Systems, Decision and Control in Energy V (Studies in Systems, Decision and Control, 481) 3031350871, 9783031350870

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Studies in Systems, Decision and Control 481

Artur Zaporozhets   Editor

Systems, Decision and Control in Energy V

Studies in Systems, Decision and Control Volume 481

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control–quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control 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. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

Artur Zaporozhets Editor

Systems, Decision and Control in Energy V

Editor Artur Zaporozhets General Energy Institute of the NAS of Ukraine Kyiv, Ukraine

ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems, Decision and Control ISBN 978-3-031-35087-0 ISBN 978-3-031-35088-7 (eBook) https://doi.org/10.1007/978-3-031-35088-7 © 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

Preface

At the beginning of 2023, Ukraine will continue to defend against the aggression of the Russian Federation. The fighting on the territory of Ukraine led to significant destruction and damage of electrical power plants, thermal power plants, boiler houses, electricity networks, heating mains, gas networks, water supply, and sanitation systems. By the end of this book writing, 50% of the power system is damaged or destroyed. In particular, more than 50 substations of various voltage classes and more than 50 high-voltage overhead power lines were damaged. Their priority restoration will require significant human and financial resources, as well as new energy efficient and ecological technologies. Thus, energy security will play a key role in the post-war reconstruction of Ukraine and its stable economic functioning. Now Ukraine is creating a legal and economic basis for the future recovery. Thus, the National Council for the Restoration of Ukraine from the Consequences of the War developed a draft Plan for the Restoration of Ukraine, containing materials from the working group “Energy Security”. This project proposes activities for the recovery of the energy industry, grouped under the following five goals: 1. European integration and ensuring efficient operation of energy markets. 2. Energy security—diversification of energy supply sources, creation of reserves, cybersecurity. 3. Decarbonization, optimization of the energy mix, and development of low-carbon generation. 4. Modernization and development of infrastructure for transportation, distribution, transmission, and storage of energy. 5. Improving energy efficiency and demand management. Within these goals, the following important measures can be identified: 1.1 Full integration of the energy markets of Ukraine and the EU by introducing the necessary regulatory changes in Ukraine and the EU. 1.2 Liberalization of electricity and gas markets, settlement of issues of accumulated debts, improvement of the system of support for vulnerable consumers.

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1.3 Restoration/development of infrastructure for exchange trade in energy resources. 2.1 Creation of an oil refinery cluster based on one of the existing oil refineries and/or new locations in Ukraine. 2.2 Resumption of energy supply and operation of energy sector facilities on the occupied territories. 2.3 Construction of an industry-specific system for protecting the unified critical infrastructure of the energy industry. 2.4 Liberalization of the process of issuing licenses for exploration and production of gas; optimization of gas and oil production from existing fields using modern technologies. 2.5 Digitization of the procedure for issuing decisions on environmental impact assessment. 2.6 Building a plant for the production of nuclear fuel and nuclear fuel storage in Ukraine to eliminate Russia’s dependence, develop uranium mining. 2.7 Coal and coal generation: providing a highly maneuverable generation, preparing for the withdrawal of certain objects. 2.8 Development of a project for the construction of 1.5–2 GW of highly flexible capacities to replace coal-fired generation, including the use of unused compressor stations of the gas transmission system. 3.1 Updating the assessment of the optimal generation structure, taking into account the state after the end of hostilities. 3.2 Nuclear generation: Modernization and optimization of the use of installed nuclear capacities, extension of the operating life of existing units, construction of new capacities at the Khmelnytskyi NPP. 3.3 Hydropower: Completion of the construction of a new hydropower capacity (Kakhovska-2 HPP, Kanivska HPP, Dnister HPP). 3.4 Solar and wind energy: Recovery of capacities taking into account new conditions and opportunities as a result of integration with ENTSO-E. 3.5 Increasing the production of biomethane (up to 1 billion m3 /year). 3.6 Hydrogen: demand analysis, production opportunities, and financing sources. 4.1 Completion of new high-voltage overhead power lines to connect new capacities (plants for the production of energy from renewable sources, hydroelectric power plants, and nuclear power plants) and provide capacity for the export of up to 3 GW of electricity to the EU. 4.2 Optimization of the operation of the gas transmission system, assistance in the creation of gas import routes from LNG terminals in Poland, Lithuania, Italy, Croatia, and/or Turkey. 4.3 Optimization of gas distribution networks. 4.4 Building of pilot projects of energy storage devices with a capacity of 50– 200 MW to ensure the balancing of the system and the fulfillment of climate obligations. 4.5 Modernization and completion of oil pipelines (Brody—Adamowo-Zastawa, etc.) and product pipelines, taking into account the needs of the Ukrainian market.

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5.1 Updated demand management strategy and improved energy efficiency with post-hostilities reflecting. 5.2 Ensuring the needs of the transport industry in access to electricity networks and electricity for transferring (rail, municipal, private) in electricity. 5.3 Ensuring the needs of heat generation in access to electricity networks and electricity for heating transferring (centralized or individual) in electricity. 5.4 Assistance to projects on energy modernization of industrial and municipal thermal power plants, as well as household consumers. The presented goals and activities are the closest reference point for domestic science in the field of energy security in near future. Of course, the proposed action plan is not exhaustive and can be supplemented. The search for optimal options for the development of the energy sector should become a subject of discussion among scientists, energy specialists, economists, environmentalists, and representatives of professional organizations. The book consists of 8 parts: Energy Informatics, Electric Power Engineering, Heat Power Engineering, Nuclear Power Engineering, Renewable Power Engineering, Fuels, Transport, and Environmental Safety. The results presented in this book are aimed at solving some of the technical issues proposed by the Ukraine Recovery Plan and other important scientific and applied problems in the field of energy. Scientists from leading Ukrainian academic institutions and universities are working on this book, among which are the General Energy Institute of the NAS of Ukraine, Institute of Engineering Thermophysics of NAS of Ukraine, Thermal Energy Technology Institute of NAS of Ukraine, Institute of Electrodynamics of NAS of Ukraine, G.E. Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, State Institution “Institute of Environmental Geochemistry of NAS of Ukraine,” Institute of Telecommunications and Global Information Space of the NAS of Ukraine, State Enterprise “State Scientific and Technical Center for Nuclear and Radiation Safety,” Research Centre of Industrial Problems of Development of NAS of Ukraine, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” National Technical University “Kharkiv Polytechnic Institute,” National Aviation University, National Aerospace University “Kharkiv Aviation Institute,” Ivano-Frankivsk National Technical University of Oil and Gas, Taras Shevchenko National University of Kyiv, Odesa National Maritime University, National University “Odesa Maritime Academy,” Kharkiv National University of Radio Electronics, National University of Civil Defence of Ukraine, Lviv Polytechnic National University, Cherkasy State Technological University, National University of Life and Environmental Sciences of Ukraine and others. This book is for scientists, researchers, engineers, as well as lecturers and postgraduates of higher education institutions dealing with energy sector, power systems, ecological safety, etc. Kyiv, Ukraine January 2023

Artur Zaporozhets

Contents

Energy Informatics Development of the New Electro-thermal Energy System Structure for Providing of Ukraine’s Energy Market Profitability . . . . . . . . . . . . . . . Vitalii Babak and Mykhailo Kulyk Modelling of Decision-Making Criteria on the Implementation of Energy-Saving Projects at the Expense of Borrowed Funds . . . . . . . . . . Olexandr Yemelyanov, Ihor Petrushka, Kateryna Petrushka, Oksana Musiiovska, and Anatolii Havryliak Accounting the Forecasting Stochasticity at the Power System Modes Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Viktor Denysov, Ganna Kostenko, Vitalii Babak, Sergii Shulzhenko, and Artur Zaporozhets Mathematical Simulation of Projecting Energy Demand for Ukraine’s Budget Institutional Buildings . . . . . . . . . . . . . . . . . . . . . . . . . Olena Maliarenko, Nataliia Maistrenko, Vitalii Horskyi, Irina Leshchenko, and Nataliia Ivanenko Two-Stage Method for Forecasting Thermal Energy Demand Using the Direct Account Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olena Maliarenko, Natalia Maistrenko, Heorhii Kuts, Valentina Stanytsina, and Oleksandr Teslenko Optimization of Coal Products Supply for the Power Industry and the Country’s Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vitaliy Makarov, Mykola Kaplin, Mykola Perov, Tetiana Bilan, and Olena Maliarenko

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Concept for Using Permutation-Based Three-Pass Cryptographic Protocol in Noisy Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emil Faure, Anatoly Shcherba, Mykola Makhynko, Constantine Bazilo, and Iryna Voronenko

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Conformal Mapping of Discontinuous Functions for Inverse Radon Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Mykola Vinohradov, Oleksandr Ponomarenko, Andrii Moshensky, and Alina Savchenko Electric Power Engineering Information Support for Identification of the Technical State of Electric Power Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Vitalii Babak, Artur Zaporozhets, Svitlana Kovtun, Mykhailo Myslovych, Yurii Kuts, and Leonid Scherbak Comparison of the Energy Efficiency of Synchronous Power Generator with Spark Ignition Engine Using Different Types of Fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Stefan Zaichenko and Denys Derevianko Synthesis of the Speed Controller of the Switched Reluctance Motor . . . 179 Serhii Buriakovskyi, Artem Maslii, and Anna Tyshchenko UAV Battery Charge Monitoring System Using Fuzzy Logic . . . . . . . . . . . 195 Anastasiia Shcherban and Volodymyr Eremenko Using the Concept of Prosumers as a Staff for Balancing at the Power Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Stanislav Fedorchuk, Oleksandr Kulapin, Andrii Ivakhnov, Dmytro Danylchenko, and Stanyslav Dryvetskyi Devising a Method for Reducing Active Power Corona Losses Based on Changing the Structural Parameters of a Power Transmission Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Sergii Shevchenko, Dmytro Danylchenko, and Stanyslav Dryvetskyi Heat Power Engineering Directions for the Rehabilitation and Modernization of Ukraine’s Heat Supply and Heat Consumption Systems in the Post-war Period . . . 269 Mykola Kyzym, Viktoriia Khaustova, Yevhen Kotlyarov, Volodymyr Shpilevskyi, and Olena Reshetnyak Innovative Technologies for Continuous Thermal Control of TPPs Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Leonid Zhukov, Dmytro Petrenko, Olena Kharchenko, and Sergii Kharchenko

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Aerodynamics and Heat Transfer Near the Conical Chimney Placed on the Thermal Power Station Site . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Artem Khalatov, Oksana Shikhabutinova, and Anna Chyrkova Review of Technologies of Thermal Energy Generation Using High Voltage Electrode Boilers in the Context of Their Application as Energy Load Regulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Pavlo Novikov, Oleksandr Teslenko, Vadym Beldii, Lenchevsky Evgen, and Olexander Bunke Thermal Energy Storage Systems in the District Heating Systems . . . . . . 371 Volodymyr Demchenko, Alina Konyk, and Oleh Dekusha Calculation Methods for Two Solid Fuels Co-combustion . . . . . . . . . . . . . . 385 Nataliya Dunayevska, Taras Shchudlo, Dmytro Bondzyk, and Ihor Beztsennyi Experimental Study of REDUXCO Fuel Additive Impact on Coal Boiler Performance, Efficiency and Emissions . . . . . . . . . . . . . . . . . . . . . . . . 411 Igor Volchyn, Wlodzimierz Przybylski, and Vitaliy Mokretskyy Anticorrosive Protection of Gas Exhaust Ducts of Boiler Plants with Heat-Recovery Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Nataliia Fialko, Raisa Navrodska, Svitlana Shevchuk, and Georgii Gnedash Improvement of Energy Efficiency and Eco-improvement of Heating Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Teodoziia Yatsyshyn, Roman Fursa, Mykhailo Liakh, Vasyl Mykhailiuk, and Tatiana Fursa Nuclear Power Engineering Is There a Future for Small Modular Reactors in Ukraine? Comparative Analysis with Large Capacity Reactors . . . . . . . . . . . . . . . . . 453 Oleksandr Popov, Anna Iatsyshyn, Valeriia Kovach, Andrii Iatsyshyn, Ihor Neklonskyi, and Alexander Zakora Features Function of Radiation Monitoring System World’s Countries of Developed Nuclear Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Oleksandr Popov, Valeriia Kovach, Andrii Iatsyshyn, Anastasiia Lahoiko, Olha Ryzhchenko, and Maksym Dement Solving the Inverse Problem of Remote Radiation Monitoring: Restoring the Surface Distribution of Radiation Pollution Based on Measurement Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Yuriy Zabulonov, Oleksandr Popov, Sergii Skurativskyi, Valeriia Kovach, Oleksandr Puhach, and Pavlo Borodych

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Means for Cognitive Analysis and Determination of Risks in Increased Danger Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Iryna Kameneva, Volodymyr Artemchuk, Oleksandr Popov, Andrii Iatsyshyn, Iryna Matvieieva, and Yurii Kyrylenko Development Boron and Gadolinium-Containing Composite Materials Based on Natural Polymers for Protection Against Neutron Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Ievhen Pylypchuk, Valeriia Kovach, Anna Iatsyshyn, Volodymyr Kutsenko, and Dmytro Taraduda Renewable Power Engineering Profitability of the PV Plant and BESS Joint Operation on the Electricity Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Ihor Buratynskyi, Tetiana Nechaieva, Iryna Leshchenko, and Sergii Shulzhenko New Approaches in the Implementation of Generation Capacity Control Systems of Nuclear, Solar and Wind Power Plants in United Energy System of Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Eugene Lenchevsky and Oleh Godun Fuels Rationale for the Creation and Characteristics of the National High-Tech Production of Motor Biofuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 Viktoriia Khaustova, Iryna Hubarieva, Dmytro Kostenko, Tetiana Salashenko, and Daria Mykhailenko Practical Application of the Lithofacial Homogeneity Criterion of Thin-Layered Thicknesses in the Analysis of the Gas Productivity of Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 Oleksii Karpenko, Mykyta Myrontsov, and Yevheniia Anpilova Some Aspects of the Use of Induction Logging . . . . . . . . . . . . . . . . . . . . . . . . 597 Mykyta Myrontsov, Oleksiy Karpenko, Yevheniia Anpilova, Oleksii Noskov, and Inesa Krasovska Transport Review on Possible Impact of Mass EVs Charging on the Power System and Ways to Mitigate It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 Ganna Kostenko and Oleksandr Zgurovets Development of the Method for Calculating the Arrival Path of the Special Services Unit to the Sites of Emergency Occurrence . . . . . . 627 Hennadii Khudov, Irina Khizhnyak, Oleksandr Makoveichuk, Vladyslav Khudov, and Rostyslav Khudov

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Ship Refrigeration System Operating Cycle Efficiency Assessment and Identification of Ways to Reduce Energy Consumption of Maritime Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 Oleg Onishchenko, Andrii Bukaros, Oleksiy Melnyk, Vladimir Yarovenko, Andrii Voloshyn, and Oleh Lohinov Simulation-Based Method for Predicting Changes in the Ship’s Seaworthy Condition Under Impact of Various Factors . . . . . . . . . . . . . . . 653 Oleksiy Melnyk, Svitlana Onyshchenko, Oleg Onishchenko, Olha Shcherbina, and Nadiia Vasalatii Comprehensive Study and Evaluation of Ship Energy Efficiency and Environmental Safety Management Measures . . . . . . . . . . . . . . . . . . . . 665 Oleksiy Melnyk, Oleg Onishchenko, Svitlana Onyshchenko, Andrii Voloshyn, and Valentyna Ocheretna Modern Aspects of Ship Ballast Water Management and Measures to Enhance the Ecological Safety of Shipping . . . . . . . . . . . . . . . . . . . . . . . . . 681 Oleksiy Melnyk, Oleksandr Sagaydak, Oleksandr Shumylo, and Oleh Lohinov Development the Method of Shipboard Operations Risk Assessment Quality Evaluation Based on Experts Review . . . . . . . . . . . . . 695 Oleksiy Melnyk, Yuriy Bychkovsky, Oleg Onishchenko, Svitlana Onyshchenko, and Yana Volianska Environmental Safety Methodological Support of Air Pollution Monitoring System . . . . . . . . . . 713 Artur Zaporozhets, Vitalii Babak, Oleksandr Popov, Leonid Scherbak, and Yurii Kuts Assessment and Analysis the Carbon Intensity Change Trends from the Electricity Production in Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 Borys Kostyukovskyi, Tetiana Nechaieva, and Sergii Shulzhenko Study of Gas-Burning Systems Emission Characteristics Due Hydrocarbon Fuels Combustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 Oleksandr Siryi, Michael Abdulin, Yurii Bietin, Olha Kobylianska, and Arina Magera Peculiarities of Using Ammonium Reagents in Technologies of Semi-dry Desulfurization of Flue Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767 Igor Volchyn, Serhii Horyanoi, Serhii Mezin, Wlodzimierz Przybylski, and Andrii Yasynetskyi

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Peculiarities of Specialized Software Tools Used for Consequences Assessment of Accidents at Chemically Hazardous Facilities . . . . . . . . . . . 779 Oleksandr Popov, Taras Ivaschenko, Liudmyla Markina, Teodoziia Yatsyshyn, Andrii Iatsyshyn, and Olha Lytvynenko Method for Detecting Natural and Anthropogenic Changes That Filled with Water in Landscapes Using Radar Satellite Imagery . . . . . . . 799 Oleksandr Trofymchuk, Yevheniia Anpilova, Oleksandr Hordiienko, Mykyta Myrontsov, and Oleksiy Karpenko Simulation of the Reagent-Free Process of Demanganation Through Aeration with Atmospheric Oxygen Without pH Correction and Using Artificial Catalysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815 Yuriy Zabulonov, Dmytro Charnyi, Serhii Marysyk, Mykhaylo Rudoman, Volodymyr Komarov, and Oleksandr Puhach

Energy Informatics

Development of the New Electro-thermal Energy System Structure for Providing of Ukraine’s Energy Market Profitability Vitalii Babak

and Mykhailo Kulyk

Abstract The energy of the world is currently experiencing a period of priority development and use of renewable energy sources (RES), first of all, wind (WPP) and solar (SPP) power plants in the structure of integrated power systems (IPS). This process is developing despite the fact that these renewable energy sources, by their physical nature, are unable to provide either a stable frequency of the produced energy or its guaranteed power. Laws on the “green” tariff assign these functions to the IPS of Ukraine through the Energy Market. In the conditions of Ukraine, in the current state of the mentioned RES development, the provision of these additional functions led to the fact that in 2020–2021, the energy market’s expenses exceeded its income, and it was in a hidden bankruptcy status. It has been proven that the current energy-economic situation of Ukraine’s IPS and its energy market is due to the hypertrophied development of SPP and WPP in the structure of Ukraine’s IPS; unreasonably large preferences granted to RES owners by “green” laws; extremely unsuccessful management in the IPS of Ukraine and its energy market, due to the laws on the “green” tariff. The purpose of the work is to create the structure and basics of the fundamentally new electro- thermal system functioning, which connects the IPS of Ukraine and the system of centralized heat supply (CHS) through the electrification of heat supply via the use of energy from autonomous RES and the capacities of nuclear power plants, which will ensure the profitability of Ukraine’s Energy Market and guaranteed profitability of RES. As a result of the construction of the integrated structure of the IPS and CHS of Ukraine, several important problems are solved: the full payment of the cost of electricity produced at SPP and WPP is ensured due to the income of the CHS system; the problems of frequency and power stabilization in IPS automatically solve, by means of which the energy market of Ukraine gets rid of losses in the amount of 15 billion dollars USA annually; 7.28 billion cubic meters of natural gas are saved; carbon dioxide emissions are reduced by 98 million tons in CO2 equivalent. Keywords Traditional technologies · Renewable energy sources · Energy system · Centralized heat supply system · Energy market · Electric heat generator V. Babak · M. Kulyk (B) General Energy Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_1

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1 Introduction Despite the lack of evidence of the decisive influence of the anthropogenic factor on climate change, the period of priority development and use of renewable energy sources, primarily wind and solar power plants in the structure of generating capacities of energy systems, continues in the world energy sector. This process has developed for quite a long time, practically without taking into account extremely important factors: WPP and SPP are energy sources with zero guaranteed capacity; due to their technological nature, WPP and SPP cannot ensure the normalized stability of the frequency and power of the electricity they supply to the power system. Withal, the relative capacities of WPP and SPP in the initial period of their use in energy systems were insignificant, and the necessary volumes of regulating capacities were forcibly drawn from the reserves of primary and secondary regulation, which are provided in each power system in accordance with regulatory requirements for stabilizing normal and emergency modes of energy system functioning. That is, in order to ensure the stable operation of WPP and SPP as part of the integrated energy systems, fastacting reserve capacities intended for absolutely another purposes were involved. This approach did not form problems in energy systems as long as the capacity of WPP and SPP was insignificant. Over time, when their power increased significantly thanks to the laws on “green” tariffs in many countries, heavy system accidents began, up to blackouts (South Australia) and disconnection from the electricity supply of large regions with a total capacity of several thousand megawatts (Germany and other countries). At the same time, all over the world, the rapid growth of the use of WPP and SPP in integrated energy systems was and is currently being carried out practically without proper scientific support, by trial and error. The IPS of Ukraine was no exception. As of October 2019, about 4,000 MW of SPP capacity and about 750 MW of WPP capacity were introduced into its structure. Two years later, the capacity of SPP was already about 6,500 MW and WPP−1,500 MW, that is, the total capacity of renewable energy sources in the IPS of Ukraine has almost doubled. Additional high-speed capacities which intended to stabilize the functioning modes of Ukraine’s IPS when using in its structure large capacities of WPP and SPP, since the acceptance of the laws “On alternative energy sources”, “On the electric energy market” (laws on the “green” tariff) was practically not introduced. It is expedient to analyze the evolution of the energy policy of industrialized countries regarding the use of WPP and SPP in the structure of their own energy systems. Already at the level of 2000−2010, it became clear to a wide range of professionals that the mentioned technologies are, of course, competitive only in energy systems that include powerful hydropower plants (Norway, Austria, etc.). The number of countries with such opportunities is very limited. The vast majority of others should take into account the specifics of their own economies to prevent loss of competitiveness when intending to use these renewable energy sources. It is indicative that the European Union, by its decisions already in 2016, prohibited the granting of any preferences to energy technologies and energy industrial installations. However, later, under the pressure of environmental lobby organizations, EU was

Development of the New Electro-thermal Energy System Structure …

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forced to change this decision and gave the right to each EU member state to solve the issue of preferences at its own discretion. Currently, Germany, in particular, has already fulfilled its environmental obligations, which it legally took 20 years ago, paying at the same time with a decrease of 2–3% of its gross domestic product. Even before 2016, Poland was able to legally renounce the preferences it had granted to the owners of WPP and SPP. Other EU members and most of the other countries of the world adhere in their actions to positions that are closer to Poland’s energy strategy. The IPS of Ukraine is close to the Polish power system in terms of its capacity structure, in particular, it also does not have powerful hydroelectric power stations. However, unlike Poland, Ukraine’s strategy in the area of using WPP and SPP in the energy system is strictly opposite. Ukrainian energy legislation exempts RES owners from installing special expensive equipment, which ensures the standard frequency on the output buses of the power plant, at their power plants. A very valuable benefit is also the fact that RES owners are freed from the installation of expensive reserve generating capacities necessary for the stable operation of the power system in the absence of wind or solar radiation. Reimbursement of expenses for these benefits the “green laws” entrust to the energy system represented by NEC Ukrenergo, i.e., to the energy market of Ukraine. In addition, as a result of the above-mentioned laws, the extremely unsuccessful management operates on Ukrainian energy market, when WPP and SPP in a complex with reserve thermal power plants push the most economical nuclear power plants out of the market. Such powerful preferences and factors probably are currently hard to find in any country in the world. The result of this attitude of the state to its own energy system was that already in pre-war times, the revenues of the Ukrainian energy market did not cover its expenses both in 2020 and in 2021. In order to correct the situation and cover the resulting deficit, the Government introduced the issuance of Eurobonds and the provision of loans by Ukrainian banks to the Energy Market of Ukraine under government guarantees to liquidate debts owed to RES owners. In addition, the state guaranteed the owners of WPP and SPP that the installed capacity of these RES will be brought to the level of 15 GW in 2030, which is almost twice the actual value of 2021, while maintaining the existing management of IPS and of the energy market. Thus, during the last two pre-war years, the energy system and the energy market of Ukraine worked in conditions of hidden bankruptcy. Poland’s experience could be a way out of the threatening economic situation in Ukraine’s energy complex. However, the Government has already granted (in 2020) and legally confirmed for the period until 2030 all owners of WPP and SPP, whose power plants operate as part of IPS of Ukraine, unjustified and destructive preferences, which in the current state have already led the energy market to a situation of hidden bankruptcy, and which in the future threaten catastrophic consequences for the entire economy of the country. That is why an urgent problem in the electric power complex of Ukraine in the postwar period is the development of a conceptually different approach to the principles using of WPP and SPP energy, which would promote to solving the national problem of ensuring the reliability of IPS and energy security of Ukraine.

