by iterating over different thresholds for the model and finally a worse than no-skill classifier can be transformed into a skillful classifier by selecting the opposite of its output. 4.2
Results Discussion
In this study we chose to compare between different popular methods which are: Table 3. Comparison of different techniques using precision, recall and f1 score. Precision Recall F1 score Logistic regression 0.47
0.50
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SVM
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0.70
0.80
Adaboost
0.94
0.77
0.83
Balanced bagging
0.99
0.96
0.97
Fig. 9. ROC curve explanation
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• AdaBoost [16]: Boosting is a method for constructing a robust classifier from a collection of weak classifiers. It is accomplished by first developing a model using a random sample of selected training data and then developing a second model that seeks to correct errors and compensate for the first model’s inadequacies. Models are mixed and added until the training set is perfectly predicted. AdaBoost was the first boosting method for binary classification to be successfully developed. • Balanced bagging [20]: Bagging, aka Bootstrap aggregation, is an ensemble learning technique that enhances the performance and precision of machine learning algorithms. It is used to address bias-variance trade-offs and decreases the variance of a prediction model. Bagging avoids data overfitting and is employed for regression and classification models, especially for decision tree algorithms. • SVM [7]: “Support Vector Machine” (SVM) represents a supervised machine learning algorithm that can be employed for classification or regression problems. It is, however, primarily used in classification problems. It aims to separate the data into classes using a hyper-plane (separating line) so that the distance between the different groups of data and the separating line is maximal. The “support vectors” are the data closest to the hyper-plane. • Logistic regression [11]: Represents a supervised machine learning technique. It is used to measure and predict the probability of a binary event (yes/no) happening. Therefore its outcome must be a categorical or discrete value. Logistic regression is very identical to Linear Regression except that Linear regression is used as a regression model, whereas Logistic Regression is an example of a classification model. Logistic regression is called for the function employed at the core of the “Logistic Function” method, also known as the sigmoid function. It is an S-shaped curve that can map any given real number into a value between 0 and 1, but never precisely at those exact limits. As one can see, the basic method which is the logistic regression had given very limited results flowed by the SVM model. This can be explained by the fact that the data collected was imbalanced. On the other hand, the AdaBoost method showed promising results in terms of precision, however, in terms of sensitivity it is still not that promising. Finally, the faulty states of the machine are much less occurring than the normal states which gives us imbalanced data. Hence, the balanced bagging method, had worked very well compared to the other proposed models. By analyzing Table 3 we conclude that the most appropriate model is balanced bagging model located in the top left corner by analyzing the ROC curve. Hence the selected classification method for our study is the balanced bagging method. The model can be used for predicting the future state of the target system.
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Fig. 10. ROC comparison of the proposed models
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Conclusion
Sudden machine failures can result in entire factories being shut down, incurring tremendous expenditures. Thus, early detection of machine anomalies is critical for avoiding such downtimes. In this research, we offer a highly generalizable endto-end procedure for detecting equipment anomalies using only primary machine data in the example of the cyclotron. The predictive maintenance strategy enables the prevention of unplanned failures, saves maintenance costs and penalties, and increases customer comfort and security [4]. Implementing a predictive maintenance strategy, on the other hand, involves numerous obstacles, including linking physical assets, obtaining essential data, and establishing accurate forecasting algorithms. On the other hand, effective predictive solutions remained prohibitively expensive and complex to execute. Additionally, recent industry progress has increased machine complexity, making it more challenging to predict failures using standard methods. Simultaneously, machine learning approaches have gained traction across various applications, from computer vision to natural language processing, from medical applications to games [6], including predictive maintenance and anomaly detection. This study provides an overview of the challenges and state-of-the-art for predictive cyclotron maintenance and a potential solution strategy. We do not give a wholly developed technical solution but offer extensive insight into the foundations and issues associated with a machine learning-based predictive maintenance solution. As a result, we may overlook technical difficulties that become apparent during solution implementation. As for our future projects, our goal is to implement an IoT-based PdM methodology by installing IoT sensors to improve bandwidth, extract more realtime data, and improve our classification method.
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21. TanakaH, H., et al.: Characteristic evaluation of the thermal neutron irradiation field using a 30 MeV cyclotron accelerator for basic research on neutron capture therapy. Nucl. Instrum. Methods Phys. Res., Sect. A 983, 164533 (2020) 22. Wagner, H.N.: A brief history of positron emission tomography (pet). In: Seminars in Nuclear Medicine, vol. 28, pp. 213–220. Elsevier (1998) 23. Wang, Y., Deng, C., Wu, J., Wang, Y., Xiong, Y.: A corrective maintenance scheme for engineering equipment. Eng. Fail. Anal. 36, 269–283 (2014) 24. Zhang, Z.: A gentle introduction to artificial neural networks. Annals Transl. Med. 4(19), 370 (2016) 25. Zonta, T., da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P.: Predictive maintenance in the industry 4.0: a systematic literature review. Comput. Indus. Eng. 150, 106889 (2020)
Weibull and Extreme Value Theory Approach to Estimate Wind Energy in the North Region Hind Sefian1,3(B) , Fatima Bahraoui1,3 , and Zuhair Bahraoui2,3 1 Laboratory of Heat Transfer and Energetic, Faculty of Sciences and Techniques, Tangier,
Morocco [email protected] 2 ESTSB, Department of Mathematics, University Chouaib Doukkali, Aljadida, Morocco 3 Meteorological Center of Tangier, Tangier, Morocco
Abstract. Wind speed fluctuates rapidly over time, the statistical characteristics of wind and the selection of suitable wind turbines are critical for optimizing profits from wind energy production and designing wind farms. To arrive at a sophisticated estimate of the energy, statistical methods must be used, The goal of a wind potential estimate is to provide the most precise estimate of wind energy recoverable on the installation site at the height of the wind turbine mast; in the presented work The estimation of Weibull parameters and the EVD is generally carried out by the maximum likelihood method eventually this approach will be compared to the EVD method in order to find out the most accurate statistical method for the north region of morocco. Keyword: Wind speed · Statistical methods · Weibull method likelihood method · EVD · Wind energy
1 Introduction In view of the wind energy potential existing on north region of Morocco, its use for the development of electrical energy in the region seems favorable. An advantageous solution for North Region is the recovery and transformation of wind energy into electrical energy. The data obtained from Meteorological center of Tangier using the Weibull distribution [1], the density distribution of the wind and expresses the statistical estimate of the wind energy potential. Then, the wind direction is established for the orientation of the wind turbines in the site. Finally, the evaluation of the prediction of wind potential is effectuated while relying on the consideration of the judicious choices of wind turbines and their load factors. The aim of this work is to verify whether wind power can meet the energy demand in tangier region. In the theoretical phase, it is a question of determining the available wind power profile and the energy estimation in this region. A comparison between two statistical distribution is made to check whether the demand for energy is met. Wind measurements are taken on the site identified for the future wind farm. A mast, equipped with anemometers and weather vanes placed at different heights, is installed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 515–522, 2023. https://doi.org/10.1007/978-3-031-35245-4_47
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for at least a year to assess the direction and average speed of the wind, which changes with the seasons. To predict wind production for horizons ranging from a few hours to a few Days, the usual process then has two successive stages. The first step is to predict the evolution of meteorological conditions, and more particularly the wind, near the production site. The second step is to convert these wind predictions into power predictions. Each of these steps influence the accuracy with which production can be predicted. The predictability of weather conditions places limits on the predictability of wind production. The uncertainty associated with meteorological predictions, the objective of an estimate of wind potential is to give with the greatest possible precision the wind energy recoverable on the installation site at the height of the wind turbine mast. The choice of turbines and their precise location requires determination more accurate wind and turbulence conditions, considering the factors local. The theoretical yield is then estimated, along with an uncertainty calculation. The wind is one of the most difficult weather phenomena to predict. It needs the use of forecasting methods to make a reliable prediction of the wind speed to confirm the efficiency of the wind farm being used. However, the experience feedback from operating parks frequently shows a production achieved lower than estimated production, with deviations of up to 20%. The main contribution to these discrepancies probably comes from the uncertainty about the determination of meteorological quantities. This is particularly the case for sites in complex terrain, for which the methods and tools currently used are often faulted [1–15]. The objective of the work is to use a computer modeling tool and statistics to predict the wind. We focused more particularly on parametric and non-parametric methods which are very popular and show great efficiency in the field of wind forecasting, Many researchers have been proposing several methods to predict the wind potential of a specific wind field Qin et al. [1] proposed to use Gaussian kernel function for the density estimation of wind speed probability distribution. Wang et al. [2] compares the popular parametric and non-parametric models for wind speed probability distribution and the estimation methods for these models parameters. C.Kalaiselvan and L.Bhaskara Rao [3] simulated the reliability techniques of parametric and non-parametric method. Arslan et al. [4] used the WBL distribution to study WSPDs of Turkish sites. Khlaifat et al. [5], demonstrated that the most adequate distribution for fitting WSPDs for four Australian sites was the WBL distribution.Wacker et al. [6, 7] estimated the energy production of wind turbines using the WBL distribution and two other parametric distributions. Alrashidi et al. [8] made comparison between the WBL distribution and mixture parametric distributions in order to arrive at the fitting distribution. Alfawzan et al. [9] estimated wind energy potential of a Saudi Arabian site by estimating WBL parameters with four typical methods. Kantar and Usta [10] modeled wind speed data and estimate wind power density using an upper-truncated Weibull distribution. Chellali et al. [11] compared wind speed distributions derived from the maximum entropy principle and Weibull distribution. Satyanarayana et al. [12] demonstrated An assessment of wind energy potential of two Indian regions. Different methods for wind speed modelling have been studied such as autoregressive moving average (ARMA) models (Kennedy and Rogers, 2003) and Markov chain (MC) models (Jones and Lorenz, 1986). The simplicity of MC makes it a valuable tool as shown by their use in many recent studies.