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The purpose of this work is to create a fundamentally new structure and basics of operation of an ultra-large electro-thermal system for the production of electric and thermal energy (mega-system), which integrates the IPS of Ukraine and centralized heat supply systems by electrification of heat supply through the use in the primary state of the energy of autonomous renewable energy sources and the capacities of nuclear power plants, which ensures reliable manufacturability, high energy-economic indicators of IPS, RES and CHS, increases energy security and significantly improves the condition of the environment in the country.

2 Justification of Work Purposes Normative and legal “green” legislation in Ukraine was formed for quite a long time, difficult and contradictory. In the period until 2020, tariffs for RES energy in Ukraine were many times higher than the market prices for electricity obtained using traditional technologies [1–5]. This factor was one of the main ones that determined the extremely high profitability of RES and the deep unprofitability of the Ukrainian electricity market. The said situation led to a significant increase of RES capacities in the IPS structure of Ukraine [6, 7]. In view of the fact of the sharp growth of RES capacities in the period after 2014, the regulatory authorities began to introduce reductions of energy tariffs for RES [8], which were later canceled in court. The regulator’s new attempt to reduce “green” tariffs [9] also did not change the situation for the better. Due to the payment crisis, already in the first months of the new electricity market operation in 2020, payments for energy produced under the “green” tariff were almost completely stopped. Electricity market actually stopped fulfilling its obligations and went into a state of hidden bankruptcy. In order to resolve the threatening situation Cabinet of Ministers, EuropeanUkrainian Energy Agency and Ukrainian Wind Energy Association accepted the “Memorandum of Understanding on the Resolution of Problematic Issues in the Renewable Energy Sector” (Memorandum) in 2020, in which producers agreed to a voluntary reduction “green” tariff for operating SPP and WPP. The state undertook to ensure the operation of the newly introduced auction model of RES support. The main provisions of the Memorandum were legislated [10] by taking into account the features of establishing a “green” tariff in the Law “On Alternative Energy Sources” [11]. As a result of the implementation of Memorandum provisions, the fixed tariffs established by law for WPP as of 2021 were close to the prices of the Ukrainian electricity market. Current SPP electricity tariffs were even lower than market prices [12]. The publication [13] provides forecast estimates of the installed capacity of WPP and SPP as part of Ukraine’s IPS for the period until 2030. In combination with the data on the tariffs determined by the Memorandum, this provides an opportunity to analyze the energy economic situation projected in the IPS of Ukraine and its energy market at the level of 2030. This task is relevant both today and in the distant future, since the extremely negative forecasts made by the authors are already confirmed

Development of the New Electro-thermal Energy System Structure …

7

in the current state of the electric energy complex of Ukraine as a whole, its energy system and the energy market in particular. This was already appeared in 2021 in the irrational use of available generating capacities, first of all, highly economical nuclear generation, unreasonably high prices for electricity on the domestic market, in due to this significant import of electricity with large surplus capacities of own generation and in a number of other negative phenomena. Therefore, there is currently an urgent need and opportunity to develop the main directions and measures to increase of the operation efficiency of Ukraine’s IPS under the conditions of deployment large volumes of RES in its structure. This problem is relevant not only for the electric power industry of Ukraine, it is no less important for the energy complexes of most industrialized countries that are moving to the principles of low-carbon development. The main difficulties in solving such problems are as follows. The presence of zero guaranteed power in RES makes it necessary to use additional specific equipment in the IPS structure, which ensures the stability of the frequency and power supplied by the RES to the system. In order to formulate the technological requirements for this equipment, it is necessary to have a toolkit for analyzing its functioning as part of the IPS. At the same time, it was necessary to develop specific mathematical models of frequency and power regulation in IPS, and composition of models had to include mathematical blocks reflecting not only the characteristics (primarily frequency) of RES and traditional technologies, but also the characteristics of the specified additional technological equipment and interconnections between all IPS equipment, including RES, additional technological and traditional equipment. An additional complication in such models is the synthesis of mathematical blocks that reflect the behavior of the wind and the Sun radiation as a working body. In the vast specialized literature devoted to RES usually issues of interrelationships and behavior between individual renewable energy sources and additional equipment of the specified purpose are investigated. A quite detailed analysis of these publications is given in [14]. Analysis of RES functioning as part of the IPS was not found among the publications known to the authors. In the current state, a large number of studies on the RES operation as part of the IPS are carried out by the General Energy Institute of the National Academy of Sciences of Ukraine. Wherein a set of several mathematical models with different functionalities is used. A model and software complex were developed for the study of the joint operation of WPP, SPP, hydroelectric power station (HPP) and storage batteries (SB) as part of the IPS of Ukraine [14, 15]. They underwent various tests and applications on real data. A modification of the model and software complex for forecasting the long-term development of power systems with wind and solar power plants using statistical information to increase the flexibility of the power system was worked out [16]. In order to evaluate the economic efficiency of the joint operation of RES, SB and a traditional reserve power plant under the conditions of ensuring a stable level of power, an appropriate model of the life cycle of such a system has been developed [17]. To forecast the long-term development of the generating capacities structure of the electric power system, taking into account the dynamics of input and output of

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capacities and changes in their technical and economic indicators during the forecast period, a partial-integer mathematical model was developed [18]. Research and modernization of energy objects, power-consuming technologies and introduction of new energy-efficient materials are based, first of all, on measurement, control and diagnosis of physical characteristics and regulation of physical processes parameters. The use of mathematical models of physical signals and fields of energy facilities operation, algorithms and programs for determining and statistical evaluation of their characteristics are the basis of information support for the operation of monitoring and diagnostic systems [19, 20]. Due to results given, in particular, in the publications [14–20], researchers received a well-founded opportunity to choose the types and power of regulators that ensure the necessary stability of the IPS frequency in the structure of which RES of one nature or another is operating. If, for example, large-capacity WPP is part of the IPS, then only SB or large-capacity HPS can provide frequency stabilization in it. Even lowland HPP can ensure the stable operation of IPS, which mainly include SPP. However, neither in the first nor in the second case thermal power plants of any physical nature can be used for frequency stabilization in such IPS due to their insufficient speed. When determining of the volume and structure of the RES use as IPS part of any country, it is necessary to take into account not only the technological capabilities and indicators of frequency regulators, which can potentially be used in this case, but also the energy-economic characteristics of both RES and IPS as a whole in the system-wide measurement.

3 Energy-Economic Indicators of SPP and WPP Functioning in the IPS Composition of Ukraine According to 2021 Energy-economic calculations were carried out by means of a system analysis of established modes of joint operation of integrated energy systems, wind and solar power plants (Table 1). At the same time, the processing of significant volumes of source information was carried out according to a large number of various dependencies and algorithms. The results of the calculations are given in the form of appropriate tables, since in this form heterogeneous and numerous indicators are perceived most accessible. Modernized coal-fired thermal power plants as reserve so and alternative power plants were used to evaluate the effectiveness of both SPP and WPP. This decision is due to the fact that in the existing structure of IPS of Ukraine these capacities were redundant as of the end of 2021, have the lowest specific capital investments and use fuel (coal), which is not included in the list of critical import goods. There are no another manoeuvrable sources of the necessary power in the IPS of Ukraine. The methodology of the calculations consists in comparing the economic efficiency

Development of the New Electro-thermal Energy System Structure …

9

indicators of two structures of Ukraine’s IPS generating capacities, namely, its structure, which was formed in accordance with the laws of Ukraine on the “green” tariff, and another, alternative structure, in which RES are completely absent. Initial data for calculating SPP indicators: installed capacity of SPP–6283 MW; term of operation-25 years; specific capital investments-$1,000/kW; the coefficient of use of the installed capacity (CUIC) is 0.17; tariff for SPP electricity–4.35 eurocents/kWh. Reserve power plants: installed capacity–6283 MW; specific capital investments−$400/kW; CUIC (calculated)–0.63; specific fuel consumption−0.345 kg.c.e. (coal equivalent); service life−35 years; coal price– 3,274 S= /t; payment for CO2 emissions−$3/t. Initial data for calculating WPP indicators: installed capacity of WPP–1529 MW; service life−25 years; specific capital investments−$1,400/kW; CUIC−0.35; the tariff for WPP electricity is 8.82 euro cents/kWh. Reserve power plants: installed capacity of TPP−1529 MW; specific capital investments−$400/kW; CUIC−0.65; specific fuel consumption−0.345 kg.c.e.; service life-35 years; the payment for carbon dioxide emissions in the amount of $3/t is currently the average for the countries of the European Union; CUIC for the reserve thermal power plant (TPP) when it works together with the SPP should be taken as 0.63, since the SPP works on average 11 h a day; $1 = 28.8 S= ; 1 e = 32.5 S= ; S= is the designation for the hryvnia. Tariffs for the energy of WPP and SPP are used in accordance with the Memorandum. The main energy-economic indicators of the operation of SPP and WPP as part of the IPS of Ukraine in 2021 are given in the Table 1. In the Table 1, the volumes of electricity production at the SPP (p.2) coincide with the volumes of its generation at the corresponding alternative TPP for the validity of comparisons of their energy-economic characteristics. A similar condition is also provided for TPP, which is an alternative to WPP. According to the Memorandum, tariffs for SPP and WPP electricity in Ukraine are legally set at the 2030 level of 3.9 and 7.72 euro cents per 1 kWh respectively. These tariffs are lower than the current prices on the Ukrainian energy market [12]. This gives grounds to assert that starting from 2023 solar and wind energy will no longer have such a destructive effect on the condition of the country’s energy complex, the demonstrations of which took place already in 2021, as can be seen from the Table 1. This study proved that such assessments and statements have no basis. At the same time, the forecast of installed capacities at the level of 2030 [13] was used for SPP with a volume of 9,947 MW and for WPP with a volume of 5,033 MW. The methodology and algorithms for calculating the energy-economic indicators of this period are similar to those used in calculating the indicators in the Table 1. It is important to compare the relevant values to recognize trends and the speed of their change.

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Table 1 Energy-economic indicators of SPP and WPP functioning as part of Ukraine’s energy system according 2021 №

Size

Measurement unit

Value

1

Installed capacity of SPP

MW

6,283

2

Production of electricity at SPP

kWh

7.485:109

3

Production of electricity at a reserve TPP

kWh

27.737:109

4

The cost of SPP electricity

$ USA

325.6:106

5

The cost of electricity of thereserve TPP

$ USA

2.617:109

6

Gross costs of the SPP owner

$ USA

289.6:106

7

Gross income of the SPP owner

$ USA

325.6:106

8

Gross profit of the SPP owner

$ USA

36:106

9

Net profit of the SPP owner

$ USA

28.8:106

SPP

10

The payback period of the SPP owner’s capital

Year

10

11

Emissions of CO2 by reserve TPP

Ton

35.1:106

12

Consumer costs for energy produced by SPP + TPP

12.1

The cost of electricity produced by the SPP

$ USA

325.6:106

12.2

The cost of electricity for SPP reservation

$ USA

1.617:109

12.3

Cost of electricity for frequency stabilization

$ USA

3.368:109

12.4

General expenses of the consumer

$ USA

5.311:109

13

The total electricity generated by the SPP + TPP complex

kWh

35.22:109

12.1

The cost of electricity produced by the SPP

$ USA

325.6:106

12.2

The cost of electricity for SPP reservation

$ USA

1.617:109

12.3

Cost of electricity for frequency stabilization

$ USA

3.368:109

12.4

General expenses of the consumer

$ USA

5.416:109

13

The total electricity generated by the SPP + TPP complex

kWh

35.22:109

14

Cost price of electricity produced at the SPP + TPP complex

$/kWh

0.153

15

An alternative TPP to the SPP + TPP complex

15.1

The installed capacity of the alt. TPP

kW

1.068:106

15.2

Fuel consumption

ton

2.582:106

15.3

CO2 emissions alt. TPP

ton

9.5:106

15.4

Total costs per alt. TPP

$ USA

338.1:106

15.5

The cost price of energy produced on alt. TPP

$/ kWh

0.0452

The installed capacity of the WPP

MW

1,529

WPP 16

(continued)

Development of the New Electro-thermal Energy System Structure …

11

Table 1 (continued) №

Size

Measurement unit

Value

17

Production of electricity at WPP

kWh

3.75:109

18

Production of electricity at a reserve TPP

kWh

6.965:109

19

The cost of WPP electricity

$ USA

330.8:106

20

The cost of electricity of the reserve TPP

$ USA

657.1:106

21

Gross costs of the WPP owner for 1 year of operation

$ USA

99.34:106

22

Owner’s gross income

$ USA

330.8:106

23

Owner’s gross profit

$ USA

231.46:106

24

Owner’s net profit

$ USA

185.17:106

25

The payback period for the owner’s expenses

Year

0.536

26

Emissions of CO2 by reserve TPP

Ton

8.81:106

27

Consumer costs for electricity WPP + TPP

27.1

The cost of electricity produced at WPP

$ USA

330.8:106

27.2

The cost of electricity for the reservation of WPP

$ USA

463.6:106

27.3

Cost of electricity for frequency stabilization

$ USA

1.6876:109

27.4

General expenses of the consumer

$ USA

2.483:109

28

Total electricity produced by the WPP + TPP complex kWh

10.72:109

29

Cost price of electricity produced at the WPP + TES complex

$/ kWh

0.234

30

An alternative TPP to the WPP + TPP complex

30.1

Production of electricity at alt. TPP

kWh

3.75:109

30.2

The installed capacity of the alternative TPP

kW

0.535:106

30.3

Coal consumption

ton

1.294:106

30.4

CO2 emissions alt. TPP

ton

4.744:106

30.5

Total costs per alt. TPP for 1 year of operation

$ USA

170.5:106

30.6

The cost price of electricity alt. TPP

$/ kWh

43.47:10–3

4 Analysis and Comments of Energy-Economic Indicators of SPP and WPP Functioning as Part of Ukraine’s IPS According to Reports (2021) and Forecast Data for the Period Until 2030 The main political argument used to justify the need for the use of SPP and WPP technologies is the need to reduce greenhouse gas emissions. However, as shown by the obtained results (Table 1), in reality the situation is completely opposite. Indeed, in the conditions of the IPS of Ukraine, which do not include powerful hydroelectric power plants, to ensure the reliable operation of renewable energy sources and the entire energy system, it is necessary to use additional reserve energy sources, among

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which coal-fired thermal power plants, the installed capacity of which coincides with the total installed capacity of SPP and WPP, are uncontested. Due to the fact that the CUIC of WPP is almost twice less than this indicator for reserve TPP, and for SPP-almost four times, CO2 emissions by the SPP + TPP complex at the level of 2030 amount to 55.6·106 tons in equivalent CO2 , while these emissions by an alternative coal-fired thermal power station are only 15·106 tons. For the WPP + TPP complex and the alternative TPP, these indicators are 29·106 tons and 15.6·106 tons, respectively. The ratios of these indicators in 2021 are similar (Table 1). Thus, due to the presence of SPP and WPP in the IPS of Ukraine, CO2 emissions will increase by 54 million tons in 2030, and the ratio of these emissions in reserve and alternative TPP is 2.8. That is, the comparative analysis of greenhouse gas emissions presented in this work completely refutes the stated political rationale for the feasibility of using SPP and WPP as part of the IPS of countries that, according to conditions of nature (lack of opportunities to build powerful hydroelectric power stations), are similar to the conditions of Ukraine. The use of RES in the IPS of Ukraine not only does not improve the environmental situation, but even significantly worsens it. In addition, in terms of energy economy, the supporters of the widespread introduction of SPP and WPP into the structure of the IPS argue their position by the fact that these energy sources do not require fuel. But at the same time, it is not taken into account that to ensure their working, it is necessary to additionally use reserve TPP. Thus, for the reservation of a SPP with a capacity of 9,947 MW (2030), TPP of the same capacity are needed, which will produce 43,916·109 kWh. and will consume 15.15·106 t of thermal coal, while the alternative TPP requires only 5.11·106 t of the same coal and will produce the same amount of electricity as provided by the SPP. That is, the SPP + TPP complex requires almost three times more coal for its operation than the alternative TPP consumes. The fact that the SPP itself does not use fuel not only does not give it any advantages, but even complicates the situation significantly. The main strategic miscalculation in the formation of directions for the use of SPP and WPP as part of the generating capacity of the IPS of Ukraine was the simultaneous use of IPS network for the transmission of energy like from both traditional energy sources so from RES. Such an approach makes it necessary, in the conditions of a unified energy system, to ensure the introduction of additional expensive equipment into its structure (reserve energy sources and fast-acting frequency regulators), which could compensate for the unevenness of power generation and the instability of the RES frequency, which are technologically inherent in these energy sources. This equipment (or the import of its energy) causes the majority of the hypertrophied costs of the consumer, who uses the energy of the IPS with large capacities of SPP and WPP. In particular, at the level of 2030, in the total costs of the consumer for the production of electricity from SPP + TPP in the amount of $8,777·109 , the costs of SPP reservation are $2,893·109 , and the costs of frequency stabilization are $5,332·109 , in total−8,225· $109 , which is 93.7% of total consumer spending. The energy market of Ukraine is the consumer of the indicated energy worth $8,777·109 . His expenses for this segment will be compensated according to “green” laws only by the cost of SPP energy in the amount of $522.2·106 . The difference between these

Development of the New Electro-thermal Energy System Structure …

13

indicators ($8,255·109 ) is the loss of the energy market. A similar situation occurs in the wind power plant sector at the level of 2030, as well as in the SPP and WPP sectors at the level of 2021 (Table 1). A very important strategic blunder during the development of laws on the “green” tariff was the introduction of a provision, which in the practice of legal and economic relations between economic entities is called the “take or pay” principle, in the Law of Ukraine “On the Electricity Market”. This principle obliges the operator (dispatcher) of the energy system to give priority in the use of energy to wind and solar power plants. In case of violation of this principle, the owner of the SPP or WPP receives compensation from the energy market of Ukraine in the amount of the lost benefit (the cost of the energy not released). The research results show that the specified principle leads to unacceptable systemic losses. Indeed, it is planned that at the level of 2030, the SPP + TPP and WPP + TPP complexes will produce a total of 91·109 kWh. In the current state of energy legislation in Ukraine, all this energy must be sold to consumers without alternatives. It is generated around the clock, so it covers part of the base zone of the electric load schedule (ELS), competing only with nuclear plants (NPP). At present, the maximum amount of energy production at NPP according to their working capacity is 97·109 kWh, therefore RES together with reserve TPP are almost completely displacing NPP units, which are the most economical among all traditional technologies, from ELS. This leads to large systemic losses and a significant increase in the unprofitability of the Ukrainian energy market. The Government’s attempts to equalize the situation by monetary means were not successful during 2020–2021, especially their use in the period up to 2030 does not make sense due to the projected doubling of the installed RES capacity. The forecast of the energy economic situation in the IPS of Ukraine at the level of 2030 shows that with the existing structure of management in the IPS of Ukraine, the IPS will not be able to function. If the installed capacity of SPP and WPP is doubled from the current one to almost 15 million kW and the existing approaches to their use are preserved, the losses will increase to a fantastic index-more than 460 billion hryvnias. It will be impossible to correct such a situation only with the specified monetary measures. The country’s economy simply will not be able to provide compensation for the losses of the IPS in the amount of more than 15 billion US dollars annually, especially in the post-war period and especially without a reasonable answer to the question of why this should be done. On the other hand, the state has already provided guarantees by the Memorandum to the owners of SPP and WPP to use all the energy produced by them at economically acceptable, fixed prices in hard currency for the period until 2030. It seems that due to a number of negative factors, a closed circle has formed, the rational exit from which is unreal. But in addition to the extremely negative phenomena that are currently occurring in the IPS of Ukraine as a result of imperfect management caused by the laws on the “green” tariff and other regulatory documents that grant unjustified preferences to the owners of SPP and WPP, in the energy sector of Ukraine (in contrast to the situation in the vast majority industrialized countries) there is an opportunity to reorganize the existing management in the IPS in such a way that the guarantees provided by the state to the owners of RES are fully implemented, and the economic activity of the IPS becomes highly profitable.

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This can be ensured by organizing a new electrothermal energy system, which will include IPS of Ukraine and centralized heat supply systems (CHSS) of large cities with specific connections between them. In countries where there are no CHSS, it will be impossible to implement such an approach.

5 Comprehensive Increase of Functioning Efficiency IPS of Ukraine in the Conditions of Growing RES Capacities in its Structure Through the Organization of its Communications with Systems of Centralized Heat Supply The research on the possibilities of using the interaction of the IPS of Ukraine and the CHSS of large cities to increase the energy-economic indicators of both systems are carried out in General Energy Institute of NAS of Ukraine. At the same time, the tasks of synthesis mathematical models for the construction of effective systems of frequency and power automatic regulation (AFPR) in emergency modes, development of new structures of AFPR, based on consumers-regulators, new peak means of electric power regulation, which are more effective than hydroaccumulating power plants, etc., were solved. In all these developments, the basic element is an electric heat generator (electric boiler or heat pump). The accumulated experience in the development of such systems allowed the scientists of the General Energy Institute of the National Academy of Sciences of Ukraine to synthesize an ultra-large electrothermal energy system (mega-system) that generates both electric and thermal energy in volumes that are not less than those that would be generated by IPS and CHSS in isolated modes. At the same time, SPP and WPP, formally (legally) being part of the IPS, are physically and functionally removed from its structure and form their own autonomous subsystem of the IPS (Electrical Heat Supply System (EHSS)). The primary energy that comes to the consumer from generator bus of SPP or WPP has a wide range of harmonics in its composition. Currently, in the IPS of almost all countries of the world, using a set of converters, they achieve the fact that only one harmonic with a standard frequency of 50 Hz remains in this spectrum, and the converted energy with this frequency is delivered to the consumer through the IPS electrical network. An alternative principle is to attract a consumer who would be insensitive to the change in frequency, that is, would be able to use electricity with the frequency spectrum that it has in its original form. Such a consumer is an electric boiler. It can work with energy that has one standard 50 Hz harmonic in the spectrum, or accept energy with a wide spectrum, then it generates heat based on the principle of superposition, when the thermal energy of each harmonic in the spectrum is integrated. Due to such a scheme, the expensive cost item for frequency stabilization disappears. However, there is another expensive item of expenses in the totality of expenses for the use of RES energy, these are expenses for reserve energy sources. The said problem is solved in the proposed megasystem by choosing the

Development of the New Electro-thermal Energy System Structure …

15

consumer of thermal energy generated by the electric boiler. Centralized heat supply systems are such an ideal consumer. A design and operational feature of CHSS systems is that in order to minimize total costs in the system, a temperature schedule with a max water temperature of 120 o C and a min of 70 °C is implemented in them. This makes it possible to organize the coverage of this schedule in such a way that the zone between the max and min temperatures of 1200 o C and 700 o C is supplied by SPP and WPP, and the base zone up to 700 o C by NPP energy. These two operations are enough so that with such management in the IPS the incomes and profits of RES owners will remain the same as those guaranteed by the Memorandum. In addition, a number of energy-economical factors appear, which, together with the ones already mentioned, also ensure high profitability of CHSS.