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2 Data Characteristics The statistical characteristics of the wind and the selection of appropriate wind turbines are important for maximizing the benefits of wind power generation and designing wind farms, as wind speeds fluctuate rapidly over time. It is essential to use statistical methods to arrive at a sophisticated estimate of energy. For modeling, we used 5-year (2013–2015) data of hourly mean wind speed (MHVV) recorded in Tangier. For the modeling, we used data from 5 years (2013–2018) of the hourly wind speed averages (MHVV) recorded in Tangier. Figures 1 and 2 show typical fluctuations on the hourly scale and daily. Comparison of hourly variations for the months of January and July shows the appearance of regular winds due to increased thermal circulation in summer. Such a finding is important for the storage of wind energy. The same figure shows that on the hourly scale, the amplitude of the fluctuation is significant especially in summer. However, a significant and lasting reduction in wind energy is less problematic than a slight fluctuation in it (Fig. 3).
Fig. 1. Evolution of the maximum daily wind speed in Tangier.
Fig. 2. Evolution of the maximum daily wind speed in Tangier (2017).
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Fig. 3. Tangier Boxplot
3 Statistical Analysis Table 1 presents the data statistical details, it can be notice by using The skewness and kurtosis the asymmetric of the data. The choice of the wind speed distribution function gives a significant impact on the estimation of the available wind energy. Table 1. . WSP
Number of data
MIN (m/s)
MAX (m/s)
AVERAGE (m/s)
SKEWNESS
KURTOSIS
Tangier
1824
3
16.7
7.977
0.681
0.200
4 Likelihood Method It is a technique which, under the assumption that the variables have a known distribution, usually the normal distribution, makes it possible to estimate the parameters of a model (of an equation or of a system, linear or nonlinear) with restrictions on the parameters (coefficients, matrix of variances and covariances) or not. More specifically, the technique consists of constructing a function called the likelihood function (constructed from the density function) and maximizing its logarithm with respect to the unknown parameters. n f (xi , θ ) L(x1 , . . . , xi , . . . xn ) = f (x1 , θ ) × f (x2 , θ ) × . . . . . . f (xn , θ ) = i=1
5 Weibull Distribution The Weibull distribution is one of the widely accepted approaches to statistically assess the behavior of the wind at a particular site. In this method, variations in wind speed are
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characterized by the probability density function (PDF) and the cumulative distribution function. The PDF indicates the fraction of time or the probability of prevalence of the wind speed in a certain direction and the CDF indicates the probability that the wind speed is equal to or less than the average speed. k v k−1 ( v )k ( ) e c (1) f (v) = c c And the distribution function or the cumulative function of distribution (Fig. 4): F(v, k, c) = 1 − e−( c )
v k
(2)
Fig. 4. Weibull density distribution
6 Extreme Value Theory Theory of extreme values the theory of extreme values proposes to approximate the tail of an experimental distribution by a particular theoretical law and then to make estimates from the latter. The extreme value theory aims to study the law of the maximum of a sequence of real random variables even if, and especially if, the law of the phenomenon is not known. Formally, consecrated! X1, X2 (,…, Xn) a sequence of n random variables (v.a.) independent and identically distributed (i.i.d) with distribution function F defined by: F(x) = Pr (Xi ≤ x) pour i = 1, . . . n
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To study the extreme behavior of events, we consider the variable random MN = max (x1 , x2 , . . . . . . xn ) the maximum of a sample of size n. Since the random variables are i.i.d., then the distribution function of Mn is given by Mn = Pr (Mn ≤ x) = (F(x))n −1 Fa,b,s (x) = exp −(1 + ε((x − a/b ) s if ε = if ε =0 Fa,b,s (x) = exp{−exp((−x − a/b )} where: a: is the location parameter b: is the scale parameter ε: is the shape parameter (Fig. 5)
Fig. 5. EVD density distribution
7 Wind Energy Estimating the wind energy available at a site where the wind turbine is located is one of the essential steps in planning a wind project. The available wind power is: P =
1 ρAv3 2
(3)
Figure 6 Present the power estimated using the two methods. Note that the nonparametric method gives the highest estimate of wind energy; Using Weibull’s pdf function, the average density power energy reaches 400 W\m2 and
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Fig. 6. Wind power density estimation
8 Conclusion In this work we have adopted a conditional approach to the theory of extreme values. This approach initially introduced by McNeil and Frey [2000] The goal of this process of filtering is to preserve the i.i.d. hypothesis, the basic hypothesis of EVD . The methods proposed here give satisfactory results, but it would also be interesting to test other filtering processes, including the class of stochastic volatility models. The wind energy is fundamentally very volatile as it depends on factors that are very difficult to predict, whether geopolitical factors, hazards climatic conditions. Thus, investors are particularly sensitive to the occurrence of substantial losses. In this context, it is essential to have the tools to better predict the available. We presented a model based on the theory of values extremes, which makes it possible to assess rare events associated with their occurrence. We have highlighted the distinctive contribution of the conditional approach of the theory of extreme values, in terms of forecasting the wind energy. In particular, these two models have attractive properties, which reflects the dynamic aspect of volatility and allows to capture the heaviness tails.
References 1. Qin, Z., Li, W., Xiong, X.: Estimating wind speed probability distribution using kernel density method. Electr. Power Syst. Res. 81, 2139–2146 (2011) 2. Wang, J., Hu, J., Ma, K.: Wind speed probability distribution estimation and wind energy assessment. Renew. Sustain. Energy Rev. 60, 881–899 (2016) 3. Kalaiselvan, C., Bhaskara Rao, L.: Comparison of reliability techniques of parametric and non-parametric method, engineering science and technology. Int. J. 19(2), 691–699 (2016) 4. Fontaine, C.: Utilisation de copules paramétriques en présence de données observationnelles : cadre théorique et modélisations. Hal archives-ouvertes (2017) 5. Genest, C., Favre, A.C.: Everything you always wanted to know about copula modeling but were afraid to ask. J. Hydrol. Eng. 12(347–368), 691–699 (2007) 6. Weibull parameters for wind energy application. Appl. Energy88 (1), 272–282
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7. D’Amico, G., Petroni, F., Prattico, F.: First and second order semi-Markov chains for wind speed modeling.PhysicaA:Stat. Mech. Appl. 392, 1194–1201 (2013) 8. D’Amico, G., Petroni, F., Prattico, F.: Wind speed and energy forecasting at different time scales : anon parametric approach.PhysicaA :Stat. Mech. Appl. 406, 59–66 (2014) 9. Soukissian, T.H., Karathanasi, F.E.: on the selection of bivariate parametric models for wind data. Appl. Energy 188(15), 280–304 (2017) 10. Akpinar, S., Akpinar, E.K.: Estimation of wind energy potential using finite mixture distribution models. Energy Convers. Manag. 50, 877–884 (2009) 11. Choi, E.C.C.: Extreme wind characteristics over Singapore, an area in the equatorial belt. J. Wind Eng. Ind. Aerodyn. 83, 61–69 (1999) 12. Riera, J.D., Viollaz, A.J., Reimundin, J.C.: Some recent results on probabilistic models of extreme wind speeds. J.Ind.Aerodyn. 2, 271–287 (1977) 13. Ahmed, A.S.: Wind energy as a potential generation source at Ras Benas, Egypt. Renew. Sustain. Energy Rev. 14, 2167–73 (2010) 14. Barthelmie, R.J., Frandsen, S.T., Nielsen, M.N., et al.: Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middel grunden. Offshore Wind Farm.Wind Energy 10, 517–528 (2007)
Selecting Key Product Characteristics to Improve the QMS in Automotive Sector Laila Benzaza1(B) , Najlae Alfathi2 , and Abdelouahid Lyhyaoui1 1 Laboratory of Innovative Technologies (LTI), National School of Applied Sciences (ENSA),
Abdelmalek Essâadi University, 90000 Tangier, Morocco [email protected] 2 Pluridisciplinary Laboratory of Research and Innovation (LPRI), EMSI, Casablanca, Morocco
Abstract. In the automotive industry, one of the major factors contributing to customer satisfaction is the product’s quality. To improve this latter, companies are implementing quality management systems, QMS, which require the usage of quality tools especially statistical ones, to control the manufacturing processes, by monitoring the product’s characteristics. However, must we track all the characteristics to achieve the product compliance and system efficiency? This paper aims to give a solution model that will allow the organizations to select the key product characteristics that should be monitored by quality tools as they have the most influence on the product’s quality. The goal is to use mathematics in industrial field to improve quality management systems and manufacturing processes performances. The paper also gives architecture of the discussed problematic and a perspective on the next work which will be an application of the proposed solution in the automotive sector. Keywords: Automotive Industry · Data Mining · Linear regression · Machine Learning · Product characteristics identification · Quality Improvement
1 Introduction Quality in automotive industry plays a crucial role in increasing business competitiveness, that’s why the organizations started a long time ago to adopt the quality management systems, studies about this topic have been performed in companies 70 years ago by Dr. Edward Deming and Dr. Joseph [1], then, international standards were created to give guidelines for QMS: Quality Management system implementation. The most widely accepted quality standard is ISO9001, which mention as principles, customer focus and continuous improvement [2], the certification in accordance with this kind of standards provides the chance for companies to work with new customers, who seem difficult to reach without certification [3]. In the automotive sector, “IATF 16949” is the quality standard which is a complementary of ISO 9001 [4], it gives more specific requirements for automotive sector [5], and its certification has increasingly became a must and can be a requirement from the customer, who may conduct audits to check the compliance of the QMS [6], this proves © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 523–531, 2023. https://doi.org/10.1007/978-3-031-35245-4_48
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that the customer satisfaction is strongly linked to the supplier’s Quality Management System due to its impact on product’s quality. One of the requirements of IATF is the mastery of common quality methods to ensure a compliant system; these methods are called the “5 core tools” and attempt to gain advantages in term of quality, cost and delay [7, 8, 9, 10, 13, 14], among these tools, there’s “SPC” and “MSA” which are statistical tools acting on real data. The first one “SPC”, helps the organizations to improve their processes by monitoring the product characteristics, and establishing suitable control charts whose interpretation determine whether a process is in or out of control, and afterwards acting on the main root causes behind the process instability and incapability [11–13], generally these causes are schematized in the 7M diagram, among these 7M causes, there’s “measure”, which can directly influence SPC results since the output of the measurement system is the input of SPC which is the measured or tested product characteristics. So if the measure is not correct/real, automatically the results of SPC won’t be properly interpreted [13], here comes the importance of “MSA” which allows the assessment of the whole measurement system including the appraisers (people who make the measurements) and measurements equipment/tools [14]. There are many other quality methodologies that can be used by companies and which are also depending on the data and the more data we have, the more in depth analysis we can get, but the problematic is what product characteristics should be controlled to maintain the quality of our processes? And if there are many product characteristics, wouldn’t it be a waste of time to track all of them while not all of them directly influence the product quality? To solve this problematic, we will use a data mining method to facilitate the identification of the key product characteristics that impacts the conformity of the product. The rest of the paper is organized as follow: Sect. 2 of this paper presents the related work which is an overview on data mining methodology and a description of the problematic, Sect. 3 presents the linear regression (LR) models and the proposed solution, the Sect. 4 provides a description of the case study that will be discussed and presented in the next work and finally Sect. 5 gives some concluding thoughts and perspectives.
2 Related Work 2.1 Data Mining Methodology The data mining is a step of KDD (knowledge Discovery from Databases) used to produce an enumeration of patterns, called models, through the collected and analyzed data [15]. This data is then validated after different steps like data cleaning through the elimination of invalid, redundant or incomplete data, data correction and data assessment etc. The management of data before usage makes it more reliable and consistent [16, 17], after that, millions of patterns can be generated, but only a few of them are interesting. When we say interesting, we mean that the pattern answer to the need, or it can prove a hypothesis already defined [17]. Data mining can be applied in many different fields and domains, it helps to avoid the subjectivity usually related to human being in making decision [18], this can be
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done by going from the known information which is the collected data to the unknown information, by analyzing and understanding the data tendency. The data mining allows either the description or the prediction taking into consideration the inputs (data), this can be made using different models generated by data mining software like (SPSS, R, IBM, SAS) [18], many methodologies are available and integrated in datamining like statistics, patterns recognition, and machine learning [17]. As we mentioned before, the data mining can be used for the prediction, thing that interests us the most in this paper since the work will be basically the prediction of the compliance of the product based on information about its characteristics, the aim is to easily identify the characteristics that have the bigger probability to influence this prediction. Some of the methodologies for data mining indicated above and that can be used for prediction are: statistics and machine learning, or we can call it statistical learning, it is the application of statistical methods to make predictions or estimations using data, this can be done through the extraction of interesting patterns from data and the understanding of their meaning; this is what we call: [learning from data] [19]. This methodology is used in different fields like chemistry, medicine, cyber security, finance and industry, where the output of statistical learning can be schematized by a variable “y” and the input by a variable “x”, these variables can be qualitative or quantitative [19]. 2.2 Problematic In our case, the input variables are the measured (quantitative) or tested (qualitative) product’s characteristics, so if we consider “x” our input, then “x” is a vector of components x i , which are the characteristics of the product. Now, if we consider the product’s conformity which is represented by the output “y”, there’s a relationship between y and the vector x [x 1 , x 2 … x n ]. In fact, if the product’s characteristics x i are not in line with the requirements, then, the product would not be compliant neither, when we say requirement, generally it’s a target to achieve, for quantitative variables, this target is an estimated value with a tolerance margin, and for qualitative variables, the target is a compliant situation (OK variable/ pass variable), and this target is the reference with which a comparison is made to judge the conformity of the variables, we will call this target, the estimated variable. The machine learning approach will attempt to predict the output “ y” by learning from the vector “x”, that means that the model is predicting the conformity of the product relying on the conformity of its characteristics [20]. However, the output “y” doesn’t consider all the product characteristics (x i ) in an equal way, there are some characteristics that influence the conformity of the products more than others. Let’s take as an example of product in the automotive industry, a small electrical wire used in the manufacturing of other automotive products like engine wire harness; there are many characteristics that could be identified in this product, hence, the vector “x” may be [color, length, section, insulation type strands type], if we chose the two following characteristics: “color” and “section” of the wire and compare them, we will notice that the section is more important than the color to judge the conformity of the wire, because the section directly impacts the functionality of this product which is the
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wire connectivity, that means that the conformity of an “electrical wire” depends mostly on its section rather than its color. To better understand this statement, let’s consider as a threshold “a green wire with 4 mm2 as a section”, a blue wire with 4 mm2 will have the same performance as the threshold, because the electrical current that will pass through these two wires is the same, even if they don’t have the same color. But the electrical current passing through another wire with green color (same as the estimated variable) and a section equal to 2 mm2 (less than the estimated variable), is small because the resistance here is greater due to the small section (according to the generic formula of electricity: U = R*I, where R: resistance, I: current and U: voltage) and this may influence the wire functionality. In the first example, the product quality is close to the estimated one even if the product is not the same color as the threshold, while in the second example; the quality of the product is poor even if the color of the product is the same as the threshold. Therefore, the conformity of the product “y” in this example mainly depends on its section rather than its color. Certainly in this example, we cannot exclude the wire color from the characteristics list to ensure -because it may be a customer requirement- and a blue wire with 4 mm2 as section is considered as NG product because it has a NG characteristic (color) compared to the threshold (Green wire with 4 mm2 as section), however, the characteristic to be closely monitored by statistical quality tools in this example, is the section of the wire, because it influence the most and directly its conformity (connectivity) and it can lead to a customer claim in case of discrepancy. This paper tries to answer to a central question: how can we identify the product characteristic that the product’s conformity mostly depends on, using Data mining and statistical learning? What we are searching for is a data mining method that can help companies in automotive sector to choose the product characteristic “x i ” which is one of the variables considered as inputs, but with the most influence on the product’s conformity “y” (the output), and consequently should be controlled and monitored the most by statistical quality tools or other quality methodologies instead of controlling all products characteristics arbitrarily. If we consider again “y” the conformity of the product, and x [x 1 , x 2 …x n ] the characteristics of that product, we have: y = f(x)
(1)
where “x i ” is quantitative or qualitative inputs (measured or tested characteristics) and “y” is a qualitative output (ok product /NG product). First, let’s define the function “f”. We notice that there’s a direct relationship between the conformity of the product and the conformity of its characteristics means that if the value of “x i ” increases, the value of “y” increases too, and vice versa, therefore, there’s a linear relationship between “y” and “x”.
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3 Linear regression model and Least Square estimator The linear regression LR is a data mining model that tries to establish a linear relationship between an output variable “y” called explained variable and one or more input variables “x i ” called explanatory variables, The generic form of the linear regression model is y = f x1, x2,..., xn (2) = x1 β1 + x2 β2 + . . . xn βn + ε n β + ε = (x ) i i i=1 where “ε” is the random error component which is the difference between y’: the estimated output variable, and the actual y. As the conformity of the product “y” depends on all its characteristics “x i ”, then, we will use the linear regression model to estimate “y”. The reason why we chose the linear regression is that this method allows an easy interpretation of the coefficients as probabilities using the LS estimator (Least Square) and an effective examination of the input effects on the output [21, 22]. For a product “yj ” where j ∈ {1… k} and k is the number of analyzed samples of a population (products), we have: n (xji βji ) + εj (3) yj = f xj1 , xj2 , . . . xjn = i=1
To estimate the parameters “β ji ” where i ∈ {1… n} and “n” is the number of product characteristics, the maximum likelihood (ML) is one of the approaches used by statisticians for estimating a mathematical model parameter. This estimation in multiple linear regressions is equal to the least squares (LS) which is another approach for parameters estimation but gives the same results for linear regression analyses when the dependent variable is normally distributed [23]. The principle of LS is to make the sum of squares of “ε” as small as possible. So, if we have k samples, and we consider a normal distribution of “x i ”, The sum of squares of errors “s” would be [24]: s= =
k
(εj )2
j=1 k j=1
yj − yj
2
(4)
where (yj ) is the output and (yj ’) is the estimated output of the sample j. And yj = And yj =
n
i=1 (xji βji )
n
(xji βji ) + εj
(5)
i=1
because there’s no error in the estimated yj’ So εj = yj − yj = yj − ni=1 (xji βji )
(6)
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From (4) and (6), we get: s=
k
yj −
n i=1
j=1
(xji βji )
2
(7)
To find the unknown parameters, βji , we minimize s, min(s) = min
k n (yj −
i=1
j=1
(xji βji ))2
Now differentiate “s” with respect to βji , n ds = −2 xj (yj − (xji βji )) = 0 i=1 d βji k
j=1
After verification, we obtain:
k
k j=1
j=1 xj (yj
yj −
−
n i=1
n
i=1 (xji βji ))
(xji βji ) = 0
=0 (8)
After the identification of the parameters “βji ”, which will represent the percentage of influence of the relevant characteristic “xji ” on the global conformity of the product “yj ”, we will hereafter select the bigger “βi ” and then associate the key characteristics “xi ” that have the most influence on the product conformity “y”. To conclude this paragraph, we will be able to easily identify the key characteristics of a product based on an analytical and statistical study.
4 Case Study In this section, we will describe the case study that will be the subject of the next work; it consists on applying the proposed solution in an affiliate of an international company for vehicles wiring harnesses production. Figure 1 gives a simplified description of the manufacturing process that begins by “cutting” where the wires are cut, then stripped (insulation removed from the end of the cut wire), then “crimping” (mechanical operation which consists in assembling two parts by deformation, in our case the two parts are wire and terminal), After that comes the pre-assembly process P2 where some of the wires –depending on the wire harness design-are welded (joined) and others are twisted (these two subprocesses are done in a parallel way), and the last process P3 consists to insert terminals into the connectors, make the lay outing and taping operations and assembly of all the semi-finished products coming from P2 process to constitute a complete wire harness, ready to be shipped to the customer after its electrical and visual inspection. In each of these processes, there is a set of characteristics to ensure, for example the output of the twisting process has as characteristics the length and the twisting pitch.
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Fig. 1. Manufacturing processes of Wire harness
According to quality data (PPM: part per million) during the 5 last years, the processes with high ratio of defect are “crimping” and “welding” processes, that’s why, the company controls them by SPC tool, this tool is considering all the characteristics in the relevant processes. The objectives of the upcoming work are: 1-to prove the reliability of the proposed solution in this paper, 2-to make sure that the company is controlling the right processes, 3-to decrease the number of characteristics by process to be considered in SPC methodology, 4-to make the process control method easier and faster. The steps of the next work will be basically the collection of data, data analysis and application of the solution presented in this paper.