6 Energy-Economic Indicators of Joint Functioning IPS and CHSS with Use of SPP and WPP Energy at the Level of 2030 The specified indicators were determined by applying a system analysis of the established electric, hydraulic and temperature modes of the joint operation of WPP, SPP, IPS and CHSS according to the fundamentally new, above-mentioned organization of their interaction (Table 2). Input data for calculating energy-economic indicators: the total length of the power transmission line of the RES is 500 km; the specific cost of the transmission line construction–1.971·103 S= /meter; specific costs for the electric boiler power (connection)–2.28·103 S= /kW; the electric boiler resource−25 years; capital investment for an electric boiler–$35/kW; efficiency coefficient (e.c.) gas boiler–0.93; e.c. electric boiler–0.98; specific emissions of CO2 of a gas boiler–1.622 t/t.c.e.; the natural gas price is $700 per 1,000 m3 ; tariff for thermal energy (2021)–2047 S= /Gcal. The energy-economic indicators of the functioning of IPS and CHSS in Ukraine based on electric heat generators using RES are given in Table 2. In the Table 2, as in Table 1, energy-economic indicators are defined in the uniform prices of 2021 for the possibility of these indicators comparison and analysis. The main advantages of the proposed mega-system in comparison with the current structure of IPS of Ukraine is that, due to fundamentally new management, the need to use expensive equipment for frequency stabilization and power reserve in the power system has disappeared. It is these two factors that determine the currently enormous unprofitability of the Ukrainian energy market, which threatens its destruction at the level of 2030. In the new megasystem, the sources of instability (WPP and SPP) are functionally removed from the structure of the IPS, which provides it with the opportunity to operate in the conditions of the classic market with appropriate profitability. During 2020−2021, the problem of market profitability existed as a result of its unsuccessful management, caused by the action of priorities for RES, determined by the laws on the “green” tariff.

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Table 2 Energy-economic indicators of the joint operation of IPS and CHSS in Ukraine using renewable technologies at the level of 2030 №

Size

Unit measurement

Value

1

Consumption of thermal energy from CHSS

Gcal

62:106

2

Including for hot water supply

Gcal

18.5:106

3

Production of electricity at SPP

kWh

11.85:109

4

Production of electricity at WPP

kWh

12.34:109

5

SPP electricity tariff

e/kWh

3.9:10–2

6

WPP electricity tariff

e/kWh

7.72:10–2

7

Payments for CO2 emissions

$/t

3

8

Efficiency coefficient of a gas boiler

–

0.93

9

Electric boiler resource

year

25

10

Specific emissions of CO2 of a gas boiler

1.622

11

Price on the electricity market: – weighted average for the day

t/t.c.e = S / kWh

12

– of the base zone

= S / kWh

0.8

13

Natural gas price

$/1000m3

700

14

Tariff for thermal energy

= S / Gcal

2,047

15

Specific costs

15.1

For the construction of power lines

1.971:103

15.2

For the electric boilers power

= S /m = S / kW

16

Total length of power lines

km

500

17

NPP energy (p. 1 × 1.161x7/12)/0.98 The NPP electricity cost (p.17 × 0.8 = S)

kWh

41.7:109

$ USA

1.16:109

19

The volume of natural gas substitution (p.p.3 + 4 + 17) × 10–3 × 130.85 (kg.c.e.)/0.94

m3

7.28:109

20

Reduction of CO2 emissions due to p.19 (p.19 × 1.15 × 1.622)

ton

13.58:106

21

Reduction of fees for CO2 emissions according to p. 20

$ USA

40.74:106

22

The replaced gas cost (p.19 × p.13)

$ USA

5.1:109

23

Reduction of CO2 emissions due to the replacement of the reserve station

ton

84.55:106

24

Reduction of fees for CO2 emissions according to p. 23

$ USA

0.254:109

25

Overall reduction of CO2 emissions (p.20 + p.23)

ton

98.13:106

18

2.717

2.28:103

(continued)

Development of the New Electro-thermal Energy System Structure …

17

Table 2 (continued) №

Size

26

Costs for the construction and operation of the electric heat supply system

26.1

Unit measurement

Value

New transmission lines (p.15.1 × p.16)

$ USA

34.2·106

26.2

Installed capacity of electric boilers (p.17/(0,8 × 8,76:103 ))

MW

20.93:106

26.3

Capital investment for 1 year, taking into account construction, voltage limiters and connection of electric boilers (p.26.1 × 1,112 + p.26,2 × 1,1x(p.15.2 + 35))/25

$ USA

106.64:106

26.4

Salary with accruals (1250 persons × 600 $/ month x12 × 1,24)

$ USA

11.16:106

26.5

Other costs (materials, etc.) (p.26.3 × 0,02)

$ USA

2.13:106

26.6

The cost of SPP, WPP and NPP electricity (p.18)

$ USA

2.762:109

26.7

$ USA

2.937:109

27

Total EHSS costs (p.p. 26.3 + 26.4 + 26.5 + 26.6) The thermal energy cost (p.1 × 2047= S)

$ USA

4.407:109

28

Gross revenues of EHSS (p.p.21 + 22 + 24 + 27)

$ USA

9.802:109

29

EHSS gross profits (p.28–p.26.7)

$ USA

6.868:109

30

EHSS net profit of (p.29 × 0.8)

$ USA

5.22:109

31

The payback period for EHSS costs (p.26.7/p.30)

Year

0.56

The gross revenues of the electric heat supply system (p. 28 of Table 2–$9.8·109 ) far exceed its expenses (p. 26.7 of Table 2–$2.937·109 ), which guarantees the payment of the cost of WPP and SPP electricity defined by the Memorandum. Such a high profitability of EHSS (payback period−0.56 years) is caused by several factors: high current tariffs for thermal energy (p. 14 of Table 2); replacing large volumes of natural gas (p.19 of Table 2–7.28·109 m3 ) by cheap energy of NPP, WPP and SPP; reduction of large volumes of greenhouse gas emissions (p. 25 of Table 2–98.13·106 t). Due to the proposed structure of EHSS, NPP are actually used as reserve sources of thermal energy, and the market cost of NPP energy is almost 3.5 times lower than the cost of TPP energy [12], which perform reserve functions in the current IPS structures with a large share of SPP and WPP. In addition, there is no need for large volumes of TPP capacity reservations, which partially block currently and will completely block the use of effective NPP energy at the level of 2030. As a result of the use of such a EHSS structure, nuclear power plants will have free access to the electricity market. If the EHSS system is implemented during the heating period, NAEC “Energoatom” will supply about 40% of its energy to this system. In the non-heating period, the use of NPP energy in EHSS will decrease to 12%, which will not affect the profitability of this company in any way, since this period is used

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by almost all power plants as a repair site. In addition, the surplus of the NPP energy is easily sold on the Ukrainian or European markets.

7 Conclusions 1. The political argument by supporters of the use of SPP and WPP that the use of these renewable energy sources leads to a decrease of greenhouse gas emissions for the conditions of Ukraine is false. The analysis demonstrates the opposite phenomenon, namely, the specified emissions in the absence of RES in the power structure of the IPS of Ukraine are reduced several times compared to the options when these RES are present in it. CO2 emissions by SPP + TPP and WPP + TPP complexes during their operation as part of the IPS of Ukraine at the level of 2030 total 84.5·106 t in CO2 equivalent. At the same time, the total emissions of these gases from alternative TPP, which produce electricity of the same volume as that generated by SPP and WPP together, amount to only 30.6·106 tons. In this particular case, alternative TPP emit 2.76 times greenhouse gases less than RES + TPP complexes. The very common statement that the use of RES does not require fossil fuels is not true. At the same time, it is not taken into account that the functioning of RES in the current conditions of the IPS of Ukraine is impossible without the use of reserve TPP. This is a gross mistake. At the level of 2030, coal consumption by reserve TPP is predicted to be 28.83·106 t c.e., while alternative TPP will need only 10.43·106 t c.e. The ratio of these indicators (2.76) strictly coincides with the corresponding ratio of greenhouse gas emissions, as it should be. The use of RES as part of the IPS of Ukraine with the current structure not only does not improve the environmental situation, but even worsens it multiple times. This condition is typical for most countries, the only exception being those that are rich in hydro resources. 2. Immediately before the war, in 2020–2021, the state carried out a series of monetary and organizational measures to settle the current threatening energyeconomic situation on the country’s energy market (establishing more or less acceptable tariffs for RES energy; issuing Eurobonds and providing loans to repay losses in the energy market caused by the action “green” laws; reducing the rate of growth of new construction of SPP and WPP by holding auctions for permits for such construction, etc.). However, despite these measures, the total losses of Ukraine’s energy market in 2021 amounted to about $7 billion (Table 1), which is estimated as its hidden bankruptcy. The energy-economic forecast of the IPS functioning and the energy market of Ukraine at the level of 2030 shows that if the management of the energy market, defined by the current laws on the “green” tariff, is preserved, annual losses of the energy market will increase to 15 billion US dollars. This will lead to the destruction of the energy market and shocks for the entire economy of the country up to and including the threat of its default.

Development of the New Electro-thermal Energy System Structure …

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3. The main strategic mistake in the formation of directions for the use of SPP and WPP as part of the generating capacity of Ukraine’s IPS was the simultaneous use of IPS network for the transmission of energy from both traditional energy sources and RES. This approach makes it necessary in the conditions of a unified power system to ensure the introduction of additional expensive equipment (reserve energy sources and fast-acting frequency regulators) into its structure, which should compensate for the unevenness of power generation and frequency instability, which are technologically inherent in RES. In this work it was established that this equipment causes the hypertrophied losses of the energy market of Ukraine in the amount of $15·109 at the level of 2030. 4. The main advantages of the proposed mega-system in comparison with the current structure of Ukraine’s IPS is that, due to a fundamentally new management, the need to use equipment for frequency stabilization and power reserve in the power system has disappeared. It is these two factors that determine the currently flagrant unprofitability of the Ukrainian energy market, which threatens its destruction at the level of 2030. In the structure of the megasystem, the sources of instability (WPP and SPP) are functionally removed from the composition of the IPS, which provides it the opportunity to work in the conditions of the classic market with acceptable profitability. During 2020–2021, the problem of its profitability existed as a result of its unsuccessful management, caused by the action of priorities for RES, determined by the laws on the “green” tariff. 5. The gross revenues of the electric heat supply system ($ 9.8·109 ) far exceed (Table 2) its expenses ($ 2.937·109 ), which guarantees payment of the cost of electricity from WPP and SPP, determined by the Memorandum. Such high profitability of EHSS is caused by several factors: high current tariffs for thermal energy; replacing large volumes of natural gas by cheap energy from NPP, WPP and SPP; reduction of large volumes of greenhouse gas emissions. 6. The proposed approach of transferring CHSS to the use of electric boilers with the combined use of electricity from both traditional and renewable sources instead of natural gas heat generators provides for the new system of centralized heat supply high economic efficiency with a capital payback period of about half a year (Table 2). This indicates that the tariff for thermal energy in the new EHSS system can be reduced by at least 4 times compared to the tariff, in particular, that was in effect at the CHSS “Ukrteploenergo” in October 2021 (before the rapid increase of world natural gas prices). This will ensure an increase in the demand for heat supplied through CHSS, with a further increase CHSS efficiency. 7. As a result of the implementation of the project to construct new, combined structures of the IPS and CHSS in Ukraine, several problems of state importance are immediately solved: – Full payment of the cost of electricity produced at SPP and WPP is ensured at the expense of EHSS system revenues; – The capacities of SPP and WPP are removed from the structure of the IPS of Ukraine, therefore the problem of frequency and power stabilization disappears in the IPS, it gets the opportunity to operate in the full extent of its

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capacities in market conditions, without fixed prices for RES energy and without other privileges for RES, which leads to huge losses on the energy market; thanks to this, the energy market of Ukraine automatically gets rid of losses in the amount of about 15 billion dollars USA annually; – 7.28 billion cubic meters of natural gas are saved, which is a significant contribution to the energy security of the country; – Emissions of carbon dioxide are reduced by more than 98 million tons in CO2 equivalent, which accounts for about 50% of all emissions in Ukraine in 2020 in the production of thermal and electrical energy. This publication was prepared in the process of joint research of the Committee for System Analysis at the Presidium of the National Academy of Sciences of Ukraine and the International Institute of Applied System Analysis (IIASA) under the program “Complex modeling of management of the safe use of food, water and energy resources for the purpose of sustainable social, economic and ecological development”.

References 1. Law of Ukraine of 25.09.2008 №601-VI.: On amending certain laws of Ukraine regarding the establishment of a “green” tariff. https://zakon.rada.gov.ua/laws/show/601-17/ed20080925# Text 2. Law of Ukraine of 1.04.2009 №1220-VI.: On amending the law of Ukraine “on electricity” to stimulate the use of alternative energy sources. https://zakon.rada.gov.ua/laws/show/1220-17# Text 3. Resolution of the National Electricity Regulatory Commission of Ukraine of 23.12.2008 N 1440.: Regarding the approval of retail electricity tariffs for January 2009, taking into account the maximum tariff levels during the gradual transition to the establishment of unified retail tariffs for consumers throughout Ukraine. https://zakon.rada.gov.ua/rada/show/v1440227-08# Text 4. Stimulating renewable energy in Ukraine with a “green” tariff. A guide for investors. IFC Advisory Program in Europe and Central Asia (2012). https://saee.gov.ua/documents/greentariff.pdf. 5. Law of Ukraine of 20.11.2012 №5485-VI.: On amending the law of Ukraine “on electricity” to stimulate the production of electricity from alternative energy sources. https://zakon.rada. gov.ua/laws/show/5485-17/ed20121120#Text 6. Installed capacity of the IPS of Ukraine values as 12/2020. https://ua.energy/installed-capacityof-the-ips-of-ukraine/ 7. National Renewable Energy Action Plan until 2020. http://zakon.rada.gov.ua/laws/show/9022014-%D1%80 8. Marina Gritsyshyna. What is wrong with the green tariff? Legal Newspaper. https://yur-gazeta. com/publications/practice/energetichne-pravo/shcho-ne-tak-iz-zelenim-tarifom.html. 9. Law of Ukraine of 04.06.2015 № 514-VIII.: On amending certain laws of Ukraine regarding ensuring competitive conditions for the production of electricity from alternative energy sources. https://zakon.rada.gov.ua/laws/show/514-19/ed20150604#Text 10. Law of Ukraine of 21.07.2020 № 810-IX.: On amending certain laws of Ukraine regarding the improvement of support conditions for the production of electricity from alternative energy sources. https://zakon.rada.gov.ua/laws/show/810-20/ed20200721#Text

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11. Law of Ukraine № 555-IV.: On alternative energy sources. https://zakon.rada.gov.ua/laws/ show/555-15?lang=en#Text 12. Draft resolution of the Cabinet of Ministers of Ukraine «About the National Action Plan for the Development of Renewable Energy for the period up to 2030». https://saee.gov.ua/sites/ default/files/blocks/02_Proekt_NPDVE-10.01.2022.docx 13. Accents of DAM and IDM December 2021 Reviews. JSC «Market operator». https://www. oree.com.ua/index.php/web/10317 14. Kulyk, M., Zgurovets, O.: Modeling of power systems with wind, solar power plants and energy storage. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 231–245. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48583-2_15. 15. Zgurovets, O., Kulyk, M.: Comparative analysis and recommendations for the use of frequency regulation technologies in integrated power systems with alarge share of wind power plants. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, vol. 346, pp. 81–99. Springer, Cham (2021). https:/ /doi.org/10.1007/978-3-030-69189-9_5 16. Nechaieva, T.P.: Accounting for use of energy storage systems in the model of the long-term power system development forecasting. Probl. Gen. Energy. 3(66), 14–22 (2021). https://doi. org/10.15407/pge2021.03.014 17. Nechaieva, T.P.: Assessment of the joint work of battery energy storage systems with power plants on renewable energy sources. Probl. Gen. Energy. 3(58), 11–16 (2019). https://doi.org/ 10.15407/pge2019.03.011 18. Nechaieva, T.P.: Model and structure of the long-term development of generating capacities of a power system with regard for the commissioning and decommis-sioning dynamics of capacities and changing their technical-and-economic indices. Probl. Gen. Energy. 3(54), 5–9 (2018). https://doi.org/10.15407/pge2018.03.005 19. Babak, V.P., Babak, S.V., Eremenko, V.S., Kuts, Yu.V., Myslovych, M.V., Scherbak, L.M., Zaporozhets, A.O.: Problems and features of measurements. In: Studies in Systems, Decision and Control, vol. 360, pp. 1–31. Springer (2021). https://doi.org/10.1007/978-3-030-70783-5_ 1 20. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch V.M.: Technical provision of diagnostic systems. In: Studies in Systems, Decision and Control, vol. 281, pp. 91–133. Springer (2020). https://doi.org/10.1007/978-3-030-70783-5_4

Modelling of Decision-Making Criteria on the Implementation of Energy-Saving Projects at the Expense of Borrowed Funds Olexandr Yemelyanov, Ihor Petrushka, Kateryna Petrushka, Oksana Musiiovska, and Anatolii Havryliak

Abstract At present, loans are the main external source of financing energy-saving projects of enterprises. At the same time, taking out loans requires a preliminary detailed assessment of the consequences of their involvement. In turn, such an assessment should be based on scientifically based decision-making criteria on the implementation of energy-saving projects at the expense of borrowed funds. Taking this into account, the purpose of this study was to model the criteria by which it is possible to make reasonable and effective management decisions regarding loan financing of energy-saving projects at enterprises. The conducted research showed that it is possible to propose a certain system of criteria for evaluating the feasibility of implementing energy-saving projects at enterprises at the expense of borrowed funds. At the same time, the types of such criteria were highlighted, namely: preliminary auxiliary criteria, which are features, without the fulfillment of which it is possible to claim in advance that loan financing of projects will be inappropriate; the main criterion, which is the result of comparing the amounts of capitalized profit, which the enterprise will receive, respectively, after and before the implementation of the energy-saving project (projects); additional auxiliary criteria, which are features that are additionally introduced into consideration by persons who will make final investment decisions. These features supplement the main criterion. Without the fulfillment of the corresponding additional ones, a positive decision based on the main criterion will not be considered to be a final one. In the process of carrying out the research, data was collected from a number of Ukrainian enterprises, which during 2020–2021 were considering the possibility of loan financing of projects to reduce natural gas consumption. At the same time it turned out that a significant part of the unrealized projects (about 40%) was screened out at the stage of checking compliance with the previous criteria. Such a share is especially significant in the investigated enterprises that manufacture metal products. As for the additional criteria, less than 20% of not implemented projects were inspected according to them.

O. Yemelyanov · I. Petrushka · K. Petrushka · O. Musiiovska (B) · A. Havryliak Lviv Polytechnic National University, Lviv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_2

23

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Keywords Energy consumption · Decision-making criteria · Modelling · Borrowed funds · Energy-saving project · Financing · Natural gas

1 Introduction Recently, in the economies of many countries, the need to save non-renewable energy resources has significantly increased [1]. This is caused by the increase in the prices of natural gas and most other types of energy carriers and by the increased risks associated with their supply. Also, one of the crucial reasons for the need to reduce the consumption of fossil energy carriers is the need to improve the environmental situation [2]. The problem of ensuring energy consumption became even more acute with the start of large-scale military operations in Ukraine in February 2022, which significantly affected the functioning of global energy resource markets. At the same time, further economic growth may require an increase in the use of fossil energy resources [3]. All these circumstances make it necessary to increase energy efficiency both at the level of individual enterprises and the economies of many countries, primarily European ones [4]. Such an increase requires the development and implementation of technical and technological, and other measures aimed at reducing the energy intensity of products [5], reducing the consumption of fossil energy resources in the residential sector [6] and increasing the amount of energy obtained from renewable sources [7]. At the same time, as the experience of implementing energy saving programs shows, implementing the energy transition is a very difficult task [8]. This is due to the presence of various obstacles that appear in the process of such a transition [9]. Among these obstacles, a special place is occupied by the lack of financial resources necessary for the implementation of energy-saving programs and projects [10]. Therefore, it is impossible to carry out the energy transition without solving the problem of adequate financial support for the implementation of measures to reduce the consumption of non-renewable types of energy resources [11]. In turn, solving this problem requires consideration of all sources of financial resources that can potentially be used to invest in energy-efficient projects. In particular, one of these sources can be funds from the state and municipal budgets. However, the amount of these funds, as a rule, is quite limited and is directed mainly to the financial support of households seeking to implement energy-saving measures. As for enterprises, the main external source of financing their energy-saving projects is bank lending and other types of loan funds [12]. Despite the fact that credit resources can act as a powerful tool for financial support for the implementation of energy-saving projects at enterprises, the use of this tool requires some caution. This is due to the fact that taking loans always puts enterprises in front of the need to take into account the interests of creditors, first of all, regarding the timely and complete fulfilment of debt obligations to them. Therefore, making decisions on loan financing of energy-saving projects requires the owners

Modelling of Decision-Making Criteria on the Implementation …

25

and managers of enterprises to carefully evaluate the effectiveness of such financing in advance. Important in the process of such an assessment is the establishment of criteria for the feasibility of implementing investment measures for energy saving at the expense of borrowed funds.

2 Literature Review and Setting Research Objectives Among the criteria for making management decisions regarding loan financing of energy-saving projects of enterprises, the central place is occupied by indicators of lending efficiency. At the same time, as noted in [13], the effectiveness of lending should be equated with the quantitative value of economic, financial, environmental, social and other consequences that result from such loans. However, in the scientific literature, various types of economic results of loan financing of enterprises and the corresponding mechanisms of their formation are distinguished. It is worth highlighting three varieties of the mentioned results, namely, economic growth, an increase in profits and profitability of companies, as well as an increase in economic risks. The effect of crediting the activity of firms on their economic growth has been proven in several works, in particular, in [14]. However, different scientists offer slightly different criteria for the economic growth of companies. In [15], such a criterion was chosen to increase production volumes. At the same time, in [16], the economic results of crediting companies are identified with the increase in their income and net assets. Also, some scientists, in particular in [17], made an attempt to estimate the impact of the volume of loan financing of firms on their profit and profitability. It should be noted that the results of such an assessment are ambiguous. First of all, it concerns the assessment of the effect of financial leverage as a possible mechanism of influence of lending on the amount of profit and the level of profitability of enterprises. Thus, in [18] the positive nature of such influence is noted. At the same time, in [19], the negative consequences of the effect of financial leverage on the financial condition of companies were revealed. In the end, the authors of the work [20] could not detect the influence of the specified effect at all. However, individual scientists, in particular in [21], suggest optimizing the effect of financial leverage. One of the reasons for conflicting estimates of the effect of financial leverage is the absence in the scientific literature of a generally accepted tool for establishing the impact of lending to companies on the level of riskiness of their activities. At the same time, it is worth noting that there is a direct and inverse relationship between the volume of loan financing of enterprises and the level of riskiness of their activity. In particular, in [22], it is noted that those enterprises whose managers feel greater uncertainty are less prone to significant volumes of lending. The negative impact of the increased riskiness of firms’ activities on the volume of their investments is also noted by other researchers, in particular, in [23]. At the same time, as shown in [24],

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investing in resource-saving, in particular, energy-saving projects, is associated with a high level of risk. When considering the patterns of loan financing of investment activities of enterprises, in particular their energy-saving projects, it is necessary to pay attention not only to efficiency but also to the availability of a loan. Thus, the analysis performed in [25] of small companies operating in some European countries showed that it was quite difficult for these companies to get access to banking services. Similar results for small firms in countries with transition economies are presented in [14]. At the same time, as noted in [26], enterprises with smaller assets are characterized mainly by internal financing, while larger firms attract significantly more funds from external sources. So, among the factors of the availability of loan financing, it is worth mentioning the size of enterprises. Also, these factors should include the ability of enterprises to deposit bail [27]. At the same time, the high profitability of economic activity, according to the data given [27], does not significantly affect the possibility of obtaining additional credit resources by enterprises. Regarding lending to energy-saving projects of enterprises, the availability of such lending should be considered through the prism of the ability of companies to overcome obstacles on the way to the implementation of these projects. First of all, this concerns financial obstacles [28]. However, financial barriers often relate only to internal sources of financing energy-saving projects, although enterprises may have opportunities for external financing of such projects. At the same time, the expediency of implementing such opportunities largely depends on the magnitude of the expected economic effect from the implementation of investment energy-saving measures at enterprises [29]. This effect will be reflected, first of all, in the reduction of the costs of enterprises for the purchase of certain types of energy resources. However, the magnitude of such an effect is not deterministic. It depends on many factors, in particular, on the future prices of energy resources, the savings of which are expected. At the same time, the level of such prices is quite difficult to predict and can be characterized by significant volatility [30]. In general, the implementation of energy-saving projects can be considered one of the ways of adaptation of enterprises to the increase in energy prices [31]. However, as proven in [32], the implementation of energy-saving projects is profitable for enterprises only in a certain range of prices for those energy carriers, the reduction of consumption of which is the goal of the implementation of these projects. This also applies to the case of loan financing of investment energy-saving measures. In particular, it was established in [33] that when natural gas prices rise too much, the profitability of companies that consume it significantly decreases. As a result, companies lose the ability to timely repay loans taken to finance projects aimed at saving natural gas. The low efficiency of loan financing of enterprises, as well as insufficient availability of credit resources, may necessitate the introduction of state regulation of the process of crediting energy-saving projects. In particular, this concerns the financial support of enterprises that plan to implement such projects based on subsidies [34], as well as by providing soft loans [35]. At the same time, the possibilities of the

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state and municipal budgets of many countries are currently quite limited. Therefore, enterprises that lack their own financial resources, first of all, need to take a reasonable approach to the formation of programs of energy-saving measures, which are supposed to be implemented at the expense of loan financing. Thus, at present, loans are the main external source of financing energy-saving projects of enterprises. At the same time, taking out loans requires a preliminary detailed assessment of the consequences of their involvement. In turn, such an assessment should be based on scientifically based decision-making criteria on the implementation of energy-saving projects at the expense of borrowed funds. At the same time, in modern scientific literature, the issue of formalization of such criteria is not finally resolved. Taking this into account, the purpose of this study was to model the criteria by which it is possible to make reasonable and effective management decisions regarding loan financing of energy-saving projects at enterprises.

3 Information Support for Decision-Making on the Implementation of Energy-Saving Projects at the Expense of Borrowed Funds Estimating economic efficiency and substantiating the feasibility of taking loans to finance energy-saving projects require the availability of significant amounts of information. This information can be divided into several blocks. In particular, according to the degree of information processing, based on which decisions are made on the implementation of energy-saving projects at the expense of loan sources of funds, it is advisable to allocate the following general blocks of this information: (1) a block of input information, which contains primary data necessary for further justification of project decisions; (2) a block of secondary information, which contains data obtained in the process of processing primary information; (3) a block of general information, which contains data obtained in the process of processing primary and secondary information; (4) a block of generalized information, the based on which a final decision is made on the enterprise’s implementation of an energy-saving project (projects) at the expense of borrowed funds. Each of the listed general blocks must contain several partial blocks of information, the list of which is presented in Tables 1 and 2. As can be seen from Tables 1 and 2, the described array of the necessary information has a hierarchical nature. At the same time, as follows from the scheme presented in Fig. 1, not only vertical but also horizontal connections can exist between different partial blocks of information. At the same time, it should be noted that the designations of the blocks of the information shown in Fig. 1 correspond to the markings presented above in Tables 1 and 2. Therefore, ensuring the adoption of well-founded and effective decisions on loan financing of energy-saving projects, which are planned to be implemented at the enterprise, requires the fulfilment of two main conditions. First, the input information

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Table 1 Content of the block of input information necessary for making decisions on the implementation of energy-saving projects at the expense of borrowed funds Names of partial blocks of information

Examples of information that partial blocks must contain

Marking of blocks

A block of internal information on the conditions of the enterprise’s activities, which plans to implement an energy-saving project (projects)

Data on the amount of resources available at the enterprise and their forecast changes, the quality of these resources, the technological processes used by the enterprise, etc.

B1

A block of external information on the conditions of the enterprise’s activities, which plans to implement an energy-saving project (projects)

Data on average expected prices for production B2 resources that the enterprise uses or plans to use, terms of supply of these resources, etc.

A block of information on the conditions for taking long-term loans by enterprises

Data on bank interest rates in various credit institutions, credit terms, conditions for providing guarantees for the return of taken loans, etc.

B3

A block of information about the energy-saving project (projects) that the enterprise plans to implement

Data on the cost of the project (projects), the specific costs of energy and other production resources for it, the period of operation of the project (projects), etc.

B4

A block of information about the probability of occurrence of certain situations in which the enterprise may find itself

Data on the possible price ranges for energy B5 resources and other parameters affecting the enterprise’s profit; the probability of different values of prices for energy resources and other parameters.

Source by authors

required for such approval must be up-to-date, complete and sufficiently accurate. Secondly, the correct organization of information processing is necessary to ensure the proper accuracy of the information contained in the partial blocks of higher levels of information support for making relevant management decisions.

4 Identification and Formalization of Decision-Making Criteria for Loan Financing of Energy-Saving Projects of Enterprises When evaluating the economic expediency of loan financing of energy-saving measures at enterprises, a number of circumstances must be taken into account. First of all, since indicators of the economic efficiency of economic activity are mostly relative in nature, indicators for evaluating the effectiveness of loan financing of this activity should also be of such a nature. Secondly, the procedure for evaluating the effectiveness of attracting loans for the purpose of financing energy-saving measures

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Table 2 Content of blocks of secondary, general and generalized summarizing information necessary for decision-making on the implementation of energy-saving projects at the expense of borrowed funds General blocks of information

Names of partial blocks of information

Examples of information that partial blocks must contain

A block of secondary information

A block of information on the expected indicators that determine the amount of the enterprise’s income

Data on natural volumes of B6 production and sales of various types of products by the enterprise, and their prices

A block of information on the specific costs of manufacturing and sales of various types of products by the enterprise

Data on the specific costs of manufacturing and sales of various types of products by the enterprise, under the condition of refusal to implement and under the condition of implementation of the planned energy-saving project (projects)

A block of information on the indicators of financial stability and riskiness of the enterprise’s activity

Data on the value of indicators of the B8 solvency of the enterprise, the structure of its capital, coefficients of variation of the enterprise’s income and profits, etc

A block of general information

A block of generalized information

A block of information Data on the loan interest rate and the on the terms of granting a loan repayment period loan for financing an energy-saving project (projects)

Marking of blocks

B7

B9

A block of information on the expected flow of the enterprise’s net profit in case of refusal to implement the energy-saving project (projects)

Projected values of the enterprise’s B10 net profit in case of refusal to implement the energy-saving project (projects) in each year of the forecast period

A block of information on the rate of capitalization of the enterprise’s profit

Data on the rate of capitalization of the enterprise’s profit

A block of information on the expected flow of net profit of the enterprise in case of implementation of the energy-saving project (projects)

Projected values of the net profit of B12 the enterprise in case of implementation of the energy-saving project (projects) in each year of the forecast period

A block of information on the results of the capitalization of the enterprise’s profit streams

The results of the capitalization of the enterprise’s net profit streams in case of refusal to implement and in case of implementation of the energy-saving project (projects)

B11

B13

(continued)

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O. Yemelyanov et al.

Table 2 (continued) General blocks of information

Names of partial blocks of information

Examples of information that partial blocks must contain

Marking of blocks

A block of information, based on which the final decision on loan financing of the energy-saving project (projects) is made

Data on the indicator of comparison of capitalization results, and the probability that this indicator will exceed one, etc.

B14

Source by authors

B12

B13

B14

B10

B9

B11

B6 B1

B7 B2

B8 B3

B4

B5

Fig. 1 Interrelationships between blocks of information necessary for decision-making on the implementation of energy-saving projects at enterprises at the expense of borrowed funds

should be based on preliminary modelling of the process of loan repayment. Thirdly, it is necessary to consider the situations in which the enterprise may find itself after the implementation of energy-saving measures. In particular, this concerns the possible values of prices for those types of energy resources, the savings of which are expected as a result of the implementation of planned measures since the level of such prices significantly affects the efficiency of investing in energy-saving measures and, accordingly, the efficiency of their loan financing. At the same time, the impact of energy prices on the level of economic efficiency of loan financing measures to reduce the consumption of these energy sources at enterprises is ambiguous. On the one hand, the increase in energy prices increases the profitability of investments in energy-saving measures. However, on the other hand, at a fairly high level of such prices, enterprises may lack the financial resources necessary to service and repay the loans taken. One of the possible indicators for evaluating the economic efficiency of debt financing of energy-saving measures at enterprises can be the ratio of the capitalized value of the net profit flow that will come in under the condition of such financing to the capitalized value of the net profit flow that will come in if the enterprise refuses to implement energy-saving measures. Taking into account the construction

Modelling of Decision-Making Criteria on the Implementation …

31

of the proposed indicator for evaluating the economic effectiveness of loan financing of energy-saving measures at enterprises, it can be noted that its minimum value, at which such financing is appropriate, is one. At the same time, as already noted above, the results of evaluating the effectiveness of investing in energy-saving measures depend significantly on the level of prices for energy carriers. In turn, such prices are often characterized by a high level of volatility. Therefore, it is necessary to evaluate the economic efficiency of loan financing of energy-saving measures at enterprises at different values of energy prices in the pre-forecasted range of fluctuations of these prices. Then, knowing the estimated probability of different values of energy prices, it is possible to establish the probability that the indicator proposed above will exceed one. Under such conditions, if the minimum acceptable level of this probability is set, it becomes possible to make a final decision on the feasibility of loan financing for the implementation of energy-saving measures at enterprises. Thus, it is worth highlighting the main and auxiliary criteria for evaluating the feasibility of loan financing of energy-saving projects of enterprises. At the same time, the formalized form of the main criterion is as follows: W =

C1 , C0

(1)

where W is the main criterion for the expediency of loan financing for the implementation of an energy-saving project (projects) at the enterprise; C 1 , C 0 is the capitalized value of the expected profit flow of the enterprise, respectively, in the case of implementation of the energy-saving project (projects) and in the case of rejection of such implementation At the same time, the risk-free rate characterizes the return on assets, investing in which is not associated with risk. Under such conditions, the capitalized value of the expected profit flow of the enterprise in case of refusal to implement the energy-saving project (projects) will be determined according to the following formula: C0 =

  R0 P0 , · 1− r Rm

(2)

where P0 is the average expected annual profit of the enterprise in case the enterprise refuses to implement the energy-saving project (projects); r is the risk-free annual rate of capitalization in fractions of a unit; R0 , Rm are, respectively, the forecast and the maximum possible value of the level of riskiness of the activity of this enterprise. Thus, formula (2) is based on the assumption of a linear relationship between the capitalized value of the company’s profit and the level of risk of its activity. As follows from the construction of expression (2), the indicator by which the level of riskiness is assessed must be characterized by a certain maximum possible value. It is worth noting that one of the most common indicators of economic risk, which is the coefficient of variation of profit based on the root mean square deviation, does not satisfy this condition. At the same time, it is satisfied by the coefficient of variation of profit according to the average linear deviation. Indeed, the last exponent

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cannot exceed 2 for non-negative values of the random variable. At the same time, to determine the coefficient of profit variation based on the average linear deviation, it is necessary to identify a number of situations in which the enterprise may find itself and establish the amount of profit in each situation and the probability of its occurrence. A more difficult task is the modelling of the capitalized value of the expected profit flow of the enterprise in the case of implementation of the energy-saving project (projects). There are two ways to solve this problem: approximate and more accurate. Under the approximate method, the process of returning the principal amount of the loan taken by the company is not considered, that is, the possibility of periodic reborrowing of the received loan is assumed. Then the capitalized value of the expected profit flow of the enterprise in the case of the implementation of the energy-saving project (projects) will be determined according to the following formula: C1 =

    P0 + (E − c − I · rc ) · (1 − t) R1 R1 P0 + P = , · 1− · 1− r Rm r Rm (3)

where ΔP is the expected average annual increase in the company’s net profit as a result of its implementation of the energy-saving project (projects); R1 is the forecast value of the level of riskiness of the activity of this enterprise after its implementation of the energy-saving project (projects); E is the average expected decrease in the annual expenses of the enterprise for the purchase of energy resources after such implementation; Δc is the average expected growth of certain types of annual expenses of the enterprise after the implementation of an energy-saving project (projects) (for example, expenses for the operation of equipment); I is the amount of investment in the implementation of the energy-saving project (projects); r c is the annual interest rate for the loan that is planned to be taken to implement the energy-saving project (projects), in fractions of a unit; t is the corporate income tax rate, in fractions of a unit. So, taking into account formulas (1)–(3), the formalized form of the main criterion for the expediency of loan financing of an energy-saving project (projects) in the case of applying the approximate approach to the capitalization of the expected profit of the enterprise after the implementation of this project will have the following form: W1 =

(P0 + (E − c − I · rc ) · (1 − t)) · (Rm − R1 ) , P0 · (Rm − R0 )

(4)

where W 1 is the value of the criterion for the feasibility of loan financing of the energysaving project (projects) in the case of an approximate approach to the capitalization of the expected profit of the enterprise after the implementation of this project. It should be noted that this study assumes the unchanged sales volume of enterprises’ products after the implementation of energy saving measures. If this assumption is not fulfilled, then it is necessary to adjust the expected amount of profit after the implementation of energy-saving measures accordingly.

Modelling of Decision-Making Criteria on the Implementation …

33

If we consider the process of returning the principal amount of a loan taken out for the purpose of financing an energy-saving project (projects), then this process must satisfy the following condition: discounted at the loan interest rate, the flow of payments aimed at repaying the loan and paying interest on it must be equal to the amount of this loan. In its formalized form, this condition looks like this:   V 1 = I, (5) · 1− rc (1 + rc )T where V is the average annual amount of payments aimed at repaying the loan and paying interest on it; T is the total duration of the loan repayment period in years. From Eq. (5) we get V =

I · (1 + rc )T · rc . (1 + rc )T − 1

(6)

Therefore, taking into account formulas (1)–(3) and (6), the formalized form of the main criterion for the expediency of loan financing of energy-saving project(s) in the case of applying a more accurate approach to the capitalization of the expected profit of the enterprise after the implementation of this project will have the following form:       I ·rc 1 P0 + E − c − 1−(1+r · (1 − t) · 1 − (1+r · (Rm − R1 )· −T )T c) + W2 = P0 · (Rm − R0 ) (P0 + E − c) · (1 − t) · (Rm − R1 ) + , P0 · (Rm − R0 ) · (1 + r )T (7) where W 2 is the value of the criterion for the feasibility of loan financing of the energy-saving project (projects) in the case of applying a more accurate approach to the capitalization of the expected profit of the enterprise after the implementation of this project. Taking into account the above, it is possible to propose a certain system of criteria for evaluating the feasibility of implementing energy-saving projects at enterprises at the expense of borrowed funds. It is worth distinguishing three types of such criteria, namely: preliminary auxiliary criteria, which are features. without the fulfilment of which it is possible to state in advance that loan financing of projects will be inappropriate; the main criterion, which is formalized in the form of formula (7), and when using the approximate approach-in the form of formula (4); additional auxiliary criteria, which are features that are additionally introduced into consideration by persons who will make final investment decisions. These features complement the main criterion. Without the fulfilment of the relevant additional criteria, a positive decision based on the main criterion will not be considered to be a final one.

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O. Yemelyanov et al.

In particular, as follows from expression (7), it is possible to distinguish the following two preliminary auxiliary criteria: (1) A criterion for ensuring the minimum acceptable level of profitability of the energy-saving project (projects): W p1 =

E − c > 1. I · rc

(8)

(2) The criterion of the sufficiency of the enterprise’s financial results for its timely fulfilment of debt obligations under the loan received to finance the energysaving project (projects).   I · (1 + rc )T · rc , W p2 = V / (1 + rc )T − 1

(9)

where W p1 , W p2 are the values of the corresponding preliminary auxiliary criteria. It is also worth highlighting at least two additional auxiliary criteria for the expediency of loan financing of energy-saving projects, namely: (1) The criterion of the absence of influence (or an acceptable level of influence) of the implementation of energy-saving projects on the financial stability of those enterprises that implement these projects. One of the ways to formalize this criterion is to assess the change in the probability of bankruptcy of the enterprise after the implementation of the planned energy-saving project (projects). A simplified approach to such formalization can be an assessment of the change in the value of the minimum possible profit of the enterprise (that is, profit under the worst conditions of economic activity). In particular, if the amount of such profit after the implementation of the energy-saving project (projects) turns out to be integral, then this will a priori mean that the given enterprise is not in danger of bankruptcy. On the other hand, there may be a relatively small probability of bankruptcy in many enterprises. Taking this into account, one of the possible variants of the formalized form of the first of the additional auxiliary criteria is as follows: Wr 1 =

Pm0 + E − c − I · rc ≥ lr 1 , Pm0

(10)

where W r1 is the value of the first additional auxiliary criterion; Pm0 is the value of the minimum possible profit of the enterprise before paying taxes, received before the implementation of the planned energy-saving project (projects); lr1 is a minimum acceptable level of the first additional auxiliary criterion pre-set by the owners (managers) of the enterprise; (2) the criterion for ensuring the appropriate level of efficiency of loan financing of the energy-saving project (projects) in most situations in which the enterprise

Modelling of Decision-Making Criteria on the Implementation …

35

implementing this project may find itself. In other words, it is necessary that, in most situations, the value of the main criterion for the feasibility of loan financing of an energy-saving project exceeds one. In particular, as already mentioned above, these situations can be linked to price fluctuations for the type of energy resources, the reduction of consumption of which is expected to be achieved as a result of the implementation of the corresponding energy-saving project (projects). At the same time, it is necessary, first of all, to establish the minimum possible value of the price of an energy resource, at which the value of the main criterion for the feasibility of loan financing will be at least one. Then the general formalized form of the second additional auxiliary criterion will be as follows: Wr 2 = p( pe ≥ pmin ) ≥ lr 2 ,

(11)

where W r2 is the value of the first additional auxiliary criterion; p is the probability that the actual price for the energy resource pe will not be less than its minimum acceptable value pmin , for which the value of the main criterion will not be less than one; lr2 is the minimum acceptable level of the second additional auxiliary criterion pre-set by the owners (managers) of the enterprise. Setting the value of pmin , requires a certain transformation of the analytical expression of the main criterion for the expediency of loan financing of energy-saving projects. Consider this using the example of expression (4). Then the transformed form of this expression will be as follows: W1m =

(P0 + (E c0 · ( pa − pe ) + (E c0 − E c1 ) · pe − c − I · rc ) · (1 − t)) · (Rm − R1 ) , (P0 + E c0 · ( pa − pe ) · (1 − t)) · (Rm − R0 )

(12) where pa is the average predicted level of prices for this type of energy carrier, which is established when calculating the value of criterion (4); E c0, E c1 are volumes of consumption of this type of energy resources at the enterprise respectively before and after the implementation of the energy-saving project (projects). Equating expression (12) to zero, you can construct the following formula for determining the pmin indicator. Thus, it is possible to preaent a certain system of criteria for evaluating the feasibility of loan financing of energy-saving projects (Fig. 2). Thus, as follows from the scheme presented in Fig. 2, making a positive decision on the feasibility of implementing an energy-saving project (projects) at the enterprise requires the step-by-step fulfilment of several conditions. These conditions are reflected in the fact that the calculated values of the corresponding criterion indicators are not less than their minimum permissible values.

36

O. Yemelyanov et al. Collection of initial data and calculation of the values of the relevant criteria

Are the previous criteria fulfilled?

Yes

No

Refusal to implement an energy-saving project (projects)

Is the main criterion met? Yes

No

Are additional criteria met? Yes

Adoption of a positive decision on the implementing the project (projects)

No

Fig. 2 A system of criteria for the feasibility of implementing an energy-saving project (projects) at the enterprise at the expense of borrowed funds

5 Empirical Analysis of the Fulfilment of Criteria for the Expediency of Debt Financing of Projects to Reduce Natural Gas Consumption Natural gas is one of the types of energy resources, the urgent need to reduce consumption of which exists in many countries. Such countries include, in particular, Ukraine. Therefore, data were collected from those Ukrainian enterprises that during 2020–2021 considered the possibility of loan financing of projects to reduce natural gas consumption. A total of 77 enterprises belonging to three industries with a high level of natural gas consumption were considered. As follows from the data presented in Table 3, a total of 126 energy-saving projects were supposed to be financed by loans. At the same time, only 52 such projects were implemented, i.e. the share of implemented projects was 41.27%. At the same time, the share of implementation of energy-saving projects that were considered did not vary significantly by industry. To identify the reasons for the rejection of loan financing for energy-saving projects, their compliance with the criteria defined above was assessed. The results of such evaluation are presented in Tables 4 and 5. As it follows from the data presented in these Tables, a significant part of the projects (about 40%) was screened out at the stage of checking compliance with the previous criteria. Such a share is especially significant in the investigated enterprises that manufacture metal products. As for

Modelling of Decision-Making Criteria on the Implementation …

37

Table 3 General data on projects to reduce natural gas consumption, which the studied enterprises tried to implement with borrowed funds during 2020–2021 Indicators

Value of indicators by industry

In total

Production of metal products

Production of glass and glass products

Production of building materials from clay

1. The number of enterprises that were considered

29

22

26

77

2. The number of projects that were considered

51

32

43

126

3. The number of projects that were implemented

22

12

18

52

37.50

41.86

41.27

4. Share of 43.14 implemented projects, % Source by authors

the additional criteria, less than 20% of not implemented projects were inspected according to them. It should be noted that taking into account the models built above, the size of the criteria for evaluating the feasibility of loan financing is influenced by a significant number of factors. In particular, such factors may include the expected reduction in Table 4 Assessment of the compliance of projects for which loan financing did not take place with the criteria for the feasibility of such financing (by the number of projects) Indicators

Values of indicators by industry

In total

Production of metal products

Production of glass and glass products

Production of building materials from clay

29

20

25

74

2. Among them, those who did not meet 13 at least one of the previous criteria

8

11

32

16

12

14

42

4. Among them, those that did not meet 11 the main criterion

9

10

30

5. The number of projects that met the main criterion, but did not meet at least one of the additional criteria

3

4

12

1. Number of projects that were not implemente

3. The number of projects that met the previous criteria

5

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O. Yemelyanov et al.

Table 5 Assessment of compliance of projects for which loan financing did not take place with the criteria for the feasibility of such financing (by the share of projects in the total number of unrealized projects) Indicators

Values of indicators by industry

In total

Production Production of Production of of metal glass and glass building products products materials from clay 1. Number of projects that were not implemente

100.00

100.00

100.00

100.00

2. Among them, those who did not meet at least one of the previous criteria

44.83

40.00

44.00

43.24

3. The number of projects that met the previous criteria

55.17

60.00

56.00

56.76

4. Among them, those that did not meet the main criterion

37.93

45.00

40.00

40.54

5. The number of projects that met the main criterion, but did not meet at least one of the additional criteria

17.24

15.00

16.00

16.22

energy resource costs and the specific capital intensity of energy-saving projects. To determine the influence of these factors in the case of the studied projects, dispersion analysis was used (Tables 6 and 7). As can be seen from the data in Table 6, the average values of the main criterion increase with an increase in the expected reduction in natural gas consumption. At the same time, as follows from the data in Table 7, the average values of the main criterion decrease if the specific capital intensity of energy-saving projects increases. Using the variance analysis method made it possible to establish that these dependencies for all industries are statistically significant, as the actual values of the F-criterion exceed its critical values with a significance level of α = 0.05.