5 Conclusion and Perspectives To effectively improve the QMS of a company in the automotive industry, statistical tools can be used to control the manufacturing processes using as input the product’s characteristics; the guidelines related to such tools give only the steps and requirements about their implementation but the identification of the product characteristics is left to the company’s decision, this latter may consider all the characteristics in her method, which can lead to a waste of time and a complicated operation. In this paper, we started a review about some important aspects related to automotive industry, we started to view the data mining methodology and its usage for quality, then we mathematically formulated the problematic as a vector containing the characteristics that impacts the conformity of the product, finally we proposed the linear regression as a solution by using the least square estimator to facilitate the identification of the key product characteristics which have the most influence on the product quality. This is our first contribution in the field of quality management system and its relationship with data mining tools, in the next work; we will apply the solution on a real data in a factory in the automotive industrial field.
References 1. Priede, J.: Implementation of quality management system ISO 9001 in the world and its strategic necessity. In: 8th International Strategic Management Conference, University of Latvia, Riga, LV-1050, Latvia (2012)
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2. ISO, ISO 9001 Quality Management Systems Requirements, 5th edition, International Quality standard book (2015) 3. Texeira Quirós, J., do Rosário Fernandes Justino, M.: ISCAL-Polytechnic Institute of Lisbon, Lisbon, Portugal, a comparative analysis between certified and non-certified companies through the quality management system. Int. J. Qual. Reliab. Manag. 30(9), 958–969 (2013) 4. IATF: International Automotive task force, Quality Management System requirements for automotive production and relevant service parts organizations, 1st edition, Automotive Quality Management Standard book (2016) 5. Laskurain-Iturbe, I., Arana-Landín, G., Heras-Saizarbitoria, I., Boiral, O.: How does IATF 16949 add value to ISO 9001? an empirical study. Journal, 1478–3371 (2020). https://doi. org/10.1080/14783363.2020.1717332 6. Hernandez, H.: Quality audit as a driver for compliance to ISO 9001:2008 standards. Journal 22(4), 454–466, Westport Innovations, Vancouver, Canada (2008) 7. Doshi, J.A., Desai, D.: Overview of automotive core tools: applications and benefits. J. Inst. Eng. (India) Ser. C 98(4), 515–526 (2016). https://doi.org/10.1007/s40032-016-0288-z 8. AIAG, Ford Motor Company, Chrysler Corporation, and General Motors Corporation, Advanced Product Quality Planning and control plan, Reference manual book, second edition (2008) 9. AIAG, Ford Motor Company, Chrysler Corporation, and General Motors Corporation: Production Part Approval Process, Reference Manual book, Fourth edition (2006) 10. AIAG, Ford Motor Company, Chrysler Corporation, and General Motors Corporation: Potential Failure Mode and Effects Analysis (FMEA) Fourth Edition book (2008) 11. Godina, R., Matias, J.C.O., Azevedo, S.G.: Quality improvement with statistical process control in the automotive industry. Int. J. 1–8. University of Beira Interior, Covilhã, Portugal (2016) 12. Antony, J., Taner, T.: A conceptual framework for the effective implementation of statistical process control. Journal Warwick Manufacturing Group, University of Warwick, Coventry, UK and Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, pp. 473– 489 (2003) https://doi.org/10.1108/14637150310484526 13. AIAG, Ford Motor Company, Daimler Chrysler Corporation and General Motors Corporation: Statistical Process Control, reference Manual book, Second edition (2005) 14. AIAG, Ford Motor Company, Daimler Chrysler Corporation and General Motors Corporation: Measurement System Analysis, reference manual book, fourth edition (2010) 15. Mikut, R., Reischl, M.: Data mining tools, advanced review. Wiley Interdiscipl. Rev. Data Min. Knowl. Dis. 1(5), 431–443. 10.1002 (2011) 16. Geekiyanage, S.C.H., Tunkiel, A., Sui, D.: Drilling data quality improvement and information extraction with case studies. J. Petrol. Explor. Product. Technol. University of Stavanger, Stavanger, Norway (2020) 17. Han, J., Kamber, M., Pei, J.: Data Mining Concepts and techniques, third edition, published book by Elsevier, Simon Fraser University (2012) 18. Tufféry, S.: Data Mining et statistique décisionnelle L’intélligence des données, book, 4th edn. University of Rennes, France (2012). (In French) 19. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, Data Mining, Inference, Prediction, Springer Series in Statistics, Second Edition (2013) 20. de Mello, R.F., Ponti, M.A.: Machine Learning A Practical Approach on the Statistical Learning Theory, ebook, Library of Congress Control Number: 2018947414 (2018) 21. Gomila, R.: Logistic or linear? estimating causal effects of experimental treatments on binary outcomes using regression analysis. J. Princeton University (2020) 22. Hellevik, O.: Linear versus logistic regression when the dependent variable is a dichotomy, Original paper published by Springer Science+Business Media (2007)
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23. Kleinbaum, D.G., Klein, M.: Logistic Regression, A Self-Learning Text, Third edition, springer, USA (2010) 24. Daniya, T., Geetha, M., Kumar, B.S., Cristin, R.: Least square estimation of parameters for linear regression. Int. J. In. 13(2), 447–452 (2020)
Berth Allocation and Quay Crane Assignment and Scheduling Problem Under Energy Constraints: Literature Review Mounir Ech-Charrat(B) , Mofdi El Amrani, and Mostafa Ezziyyani Faculty of Science and Technologies of Tangier, Abdelmalek Essaâdi University, Km 10, Ziaten, Tangier, Morocco [email protected], [email protected], [email protected]
Abstract. Given the extent of the sea lanes in international trade, the problems of management of quays and port infrastructure are receiving increasing interest in the literature. In this context, this paper reviews the literature on optimization problems mainly related to quay management: berth allocation, quay cranes assignment/scheduling problems under energy constraints. A set of criteria were considered, then a comparative study of the work carried out was carried out, and research horizons are highlighted. Keyword: Berth Allocation · Quay crane scheduling · Uncertainty Review · Energy constraint
1 Introduction Port terminals are very important since they are the main nodes responsible for connecting sea and land transportation. When vessels arrive at a terminal, they stop in the anchorage area, and they wait until adequate space slots are assigned to them (known as berths). Once a ship is berthed, loading and unloading operations on those ships are performed by quay cranes. The berthing plan is the first level of terminal planning and is used as a very important element for the assignment and deployment planning of personnel/equipment. This means that efficient use of berths is imperative to improving customer satisfaction and increasing throughput, which leads to higher at terminal management revenue. Therefore, it is necessary to organize the berthing by optimal methods to determine its Efficiency within the global maritime network. This problem which is associated by the berth spaces and berthing times to incoming vessels is known as the berth allocation problem (BAP) or berth planning problem (BPP) (Lim 1998) and it has been studied in literature since 1990s. (Filipe Rodrigues & Agostinho Agra 2021). The second level is the assignment of quay cranes for a ship, this problem consists of assigning an optimal number of quay cranes for each ship without knowing the start and end times of operation by the cranes, is known as the quay crane assignment © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 532–544, 2023. https://doi.org/10.1007/978-3-031-35245-4_49
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problem (QCAP) The scheduling of cranes to ships is also a fundamental level for efficient terminal operations. This problem, known as the quay crane scheduling problem (QCSP) is a NP-complete problem (Zhu & Lim 2006). The BAP and the QCSP were first studied separately in the literature; however, since these two problems are closely related, it is desirable to integrate them to achieve higher performances in port terminals. The simultaneous BAP and QCAP is known as the berth allocation and quay crane assignment problem (BACAP), and it was first considered by Park & Kim (2003), while the simultaneous BAP and QCSP is known as the berth allocation and quay crane assignment and scheduling problem (BACASP).
2 Selection and Analysis Criteria In this literature review, we considered papers and conference proceedings published in English in Scopus-indexed international journals. For our research, we considered four main criteria for analysis and comparisons, namely: 1) The class of problem treated by each paper; 2) The approach adopted; 3) The resolution method applied; 4) consideration of energy constraints.
3 Problems and Approaches In this section, we review the general models for berth allocation problems, quay crane assignment problems and quay crane scheduling problems give a general overview of the corresponding publications (Table 1), and classify the strategic approaches used (Table 2) 3.1 Berth Allocation Problem To improve the quality of port service, a precise berth plan must be drawn up so that each vessel occupies a specific position for a specific period. Therefore, all parameters affecting the location and duration of vessel mooring must be taken into account like the dimensions of the ship, the safety margin, service time, the service types… It is also necessary to take into consideration all the constraints like not allowed to occupy the same berth at a time for 2 vessels, The (Fig. 1) shows an example of a berth plan representation for four ships. Berth planning has been shown to be an NP-hard problem by relating it to the set partitioning problem (Lim 1998), the single machine scheduling problem with release dates (Hansen and Oguz 2003), and the two-dimensional cutting stock problem (Imai et al. 2005). There may be further constraints involved in berth allocation, which leads to a multitude of BAP formulations. Spatial constraints restrict the feasible berthing positions of vessels according to a preset partitioning of the quay into berths. According to Imai et al. (2005) the following cases are distinguished: (a) Discrete layout: the quay consists of a finite number of sections (berths) and only one vessel can be served at each single berth at a time. The partitioning can either follow the construction of the quay (Fig. 2a) or is organizationally prescribed to ease the planning problem (Fig. 2b)
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Fig. 1. Space-time representation of a berth plan
(b) Continuous layout: There is no partitioning of the quay, i.e. vessels are allowed to berth at any place along the boundaries of the quay (Fig. 2c) or a continuous layout, berth planning is more complicated than for a discrete layout at the advantage of better utilizing quay space. Hybrid Layout: Like in the discrete case, the quay is partitioned into berths, but large vessels may occupy more than one berth (Fig. 2d) while small vessels may share a berth (Fig. 2e). An indented berth results if two opposing berths exist, which can be used to serve a large vessel from both sides (Fig. 2f).
Fig. 2. Berthing layouts in port terminals.