6 Conclusions Loan sources of funds are one of the main tools for financing investment projects, in particular projects involving a reduction in the consumption of renewable energy sources. At the same time, loan financing of energy-saving projects is characterized by a high level of risk and requires detailed justification. To carry out such a justification, scientifically based criteria are needed. The conducted research showed that it is possible to propose a certain system of criteria for evaluating the feasibility of implementing energy-saving projects at enterprises at the expense of borrowed funds. At the same time, the types of such criteria were highlighted, namely: preliminary auxiliary criteria, which are features, without the fulfilment of which it is possible to claim in advance that loan financing of

Modelling of Decision-Making Criteria on the Implementation …

39

Table 6 Average values of the main criterion indicator of the expediency of loan financing for implemented projects depending on the expected decrease in the specific consumption of natural gas Indicators

Values of indicators by industry Production of metal products

Production of glass and glass products

Production of building materials from clay

1.23

1.31

1.16

2.1. Up to 20% of the basic values of unit costs

1.07

1.11

1.05

2.2. From 20 to 40% of the basic values of specific costs

1.21

1.32

1.17

2.3. More than 40% of the basic values of unit costs

1.30

1.39

1.25

3. The actual value of the F-criterion

6.04

5.66

6.72

1. The average level of the main criterion indicator of the feasibility of loan financing by project 2. Including projects that provided for a decrease in the specific consumption of natural gas:

Source by authors

Table 7 The average value of the main criterion of the indicator of the feasibility of loan financing for implemented objects depending on the level of their specific capital intensity Value of indicators by industry

Indicators

Production of metal products

Production of glass and glass products

Production of building materials from clay

1.34

1.41

1.27

1.22

1.32

1.15

1.3. More than $ 0.2 per 1 m3 of natural gas savings 1.08

1.10

1.05

2. The actual value of the F-criterion

6.14

7.09

1. The average level of the main criterion indicator of the expediency of loan financing for projects for which the specific capital intensity was: 1.1. Up to $ 0.1 per 1 m3 of natural gas savings 1.2. From $ 0.1 to $ 0.2 per 1 savings

m3

of natural gas

5.96

projects will be inappropriate; the main criterion, which is the result of comparing the amounts of capitalized profit, which the enterprise will receive, respectively, after and before the implementation of the energy-saving project (projects); additional auxiliary criteria, which are features that are additionally introduced into consideration by persons who will make final investment decisions. These features supplement

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the main criterion. Without the fulfilment of the corresponding additional ones, a positive decision based on the main criterion will not be considered to be a final one. In the process of carrying out the research, data was collected from a number of Ukrainian enterprises, which during 2020–2021 were considering the possibility of loan financing of projects to reduce natural gas consumption. At the same time it turned out that a significant part of the unrealized projects (about 40%) was screened out at the stage of checking compliance with the previous criteria. Such a share is especially significant in the investigated enterprises that manufacture metal products. As for the additional criteria, less than 20% of not implemented projects were inspected according to them.

References 1. Mohsin, M., Kamran, H.W., Nawaz, M.A., Hussain, M.S., Dahri, A.S.: Assessing the impact of transition from nonrenewable to renewable energy consumption on economic growthenvironmental nexus from developing Asian economies. J. Environ. Manage. 284, 111999 (2021). https://doi.org/10.1016/j.jenvman.2021.111999 2. Zaporozhets, A.O.: Correlation analysis between the components of energy balance and pollutant emissions. Water Air Soil Pollut. 232, 114 (2021). https://doi.org/10.1007/s11270-02105048-9 3. Yemelyanov, O., Symak, A., Petrushka, T., Vovk, O., Ivanytska, O., Symak, D., Havryliak, A., Danylovych, T., Lesyk, L.: Criteria, indicators, and factors of the sustainable energy-saving economic development: the case of natural gas consumption. Energies 14, 5999 (2021). https:/ /doi.org/10.3390/en14185999 4. Tutak, M., Brodny, J., Siwiec, D., Ulewicz, R., Bindzár, P.: Studying the level of sustainable energy development of the European union countries and their similarity based on the economic and demographic potential. Energies 13, 6643 (2020). https://doi.org/10.3390/en13246643 5. Trianni, A., Cagno, E., Worrell, E.: Innovation and adoption of energy efficient technologies: an exploratory analysis of Italian primary metal manufacturing SMEs. Energy Policy 61, 430–440 (2013). https://doi.org/10.1016/j.enpol.2013.06.034 6. Jia, J.J., Xu, J.H., Fan, Y.: Public acceptance of household energy-saving measures in Beijing: heterogeneous preferences and policy implications. Energy Policy 113, 487–499 (2018). https:/ /doi.org/10.1016/j.enpol.2017.11.024 7. Opeyemi, B.M.: Path to sustainable energy consumption: the possibility of substituting renewable energy for non-renewable energy. Energy 228, 120519 (2021). https://doi.org/10.1016/j. energy.2021.120519 8. Matsumoto, M., Doumpos, M., Andriosopoulos, K.: Historical energy security performance in EU countries. Renew. Sustain. Energy Rev. 82(2), 1737–1748 (2018). https://doi.org/10.1016/ j.rser.2017.06.058 9. Kangas, H.L., Lazarevic, D., Kivimaa, P.: Technical skills, disinterest and non-functional regulation: barriers to building energy efficiency in Finland viewed by energy service companies. Energy Policy 114, 63–76 (2018). https://doi.org/10.1016/j.enpol.2017.11.060 10. Trianni, A., Cagno, E., Worrell, E., Pugliese, G.: Empirical investigation of energy efficiency barriers in Italian manufacturing SMEs. Energy 49, 444–458 (2013). https://doi.org/10.1016/ j.energy.2012.10.012 11. Tsurkan, M., Andreeva, S., Lyubarskaya, M., Chekalin, V., Lapushinskaya, G.: Organizational and financial mechanisms for implementation of the projects in the field of increasing the energy efficiency of the regional economy. Probl Perspect Manag 15(3), 453–466 (2017). https://doi. org/10.21511/ppm.15(3-2).2017.13

Modelling of Decision-Making Criteria on the Implementation …

41

12. Jude, F.A., Adamou, N.: Bank loan financing decisions of small and medium-sized enterprises: the significance of owner/managers‘ behaviours. Int. J. Econ. Financ. 10(5), 231–241 (2018). https://doi.org/10.5539/ijef.v10n5p231 13. Yemelyanov, O., Petrushka, T., Symak, A., Trevoho, O., Turylo, A., Kurylo, O., Danchak, L., Symak, D., Lesyk, L.: Microcredits for sustainable development of small Ukrainian enterprises: efficiency, accessibility, and government contribution. Sustainability 12, 6184 (2020). https:// doi.org/10.3390/su12156184 14. Rostamkalaei, A., Freel, M.: The cost of growth: small firms and the pricing of bank loans. Small Bus. Econ. 46, 255–272 (2016). https://doi.org/10.1007/s11187-015-9681-x 15. Yang, W.: Empirical study on effect of credit constraints on productivity of firms in growth enterprise market of China. J. Financ. Econ. 6, 173–177 (2018). https://doi.org/10.12691/jfe6-5-2. 16. Lin, Y., Li, L.: Empirical analysis of microcredit in western China: based on empirical analysis. J. Chongqing Technol. Bus. Univ. 5, 1 (2018). https://en.cnki.com.cn/Article_en/CJFDTotalCQYZ201805001.htm 17. Akinleye, G.T., Olarewaju, O.O.: Credit management and profitability growth in Nigerian manufacturing firms. Acta Univ. Danub. Oecon. 15, 445–456 (2019). http://journals.univ-dan ubius.ro/index.php/oeconomica/article/view/5281/5232 18. Gill, A.S., Mand, H.S., Sharma, S.P., Mathur, N.: Factors that influence financial leverage of small business firms in India. Int. J. Econ. Financ. 4, 33 (2012). https://doi.org/10.5539/ijef. v4n3p33 19. Javed, Z.H., Rao, H.H., Akram, B., Nazir, M.F.: Effect of financial leverage on performance of the firms: empirical evidence from Pakistan. SPOUDAI J. Econ. Bus. 65, 87–95 (2015). https:/ /EconPapers.repec.org/RePEc:spd:journl:v:65:y:2015:i:1-2:p:87-95 20. Hoque, M.A.: Impact of financial leverage on financial performance: evidence from textile sector of Bangladesh. IIUC Bus. Rev. 6, 75–84 (2017). http://dspace.iiuc.ac.bd:8080/xmlui/ handle/88203/687 21. Adenugba, A.A., Ige, A.A., Kesinro, O.R.: Financial leverage and firms’ value: a study of selected firms in Nigeria. Eur. J. Res. Reflect. Manag. Sci. 4, 14–32 (2016). https://www.idp ublications.org/wp-content/uploads/2016/01/Full-Paper-FINANCIAL-LEVERAGE-ANDFIRMS%E2%80%99-VALUE-A-STUDY-OF-SELECTED-FIRMS-IN-NIGERIA.pdf 22. Agarwal, S., Chomsisengphet, S., Driscoll, J. C.: Loan commitments and private firms. FEDS Working Paper No. 2004–27 (2004). https://ssrn.com/abstract=593862. https://doi.org/ 10.2139/ssrn.593862 23. Choi, S., Furceri, D., Huang, Y., Loungani, P.: Aggregate uncertainty and sectoral productivity growth: the role of credit constraints. J. Int. Money Financ. 88, 314–330 (2018). https://doi. org/10.1016/j.jimonfin.2017.07.016 24. Lesinskyi, V., Yemelyanov, O., Zarytska, O., Symak, A., Koleshchuk, O.: Substantiation of projects that account for risk in the resource-saving technological changes at enterprises. East. Eur. J. Enterp. Technol. 6, 1 (2018). https://doi.org/10.15587/1729-4061.2018.149942 25. Angori, G., Aristei, D.: A panel data analysis of firms’ access to credit in the Euro area: endogenous selection, individual heterogeneity and time persistence 2018. https://ssrn.com/ abstract=3254358. https://doi.org/10.2139/ssrn.3254358. 26. Bhalli, M., Hashmi, S., Majeed, A.: Impact of credit constraints on firms growth: a case study of manufacturing sector of Pakistan. J. Quant. Methods. 1(1), 4–40 (2017). https://doi.org/10. 29145/2017/jqm/010102 27. Krasniqi, B.A.: Are small firms really credit constrained? empirical evidence from Kosova. Int. Entrep. Manag. J. 6, 459–479 (2010). https://doi.org/10.1007/s11365-010-0135-2 28. Dong, J., Huo, H.: Identification of financing barriers to energy efficiency in small and mediumsized enterprises by integrating the fuzzy delphi and fuzzy DEMATEL approaches. Energies 10, 1172 (2017). https://doi.org/10.3390/en10081172 29. Malakhov, V.A.: Assessing the economic effect from introduction of energy-saving technologies in the field of heat supply. Therm. Eng. 59, 250–257 (2012). https://doi.org/10.1134/S00 40601512030093

42

O. Yemelyanov et al.

30. Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30(4), 1030–1081 (2014). https://doi.org/10.1016/j.ijforecast.2014. 08.008 31. Yemelyanov, O., Symak, A., Petrushka, T., Lesyk, R., Lesyk, L.: Evaluation of the adaptability of the Ukrainian economy to changes in prices for energy carriers and to energy market risks. Energies 11, 3529 (2018). https://doi.org/10.3390/en11123529 32. Yemelyanov, O., Symak, A., Petrushka, T., Zahoretska, O., Kusiy, M., Lesyk, R., Lesyk, L.: Changes in energy consumption, economic growth and aspirations for energy independence: sectoral analysis of uses of natural gas in Ukrainian economy. Energies 12, 4724 (2019). https:/ /doi.org/10.3390/en12244724 33. Lesinskyi, V., Yemelyanov, O., Zarytska, O., Symak, A., Petrushka, T.: Development of a toolkit for assessing and overcoming barriers to the implementation of energy saving projects. East.-Eur. J. Enterp. Technol. 5(3(107)), 24–38 (2020). https://doi.org/10.15587/1729-4061. 2020.214997. 34. Hui, J., Cai, W., Wang, C., Ye, M.: Analyzing the penetration barriers of clean generation technologies in China’s power sector using a multi-region optimization model. Appl. Energy 185(P2), 1809–1820 (2017). https://doi.org/10.1016/j.apenergy.2016.02.034 35. Yemelyanov, O., Petrushka, T., Lesyk, L., Symak, A., Vovk, O.: Modelling and information support for the development of government programs to increase the accessibility of small business lending. In: 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), pp. 229–232 (2020). https://doi.org/10.1109/CSIT49958. 2020.9322040

Accounting the Forecasting Stochasticity at the Power System Modes Optimization Viktor Denysov , Ganna Kostenko , Vitalii Babak , Sergii Shulzhenko , and Artur Zaporozhets

Abstract A formulation of the problem of finding the optimal algorithm for power system control, taking into account the randomness of external influences and forecasting errors, proposed in the framework of the stochastic control theory with adaptation. This made it possible to obtain an optimization procedure in the form of a sequential process with a finite number of steps and a quadratic loss function at each step. This type of loss function made it possible to use optimal decision-making procedures based on linear functions of control actions. The proposed approach tested on real data of the consumption schedule for the day ahead of the Ukrainian Integrated Power System. Estimates for the accuracy of coverage of the load schedule and the cost of errors arising under influence of random disturbances and inaccuracies in forecasting the hourly parameters of the system obtained. The application of the above approach made it possible to reduce the time for calculating the sequence of optimized controls by dozens of times and reduce this time to less than one minute when using a conventional personal computer. Keywords Stochasticity external influences · Forecasting errors · Quadratic loss function

1 Introduction One of the key aspects of system research in the energy industry is mathematical modeling for optimal management of energy systems. The basic importance of formalized methods of modeling and justification of decisions, which is the main apparatus for the study of energy systems and their management, grows over time in connection with the growth of the complexity of the systems themselves and the problems associated with their development and functioning. Considering the complexity of the studied systems, as a rule, it is necessary simplify as much as possible the model without significantly reducing the accuracy of the results. At the V. Denysov (B) · G. Kostenko · V. Babak · S. Shulzhenko · A. Zaporozhets General Energy Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_3

43

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same time, one should certainly not oversimplify the model. Important point is the ratio of the accuracy of the calculation results with the accuracy of the information used. This especially applies to planning and design calculations, in which the reasonable accuracy of the results cannot be greater than the accuracy of the used initial indicators. The uncertainty of the source information increases with increasing distance from the considered period, and the general indicators for the country as a whole are usually more stable than for individual districts. The saving of energy costs and energy resources, the problem of maneuverability of electricity supply, increasing the efficiency of their production are among the extremely urgent tasks of state importance. Therefore, all models must take into account the need to ensure the maneuverability of fuel supply to compensate for daily, weekly and seasonal irregularity of consumption. A number of circumstances important for mathematical modeling emerge from the main properties of energy systems [1]. During its life cycle, including the development and operation management stages, energy systems influenced by a number of natural and random factors. Currently, most energy systems not newly created, they are existing systems, that constantly being developed and reconstructed. This forces us to take into account the presence of existing parts, as well as the limited and discrete possibilities of development and reconstruction of the energy system. Specific properties of energy systems dictate requirements for mathematical models and methods. Firstly, any one absolute mathematical model cannot be created, for example, of the country’s energy system or its main components. A variety of sets of mathematical models, differentiated by management, goals and objectives, type and purpose of the system, level of detail, etc., are necessary. Most of the system analysis problems consist in finding the optimal value of the criterion function in the presence of certain restrictions. In practice, they often formulate not one, but a set of goals for which the establishment of a hierarchy formalized through the construction of multi-criteria models envisaged. In real conditions, the model complicated by the fact, that significant part of the determining parameters are stochastic. Therefore, this stochasticity must be taken into account. In this paper, a model for optimizing the operating modes of the power system based on the consumption forecast for the day ahead proposed. It takes into account the stochasticity of both external disturbances and forecasting errors.

2 Model Formulation The classical formulation of the control problem for a dynamic system [2] assumes that control is carried out from a single center based on a single optimality criterion μ, and is written as: μ=

T E τ =1

g(u(τ ), u(τ ), ξ (τ )) → max,

Accounting the Forecasting Stochasticity at the Power System Modes …

45

where, at a time τ: g—value of the optimality criterion, Ω—system status, u—vector of control actions, ξ —vector of random external actions. Works [3, 4] on the theory of optimal control are devoted to solving this problem in various versions. At the same time, in the case of modeling a complex hierarchical quasi-dynamic system, such as, for example, the energy system of a country or an integrated energy system of several neighboring countries, such a model turns out to be insufficient. In our case, it generalized in the form of a model of a hierarchical controlled quasi-dynamic system with r ∈ R levels of administrative-territorial hierarchy and branch (sub-branch) infrastructure, detailed in accordance with the structure of its technological k ∈ K content. The task of managing such a system formulated as follows: uτ r k ∈ oτ r k |τ = 1, 2, . . . , T ; uτ r k |u(τ, r, k), ξ (τ, r, k) ⇒ u(τ +1)r k |τ = 1, 2, . . . , T ; μτr k =

R E K E

g(u(τ, r, k), u(τ, r, k), ξ (τ, r, k)) → max;

r =1 k=1

u(τ, r, k) ∈ U (τ, r, k)|τ = 1, 2, . . . , T ; ξ (τ, r, k) ∈ E(τ, r, k)|τ = 1, 2, . . . , T ; u∅r k ∈ u∅ |τ = 1, 2, . . . , T , where uτ r k —the state vector of structure of the technological content k of the level r at the moment τ ; oτ r k —the set of feasible states; u(τ, r, k)—the control actions vector; ξ (τ, r, k)—vector of random external influences; μτ r k —the optimality criterion; U (τ, r, k), E(τ, r, k)—the set of possible values of controls and random external influences; u∅ —known initial system state; T —the simulation period. The task of finding the optimal control algorithm for energy system, taking into account the randomness of external actions and forecasting errors, can be formulated in terms of stochastic control theory with adaptation [5]. The optimal procedure for a sequential process with a finite number of steps and a quadratic loss function at each step is determined. This form of the loss function allows the use of optimal decision procedures based on linear functions of the controlling influences. For a one-dimensional problem, this procedure is as follows. Let u∅ , . . . , uk be the final sequence of states of a stochastic hierarchically controlled dynamic system at different steps of a sequential process. u∅ —initial state of the system, and u1 , . . . , uτ —the states of the system at the next steps. The

46

V. Denysov et al.

process ends if, at the next step τ, the value of uτ differs from the target value tτ by an amount less than or equal to the determined ε. If at some step τ (τ = 1, …, T ) the distribution of the next state uτ +1 depends only on the current state uτ and on the control value u τ , then the process can be described by the following system of equations: uτ +1 = ατ uτ + βτ + u τ + ξτ .

(1)

Here α and β—given constants, ατ /=0, u τ —the control value that is selected at the next step τ taking into account the values of uτ and ξτ , which are normally distributed random variables with zero mean and variance γτ2 . Values ξ1 , . . . , ξT — random disturbances of the system, which considered independent. The initial state of the system u∅ is known. Under the action of the selected control u 1 the next state u1 has a normal distribution with mean α0 u∅ + β0 + u 1 and variance γ12 . Similarly, the process continues until the state uT reached. At each step, control u τ chosen, that the next system state uτ +1 closest to target value tτ +1 . In practice, it is necessary to take into account the cost of choosing a control u τ , which we denote as ρτ = rτ u 2τ , for rτ ≥ 0. It is shown [5], that for the movement of the system along the optimal trajectory u1 , . . . , uT , where, the minimum value: L=

T E

2

aτ −1 (θτloss ) + cτ −1

(2)

τ =1

of the average total loss reached, it is enough to choose at each step τ the control u τ , for which the coefficients aτ −1 , bτ −1 and cτ −1 can be calculated explicitly according to the following formulas: ( aτ −1 =

) ( ) ∞2τ pτ (θτ + aτ ) 1 θτ tτ + aτ bτ , bτ −1 = − βτ ; θτ + aτ + ρτ ∞τ θτ + aτ cτ −1 =

aτ θτ (tτ − bτ )2 + (θτ + aτ )γτ2 + cτ , θτ + aτ

(3)

(4)

where ρτ —the current losses associated with choice of control u τ ; θτloss —current value of the deviation of uτ from the target value of tτ . Namely, u τ can be calculated as a linear function of uτ with known tτ and specified loss values at each step according to the following formula: uτ =

θτ tτ + aτ bτ − (θτ + aτ )(∝τ ∗ uτ + βτ ) . θτ + aτ + ρτ

(5)

It is important, that if random perturbations ξ1 , . . . , ξk are independent with zero mean and finite variance, then the control values u τ , found in this way, do not depend on the form of distributions of random perturbations and their variance. In addition,

Accounting the Forecasting Stochasticity at the Power System Modes …

47

the found control values remain optimal for any process described by the system of Eqs. (1). In reality, the task of optimizing control is more complicated, because the initial information is not the real state of the system at the moment τ —uτ , but the forecast for a step (in our case, an hourly forecast for the day) forward Wτ = ετ uτ + £τ . Here ετ is a given constant, and £τ is a random, normally distributed variable with zero mean and variance στ2 . In this case, the assumption about the independence of uτ and £τ is fulfilled. It is possible, that at some steps τ, at which ετ /= 0 and the variance στ2 = 0, the predicted value of Wτ coincides with ετ uτ . In the situation under consideration, the true value of u0 is unknown, since it is a normally distributed random variable with mean m ∗0 and variance ω02 . Based on the predicted value Wτ , the posterior distribution of u1 and the optimal control u 0 are determined. Then, in a similar way, the sequence of controls u 1 , . . . , u T calculated, which minimizes the average value of loss. Proofs, that formulas (2) can be directly used to find the sequence of optimal controls are given in [5]. In turn, in formula (3), it suffices to replace the value uτ with m ∗τ . The next stage is to consider a one-dimensional process in which random control errors added at each step. These errors affect the subsequent state of the system and increase the variance of the latter. More precisely, the process described by Eq. (6): uτ +1 = ατ uτ + βτ + u τ + Hτ u τ + ξτ .

(6)

In Eq. (6), constant coefficients ∝τ and βτ , as well as control u τ and random perturbations ξ1 , . . . , ξk , have the same meaning as in the previous stage of consideration. The product u τ Hτ of the control u τ and of the random variable Hτ determines the value of the additional error due to the control error. Assuming that Hτ has zero mean with variance ητ2 and all random variables included in (4) are independent, then the uτ +1 value of the system state at the next step, as at the previous stage of consideration, is determined from relation (7): uτ +1 = ∝τ uτ + βτ + u τ ,

(7)

but the variance—from relation (8): 2 2

στ2 = u τ ητ + γτ2 .

(8) 2

Thus, the variance στ2 is greater by the value of u 2τ ητ . If the total damage is still determined by Eq. (2), then the optimal control at each step is a linear function of the state of the system at that step uτ . It can see, that the problem considered at this stage is a generalization of problem (1), assuming that ητ2 is zero at each step. As a result, [5]: ( aτ −1 =

) ∝2τ ρτ (θ τ + aτ )[rτ + ητ2 ((θ τ + aτ )] , (θτ + aτ )(1 + ητ2 ) + ρτ

(9)

48

V. Denysov et al.

βτ and cτ are determined, as in the previous stages, from formulas (2) and (3) and u τ by formula (10): uτ =

θτ tτ + aτ bτ − (θτ + aτ )(∝τ ∗ uτ + βτ ) . (θτ + aτ )(1 + ητ2 ) + ρτ

(10)

As well as at the first stage, the optimal sequence of controls u 1 , . . . , u T does not depend on the variance στ2 values and, therefore, there is no need to determine these values for optimal control. The optimality of the controls sequence u 1 , . . . , u T , defined in (10) will be ensured if the assumption about the independence of the random variables Hτ and ξτ is true. The generalization of the considered problem to the real case when all parameters of the system are multidimensional vectors is as follows. As in the previous case, u∅ , . . . , uT is the finite sequence of k-dimensional state vectors of the stochastic hierarchically controlled dynamic system at different steps of the sequential process. Also t∅ , . . . , tT is a sequence of k-dimensional target vectors of system states. u∅ —vector of the initial state of the system, and u1 , . . . , uτ are the states of the system at the next steps. The process ends if, at the next step τ, the distance between the vector uτ and the target vector tτ does not exceed the specified ε. As in the previous case, we believe that the process can be described by the following system of equations: uτ +1 = ατ uτ + βτ + u τ + ξτ ,

τ = 1, ..., T.

(11)

But here: ατ uτ —given non-degenerate k x k-matrix; βτ —given k-dimensional vector; u τ —k-dimensional control vector, which is selected at the next step τ taking into account the values of uτ and ξτ . ξτ —normally distributed k-dimensional random vector with zero mean vector, k x k covariance matrix of which is equal to |τ (has a gamma distribution). Vectors ξ1 , . . . , ξT are random disturbances of the system, which are considered independent and independent of u1 . The system’s initial state vector u∅ is known, with a normal distribution, vector of means m 0 and a non degenerate k x k-matrix G 0 . Under the influence of the selected control vector u 1 , the next state u1 has, like u∅ , a normal distribution with a vector of means m 1 and a non-degenerate k x k-matrix G 1 . Similarly, the process continues until the state uT reached. At each step, the control vector u τ is chosen so that the next state of the system uτ +1 is the closest to the target value tτ +1 . It was proved in [5] that for the movement of the system )' ( trajectory )u1 , . . . , uT , on which E ( along the optimal the minimum value L = τN=1 m ∗τ − bτ −1 aτ −1 m ∗τ − bτ −1 + cτ −1 of the average total loss achieved, it is enough to choose at each step τ the control u τ , for which the coefficients aτ −1 and bτ −1 can be calculated explicitly according to the recurrent formulas (12) and (13). | | aτ −1 = ∝'τ (θτ + aτ ) − (θτ + aτ )(θτ + aτ + ρτ )−1 (θτ + aτ ) ∝τ ;

(12)

Accounting the Forecasting Stochasticity at the Power System Modes …

| | −1 bτ −1 = ∝−1 τ (θτ + aτ ) (θτ tτ + aτ bτ ) − βτ ,

49

(13)

u τ itself can be calculated according to formula (14) as a linear function of uτ with known tτ and given values of ρτ —current losses associated with the choice of control u τ and θτ —the current value of the deviation of the vector uτ from the target vector tτ at each step. u τ = (θτ + aτ + ρτ )−1 [θτ tτ + aτ bτ − (θτ + aτ )(∝τ m τ + βτ )],

(14)

where aτ −1 , θτ , ρτ —symmetric non-negative definite k x k-matrices; bτ −1 —kdimensional vector. From relations (12), (13), (14), under the assumption that aT = 0 and bT = 0, we find the optimal control sequence u ∅ , . . . , u T −1 .

3 Materials and Methods The model, considered below, is a special case of the model of a hierarchical controlled quasi-dynamic system formulated above. It belongs to the class of optimization tasks–Optimal unit Commitment of Power System (tasks of optimal utilization of generating capacities of power systems) with the criterion of minimizing the cost of generating, reserving and importing/exporting electricity. It can define more precisely as a mathematical model with integer variables for optimizing the composition and load modes of generating, reserve and storage capacities of power systems. The specifics technological conditions of the united power system of Ukraine discussed in [6, 7]. The initial information for evaluating and forecasting the optimal modes of using the Ukrainian Integrated Power System is the set of vectors and matrices below: • L τFor , L τReal |τ = 1,2,…,T —the vector of hourly day-ahead power forecasted L τFor , real consumption L τReal (MW). • £τ |τ = 1,2,…,T;—the control error vector—random, normally distributed variable with zero mean and variance ητ2 . • Ccj —technologies self cost matrix ($/MWh), where J—the technologies list, j = 1,…,J. EL • C FU —fuel f price matrix ($/MWh). fj • W jC O2 —CO2 emission vector (tons/MWh). • P jRτ E S —hourly power of nondispatchable renewable energy technologies (MW). ( ) ramp • B Disp n j , P jinst , P jmax , P jmin , E max , Pj —matrix of parameters and limitaj tions for dispatchable technologies, where for technology j: nj —number of power units; P jinst , P jmax , P jmin —installed, maximum and minimum permissible oper—maximum of electricity during the day (MWh); ating capacities (MW); E max j ramp Pj —permissible rate of power change (%/sec).

50

V. Denysov et al.

( ) • B R E S n j , P jinst , P jmax —matrix of parameters and limitations for nondispatchable renewable energy sources. All these resources are used to solve a problem where, at each step τ of the modeling horizon T, the problem of finding the optimal set of generation, reservation, accumulation and import/export modes is solved, taking into account technological limitations and parameters of the forecasting stochasticity. As already noted, technological limitations are discussed in more detail in [6, 7]. The result of solving the problem is a set of matrices, that provide optimal conditions of generation G j τ (L, C, P, B), reservation R jτ (L, C, P, B), accumulation A jτ (L, C, P, B) and import/export (flows) Fτ (L, C, P, B), £τ —the control error vector—random, normally distributed variable with zero mean and variance ητ2 . During forming the input data parameters, the requirements for the required redundancy volumes [8, 9] are taken into account: – – – – – –

automatic frequency recovery reserves; frequency support reserves; frequency support reserve in emergency modes; frequency support reserve in normal mode; frequency recovery reserves; frequency recovery reserves by the operator.

It also takes into account the available volumes of technological electricity flows due to the parallel operation of the power systems of Ukraine and neighboring power systems included in European Network of Transmission System Operators for Electricity (ENTSO-E). Calculations of parameters of generating, accumulating, reserve capacities and predicted parameters of interconnectors in synchronous operation modes of power systems of Ukraine and neighboring power systems included in ENTSO-E are performed using the software implementation of the mathematical model for optimizing the coverage of the electrical load schedule. At the same time, the costs caused by the discrepancy between the predicted and actual load schedule minimized, including due to the presence of a stochastic component in the latter. The model implemented in the MathProg algebraic modeling language using the COIN-OR PuLP modeling language on the SolverStudio platform [10]. The model listing contains 761 lines and the size of the model parameters file is 8 KB.

4 Results of Simulation The following are the results of calculations for the day of maximum flooding on April 14, 2018, using the example of actual values of installed power [11, 12] and load schedules [13, 14] in 2018. Values of the most significant parameters that were used in the mathematical modeling process for the maximum flood day on April 14, 2018.

Accounting the Forecasting Stochasticity at the Power System Modes …

51

Below: • • • • •

CτGen_Cost —hourly generation cost at each step (m/MWh). CτLoss_Price —hourly loss price at each step (m/MWh) [15]. CτLoss_Cost —hourly loss cost at each step (m/MWh). C DGen —total generation cost for day (m). C DLoss —total loss cost for day (m).

Distribution by technologies of hourly generation of the Ukrainian energy system as of April 14, 2018 (MW) presented in Table 1 and Fig. 1. Accounting for losses from errors as result of random effects and stochastic errors in the management of the Ukraine power system for April 14, 2018 shown in Table 2. _total Total day-ahead forecasted generation L For was 355 952 MWh. Total day D Real_total was 344 604 MWh and its cost C DGen was 6 479 180 m. real consumption L D The total power of the error per day £ D was 11 348 MWh (3%) with minimum value 66 MW (0.4%) and maximum value 1 355 MW (9%). Its cost C DLoss was 405 622 m, which is 6% of the total cost. Table 1 Hourly generation of the Ukraine power system for April 14, 2018 (MW) T

L τFor

L τReal

£τ

PV

WIND

NPP

TPP

WPPG

HPPS

IMP

EXP

1

13932

13494

438

0

16

9212

3390

1314

0

0

0

2

13187

12685

502

0

5

9212

3373

814

−216

0

0

3

13178

12691

487

0

7

9212

3390

786

−216

0

0

4

12983

12591

392

0

13

9212

3382

1014

−637

0

0

5

13202

12859

343

0

18

9212

3390

1003

−421

0

0

6

13343

13064

279

26

37

9212

3390

1185

−507

0

0

7

14016

13009

1007

43

53

9212

3380

1631

−302

0

0

8

14412

13652

760

78

53

9212

3360

2069

−302

0

58

9

15207

14735

472

132

66

9212

3390

2569

−216

55

0

10

15356

14955

401

201

63

9212

3390

2166

421

0

0

11

15662

15154

508

276

52

9212

3390

2408

421

0

0

12

15383

14977

406

347

32

9212

3390

2078

421

0

0

13

15284

15038

246

344

33

9212

3390

2305

0

0

0

14

15212

14888

324

326

61

9212

3360

2674

−421

0

0

15

15323

15130

193

275

56

9212

3390

3174

−842

58

0

16

15135

15069

66

237

38

9212

3390

3143

−885

0

0

17

15390

15167

223

189

37

9212

3390

2849

−464

177

0

18

15026

14840

186

125

23

9212

3360

2349

−43

0

0

19

15168

14712

456

54

11

9212

3330

2265

421

0

28

20

16144

14789

1355

0

8

9212

3360

2765

1059

0

0

(continued)

52

V. Denysov et al.

Table 1 (continued) T

L τFor

L τReal

21

17149

16511

£τ 638

PV 0

WIND 33

NPP

TPP

WPPG

HPPS

IMP

9212

3390

3265

1404

188

EXP 0

22

16461

15995

466

0

72

9212

3390

3043

983

7

0

23

15474

14887

587

0

97

9212

3360

2543

346

0

0

24

14325

13712

613

0

139

9212

3330

2043

0

0

399

*

PV—Solar Photovoltaic Station; WIND—Wind Power Plant; NPP—Nuclear Power Plant; TPP— Thermal Power Plant; WPPG—Hydro Power Station; HPPS—Hydro Power Pumping Station; IMP—Import power; EXP—Export Power

Fig. 1 Hourly generation of the Ukraine power system for april 14, 2018

5 Discussion The proposed approach tested on real data [15] of the day-ahead consumption schedule of the Ukrainian Integrated Power System. Estimates for the accuracy of covering the load schedule and the cost of errors arising under influence of random disturbances and inaccuracies in forecasting the hourly parameters of the system obtained. The problem of finding the optimal algorithm for power system control, taking into account the randomness of external influences and forecasting errors, formulated in the framework of the stochastic control theory with adaptation [5]. Thanks to this, it was possible to obtain an optimization procedure in the form of a sequential process with a finite number of steps and a quadratic loss function at each step. This type of loss function made it possible to use optimal decision-making procedures based on linear functions of control actions.

Accounting the Forecasting Stochasticity at the Power System Modes …

53

Table 2 Losses caused by errors as result of random influences and stochastic control errors of the Ukraine power system for April 14, 2018 T

Disp

Lτ

L τReal

£τ

Loss_Cost

£τ , % (%) CτGen Cost , CτLoss_Price m/ Cτ MWh m/MWh m/h

Loss_Cost

, Cτ (%)

1

13932 13494

438 3.25

228650

30

13126

5.74

2

13187 12685

502 3.96

214462

30

15044

7.01

3

13178 12691

487 3.84

215184

30

14580

6.78

4

12983 12591

392 3.11

221060

30

11735

5.31

5

13202 12859

343 2.67

220400

30

10269

4.66

6

13343 13064

279 2.14

219740

30

8361

3.81

7

14016 13009 1007 7.74

214723

30

30179

14.05

8

14412 13652

760 5.57

247985

41

30851

12.44

9

15207 14735

472 3.20

254865

39

18393

7.22

10 15356 14955

401 2.68

292662

42

16855

5.76

11 15662 15154

508 3.35

274265

36

18256

6.66

12 15383 14977

406 2.71

265985

34

13956

5.25

13 15284 15038

246 1.64

256430

36

8841

3.45

14 15212 14888

324 2.18

263225

38

12150

4.62

15 15323 15130

193 1.28

268850

41

7961

2.96

16 15135 15069

66 0.44

279725

45

2991

1.07

17 15390 15167

223 1.47

278968

42

9408

3.37

18 15026 14840

186 1.25

272060

44

8138

2.99

19 15168 14712

456 3.10

268400

36

16388

6.11

20 16144 14789 1355 9.16

318591

36

48695

15.28

21 17149 16511

638 3.86

463038

42

26816

5.79

22 16461 15995

466 2.91

270133

42

19587

7.25

23 15474 14887

587 3.94

307830

42

24672

8.01

24 14325 13712

613 4.47

361950

30

18371

5.08

,

Calculations made on real data showed that the total error for the selected characteristic day was 11 348 MWh–3% of the total consumption. At the same time, the minimum error value was 66 MWh (0.4%), and the maximum was 1 355 MWh (9%). The total cost of losses for the selected characteristic day was 405622 m, which is 6% of the total cost of consumed energy. The application of the above approach made it possible to reduce the time for calculating the sequence of optimized controls by dozens of times and bring this time to less than one minute when using a conventional personal computer with CPU an Intel(R) Core(TM) i5–3570 @ 3.40 GHz and just 8 GB of RAM.

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6 Conclusions Today, we can state the rapid development of mathematical modeling for optimal management in the energy industry. Particularly promising and among the extremely urgent tasks of national importance in the field of energy and ecology are the problems of saving energy resources, the cost of generating electricity, the flexibility of power supply, and increasing the efficiency of its production. Therefore, in all models, it is necessary to take into account the need for flexibility to compensate for daily, weekly and seasonal irregularities in the consumption schedule. Number of important circumstances follow from the main properties of energy systems, which must be taken into account in mathematical modeling. This work provides a formulation of the problem of finding the optimal algorithm for power system control, taking into account the randomness of external influences and forecasting errors, proposed in the framework of the stochastic control theory with adaptation. It made it possible to obtain an optimization procedure in the form of a sequential process with a finite number of steps and a quadratic loss function at each step. This type of loss function made it possible to use optimal decision-making procedures based on linear functions of control actions. The analysis showed the effectiveness of using the proposed approach. Its distinguishing feature is a significant reduction of the calculations amount required to obtain the parameters of optimal system control. In general, the use made it possible to reduce the time for calculating the sequence of optimized controls by dozens of times and bring this time to less than one minute when using a conventional personal computer, while maintaining an acceptable accuracy of the resulting estimates.

References 1. Shulzhenko, S.V.: Estimation of cost indicators for the task of electrical power system development forecast under the market liberalization conditions. Probl. Gen. Energy. 2(18), 16–20 (2008) 2. Ugolnitsky, G., Usov, A.: Control of complex ecological-economic systems. Avtomat. i Telemekh. 5, 169–179 (2009) 3. Bellman, R.: Adaptive Control Processes: A Guided Tour, p. 276. Princeton University Press (1961). 4. Blum, E.K.: The Mathematical Theory of Optimal Processes. by Pontryagin, Boltyanskii, Gamkrelidze, Mishchenko. 5. DeGroot, M.H.: Optimal Statistical Decisions, p. 489 (2004). 6. Denisov, V.: Determination of optimal operating modes of the Ukrainian power system when covering the daily schedule of electrical loads, ensuring the necessary volumes of redundancy and using storage capacities. Probl. Gen. Energy. 4(63), 33–44 (2020). https://doi.org/10.15407/ pge2020.04.033. 7. Shulzhenko, S., Turutikov, O., Tarasenko, P.: Model of mathematical programming with integer variables for determining the optimal regime of loading of hydroelectric pumped storage power plants for balancing daily profile of electric loads of the power system of Ukraine. Probl. Gen. Energy. 4(59), 13–23 (2019). https://doi.org/10.15407/pge2019.04.013.

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8. Frequency Containment Reserves or FCR, source: «Transmission Network Code» URL: https:/ /ua.energy/ 9. Network codes by ENTSO-E. URL: https://www.entsoe.eu/network_codes/sys-ops/ 10. SolverStudio. URL: https://solverstudio.org/. Accessed 16 September 2022. 11. Installed capacity of the Integrated Power System of Ukraine, National Power Company “Ukrenergo”. https://ua.energy/installed-capacity-of-the-IPS-of-Ukraine/#10-2018. Accessed 04 February 2022 12. ENTSO-E Installed Capacity per Production Type. Installed Generation Capacity Aggregated [14.1.A] 2018. URL: https://transparency.entsoe.eu/generation/r2/installedGenerationCa pacityAggregation/show. Accessed 04 February 2022. 13. Daily electricity generation/consumption schedule, National Power Company “Ukrenergo”. https://ua.energy/transmission-and-dispatching/dispatch-information/daily-electricityproduction-consumption-schedule. Accessed 04 February 2022. 14. ENTSO-E Total Load–Day Ahead/Actual. https://transparency.entsoe.eu/load-domain/r2/tot alLoadR2/show. Accessed 04 February 2022. 15. Hourly electricity purchase and sale prices on the day-ahead market in the IPS of Ukraine for April 2020 (UAH/MWh). https://www.oree.com.ua/index.php/pricectr. Accessed 24 December 2022.

Mathematical Simulation of Projecting Energy Demand for Ukraine’s Budget Institutional Buildings Olena Maliarenko , Nataliia Maistrenko , Vitalii Horskyi , Irina Leshchenko , and Nataliia Ivanenko

Abstract The constant increase in the fuel and energy cost leads to radical changes in the structure of energy consumption, which is important when determining the need for energy resources at all hierarchical levels of the economy. The budgetary sphere of the economy is present in any country in the world. As a rule, this area is provided by the most available energy resources, which at the same time are not the most expensive. Using the example of Ukraine, the structure and volumes of consumption of various types of fuel and energy in the budgetary sphere were analyzed. A method of forecasting the demand for energy resources is proposed, taking into account prospective amounts of energy savings in terms of the use of energy resources and the replacement of scarce types of fuels with more accessible and cheaper types of fuel and energy. Forecast estimates of the demand until 2040 for the main types of energy resources consumed by the budgetary sphere of Ukraine were carried out, with an assessment of possible changes in the structure of energy consumption. Keywords Demand · Energy resources · Energy consumption model · Budgetary sphere of the economy

1 Introduction The importance of sufficient provision of the domestic economy, especially its budget sector, with the necessary fuel and energy is obvious. According to KVED-2010 classifier the budgetary sphere of Ukraine’s economy includes the following components: Section O-Public administration and defense; compulsatory social security, partly Section M-Professional, scientific and technical activities, Section N-Administrative and support service activities, Section P–Education, Section Q-Human health and social work activities, Section S-Other services activities. Sections O, P, Q consume the main part of energy resources. O. Maliarenko · N. Maistrenko (B) · V. Horskyi · I. Leshchenko · N. Ivanenko General Energy Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_4

57

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A set of following economic, environmental and political factors causes the determinant influence on the energy consumption of the budgetary sphere, including the sector of general public administration, non-profit organizations and households: migration of people, labor force and capital; open data (information availability); political populism; growth of capital investments; a sharp change in the population; global warming; changes in National Bank of Ukraine discount rates and tightening of monetary policy; globalization. Tables 1, 2 and 3 present the distribution of fuel, energy and heat consumption by the main consumers in the budgetary sphere. Table 1 Fuel consumption in Ukraine according to KVED-2010 classifier in 2010–2017, thous tce 2010

2011

2012

2013

2014

2015

2016

2017

O−public administration and defense; compulsatory social security

467.8

451.0

427.1

367.7

259.4

249.1

198.4

298.8

P−education

126.3

130.2

126.6

101.7

63.7

55.2

50.5

38.0

Q−human health and social work activities

181.9

198.3

191.2

152.1

104.1

95.4

87.3

78.3

Table 2 Heat consumption in Ukraine according to KVED-2010 classifier in 2010–2017, thous Gkal 2010 2011 2012 2013 2014 2015 2016 2017 O−public administration and defense; 7367 7050 6877 3357 2607 2470 5450 5298 compulsatory social security P−education

4214 4145 3985 1232

940

757 2942 2634

Q−human health and social work activities

2997 3088 2986

691

583

957

188 2305

Table 3 Electricity consumption in Ukraine according to KVED-2010 classifier in 2010–2017, mln kWh 2010 2011 2012 2013 2014 2015 2016 2017 O−public administration and defense; 1950 2108 2403 1352 1133 1671 1883 1983 compulsatory social security P−education Q−human health and social work activities

1060 1077 1084

371

270

264

863

835

966 1043 1038

357

235

310

111

839

Mathematical Simulation of Projecting Energy Demand for Ukraine’s …

59

2 Energy Consumption Projection Methodology of Budgetary Institutions in Ukraine The aim of this study was to clarify the normative method of forecasting the demand of energy resources at different hierarchical levels of the economy by including an improved three-level methodology for calculating forecasts of energy consumption, taking into account regional characteristics by current types of economic activity. The complex method of forecasting the fuel and energy resources (FER) demand [1], which was developed at the Institute of General Energy of the National Academy of Sciences of Ukraine, uses a normative method at the first stage, a component of which is an improved three-level methodology of forecasting energy consumption by the regions of Ukraine. It takes into account both the general energy saving potential (from structural and technological changes) as a whole, as well as the specifics at the regional levels (oblast, city, village, settlement, territorial community) in certain (selected) kinds of economic activity in the provision of services (production). The following groups of energy efficiency indicators to calculate the total and regional fuel and energy consumption by the kinds of economic activity were selected: 1st level:—macro level—country: energy intensity of “total gross value added (GVA)” (instead of Gross Domestic Value (GDP)), energy intensity of services and production at the country level; 2nd level:—meso level—regions (regions) by kinds of economic activity (KEA): energy intensity of GVA by KEA, energy intensity of services and production at the regional level by a certain (selected) KEA; 3rd level:—micro level—energy intensity of services and production by KEA at a lower administrative level (district, city, territorial community, village, settlement). At the national level (1st macro level), the total forecast of FER consumption should be determined by kinds of economic activity (regions) and population: Pst =



t Pqt + Ppop ,

(1)

t P tf + Ppop ,

(2)

q

or Pst =

 f

where q—kind of economic activity accoding to the current classifier; f —region t according to the statistical data; P pop —forecast of FER consumption by population (estimated by a specific method); f P tf —forecast of FER consumption by regions;  t q Pq — total FER consumption by KEA, which is calculated by the following equation:  q

Pqt =

 q

b t eGV Aq VGV Aq −

 q

E qt ,

(3)

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O. Maliarenko et al.

and (or) for regions 

P tf =



f

b t eGV A f VGV A f −



f

E tf ,

(4)

f

b where eGV Aq —GVA energy intensity GVA of q-th kind of economic activity; b t eGV A f —GVA energy intensity for f -th region; VGV Aq —GVA in projected year, which t is defined by projected GVA structure; VGV A f —regional GVA in projected year,  t which is defined by projected GVA structure; f E f —total forecasted energy saving potential in t-th year for each f -th region in projected year for the whole economy:



E tf =



f

F E tf +

f



E tf a ,

(5)

fa

 where f F E tf —forecasted energy saving potential in t-th year for each f -th region; f a E tf a —forecasted energy saving potential in t-th year, whichwas taken into account at the lower stages of the domestic administrative system; q E qt — total forecasted energy saving potential in projected year for all q-th KEA: 

E qt =



q

E qints +

q



Ert ,

(6)

q

 where q E qints —forecasted energy saving potential in t-th year from intersectional structural shifts, q E qt —forecasted energy saving potential in t-th year, which was taken into account at the lower stages of the domestic economic system. It should be noted that the national GDP and the gross regional product (GRP) calculated at the country level are the same value, since the sum of regional consumptions calculated for the regional level is not identical to national consumption. The forecasted regional service provision in the t-th year includes volumes t intended for the internal needs of the region (Vint f ), for export (Vexp f ) and t interregional (Vintr f ) volumes (calculated by intermediate consumption): t t V t = Vint f + Vexp f + Vintrf .

(7)

Aggregate demand for fuel or energy of j—type in the t-th year when providing services of the k-th type can be expressed as: t P jk =

 k

Pint jk +



t Pexp jk ,

(8)

k

 where k Pint jk —internal demand for the j-th type of fuel or energy, which is determined by the demand of k-th consumers for this type of fuel or energy to meet

Mathematical Simulation of Projecting Energy Demand for Ukraine’s …

61

 t the demand of the domestic regional market in the t-th year; k Pexp jk —external demand for the j-th type of fuel or energy, which is equal to the costs of the j-th type of fuel or energy in the region for the provision of export services of the k-th type in the t-th year. Internal demand for a certain type of j-th fuel or energy at the level of the economy consists of the need for a certain type of fuel for the provision of k-th services (including energy carriers: electric and thermal energy, water, air, etc.), which is determined by the sum of the fuel of own production or fuel processing products of own production from imported raw materials and imported types of fuel and their processing products. It is calculated through the energy intensity of the j-th type of energy resource when providing services of the k-th type: Pint jk =



t t eGV A jk Vink ,

(9)

k t where eGV A jk —GVA energy intensity of j-th fuel or energy for the provision of k-th t —the volume of k-th service for domestic consumption, services in the t-th year; Vink which is determined by the consumption of the product per capita or group (number of families or 1000 inhabitants): t Vink = ϑkt N t ,

(10)

where ϑkt —consumption per capita of the k-th service in the country in the t-th year; N t —population in the t-th year or number of groups under statistical observation. The external demand for the j-th type of fuel or energy, which is equal to the use of the j-th type of fuel or energy for the provision of export services of the k-th type in the t-th year, consists of the need for a certain type of fuel for the provision of services (including energy carriers: electric and thermal energy, water, air, etc.) for the k-th KEA, which is determined by the of own production or own production of fuel processing products from imported raw materials and imported fuel and their processing products, and the ratio of prices of external and the internal market or their indicators: t Pexp jk =



t t eGV A jk Vexp k

k

Iexp k , Ivck

(11)

t where Vexp k —services of k-th type for external use; Iexp k —export prices for services or their indices, thousand or mln C ($), or % (portion); Ivck —internal prices for services or their indices, thousand or mln UAH ($), or % (portion). At the second level (1st mezo level) in region f for q-th sections of the economy, the projected consumption of FER can be determined by types of economic activity:

Pqt f =

 r

Prt f ,

(12)

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where r—type of economic activity in the region, which is included in a certain  section q, according to the current classifier; r Prt f —total energy consumption in the region f by kinds of economic activity, which is determined by the equation: 

Prt f =

r



b t eGV Ar f VGV Ar f −



r

Ert f ,

(13)

r

b where eGV Ar f —GVA energy intensity of the r-th type of economic activity in the t base year, included in a certain region f ; VGV Ar f —GVA in the projected year for the kind ofeconomic activity (section) r, given by the projected structure of regional GVA; r Ert f —total projected energy saving potential in the t-th year in region f for each r-th kind of economic activity (section). The final energy consumption, which is used to calculate total energy consumption for level 1 (country) and level 2 (region), are determined by kinds of economic activity: t t P tf ec = eGV A jk VGV Ak ,

(14)

t where eGV A jk —GVA energy intensity of the j-th type of energy (specific fuel, heat and electrical consumption) of the k-th kind of economic activity in the t-th year for the overall economy or the r-th kind of economic activity (section) of the economy b in the region, kg c.e./UAH; VGV Ak —projected GVA of the k-th kind of economic activity in the t-th year for the overall economy or the r-th kind of economic activity (section) in the region, thousand UAH/year. The projected provision of the k-th type of services in the t-th year at the sectional t t , export Vexp level includes volumes intended for domestic consumption Vintkr kr and t : interregional consumption Vint s f kr t t t Vkrt = Vint kr + Vexp kr + Vint s f kr .