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The duration of the vessel’s presence in the port is a major indicator of the quality of the service of the vessels inside the port. solving the BAP i.e. improve the quality of vessels services in port terminal by developing a precise plan for the distribution of ships on the berthing sites so as to minimize handling times and also waiting times, which will reduce the number of ships rejected by the port. On the other hand, reduce the workload on port resources (workers, equipment, energy). so as not to exceed the total cost allocated to the berth plan 3.2 Quay Crane Assignment Problem Once the berthing location is assigned and the section contains a set number of quay cranes available for loading and unloading operations. For each ship in the berth plan, the volume of containers to be loaded and unloaded is known as well as the number of cranes allowed to serve it simultaneously. Cranes can be moved to every vessel but they are not able to pass each other. The problem is to assign cranes to ships in such a way that all required transshipments of containers can be carried out the quay crane assignment problem (QCAP), see Fig. 3.
Fig. 3. Assignment of cranes to vessels (Christian Bierwirth & Frank Meisel 2009)
BAP is not enough to ensure perfect maritime service in ports Because the handling time is also linked to the number of cranes assigned to each ship, so we can’t get perfect service without solving the QCAP. A quay crane assignment problem can take different positions to determine from: 1. Ship handling is done with a predetermined set of cranes that do not change later. (shown for ships 1, 4, and 5 in Fig. 3). In contrast, a variable-in-time assignment of cranes (ships 2 and 3 in Fig. 3) is enabled in some approaches.
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2. The minimum number of cranes serving the vessel during the entire handling period can be specified. 3.3 Quay Crane Scheduling Problem Crane assignment decisions play a major role in handling time, the quay crane scheduling program includes a set of tasks represented in the operations of loading and unloading the ship, as well as the development of a stowage plan on the deck of the ship or in the warehouse. The start times and end of each operation are determined on the crane. Tasks can be defined based on bay zones or individual bays (Fig. 4a), or on the basis of container stacks, container groups, or individual containers (Fig. 4b):
Fig. 4. Storage location structure of a vessel (a) and a cross-sectional view of a bay (b). (Christian Bierwirth & Frank Meisel 2009)
The greater the number of ship handling tasks, the more complex the distribution and arrangement of dockside cranes, especially since the number of cranes assigned to each ship is unchangeable. Therefore, ensuring the shortest handling time, especially for ships that require hundreds of tasks, becomes complicated and impossible to find an exact solution to QCSP. Suggest a plan or solution to the QCSP. The plan or solution to the QCSP becomes more complex under energy constraints. The priority is always given to the benefit of port resources so as not to exceed the total cost of handling Which is directly affected by energy consumption inside the port and indirectly affected by ship waiting time and gas emission. Therefore, energy and emissions constraints must be considered when developing a plan or solution to the QCSP. Unfortunately, all the literature neglects these important constraints, which strongly affect container port revenues.
4 General Overview and Solution Methods This section provides insights into the articles addressing BAP, QCSP, BACAP and BACASP published in the literature until the end of 2022 (Table 1), and classify the strategic approaches used (Table 2). We use the following acronyms in Table 2: GA: genetic algorithm, MCS: Monte Carlo simulation, LS: local search, TS: - Tabu search, SIH: sequence insertion heuristic, MIP: mixed-integer programming, SWO: squeaky wheel optimization, SA: simulated
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annealing, B&B: - branch-and-bound, ABC: artificial bee colony algorithm, EA: evolutionary algorithm, PFE: Pareto front estimation, MCS: Monte Carlo simulation, BPDM: behaviour perception disruption mode, RHOH: - rolling horizon optimization heuristic PSO: particle swarm optimization and ALNS: adaptive large neighbourhood search. Table 1. Overview of the corresponding publications Paper
Explanation of study
Moorthy & Teo (2006)
this article proposed a framework to address the home berth design problem. And model this as a rectangle packing problem on a cylinder and use a sequence pair based simulated annealing algorithm to solve the problem
Pengfei Zhou (2006)
This paper proposes a dynamic berth allocation model based on stochastic consideration. For quick turnaround in MUT systems, the ship-to-berth assignments of less total waiting time should be gained without the FCFS basis
Zhou & Kang (2008)
this article proposed a berth & quay-crane allocation model in container terminal. And a genetic algorithm with reduced search space is developed based on the characteristics of the optimal solution. The experiments reveal that the stochastic programming model can effectively treat the related random factors and reflect the risk preference of decision-maker
Hendriks (2008)
The research in this paper focuses on incorporating pro-active robustness in a cyclic nominal berth plan for container vessels with developing a robust mixed integer linear program (MILP) model, which explicitly incorporates the process time agreements and minimizes the maximally required crane capacity reservation
Han (2010)
A mixed integer programming model is proposed, and a simulation based Genetic Algorithm (GA) search procedure is applied to generate robust berth and QC schedule proactively. Computational experiment shows the satisfied performance of the developed algorithm under uncertainty
Golias (2011)
formulate the berth allocation problem as a bi-objective mixed-integer programming problem with the objective to maximize berth throughput and reliability of the schedule under the assumption that vessel handling times are stochastic parameters, being a function of other stochastic parameters A combination of an exact algorithm, a Genetic Algorithms-based heuristic and a Monte Carlo simulation are proposed as the solution approach for the resulting problem
Zeng (2011) (continued)
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M. Ech-Charrat et al. Table 1. (continued)
Paper
Explanation of study
Zhen & Chang (2012)
This paper develops a two-stage decision model for berth allocation problem (BAP) under uncertainties. The model is designed to balance the cost associated with the initial (baseline) schedule and the expected cost of deviation from the initial schedule
Xu (2012)
The main contributions of this paper can be summarized as follows: -A reactive strategy is presented for the integration of BAP and QCAP to handle disruptions A rolling horizon optimization algorithm (RHOA) is provided to solve the problem
Guldogan (2012)
This study proposes a dynamic berth allocation model that reflects the stochastic nature of both vessel arrivals times and handling times. For the discrete quay structure, the model aims to minimize the total waiting time of the vessels. Vessel priorities are imposed by customer differentiation rules. To solve the proposed model two different heuristics hybridized with a local search method, based on artificial bee colony and genetic algorithms are developed
Karafa (2013)
In this paper, the berth allocation problem with stochastic vessel handling times is formulated as a bi-objective problem. To solve the resulting problem, an evolutionary algorithm-based heuristic and a simulation-based Pareto front pruning algorithm is proposed
Rodriguez-Molins (2014) his paper studies the problem of how to allocate berths to deep-sea vessels and schedule arrivals of feeders for congestion mitigation at a container port where the number of feeders to be served is significantly larger than the number of deep-sea vessels, and where the service times of feeders are uncertain. We develop a stochastic optimization model that determines the berth plans of deep-sea vessels and arrival schedules of feeders, so as to minimize the departure delays of deep-sea vessels and schedule displacements of feeders. The model controls port congestion through restricting the expected queue length of feeders Golias (2014)
this paper studies the discrete space and dynamic BSP where vessel arrival time is optimized to account for the minimization of port-related emissions, waiting time of vessels and delayed departures. The problem was formulated as a mixed integer optimization problem, and a GA-based heuristic was used to solve the resulting problem
Li (2015)
This paper studies disruption recovery optimization for the integrated berth allocation and quay crane assignment problem in container terminals. The proposed reactive recovery strategy adjusts the initial plan to handle realistic disruptions. In the proposed recovery strategy, new berthing positions for vessels are restricted within a certain space (continued)
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Table 1. (continued) Paper
Explanation of study
Zhang (2016)
In this paper, the problem of berth schedule recovery is addressed to reduce the influences caused by disruptions. A multi-objective, multi-stage model is developed considering the characteristics of different customers and the trade-off of all parties involved. An approach based on the lexicographic optimization is designed to solve the model. Numerical experiments are provided to illustrate the validity of the proposed Model A and algorithms
Alsoufi (2016)
this paper proposes a new mathematical model of the mixed integer programming type that addresses robust berth allocation is proposed. Its solutions help mitigate the uncertainty in arrival times and handling times of vessels
JunliangHe (2016)
This paper addresses the problem of integrated berth allocation and quay crane (QC) assignment for the trade-off between time-saving and energy-saving. This problem is formulated in order to minimize the total departure delay of all vessels and the total handling energy consumption of all vessels by QCs
Lu Zhen (2016)
This article investigates the method of allocating arriving vessels to the terminals in transshipment hubs. The terminal allocation decision faced by a shipping alliance has the influence on the scheduled arrival time of vessels and further affects the bunker consumption cost for the vessels. A model is formulated to minimize the bunker consumption cost as well as the transportation cost of inter-terminal transshipment flows/movements
Segura (2017)
this paper develops a model of fully fuzzy linear programming (FFLP) for the continuous and dynamic BAP. The vessel arrival times are assumed to be imprecise, meaning that the vessel can be late or early up to a threshold permitted. Triangular fuzzy numbers represent the uncertainty of the arrivals
Xiang (2018)
This paper studies a reactive strategy for integrating BAP and QCAP to address the four types of disruptions, namely, the deviation of vessel arrival time, the deviation of vessel loading and unloading operation time, the calling of unscheduled vessels and QC breakdowns
Lam (2019)
This paper aims at developing a recoverable robust optimization approach for the weekly berth and quay crane planning problem. In order to build systematic recoverable robustness, a proactive baseline schedule with reactive recovery costs has been suggested. The uncertainty of vessel arrivals and the fluctuation in the container handling rate of quay cranes are considered (continued)
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M. Ech-Charrat et al. Table 1. (continued)
Paper
Explanation of study
Lv Xiaohuan (2020)
In this paper, the author formulates a mathematical model for berthing recovery problems, in which the transhipment connections between correlated feeder and mother vessels are specially considered. Besides, a metaheuristic based on SWO is designed, and then we design comprehensive numerical experiments to show satisfactory performance of proposed solution approach
Pérez-Cañedo (2020)
This paper takes fuzzy uncertainty into account and presents a fully fuzzy two-objective BA problem, by considering the minimisation of the total waiting time of vessels and the makespan of the wharf operation from the perspectives of vessels and wharves operators, respectively. A fuzzy epsilon-constraint method and a lexicographic method for fully fuzzy linear programming with inequality constraints are used jointly to solve the problem
Tan & He (2021)
this paper addresses the optimization of BACAP under the uncertain vessels’ arrival times and fluctuation of loading and unloading volumes. it proposes a proactive BACAP strategy considering minimum recovery cost under uncertainty using a reactive strategy. A stochastic programming model is formulated to minimize the basic cost in the baseline schedule, and the recovery cost in real uncertain scenarios