(15)

Accordingly, the forecast of the FER consumption in the region for the KEA (sections), which include export-oriented types of services, should be determined as the sum of the projected need for energy resources to ensure the internal market, the provision of interregional services and external demand for services. Aggregate demand for fuel or energy j in the t-th year for the provision of the k-th kind of services can be expressed as follows: t P jkr =

 k

t Pint jkr +

 k

t Pexp

jkr

+



t Pint s f kr ,

(16)

k

t where P jkr —aggregate demand or energy j in the t-th year for the provision for fuel t of the k-th kind of services; r Pint jkr —internal demand of j-th FER, which is determined of k-th consumers of r-th KEA (section) for internal trade in the by need t t-th year; r Pexp jkr —external demand of j-th FER, which equals to needs to provide

Mathematical Simulation of Projecting Energy Demand for Ukraine’s …

63

 t k-th export service of r-th KEA (section) in the t-th year, k Pints f kr —intersectional demand of j-th FER, which is determined by need of k-th consumers of r-th KEA (section) for trade in the t-th year. Internal demand for j-th type of FER consists of the need for the provision of k-th service (including energy carriers: electric and thermal energy, water, air, etc.), which is determined by the domestic fuel or fuel processing products from imported raw materials and imported types of fuel and their processing products: t Pint jkr =



t t eGV A jkr Vintkr ,

(17)

k t where eGV A jkr —GVA energy intensity of j-th type of FER for provision of k-th t service in the t-th year; Vint jkr —production of the k-th type of the r-th KEA (section) for domestic consumption. The external demand for the j-th type of fuel or energy, which is equal to the costs of the j-th type of fuel or energy for the provision of export services of the k-th type in the t-th year, consists of the need for FER for the provision of k-th service (including energy carriers: electric and thermal energy, water, air, etc.), which is determined by the sum of fuel of own extraction or own production of fuel processing products from imported raw materials and imported types of fuel and their processing products, the ratio of foreign and domestic market prices or their indices: t Pexp jkr =



t t eGV A jkr Vexp kr

k

Iexp kr , Ivckr

(18)

t where Vexp kr -the volume of k-th service for domestic consumption; Iexp kr —export prices for services or their indices, thousand or million UAH ($) /year, or % (portion); Ivckr —internal prices for services or their indices in r-th KEA (section), thousand or million UAH ($) /year, or % (portion). Accordingly, the projected FER consumption for the KEA level (section), which includes export-oriented types of services, should be determined similarly to level 1, but the internal prices for services or their prices, which are compiled at the level of the section, may differ from the corresponding prices of individual business entities, because the coverage of the higher level (country) is more than in the region, since the additional services provided in the economic sectors at the country level are not taken into account in a sectoral level. This is explained by the coverage of each section with additional services not included in the main type of activity. For the 3rd level (micro level)—the local administrative level−the projected consumption (fuel, electricity, thermal energy) of the j-th type of FER for the provision of the k-th service in the t-th year is determined by the equation: t P jka =

 k

t pka Vka ,

(19)

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O. Maliarenko et al.

where pka —specific fuel consumption (electric, heat) for k-th service, k c.e., Mkal/ t −the volume of k-th service, thous or mln t. t, kWh/t; Vka The consumption of fuel (electricity, heat) of the k-th service is converted into I kr conditional fuel, energy intensity of the k-th product is determined (if Iexp ≥ 1, the vckr ratio of export and domestic prices is greater than (or equal to) one): t P jka

b ekna =

,

(20)

b t ekna Vka .

(21)

t Vka

and energy intensity of the k-th service: Pkna =

 k

Energy costs for export services are calculated according to end-to-end energy intensity: t Pexka =



b t ekna Vexka ,

(22)

k

Estimated final consumption of fuels (energy) for export services by types of products, for example, in the provision of education (healthcare) services, taking into account the energy saving potential, can be described by the following dependence: t Pexka =



b t ekna Vexka −

k



Hka ,

(23)

k

 where k Hka —total energy saving potential k-th service, which at the administrative level includes the technological potential of energy saving for each type of service k;   tex E kr Hka . (24) br b = k

k

Aggregate demand for fuel or energy of the j-th type in the t-th year for the production of the k-th type of products at this level is determined similarly to the previous one. Internal demand for a certain j-th type of fuel or energy consists of the need for a certain type of fuel for the production of the k-th product (including energy carriers: electric and thermal energy, water, air, etc.) or the provision of services, which is determined the sum of fuel of own extraction or own production of fuel processing products from imported raw materials and imported types of fuel and their processing products: t Pintka =

 k

t ent jka Vint ka ,

(25)

Mathematical Simulation of Projecting Energy Demand for Ukraine’s …

65

where ent jka —end-to-end energy intensity of j-th FER for the production of k-th t —production of k-th type for domestic use. product in t-th year; Vintka The external demand for the j-th type of fuel or energy, which is equal to the costs of the j-th type of fuel or energy for the production of the k-th export product in the t-th year, is calculated taking into account the ratio of foreign and domestic market prices or their indices: t Pex jka =



t ent jka Vexka

k

Iexka , Ivcka

(26)

t where Vexka —production of k-th type for external use, Iexka —export prices for services or their indices, thousand or million UAH ($) /year, or % (portion); Ivckr — internal prices for services or their indices in r-th KEA (section), thousand or million UAH ($) /year, or % (portion).

3 Analysis of Results Obtained The indicator of the volume of products for domestic consumption can be calculated by other indicators, except consumption per capita, depending on the specifics of the type of product. Assessment of energy saving potential in the sector of general public administration and non-profit organizations. Sector of general public administration (O−Public administration and defense; compulsatory social security), non-profit organizations, which combine several sections (M−Professional, scientific and technical activities, N-Administrative and support service activities, P−Education, Q−Human health and social work activities, S.94−Activities of membership organizations) need priority energy saving, since most of them are budget institutions. Consumption structure for 2017 (was assumed as a baseline to determine directions of possible reduction of fuel consumption by types. The data are taken from the statistical reporting form of 4-MTP for 2017 of the State Statistics Service of Ukraine [2]. As can be seen from this statistical report, the considered sections can be divided into two groups by directions of fuel use. The first group−most of the fuel was used for conversion into thermal energy. It includes sections “O−Public administration and defense; compulsory social security”, “P−Education”, “Q−Human health and social work activities”. For example, in the “O” section, 61% of the consumed fuel was used for conversion, and 39%−for final consumption. Section “O” uses more than 80% of solid fuels (coal, peat, their briquettes, biofuels, shavings and wood shavings, etc.) and fuel oil, and more than 60% of natural gas in boiler houses and other heat-generating installations. The remaining types of fuel, mainly petroleum products, are used in

66

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the direction of end consumption in vehicles. Oils and lubricants−as raw materials. In the “P” section, more than 80% of the fuel is used for conversion in boiler houses and heat generating units. In the “Q” section, more than 60% of fuel is spent on conversion, the rest is final consumption. The second group includes sections in which the majority of fuel (more than 70%) is used in the direction of final consumption: “M−Professional, scientific and technical activities”, “N−Administrative and support service activities “, and “94S−S.94−Activities of membership organizations” (more than 59%). These types of economic activity are dominated by transport services, a small share of final consumption is the use of solid fuels in small boilers for heating. Based on the directions of fuel use in the specified sections, modernization and replacement of existing heating boilers and improvement of vehicles used in these types of economic activities should be considered as the main measures to reduce fuel consumption. Electricity consumption by budgetary institutions is carried out for the needs of lighting and operation of office equipment and medical equipment. Accordingly, measures to reduce electricity consumption are related to increasing the efficiency of lighting systems and updating the used equipment to a more state-of-the-art one with lower electricity consumption and a higher energy efficiency class. For lighting systems, the introduction of energy-saving lamps, in particular lightemitting diodes (LED), is recommended. A proven study [3] showed that when introducing energy-saving lamps to replace the existing 326.5 million units, it is possible to save 40 billion kWh of electricity annually. According to the data of 11MTP [4], the electricity consumption of the specified sections in the base 2017 year belongs to small consumers, whose percentages to national electricity consumption in the country as a whole (net) are within 0.7–1.7%. The electricity consumption by the specified non-commercial sections depends on the use of energy-saving office equipment and medical devices. The last group of devices is expected to be enlarged so increase of the consumption of the Q section is envisaged. The set of technological measures addressed at reducing electricity consumption by section for the forecast period until 2040 includes the following [3]: • Application of economical lighting schemes using LED lamps; • Replacement of existing computer equipment with low-consumption "green" computers; • Introduction of energy efficient technological equipment and household appliances in hospitals (medical equipment, refrigerators, washing machines, electric stoves for cooking, etc.) instead of current energy-consuming ones; • The use of automatic systems for regulating the operating modes of equipment for the production of heat at the expense of heat pumps in budgetary institutions; • Modernization of engineering equipment (including pumps) of heating, ventilation, air conditioning and hot water supply systems; introduction of highly efficient energy conversion technologies that completely replace the use of natural

Mathematical Simulation of Projecting Energy Demand for Ukraine’s …

67

Table 4 Projected national electricity consumption until 2040 in 2015 prices Indicators

2017

I—Gross electricity consumption by specific electricity consumption of 2017 GDP, mln kWh

129,058 171,828 209,044 253,280 286,563

2025

2030

2035

2040

II—Gross electricity consumption by specific electricity consumption of 2017 GDP taking into account structural shifts, mln kWh

129,058 182,888 224,931 270,827 297,805

III—Gross electricity consumption by specific 129,058 187,283 229,018 274,526 300,056 electricity consumption of 2017 GDP taking into account structural and technological changes, mln kWh

gas and coal in heat-generating sources with technologies with direct use of electricity (electric boilers, electric boilers and other equipment), which can be used as independent heat-generating sources for individual use; • Accounting and control of electricity use. Taking into account energy saving technological measures, the projected electricity use by non-commercial sections for the forecast period until 2040 were evaluated. According to our estimates, this amount will reach: in the “Public administration and defense; compulsory social security” section−5194.8 million kWh, in the “Education” section−2728.1, in the “Health Protection and Social Assistance” section−44,471.7 million kWh. For such sections as “Professional, scientific and technical activities”, “Human health and social work activities”, it is envisaged increasing the electricity consumption in the future, which is caused by the growth and development of scientific and technical progress in their production activities and a wide introduction of energy-saving types of equipment and quality services. If the economy of Ukraine is restored to pre-war indicators by 2030, the total level of electricity consumption in Ukraine in 2040 will reach 252,480.2 million kWh. Calculations of electricity consumption for KEA in 2015 prices at the country level until 2040 (macro level) are presented in the Table 4. Projected gross electricity consumption by KEA by GVA electricity capacity from 2017 to 2040 (meso level) is presented in the Tables 5 and 6.

4 Conclusions A comprehensive method of long-term forecasting of energy consumption has been improved, taking into account the specifics of energy use in the general public administration sector, non-profit organizations and households based on energy efficiency indicators. The two-level fuel demand forecasting model with the allocation of fuel

68

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Table 5 Projected national electricity consumption by GVA electricity capacity from 2017 to 2040, mln kWh Indicators

2017

2025

2030

2035

2040

1

2

3

4

5

6

Other KEA Projected electricity consumption by KEA, mln kWh (1) 2017 economy’s structure and 2017 energy capacity

12,717.6

15,814.8

19,240.1

23,311.5

26,374.8

(2) Structural changes in economy and 2017 energy capacity

12,717.6

26,014.9

36,869.0

45,503.2

52,424.6

Energy saving potential in case of structural changes

0.0

−10,200.1

−17,628.8

−22,191.7

−26,049.8

Technological energy saving potential

0.0

26.6

32.8

36.2

36.4

(3) Taking into account technological energy saving potential

12,717.6

25,988.3

36,836.2

45,467.0

52,388.2

Including public administration and defense; compulsory social security Projected electricity consumption by KEA, mln kWh (1) 2017 economy’s structure and 2017 energy capacity

1983.7

2641.1

3213.1

3893.1

4404.7

(2) Structural changes in economy and 2017 energy capacity

1983.7

2880.5

3701.8

4544.9

5209.8

Energy saving potential in case of structural changes

0.0

−239.4

−488.6

−651.8

−805.2

Technological energy saving potential

0.0

10.0

12.0

13.0

15.0

(3) Taking into account technological energy saving potential

1983.7

2870.5

3689.8

4531.9

5194.8

Education Projected electricity consumption by KEA, mln kWh (1) 2017 economy’s structure and 2017 energy capacity

835

1112

1353

1639

1854

(2) Structural changes in economy and 2017 energy capacity

835

1240

1688

2231

2734

(continued)

Mathematical Simulation of Projecting Energy Demand for Ukraine’s …

69

Table 5 (continued) Indicators

2017

2025

2030

2035

2040

Energy saving potential in case of structural changes

0.000

−128.2

−335.0

−591.7

−879.8

Technological energy saving potential

0.000

3

4

5

6

(3) Taking into account technological energy saving potential

0.000

1237.1

1683.6

2225.6

2728.1

Human health and social work activities Projected electricity consumption by KEA, mln kWh (1) 2017 economy’s structure and 2017 energy capacity

9060

12,062

14,674

17,780

20,116

(2) Structural changes in economy and 2017 energy capacity

9060

21,894

31,480

38,728

44,481

Energy saving potential in case of structural changes

0.0

−9832.6

−16,805.2

−20,948.2

−24,364.8

Technological energy saving potential

0.0

4.0

6.0

7.0

9.0

(3) Taking into account technological energy saving potential

9060

21,890.4

31,473.6

38,720.7

44,471.7

Table 6 Projected FER consumption by section “Public administration and defense; compulsory social security” taking into account structural and technological changes Indicators

2017-actual

2025

2030

2035

2040

Heat energy, thous Gkal

55,370.7

62,313.0

74,385.6

84,226.0

92,081.6

Heat energy, thous tce

9042.04

9814.30

11,604.2

13,055.0

14,088.5

Electricity, mln kWh

5654.8

7852

10,090

12,175

14,034

Electricity, thous tce

2261.9

3141

4036

4870

5614

415.0

582.7

714.2

822.6

881.0

13,538

16,354.4

18,747.6

20,583.5

Fuel, thous tce Total, thous tce

11,718.94

volumes for transformation in sections of the economy has been refined. According to this model, forecasts of total fuel including coal and natural gas by 2040 for the new structure of generating capacities, which provides for the development of renewable energy in Ukraine have been developed. The impact on the country’s energy consumption of the general public administration sector, which functions at the state, regional, and regional economic activity levels, was studied. These costs of energy resources have their own specificity for each hierarchical level. For non-profit organizations, which include organizations

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that provide housing services, health care services, recreation and culture services, education, social protection, religious, political parties, trade union and professional organizations, environmental protection, etc., fuel and energy consumption spent on the maintenance of buildings and premises, which does not depend on the volumes of services provided, are determined. When determining the forecasted demand for heat energy for organizations that provide services, estimates of the forecast demand per 1 m2 of heating area were made. The power energy consumed in the respective sectors depends on the lighting performance of buildings, architectural features and the set of electrical appliances used in public institutions and non-profit organizations. Fuel consumption depends on heating systems and the availability and number of vehicles in organizations. For the types of economic activity studied in the work−“Public administration and defense; compulsatory social security”, “Education”, “Human health and social work activities”, the forecasted energy consumption is determined according to the new model. For the “Other KEA” Section electricity consumption is expected to increase by 4.12 times in the forecasted period due to the expansion of types of services, in the Section “Public administration and defense; compulsatory social security” −by 2.62 times, in the Section “Education”−by 3.27 times, in the Section “Human health and social work activities”−4.9 times due to the increase of relevant services, development of the defense sector, in the section of industrial foreign exchange−in 2 times due to the development of electrotechnical processes and technologies. Heating, hot water and air conditioning services will also grow to meet regulatory requirements. It is expected to reduce the consumption of organic fuel in Ukraine from 165.8 to 108.6 bln tce and, accordingly, to increase the consumption of electric energy from renewable energy sources.

References 1. Kulyk, M.M., Malyarenko, O.E., Maistrenko, N.Y., Stanitsyna, V.V., Spitkovskyi, A.I.: Application of the complex forecasting method to determine the prospective demand for energy resources. Probl. Gen. Energy. 1(48), 5–15. (2017) https://doi.org/10.15407/pge2017.01.005 2. Form of statistical reporting No. 4-MTP “Report on residues and use of fuel and fuel and lubricants” (annual) for 2010–2017: Stat. bulletins—K.: State Statistical Service of Ukraine, 2010–2017. 3. The best available technologies for housing and communal services in Ukraine. USAID project “Municipal energy reform in Ukraine”. Technology Selection Guide. Under the editorship S. Yermilova. K.: “Polygra PLUS”. 2016. 134 p. ISBN 978–966–8977–63–3. 4. Form of statistical reporting No. 11-MTP “Report on the results of the use of fuel, heat energy and electricity” (annual) for 2010–2017: Stat. bulletins—K.: State Statistical Service of Ukraine, 2010–2017.

Two-Stage Method for Forecasting Thermal Energy Demand Using the Direct Account Method Olena Maliarenko , Natalia Maistrenko , Heorhii Kuts, Valentina Stanytsina , and Oleksandr Teslenko

Abstract The method of two-stage forecasting of consumption of energy resources developed by specialists of the Institute of General Energy of the National Academy of Sciences of Ukraine. This method allows simultaneous determination of forecasts at two levels: TOP and DOWN. The difference in the obtained forecasts results reconciled by Kulyk’s vector method. Kulyk’s method allows to reconcile forecasts based on the proposed analytical dependencies without using successive iterations. The methodical approach using the two-stage method based on the generally accepted method of forecasting at the TOP- level (country) and DOWN-level (types of economic activity) analyzed in our previous articles. In this article, we investigated the peculiarity of using the direct account method to determine the forecast demand at the TOP- level (country) and DOWN- level (energy-intensive productions, the share of which is at least 70% in total energy consumption). This forecasts obtained by the method of direct calculation are also consistent with the use of Kulyk’s method. It is shown that using a two-stage method based on the direct calculation method provides better convergence between forecasts at the TOP- level and DOWN- level for forecasting heat energy consumption. The TOP- level can be either national (country) or regional (region of the country). DOWN-level (by energy-intensive types of production) allows to use forecasts of production volumes in natural dimensions (not in cost dimensions). As a result, a possible reduction in energy consumption in the case of an increase in energy efficiency of energy-intensive industries more accurately taken into account. The replacement of outdated technologies with innovative ones, the modernization of existing production and the implementation of individual energy-saving measures contribute to the increase in energy efficiency. The recovery of the economy after the COVID- pandemic is taken into account when forecasting production volumes. Keywords Energy · Consumption · Thermal energy · Forecast · Method

O. Maliarenko (B) · N. Maistrenko · H. Kuts · V. Stanytsina · O. Teslenko General Energy Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_5

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1 Introduction In the conditions of the economic restructuring of the country, an important issue is the determination of the strategy for the development of the economy and its energy supply, in particular, the development of the country’s heat supply systems for the prospective period. There are a significant number of methods for forecasting the demand for energy resources. However, for each stage of the functioning of the economy, it is necessary to determine the most relevant method that will allow estimating the forecast volumes of energy resources with a greater degree of probability. This study compared the two-stage method in two modifications: with the use of the direct account method and with the use of the normative method that was used earlier. All methods take into account the potential of thermal energy savings during its consumption by different groups of consumers or types of economic activity, as well as by the country as a whole. The direct account method, which is presented in this study, has been improved in terms of the selection of three groups of consumers according to the relevant energy efficiency indicators, and an algorithm has been created for forecasting the demand for thermal energy at the levels of product production, service provision, and consumption by the population for the country as a whole.

2 Literature Review and Problem Statement Forecasting the demand for thermal energy is both a separate task and a component of a complex of tasks in which it is necessary to use the initial data on the forecast of thermal energy consumption to obtain the final result. These forecasts can have different accuracy: determined at different hierarchical levels and for different time intervals. These forecasts are necessary when determining the required amount of fuel and its types and electricity to ensure the necessary consumption, the adequacy of heat generating capacities and the possibility of fuel export, if necessary. Forecast energy and product (by type of energy resources) balances of the country and enterprises, in particular, thermal energy balances for the regions of the country, are compiled on the basis of energy resource consumption forecasts. Regional programs for improving energy efficiency and energy saving are also being developed. As a rule, economic and mathematical models are created to determine forecasts of consumption of energy resources and, in particular, thermal energy. Among foreign researchers, the works of J. Dickman, J. Horn, H. Goldstein, U. Remme, K. Kempfert, H. Markowitz and others are devoted to the theoretical and applied aspects of energy modeling [1–9]. The models for perspective assessment of energy demand developed by the IAEA are well-known in the world: the so-called MAED, WASP and ENPEP-balance [2, 10].

Two-Stage Method for Forecasting Thermal Energy Demand Using …

73

MAED makes it possible to estimate forecasts of energy demand based on medium-term or long-term scenarios of socio-economic, technological and demographic development. The demand for energy is distributed among a significant number of end-user categories corresponding to various goods and services. Assessment of social, economic and technological factors affecting specific development scenarios is carried out. The WASP model is the most common energy system planning model in developing countries. Taking into account the limitations defined by the user, WASP allows to develop an optimal long-term plan for the development of the power generation system. The WASP model is the most common energy system planning model in developing countries. Taking into account the limitations defined by the user, WASP allows developing an optimal long-term plan for the development of the power generation system. Limitations may include insufficient fuel resources, gaseous waste emissions, system reliability requirements, and other factors. Optimal development is determined by minimizing total reduced costs. The energy assessment program ENPEP-balance, which is also widely used in developing countries, allows for a multi-faceted assessment of energy system development strategies. The ENPEP-balance software package includes the following modules: energy demand assessments (MAED), determination of prices that ensure the balance of supply and demand and drawing up a balance of supply and demand for energy in market conditions; optimization of the development of the electric power sector (WASP), assessment of the environmental impact of a certain energy system. In Ukraine, scientists of the Council for the Study of Productive Forces of the National Academy of Sciences of Ukraine [3], the Institute of General Energy of the National Academy of Sciences of Ukraine [1, 2, 10–15], Institute of Economics and forecasting of the National Academy of Sciences of Ukraine [16], Kyiv National Aviation University [17] and other scientific institutions [18, 19] were engaged in solving these problems. Mathematical models for forecasting energy consumption have been developed at the Institute of General Energy (IZE) and the former Institute of Energy Conservation Problems of the National Academy of Sciences of Ukraine for more than 20 years. The optimization model of the fuel and energy complex of Ukraine and its branch systems, developed under the leadership of Kulyk in 1992 [20]. This model is based on an optimization and simulation approach to the medium-term forecast of the development of Ukraine’s energy complex. The methodological developments of IZE specialists for forecasting energy consumption levels were used in the development of the Energy Strategy of Ukraine until 2030 in the sections “Forecasting Ukraine’s demands for fuel and energy resources” and “Prospective fuel and energy balances. Import–export policy and energy diplomacy of the country”.