Kolley (2021)
In this paper, Machine Learning techniques are applied to enable the determination of patterns in AIS data and hence to develop forecasts of the arrival times. Moreover, with a robust optimization approach based on Dynamic Time Buffers, uncertainty is proactively considered in the planning phase, resulting in a robust berthing schedule
Park (2021)
this paper investigates the robust berth allocation problem in container terminals. To handle the uncertainties in vessel arrivals, the problem is formulated as a scenario-based two-stage stochastic programming model. Furthermore, the authors introduce the time buffers to the model. They then develop an algorithm for time buffer insertion, which accommodates the adaptive search procedure for the time buffer into the Particle Swarm Optimization (PSO) algorithm
Shoufeng Ma (2021)
this paper investigate the unidirectional quay crane scheduling problem for a stochastic processing time, which requires that all the quay cranes move in the same direction either from bow to stern, or vice versa, throughout the planning horizon
LuZhen (2022)
This paper integrates the planning and operations at container ports to jointly optimize strategical level planning and tactical level berth and yard space allocation under uncertain vessel arrival times and uncertain numbers of loading/unloading containers
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Table 2. Classification of the papers according to the problem, approach and Solution methods Paper
Problem
Approach
Solution methods
Moorthy & Teo (2006)
BAP
Proactive
Sequence pair-based approach
Zhou (2006)
BAP
Proactive
Chance-constraint model + GA
Zhou & Kang (2008)
BACAP
Proactive
Chance-constraint model + GA
Hendriks. (2008)
BACAP
Proactive
Robust model
Han (2010)
BACAP
Proactive
GA(LS + MCS)
Golias (2011)
BAP
Proactive
Exact partition method + GA
Zeng (2011)
BACAP
reactive
Disruption model (Local rescheduling + TS)
Zhen & Chang (2012)
BAP
proactive
Heuristic(SIH + SWO + Buffers)
Xu (2012)
BAP
Proactive
Model, Heuristic(SIH + SA + B&B + Buffers)
Guldogan (2012)
BAP
Proactive
GA(LS insertion), ABC(LS insertion)
Karafa (2013)
BAP
Proactive
EA + PFE(MCS)
Rodriguez-Molins BACAP (2014)
Proactive
Model, Heuristic(GA + Buffer) + PFE
Golias (2014)
BAP
Proactive
(Bi-level optimization + GA) + PFE
Li. (2015)
BACAP
reactive
SWO
Zhang (2016)
BACAP
reactive
Heuristic(Lexicographic optimization + SA)
JunliangHe (2016)
BACAP
Proactive
SA + GA
Lu Zhen (2016)
BAP
Proactive
MIP + PSO
Xiang (2018)
BACASP reactive
Lam (2019)
BACAP
proactive/reactive Model, Heuristic(ALNS + Buffers)
Lv Xiaohuan (2020)
BAP
proactive
Pérez-Cañedo (2020)
BAP
proactive/reactive Fuzzy models
Tan & He (2021)
BACAP
proactive/reactive Model, Metaheuristic framework (SA, GA)
Kolley (2021)
BAP
proactive/reactive Machine Learning + Buffers
Park (2021)
BAP
proactive/reactive PSO(SWO + Buffers + RHOH)
Shoufeng Ma (2021)
QCSP
proactive
Heuristic(BPDM + RHOH) SWO(SIH) Fuzzy models
stochastic + MIP (continued)
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M. Ech-Charrat et al. Table 2. (continued)
Paper
Problem
Approach
Solution methods
LuZhen (2022)
BAP
proactive/reactive stochastic integer programming models
5 Conclusions and Future Research In this article, we have reviewed the publications on BAP, QCSP, QCASP, BACAP and BACASP. These publications have focused on the last 16 years. This survey shows that the most common problem in BAP, QCSP, QCASP, BACAP and BACASP is the arrival time and processing times, and that uncertainty is often included in solution approaches through scenarios in terms of objectives, waiting times, deviations from desirable mooring positions and delays are the most frequent objectives to be optimized (Filipe & Agostinho 2021). The BAP, QCASP, BACAP and BACASP under uncertainty has been extensively studied in the literature under different approaches: stochastic programming, robust optimization, fuzzy theory, and deterministic approaches (Table 2). Optimum energy utilization and constraint release of greenhouse gases (CO2 emissions) are essential considerations in any process (Ech-Charrat 2022), so as future work, we aim to extend the proposed algorithm and approach to more complex problems, such as Berth allocation and quay crane assignment/scheduling problem under uncertainty and energy constraints.
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Iris, C., Lee Lam, J.S.: Recoverable robustness in weekly berth and quay crane planning. Transp. Res. Part B Methodological 122, 365–389 (2019). https://doi.org/10.1016/j.trb.2019.02.013 Lv, X., Jin, J.G., Hu, H.: Berth allocation recovery for container transshipment terminals. Marit. Policy Manag. 47(4), 558–574 (2020). https://doi.org/10.1080/03088839.2020.1725672 Pérez-Cañedo, B., Verdegay, J.L., Rosete, A., Concepción-Morales, E.R.: Fully fuzzy multiobjective berth allocation problem. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds.) HAIS 2020. LNCS (LNAI), vol. 12344, pp. 261–272. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61705-9_22 Tan, C., He, J.: Integrated proactive and reactive strategies for sustainable berth allocation and quay crane assignment under uncertainty. Ann. Oper. Res. , 1–32 (2021). https://doi.org/10. 1007/s10479-020-03891-3 Kolley, L., Rückert, N., Fischer, K.: A robust berth allocation optimization procedure based on machine learning. In: Buscher, U., Lasch, R., Schönberger, J. (eds.) Logistics Management. LNL, pp. 107–122. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85843-8_7 Park, H.J., Cho, S.W., Lee, C.: Particle swarm optimization algorithm with time buffer insertion for robust berth scheduling. Comput. Ind. Eng. 160, 107585 (2021). https://doi.org/10.1016/j. cie.2021.107585 Ma, S., Li, H., Zhu, N., Fu, C.: Stochastic programming approach for unidirectional quay crane scheduling problem with uncertainty. J. Sched. 24(2), 137–174 (2020). https://doi.org/10.1007/ s10951-020-00661-8 Rodrigues, F., Agra, A.: Berth allocation and quay crane assignment/scheduling problem under uncertainty: a survey. Eur. J. Oper. Res. 303(2), 501–524 (2022). https://doi.org/10.1016/j.ejor. 2021.12.040 Agra, G., Rodrigues, F.: Distributionally robust optimization for the berth allocation problem under uncertainty. Transp. Res. Part B Methodological 164, 1–24 (2022). https://doi.org/10. 1016/j.trb.2022.07.009 Zhen, L., Zhuge, D., Wang, S., Wang, K.: Integrated berth and yard space allocation under uncertainty. Transp. Res. Part B Methodological 162, 1–27 (2022). https://doi.org/10.1016/j.trb. 2022.05.011 Ech-Charrat, M., Ech-Charrat, M.R., Amrani, M., Cherrat, L., Zouadi, T., Ezziyyani, M.: Variable neighborhood search to design a green dynamic cellular manufacturing system considering carbon emission minimization. In: Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol. 1418, pp.1261–1272. Springer, Cham. https://doi.org/10.1007/978-3-030-90639-9_105
Simulation of Thermal Conductivity with Comsol Multiphysics of Clay Zakaria Kbiri1 , Bouazza Tbib1 , Mohamed Faoussi2 , and Khalil El-Hami1(B) 1 Mohammed V University in Rabat, Scientific Institute, Av. Ibn Batouta, Agdal, BP 703, Rabat,
Morocco [email protected] 2 Health Science Laboratory, Euro-Mediterranean University, Fez, Morocco
Abstract. This work focused on the simulation of thermal conductivity for Moroccan Clay. It was obtained that the finite element simulation in Comsol Multiphysics has a remarkable reproducibility with respect to the conventional theoretical model. Besides this type of analysis provides a substantial advantage, being able to vary the different parameters of the experiment, such as the radial distance, the heat flux, the initial temperature, among others, and thereby optimize the results. Keywords: Simulation · Thermal conductivity · Clay · Finite element · Comsol Multiphysics · Theoretical model and heat flux
1 Introduction This Heat transfer is the area which describes the energy transport between material bodies due to a difference in temperature, and its development and applications is of fundamental importance in many branches of engineering since provides economical and efficient solutions for critical problems encountered in many engineering items of equipment. Among the parameters that determine the thermal behaviour of a material, the thermal conductivity is especially important because it represents the ability of a material to transfer heat, and it is one of the physical quantities whose measurement is very difficult and it requires high precision in the determination of the parameters involved in its calculations [1, 2]. The hot wire technique is an absolute, no steady state and direct method which is considered an effective and accurate procedure to determining the thermal conductivity of a variety of materials, including ceramics, fluids, food and polymers [3–6]. However, this technique is based in a conventional mathematical model which is an approximation of the physical reality in the experimental setup because the complexity of the mathematical problem has been an obstacle to obtain a more realistic theoretical model [6, 6]. Fortunately, nowadays the development of the advanced numerical methods and computing systems allow the application of high level software for obtain an approximate solution to a complex mathematical problem with a boundary conditions congruent with the physical reality. In particular, Comsol Multiphysics is a powerful Finite Element © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 545–559, 2023. https://doi.org/10.1007/978-3-031-35245-4_50
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(FEM) Partial Differential Equation (PDE) solution engine [6] useful to obtain a numerical solution in complex problems. In this work, the Comsol Multiphysics software is used to determinate the numerical solution of a transient temperature distribution in a sample measured by the hot wire technique configuration. The COMSOL Multiphysics software is used to calculate the problem of interest to us, namely the heating of a wire of platinum immersed in Clay. It is a tool for solving finite element partial differential equations. The modelling of a system such as the one studied here takes place in several stages. The first step is to define the physics “modules” that will be used. In this case, it will be the thermal module “heat transfer in solids” and the electricity module “Joule heating”. The following phases of modelling are common to other physical modelling software. In chronological order, there are: 1. Drawing of the geometry of the system. 2. Choice of different materials in the property library. 3. Setting up of the boundary and initial conditions in each module with the possible coupling of these. 4. Mesh of the elements of the structure. 5. Choice of the solver and the convergence parameters, then resolution of the problem. 6. Post processing of the calculated data (here temperature and electric current). In the last phase to process the results, there are several tools available to represent any computable physical quantity in the complete model, along a plan, along a line or a point. The parameters represented in 3D can also depend on time if the study is carried out in an unsteady state. We can then process the results directly in the software via the “report” tab or under another software such as Matlab, Excel or Origin Pro as in our case. The particular interest of COMSOL lies in the possibility that we have to couple different physical models. It is also possible to work in a steady state and in a transient state. The convergence of calculations in stationary mode is obviously faster. The work that is presented below therefore involves following the different stages of creating a model under COMSOL. One of the most delicate parts of this work consists in introducing the physics and the parameters used during the modelling (equation, initial conditions, boundary conditions, electricity modulus). This point and the associated problems are discussed below. For this study we approach the problem according to two approaches. In the first case, we are interested in the two-dimensional (3D) modelling of a section of the experimental device. Secondly, taking into account the limitations of the 2D model, the last part of this study is centered on the comparison between the results of numerical simulations and the experimental measurements obtained thanks for this study we approach the problem according to two approaches. In the first case, we are interested in the two-dimensional (2D) modelling of a section of the experimental device. Secondly, taking into account the limits of the 2D model, the last part of this study is centred on the comparison between the results of numerical simulations and the experimental measurements obtained thanks to the experimental device.
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2 Numerical Modelling In this first section, the basic equations and the boundary condition equations used in COMSOL to make numerical simulations are recalled. We then present the main stages of a typical modelling. A sub-section is also devoted to the determination of the convective exchange coefficient. 2.1 Thermal Transfer Module In a solid, the heat transfer equation is written: ρCp ∂T/∂t − λ T = Q
(1)
With: • • • • •
ρ the density, in kg.m-3 Cp the thermal capacity, in J/kg. λ thermal conductivity, in W/m.K Q the term internal heat source, in W. T the temperature field, in K. Conditions to the limits:
• Thermal insulation: -n. (-k.T) = 0. • Natural convection: heat transfer coefficient by convection h imposed. • Imposed initial temperature, equal to the ambient temperature (Tinit = T0 = 293.15 K). 2.2 Electric Module The equations solved by the electrical module are: .J = Qc
(2)
E = −∇V
(3)
J = σE + Je Localized ohm’s law
With: • • • • •
J is the density of the electric current, in A.m-2 σ: electrical conductivity, in S.m-1 E: electric field, in V.m-1 V: the electric potential, in Volt V. Qc: the source term, in W.
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The experimental heating system is modelled by the term internal heat volume source Q and which represents the energy dissipated by the Joule effect in the control volume considered. This energy results from an electric power dissipated by the Joule effect, namely: P = U2/R = U2/(ρ ∗ l/s) = R ∗ I2 With: • P the electrical power, in W. • R the resistance, in . • I the intensity of electric current, in A. Boundary condition: • Electrical insulation: − n. J = 0 • Density of electric current imposed: − n.J = Jn.
3 System Design and Mesh In order to represent the heating system used during the experiments, we will simplify the data of the problem by keeping only the characteristic dimensions of the key elements, namely the geometry of the platinum track and the overall size of the substrate. 3.1 System Design The platinum wire is 10 mm in diameter, 32 cm long. The sample of the sylinder of 32 cm in height and 16 cm in the diameter. The Fig. 1 presents the sample to be characterized with the heating wire. 3.2 Mesh The mesh chosen is of fine size at the micrometric scale, the results of these parameters are given by the following Fig. 2. • • • • •
J is the density of the electric current, in A.m-2 σ: electrical conductivity, in S.m-1 E: electric field, in V.m-1 V: the electric potential, in Volt V. Qc: the source term, in W.
The experimental heating system is modelled by the term internal heat volume source Q and which represents the energy dissipated by the Joule effect in the control volume considered. This energy results from an electric power dissipated by the Joule effect, namely: P = U2/R = U2/(ρ ∗ l/s) = R ∗ I2 With:
Simulation of Thermal Conductivity with Comsol Multiphysics of Clay
Fig. 1. Represents the sample to be characterized with the heating wire
Fig. 2. Represents the sample to be characterized with the heating wire
• P the electrical power, in W. • R the resistance, in . • I the intensity of electric current, in A. Boundary condition:
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• Electrical insulation: − n. J = 0 • Density of electric current imposed: − n.J = Jn or formulas are centered and set on a separate line (with an extra line or half line space above and below). Equations should be numbered for reference. The numbers should be consecutive within the contribution, with numbers enclosed in parentheses and set on the right margin. Please do not include section counters in the numbering. x+y =z
(4)
4 3D Simulation Results On the following diagram, we present the evolution of the temperature field in the 3D model. The calculations were made for a linear electric power of 24.5W, which corresponds to the experiment exposed in the part of measurement experimental. The temperature field was obtained in transient permanent regimes with the chosen medium is Clay in Fig. 3.
Fig. 3. Temperature at 120 s
To have a good precision of thermal conductivity measurement it is necessary to avoid the effect of convection, and this requires a long time for opting the measurement in a permanent regime.
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4.1 The Temperature Variation in the Wire Over time the temperature of the wire increases in a logarithmic manner which is in agreement with the theory of the hot wire, the following figure represents the variation of the temperature of the 3 position as a function of time in Fig. 4.
Fig. 4. Evolution of the temperature in 3 point r = 6 mm; r = 40 mm; 80 mm
4.2 Calculates the Thermal Conductivity of Clay The values found of the thermal conductivity in all the experimental, bibliographic and simulated are: With ϕ is the electrical power ϕ = 24, 5W . The value of simulation current work r3 24, 5ln 80 ϕln r2 40 = = 0, 84 W.m − 1.K − 1 λcal = 2π L(T 2 − T 3) 2π ∗ 0, 32(303 − 293) The bibliographic value λcal ==0,7 W.m − 1.K − 1 4.3 Manufacture of Specimens A metal mold 16 cm in diameter by 32 cm in height and a 12-tonne hydraulic compacting press are used to make the test specimens. The water content is assessed during the
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preparation of the specimens. Once the mixture is well homogenized, it is introduced into the mold and subjected to a pressure of some MPa to obtain a cylindrical specimen (see Fig. 37). It is first dried at room temperature within the company for a week and then in an oven for 24 h at 80 °C. The specimens are arranged for the measurement of the conductivity according to the paragraph (experimental device above) and the results are presented in tables 1 to 6 and Fig. 5 to 13.
Fig. 5. EManufacture of specimens [current work].
4.4 Experimental Apparatus The thermal characterizations were carried out at the LERMAB Laboratory. The diagram of the experimental device is shown in Fig. 33. It consists: • A special stainless steel ACIM JOUANIM heating cartridge with 50 W power and 10 mm in diameter. • From a 10/12 copper tube. • Type K thermocouples, jacketed 0.5 mm in diameter with welding • warm insulated. Data acquisition (temperatures) is done using an ALMEMO 2290–8 acquisition unit which has been calibrated beforehand. • Voltage and current are measured by a BBC M 2042 precision multimeter. • The voltage is adjusted by a “Variac” type variable resistor. Temperature measurements are made with type K thermocouples, jacketed 0.5 mm in diameter with insulated hot junction. The position of the thermocouples is represented by Fig. 6 and the photograph of the experimental device is represented by Fig. 5 and 6.
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Fig. 6. Experimental device [current work]
4.5 Thermal Conductivity Measurement
Fig. 7. Variation of temperature according the time, Blue curve at r = 0, Red curve at r = 8 cm and Green curve at 4 cm [current work]
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Table 1. Illustrate the Thermocouple temperature of deferent percentage of coffee in the range permanent [current work. (Color figure online) Coffee percent in Clay
Thermocouple temperature (r = 0 cm) °C
Thermocouple temperature (r = 8 cm) °C
Thermocouple temperature (r = 4 cm) °C
0
82,25
22,25
28,25
2,5
75,5
23
32,5
5
67,75
21
29,75
7,5
82,25
22,5
29,75
Table 2. Illustrate the Temperature difference of thermocouples of deferent percentage of coffee in the range permanent [current work]. Coffee percent in Clay %
Temperature difference of thermocouples (r = 8; r = 0) °C
Temperature difference of thermocouples (r = 0; r = 4) °C
Temperature difference of thermocouples (r = 8; r = 4) °C
0
60
54
6
2,5
52,5
43
9,5
5
46,75
38
8,75
7,5
59,75
52,5
7,25
Fig. 8. Variation of temperature according the time, Blue curve at r = 0, Red curve at r = 8 cm and Green curve at 4 cm [current work]. (Color figure online)
The In this part, the thermal conductivities and the mechanical characteristics of the specimens prepared in several formulations will be analyzed and discussed in order to understand the influences of coffee in the mixture as well as the compaction stress and
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Fig. 9. Variation of Temperature difference of thermocouples of deferent percentage of coffee in the range permanent [current work]
Table 3. Illustrate the Thermocouple temperature of deferent percentage of coffee in the range permanent [current work] Coffee percent in Clay %
0 2,5 5
Thermocouple temperature (r = 8 cm) °C
Thermocouple temperature (r = 4 cm) °C
Thermocouple temperature (r = 0 cm) °C
22,5
39,25
82,25
23,25
34,25
82
22
28,25
78,5
7,5
22,5
30,5
82,75
10
22,25
32
84
the rate of sand. The thermal conductivity values of all formulations vary as shown in the figures below. The highest value is given by the formulation prepared with 90% clay and 10% coffee. A material composed of fine and coarse elements gives a much denser final product than a material prepared with only fine elements. Indeed, the value of its thermal conductivity is also high. For coffee-stabilized specimens, the higher the coffee content, the lower the thermal conductivity value. This phenomenon is explained by the fact that 7.5% of coffee in the mixture is enough for high values. Voids are created
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Table 4. Illustrate the Temperature difference of thermocouples of deferent percentage of coffee in the range permanent [current work] Coffee percent in Clay %
Temperature difference of thermocouples (r = 8; r = 4) °C
Temperature difference of thermocouples (r = 8; r = 0) °C
0
16,75
59,75
43
11
58,75
47,75
56,5
50,25
2,5 5
6,25
Temperature difference of thermocouples (r = 0; r = 4) °C
7,5
8
60,25
52,25
10
9,75
61,75
61,75
Fig. 10. Variation of Temperature difference of thermocouples of deferent percentage of coffee in the range permanent [current work]
in the sample. This causes the thermal conductivity value to drop. As the amount of coffee increases, all pores are sealed and the material becomes compact. Consequently, the thermal conductivity value increases up to 6.5% and then remains stable up to 10% coffee.
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Fig. 11. Variation of temperature according the time, Blue curve at r = 0, Red curve at r = 8 cm and Green curve at 4 cm [current work] (Color figure online)
Table 5. Illustrate the Thermocouple temperature of deferent percentage of coffee in the range permanent [current work] Coffee percent in Clay %
Thermocouple temperature (r = 8 cm) °C
Thermocouple temperature (r = 4 cm) °C
Thermocouple temperature (r = 0 cm) °C
0
22,25
36,75
86,5
23,5
33
71,5
2,5 5
23
29,75
92,5
7,5
22,75
32,5
91,15
10
22,5
31,75
94,25
Table 6. Illustrate the Temperature difference of thermocouples of deferent percentage of coffee in the range permanent [current work] Coffee percent in Clay %
Temperature difference of thermocouples (r = 8; r = 4) °C
Temperature difference of thermocouples (r = 8; r = 0) °C
Temperature difference of thermocouples (r = 0; r = 4) °C
0
14,5
64,25
49,75
2,5
7,5
5
6,75
69,5
49
62,75
48,5
7,5
9,75
69,5
58,6
10
9,25
71,75
62,50
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Fig. 12. Variation of Temperature difference of thermocouples of deferent percentage of coffee in the range permanent [current work]
Fig. 13. Heat conductivity of clay according percentage of coffee [current work]
5 Conclusion This research work has highlighted the advantage of local materials in construction. They are economical and have a low environmental impact. A new method for determining thermal conductivities has been implemented at jball nour. This is the hot wire adapted to a cylindrical test piece 16 cm in diameter by 32 cm in height. This device takes into account some difficulties stated on the other methods, such as radial and axial heat leaks and edge effects. It can be validated as a new hot wire method if the experimental conditions are improved (control of atmospheric conditions). The thermal conductivity values obtained by this method prove that the different formulations tested in this research work do not lead to insulating materials. We rely on the high thermal inertia of the material to improve interior comfort.
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References 1. Pope, A.L., Tritt, T.M.: Thermal conductivity of quasicrystalline materials. In: Tritt, T.M. (eds.) Thermal Conductivity. Physics of Solids and Liquids. Springer, Boston, MA (2004). https://doi.org/10.1007/0-387-26017-X_11 2. Lewis, R.W., Nithiarasu, P.: Fundamentals of the Finite Element Method for Heat and Fluid Flow (2004) 3. Nithiarasu, P., Seetharamu, K.N.: Wiley (2008) 4. dos Santos, W.N.: Effect of moisture and porosity on the thermal properties of a conventional refractory concrete. J. Eur. Ceram Soc. 23(5), 745–755 (2003) 5. Khayet, M., Ortíz Zarate, J.M.: Application of the multi-current transient hot-wire technique for absolute measurements of the thermal conductivity of glycols. Int. J. Thermophys. 26, 637–646 (2005) 6. Nahor, H.B., Scheerlinck, N., Van Impe, J.F., Nicolai, B.M.: Optimization of the temperature sensor position in a hot wire probe set up for estimation of the thermal properties of foods using optimal experimental design J. Food Eng. 57(1), 103–110 (2003) 7. dos Santos, W.N.: Polym. Test. 26, 556–556 (2007) 8. Carslaw, H.S., Jaeger, J.C.: Conduction of Heat in Solids, Second Edition, pp. 255–262 Editorial Oxford Science Publications (1954) 9. dos Santos. W. N.: Advances on the hot wire technique. J. Eur. Ceramic Soc. 28, 15–20 (2008) 10. Pryor, R.W.: Multiphysics Modeling using Comsol 4 A First Principles Approach, Mercury Learning and Information (2012) 11. Arfken, G.B., Weber, H.J.: Mathematical Methods for Physicists, 4th edn. Academic Press, London (1995)
Author Index
A Abbou, Ahmed 480 Abdi, Hamda 87 Abdia, Rachid 113 Abdoun, Farah 435 Abouharim, Abdelhafid 113, 123 Aboulfatah, Mohamed 448 Abouloifa, Houria 200 Agouzzal, Kawtar 480 Ait-Allal, Abdelmoula 351 Akarne, Youssef 130 Alfathi, Najlae 523 Allouhi, A. 28 Allouhi, H. 28 Annoukoubi, Maha 40 Aqqal, Abdelhak 351 Aroudam, Elhassan 179 Azizi, Mostafa 99 Azrar, Lahcen 399, 435 Azrour, Mourade 343
B Bahaj, Mohamed 200 Bahraoui, Fatima 515 Bahraoui, Zuhair 515 Bahri, Hicham 448 Bahri, Mohamed 448 Bahsine, Saida 230 Bajja, Salwa 360 Bakkari, Fatima El 240 Baraka, Kamal 230 Begdouri, Ahlame 387 Belghaddar, Yassine 387 Belkourchia, Yassin 399 Bellamine, Insaf 378 Belloulid, Mohamed Oussama 14 Ben-Bouazza, Fatima-ezzahraa 497 Benchikh, Salma 462 Bendrhir, Sara 435
Bensassi, Bahloul 59 Benzaza, Laila 523 Bouchaala, Kenza 435 Boulaala, Mohammed 169, 190, 293 Boumait, El Mahdi 343 Boumane, Abdrazak 250 Boutahir, Mohamed Khalifa 343 Bouyghrissi, Soufiane 360 C Chahinian, Nanée 387 Chenal, Jerome 360 D Dachry, Wafaa 59 Dahbi, Aziz 351 Dani, Abdelfattah 190 Dehbi, Zineb El Otmani 497 Delenne, Carole 387 Dine, Khalid Zine 378 E Ech-Charrat, Mounir 532 Egube, Ona 250 El Allaoui, Ahmed 343 El Amrani, Mofdi 532 El Aoumari, Abdelaziz 316 El Ass, Khalid 14 El Bakali, Saida 368, 408 El Majaty, Salma 259 El Markhi, Hassane 473, 488 El Marzougui, Mustapha 59 EL Moussaoui, Hassan 473 El Moussaoui, Hassan 488 El Moussaoui, Tawfik 14 El Moutarajji, Abdelghafour 113, 123 El Mrabet, Mhamed 190 El Outmani, Ayyoub 99 El Qouarti, Ouassima 162
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kacprzyk et al. (Eds.): AI2SD 2022, LNNS 714, pp. 561–563, 2023. https://doi.org/10.1007/978-3-031-35245-4
562
Elatife, Khalid 149 El-Hami, Khalil 113, 123, 545 Elharbili, Redouane 14 Elmehdi, Nasri 462 Elmisaoui, Safae 422 Elmisaoui, Sanae 422 Elouatouat, Hasnae 138 En-nay, Zineb 473, 488 Essadiqi, Elhachmi 435 Essadki, Ahmed 40, 130, 138, 162 Et-targuy, Omar 387 Ez-Zahraouy, H. 70 Ezziyyani, Mostafa 532 F Fadlaoui, Elmahdi 272 Faoussi, Mohamed 545 Farhaoui, Yousef 343 Ferehoun, Sarah 207 G Ghazal, Hassan 497 Gheouany, Saad 368, 408 H Haidine, Abdelfatteh 351 Hairach, M. Limam El 378 Hboub, Hamza 272 Housni, Farah 250 I Ibrahim, Abdoulkader 87 Idris, Abdou 87 Idrissi, Ayoub El 351 Ihia, Otman Ait 1 J Jaara, El Miloud 99 Jamil, A. 28 Jilbab, Abdelilah 281 Jrhilifa, Ismael 281 Jupsin, Hugues 14 K Kaddiri, Mourad 50 Karakhi, A. 70 Kasseh, Youssef 302 Kbiri, Zakaria 545
Author Index
Khallouk, A. 70 Khamar, Lhachmi 422 Khanniba, Maha 360 Khayat, Mohammed 169 Khomsi, Driss 1 L Laarej, A. 70 Laghridat, Hammadi 40, 130, 162 Laghzaoui, Fadoua 207 Lakhmas, Kamal 250 Lakouari, N. 70 Lamhamdi, Tijani 473, 488 Lyhyaoui, Abdelouahid 523 M Machhour, Zineb 293 Manchadi, Oumaima 497 Mandi, Laila 14 Marah, Rim 179 Marjani, Abdellatif El 149 Masaif, Noureddine 272 Maurady, Amal 250 Mekrini, Zineb 169, 190, 293 Messaoudi, Najat 59 Mestouri, Hind 230 Mohamed, Assabo 87 Moufid, Ismail 473, 488 Mounir, Hamid 240 Mounir, Nada 331 Mrabet, Mhamed El 169, 293 N Naciri, Soukaina 473 Nasser, Tamou 40, 130, 138, 162 O Ouadi, Hamid 281, 316, 331, 368, 408 Ouazzani, Naaila 14 Ouberri, Youness 179 R Radoine, Hassan 360 Rhalem, Wajih 497 S Saadi, Nabiha 462 Sadki, Brahim 50
Author Index
Sedqui, Abdelfettah 250 Sefian, Hind 515 Seriai, Abderrahmane 387
T Talea, Mohamed 448 Tarik, Jarou 462 Tassi, Nada 399 Tbib, Bouazza 545
563
Tmiri, Amal 378 Touzani, Abdellatif
259, 302
Y Yatimi, Hanane 179 Youssef, Elomari 123 Z Zerouaoui, Hasnae 422 Zouhir, Fouad 14