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3 Purpose and Objectives of the Study The purpose of this study is the development of a two-stage method of forecasting demand for thermal energy using the direct account method at two levels: TOP−level (country or region) and DOWN−level (consumer groups: energy-intensive industries, population and social sphere). DOWN−level allows to use forecasts of production volumes of goods and services supplied in natural dimensions (not in cost dimensions).Thanks to this, a possible reduction in energy consumption in the case of implementing measures to increase the energy efficiency of energy-intensive industries and the provision of services is more accurately taken into account. The task of this study is to use a two-stage method based on the direct account method, provided there is retrospective statistical information on the consumption of energy resources by energy-intensive types of production, the provision of heating, ventilation, hot water supply services and the provision of communal needs of the population and the social sphere.

4 An Improved Direct Calculation Method for Forecasting the Thermal Energy Demand, Taking into Account the Energy Saving Potential The total forecast demand for energy resources Est in the country as a whole is generally calculated according to the following algorithm: E st =

E

t E tpr m + E tpop + E ss ,

(1)

m

where t–forecast year; m−type of product or service, according to the current clasE sifier of product types; m E tpr m —forecast of total energy consumption by types m of production of goods or service for year t; E tpop —forecast of energy resource t —forecast of energy resource consumption by the population (pop) for year t; E ss consumption for the social sphere (ss) for year t. Forecast of total energy consumption by types m of production of goods or services for year t is determined by the formula: E m

E tpr m =

E m

t bbas pr m V pr −

E

/\E tpr m ,

(2)

m

where bbas pr m —specific thermal energy consumption for the production of the m-th type t —the volume of production of goods or provision of product in the base year bas; V pr of services in the forecast year t,Ewhich is accepted according to the estimates of expert economists (Delphi method); m /\E tpr m – total forecast energy saving potential in

Two-Stage Method for Forecasting Thermal Energy Demand Using …

75

the production of the type m of product in the year t; is determined according to the directions given in [21–41]. This energy saving potential is determined according to DSTU [42–44] according to the method given in monograph [45]. Forecast values of the specific consumption of energy resources for the future can be expected taking into account the global trends of transition to other technologies and their actual implementation volumes in Ukraine [46, 47]. Forecast of energy resource consumption by the population for year t are determined as follows: t t t E tpop = E h. pop + E hw. pop + E com. pop ,

(3)

t where E h. pop —thermal energy demand for residential buildings heating by the poput lation; determined according to [45, 48–52]; E hw. pop —thermal energy demand for t hot water supply by the population [48–50]; E com. pop —thermal energy demand for washing and other household needs by the population [48–50]. The formation of the amount of energy consumption by the population is carried out under the influence of both external and internal factors. A specific feature of the energy consumption system in the sphere of housing and communal services for the population is its high dependence on the demand for various services of enterprises and institutions of various subordinates, i.e. the amount of fuel and energy use depends on the lifestyle of the population (income), its size and structure (urban, rural) and other demographic characteristics (gender, age). In addition, energy consumption is influenced by such external factors as climatic and regional conditions, landscaping of cities, urban-type villages and rural settlements, their planning and construction, engineering equipment of buildings. The internal factors that determine the consumption of energy resources include the peculiarities of energy use in the residential and communal services, the impact on energy consumption of social, economic and environmental indicators of its development, the volume and structure of services, the level of energy efficiency and the possibility of realizing energy saving reserves, the characteristics of energy-using installations, equipment and devices, their modes of operation, etc. The following algorithm is used for forecast of energy resource consumption for the social sphere for year t (hospitals, schools and pre-school educational institutions, sanatoriums, preventive clinics, dormitories of educational institutions of various levels, homes for the elderly, etc.): t t t t t E ss = E h.ss + E vent.ss + E hw.ss + E com.ss ,

(4)

where t E h.ss —forecast thermal energy consumption in the social sphere for heating needs in the year t, thousand Gcal; determined according to [48–50]; t E vent.ss —forecast thermal energy consumption in the social sphere for ventilation needs in the year t, thousand Gcal; determined according to [48–50];

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t E hw.ss —forecast thermal energy consumption in the social sphere for the needs of hot water supply in the year t, thousand Gcal; determined according to [48–50]; t E com.ss —forecast thermal energy consumption in the social sphere for communal needs (laundry, dishwashing, sanitary and hygienic needs) in the year t, thousand Gcal; determined according to [48–50]. The levels of thermal energy demand for the forecasted and current years were calculated for non-profit enterprises and organizations based on the specific thermal energy consumption for the production of a unit of products or services for non-commercial institutional orders (schools, kindergartens, budget hospitals, etc.) according to the norms of thermal energy consumption per unit measurement of communal and household needs.

5 Two-Stage Method of Forecasting Thermal Energy Demand Based on the Direct Account Method A two-stage method of forecasting the energy resources demand was proposed by Kulyk to solve the problem of the difference in forecasting results at the TOP— and DOWN—levels, calculated for the same energy system (energy consumption by country and types of economic activity or types of products, energy consumption by region and types of economic activity or types of products in the region). According to [13], the existing methods of forecasting the demand for energy resources (regression analysis, direct calculation, normative method) at the TOP— and DOWN—levels use different indicators of energy efficiency. At the country level–energy intensity of Gross Domestic Product (GDP), thermal energy intensity of GDP, etc. At the level of types of production and provision of services–the energy intensity of the production of products and services provided. These indicators are determined according to normative documents [42–44]. At the first stage, using the method of direct calculation, taking into account energy-saving potentials in energy-intensive technologies, the forecast volumes of thermal energy consumption by groups of consumers are determined according to formulas (1)–(4) for DOWN- level, as well as the forecast of thermal energy consumption at TOP- level equal according to its specific thermal energy consumption for the production of products and of the country (region) or the normative method due to the thermal capacity of GDP: ( bas ) t bas−t E Tt O P = eGbasD P × VGt D P − /\E Tbas−t O P = eG D P − /\eG D P VG D P , where bas—base year; eGt D P —calorific value of GDP of the base year bas; VGt D P —forecast GDP of the country in year t;

(5)

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77

/\E Tbas−t O P —the overall energy saving potential, including structural and technological components, at the country level, in year t relative to the base year bas; /\eGbas−t D P —decrease in the energy intensity of GDP in year t relative to the base year bas. At the second stage, the forecasts obtained at the previous stage are compared. In the case of a difference of more than 5%, they must be reconciled, since the choice of one forecast will lead to an overestimation or underestimation of the forecasted energy consumption. According to Kulyk’s forecast decision matching method [11], two value vectors are formed: a forecast for year t at the TOP-level and a total forecast at the DOWNlevel by types of products, services provided in the social sphere and population. The difference between these forecasts is determined. Consumer groups are aggregated and their minimum and maximum number is determined. The final value of the TOP-level indicator according to [13] is determined by the following formula: ( ) ( ) ( ) YTt O P = YTt O P n tmin × 1 − /\n t + YTt O P n tmax /\n t .

(6)

6 Analysis of Results Obtained Forecast of the thermal energy demand for the energy-intensive productions for the long-term perspective by the direct calculation method 51 types of products and services for thermal energy consumption from the state statistical reporting of Ukraine were analyzed in order to select the types of production that form the country’s energy consumption levels [53, 54]. This list includes energy-intensive products that had high specific thermal energy consumption during 2006–2015. As a result, the following industries were selected: electricity generation at thermal power plants and combined heat and power plants, coal mining, oil recovery, primary oil processing, iron and steel production, ferrous metal rolling, pipe production, coke production, alumina and aluminum production, productions of ammonia, caustic soda, urea and ammonium nitrate, oxygen production, productions of butter, sugar, foodstuff oil, alcohol and beer. The thermal energy consumption for selected 19 types of thermal-intensive products in 2015 were analyzed. The production forecast for selected types was developed on the basis of the analysis of the production development of selected product types for 2015–2020 and is given in Table 1. This forecast was made at the end of 2021 based on the analysis of actual statistical data for 2020 and corresponds to a pessimistic scenario of the development of Ukraine’s economy, taking into account the gradual stabilization after the COVID- pandemic. The production forecast given in the monograph [45] was based on forecast indicators for 2020 and is an optimistic scenario taking into account the development of Ukraine’s export potential.

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Table 1 Production forecast of certain types of thermal-intensive products until 2040 under a pessimistic scenario (pandemic, insignificant economic growth) No Types of products 1

2015 fact

Electricity produced by TPPs and CHPs for 60.9 general use, billion kWh

2020 2025 2030 2035 2040 fact 54.4

35.1

19.6

19.6

19.6

2

Hard coal, million tons

39.7

24.2

44.9

46.3

43.3

41.8

3

Crude oil and gas condensate, million tons

2.5

2.5

2.5

2.5

2.5

2.5

4

Primary processing of oil (including gas condensate), million tons

2.9

2.5

2.5

2.5

2.5

2.5

5

Coke from coal, million tons

11.6

9.5

13.4

14.3

15.0

16.0

6

Cast iron, million tons

21.9

20.2

22.0

24.0

27.0

29.0

7

Rolled finished ferrous metals, million tons

24.5

26.0

28.0

30.5

32.5

35.0

1.2

1.4

1.6

1.8

8

Pipes, etc. of steel, million tons

1.0

1.0

9

Ammonia, thousand t

2168

2304 2350 2400 2450 2500

10

Caustic soda, thousand t/thousand t of NaOH

35.7

H.d

47

67

87

107

11

Alumina, million tons

1.5

1.6

1.8

2.0

2.2

2.5

12

Oxygen, billion m3

2.9

3.0

3.0

3.0

3.0

3.0

13

Urea, million tons

0.98

1.16

1.16

1.18

1.19

1.2

14

Ammonium nitrate, thousand tons

361

548

650

750

800

820

15

Butter, thousand tons

102.0

87.5

87.5

87.5

87.5

87.5

16

Sugar, thousand tons

1459

1000 1100 1250 1400 1500

17

Food oils, thousand tons

3716

6100 6100 6100 6100 6100

18

Ethyl alcohol, thousand dal

10,778 7384 7400 7500 7500 7500

19

Beer, million dal

195

181

182

185

190

195

Table 2 gives an estimate of the thermal energy saving potential, according to the direction of reduction of the thermal energy consumption of certain types of production [55]. The specific thermal energy consumption was calculated taking into account the energy saving potential, the method of determining which is provided in [45] (Table 3). The forecast of the thermal energy demand until 2040 for the DOWN- level was determined using the improved direct calculation method (Table 4) based on the database of the forecast of the production of selected types of products (Table 1), the estimated potential for thermal energy saving (Table 2) and the forecast specific thermal energy consumption for the production of these products (Table 3).

Two-Stage Method for Forecasting Thermal Energy Demand Using …

79

Table 2 The thermal energy saving potential for certain types of production until 2040, thousand Gcal (1 Gcal = 4,1868 GJ) No

Types of products

2020

2025

2030

2035

2040

1

Electricity produced by TPPs and CHPs for general use, billion kWh

−

−

−

−

−

2

Hard coal, million tons

10.3

20.5

30.8

41.1

51.4

3

Crude oil and gas condensate, million tons

6.9

11.7

16.9

23.0

24.2

4

Primary processing of oil (including gas condensate), million tons

57.75

115.5

173.5

231.2

289

5

Coke from coal, million tons

112.5

207.7

314.6

405

552

6

Cast iron, million tons

24.2

61.6

115.2

183.6

284.2

7

Rolled finished ferrous metals, million tons

15.6

28.0

42.7

58.5

70.0

8

Pipes, etc. of steel, million tons

4.2

11.0

19.9

30.7

40.0

9

Ammonia, thousand t

36.7

85.8

135.1

184.4

233.8

10

Caustic soda, thousand t/thousand t of NaOH

0.0

11.8

25.9

45.0

68.8

11

Alumina, million tons

9.2

21.2

29.2

40.9

56.5

12

Oxygen, billion m3

2.6

5.4

8.1

10.8

14.4

13

Urea, million tons

10.3

20.4

30.7

40.8

50.8

14

Ammonium nitrate, thousand tons

0.6

1.3

2.2

3.1

3.9

15

Butter, thousand tons

3.9

7.7

11.4

15.1

18.6

16

Sugar, thousand tons

166.7

204.3

214.1

304.2

399.3

17

Oils, thousand tons

104.3

126.5

154.2

185.1

209.6

18

Ethyl alcohol, thousand dal

34.5

53.8

70.9

89.1

110.9

19

Beer, million dal

17.0

26.3

31.7

38.6

47.5

Total

579.4

1003.4

1403.7

1897.2

2484.6

Forecasting the thermal energy demand for the long-term perspective by a two-stage method The forecast of thermal energy consumption at two levels of the country’s economy before and after the agreement is given in the Table 5. Explanation of Table 5. The DOWN- level forecast was determined for three groups of consumers. The largest consumer of thermal energy is the population. The difference in thermal energy consumption between the TOP- and DOWN- levels was 11.36% in 2015. This difference decreased to 5% in 2020. Taking into account the actual data of 2020, the thermal energy forecast was updated and presented in the monograph [45]. The decrease in thermal energy consumption by the population during 2025–2035 corresponds to the current trend in the country, which is associated with a full-scale war against Ukraine, the destruction of housing, population migration to rural areas, western regions of the country and to other countries. The productions and providing services, which create the largest gross domestic product,

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Table 3 Forecast of specific thermal energy consumption for the production of certain types of energy-intensive products until 2040, Mcal/t (1 Mcal = 4,1868 MJ) No Types of products 1

Electricity produced by TPPs and CHPs for general use, billion kWh

2015 fact 49.5

2020 fact 49.5

2025 49.5

2030 49.5

2035 49.5

2040 49.5

2

Hard coal, million tons

29.8

29.7

29.6

29.5

29.5

29.3

3

Crude oil and gas condensate, million tons

64.2

59.7

57.1

54.6

52.0

51.4

4

Primary processing of oil (including gas condensate), million tons

155.6

132.5

109.4

86.2

63.1

40.0

5

Coke from coal, million tons

279.0

270.0

263.5

257.0

252.0

244.5

6

Cast iron, million tons

59.8

58.6

57.0

55.0

53.0

50.0

7

Rolled finished ferrous metals, million tons

20.8

20.2

19.8

19.4

19.0

18.8

8

Pipes, etc. of steel, million tons

187.2

183.0

178.0

173.0

168.0

165.0

9

Ammonia, thousand t

530.9

515.0

494.4

474.6

455.6

437.4

10

Caustic soda, thousand t/thousand t of NaOH

3651.1

3541.6 3399.9 3263.9 3133.4 3008.0

1422.6

1417.5 1412.0 1408.0 1404.0 1400.0

11

Alumina, million tons

12

Oxygen, billion m3

90.3

89.4

88.5

87.6

86.7

85.5

13

Urea, million tons

355.8

346.9

338.2

329.8

321.5

313.5

14

Ammonium nitrate, thousand tons

15

Butter, thousand tons

16

Sugar, thousand tons

17

Oils, thousand tons

18

Ethyl alcohol, thousand dal

19

Beer, million dal

40.2

39.2

38.2

37.3

36.3

35.4

211.17

201.0

194.0

188.5

183.0

175.0

1377.4

1308.5 1295.7 1281.0 1267.2 1239.7

468.6

445.2

440.5

435.8

431.1

426.4

39.9

37.9

37.5

37.1

36.7

36.3

1509.3

1433.8 1418.7 1403.7 1388.6 1373.5

will be restored first. The recovery and growth of thermal energy consumption by the population is expected until 2040, with an increase in its quantity compared to 2022. When using the traditional normative method in order to reduce the differences in forecasts between the TOP- and DOWN- levels, the consumption of thermal energy by the population is excluded from the calculation of the agreed level. First, the TOP and DOWN levels are agreed without the population, and the next stage takes into account consumption by the population. Comparing the qt indicator (coefficient of the distribution of the agreed level of energy consumption by consumers) from Table 5 and a similar indicator when forecasting thermal energy by a two-stage method based on the traditional regulatory method [45], it can be seen that when using the direct calculation method, the qt

Two-Stage Method for Forecasting Thermal Energy Demand Using …

81

Table 4 Forecast of the thermal energy demand for the production of products until 2040, thousand Gcal (1 Gcal = 4,1868 GJ) No Types of products 1

2015 fact

Electricity produced by TPPs and 3.4 CHPs for general use, billion kWh

2020 fact

2025

2030

2035

2040

2.7

1.7

1.0

1.0

1.0

2

Hard coal, million tons

1027.4 718.7

1329.0 1365.9 1273.0 1224.7

3

Crude oil and gas condensate, million tons

72.0

67.0

64.1

61.2

58.3

57.6

4

Primary processing of oil 451.6 (including gas condensate), million tons

331.3

273.5

215.5

157.8

100

5

Coke from coal, million tons

3236.4 2565.0 3530.9 3675.1 3780.0 3912.0

6

Cast iron, million tons

1309.6 1183.7 1254.0 1320.0 1431.0 1450.0

7

Rolled finished ferrous metals, million tons

509.6

525.2

554.4

591.7

617.5

658.0

183.0

213.6

242.2

268.8

297.0

8

Pipes, etc. of steel, million tons

187.2

9

Ammonia, thousand t

1151.0 1186.5 1161.8 1139.0 1116.3 1093.5

10

Caustic soda, thousand t/thousand t of NaOH

130.5

H.d

159.8

218.7

272.6

321.9

11

Alumina, million tons

2107.0 2197.1 2541.6 2816.0 3088.8 3500.0

12

Oxygen, billion m3

258.2

263.8

265.8

263.4

260.8

257.4

13

Urea, million tons

348.7

402.4

392.3

389.1

382.6

376.2

14

Ammonium nitrate, thousand tons

14.5

21.5

24.8

27.9

29.1

29.0

15

Butter, thousand tons

149.3

152.6

148.8

145.1

141.5

137.9

16

Sugar, thousand tons

2010

3167

3239

3395

3497

3595

17

Oils, thousand tons

1530

1981

1982

2048

2127

2119

18

Ethyl alcohol, thousand dal

431

654

841

939

1022

1118

19

Beer, million dal

303

323

411

421

444

481

16.11

18.55

19.31

20.00

20.77

Selected products together, million 15.45 Gcal

indicator is smaller. That is, energy consumption forecasts that agreed between the TOP-and DOWN-levels had a smaller discrepancy.

7 Conclusions This article shows another methodological approach of applying the two-stage method of forecasting the energy resources demand using the direct calculation method at the DOWN- level in the example of thermal energy consumption. The authors’ previous articles showed the application of a complex method (in two-stage

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Table 5 Forecast of thermal energy consumption until 2040 calculated using the two-stage method, million Gcal (1 Gcal = 4,1868 GJ) No Indicators

2015 fact

2020 fact

2025

2030

2035

2040

1

Forecast of consumption by 15.4 types of products–Fd1

16.1

18.6

19.3

20.0

20.8

2

Consumption by the population–Fd2 = Fdi max

160.8

170.0

154.4

147.9

147.6

178.1

3

Other services−Fd3

12.6

12.9

13.5

14.0

15.3

16.3

I

DOWN level together (E №1–3)−Fd t

188.8

198.8

186.5

181.2

183.0

215.2

II

In total, TOP-level in the country–FT t

213

209.3

194.3

188.8

190.6

224.2

% Fd t of FT

11.36

5.0

4.0

4.0

3.99

4.0

24.2

10.5

7.8

7.6

7.6

9.0

t

Difference Rt = FT t −Fd

t

kt = Fd t / Fdi max

t

1.17

1.169

1.208

1.225

1.24

1.208

/\nt = kt –[kt ]

0.17

0.169

0.208

0.225

0.24

0.208

nmin = t

[kt ]

+1

2

2

2

2

2

2

nmax t = nmin t + 1

3

3

3

3

3

3

S(nmin t ) za [13, 45]

0.5

0.5

0.5

0.5

0.5

0.5

t)

S(nmax za [13, 45]

0.41667 0.41667 0.41667 0.41667 0.41667 0.41667

Yt (nmin ) = FT t −S(nmin t ) Rt

200.9

204.05

190.4

185.0

186.8

219.7

Yt (nmax ) = FT t − −S(nmax t )Rt

202.9

204.92

191.05

185.63

187.43

220.45

YT t = Yt (nmin )(1−/\nt ) + 201.24 Yt (nmax ) /\nt

204.20

190.53

185.14

186.95

219.87

qt = YT t /Fd t

1.0271

1.0216

1.0217

1.0216

1.0217

1.0659

or three-stage modifications) using only the traditional normative method, in which the main criterion is the energy intensity of the gross added value of a separate section of the economy. The new methodological approach of forecasting is carried out by groups of consumers of energy resources: production of products, provision of services and population. This approach provides an opportunity to more widely use the two-stage method based on available input data with different aggregation.

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References 1. Kulyk, M.N.: Methods of System Analysis in Energy Research, p. 198. Naukova dumka, Kyiv, Ukraine (1987). 2. Dobrovolskyi, V.K., Stohnii, O.V., Kostiuk, V.O., Kaplin, M.I.: Economic and Mathematical Modeling of Energy Systems, p. 250. Naukova dumka, Kyiv, Ukraine (2013). 3. Pyryashvyly, B.Z., Voronchuk, M.M., Halynovskyi, E.Y.: Simulation Modeling in the Energy Sector. In: Danylyshyn, B.M. (eds.) p. 207. Naukova dumka Kyiv, Ukraine (2008). 4. Wanke, P., Saliby, E.: Top-down or bottom-up forecasting?. Pesqusia Operacional. 3(27), 591– 605 (2007). 5. Idowu, S., Saguna, S., Ahlund, C., Schelen, O.: Forecasting heat load for smart district heating systems: a machine learning approach (2014). https://core.ac.uk/download/pdf/205368718.pdf 6. Bansal, A., Rompikuntla, S.K., Gopinadhan, J., Kaur, A., Kazi, Z.A.: Energy consumption forecasting for smart meters (2015). https://arxiv.org/ftp/arxiv/papers/1512/1512.05979.pdf 7. Saloux, E., Candanedo, J.A.: Forecasting district heating demand using machine learning algorithms. Energy Procedia. 149, 59–68 (2018). https://www.sciencedirect.com/science/article/ pii/S187661021830465X. https://doi.org/10.1016/j.egypro.2018.08.169 8. Leoni, P., Geyer, R., Schmidt, R.: Developing innovative business models for reducing return temperatures in district heating systems: approach and first results. Energy, 195, 116963 (2020). https://doi.org/10.1016/j.energy.2020.116963 9. Idowu, S., Saguna, S., Åhlund, C., Schelén, O.: Applied machine learning: forecasting heat load in district heating system. Energy Build. 133, 478–488 (2016). https://doi.org/10.1016/j. enbuild.2016.09.068. 10. Kostyukovskyi, B.A., Maksimets, A.A., Spitkovskyi, A.I., Sas, D.P., Parasyuk, M.V.: Formation of the coordinated prognoses of economy and energetics development with the use of optimization models. Probl. Gen. Energy. 2(18), 21–23 (2008). 11. Kulyk, M.M.: Methoads for adjusting predictive decisions. Probl. Gen. Energy. 2(37), 5–14 (2014). 12. Maliarenko, O.Y., Maistrenko, N.Y., Stanytsina, V.V.: Advanced technical approach to identify appropriate energy saving potential in energy-intensive economic activities. Probl. Gen. Energy. 3(42), 23–31 (2015). https://doi.org/10.15407/pge2015.03.023 13. Kulyk, M.M., Maystrenko, N.Y., Malyarenko, O.Y.: Two-stage forecasting method of the future energy demand. Energy Technol. Resour. Conserv. (5–6), 25−33 (2015). 14. Maistrenko, N.Y.: Improved four-level methodology for forecasting the levels of energy consumption with regard for structural changes in the economy. Probl. Gen. Energy. 3(50), 15–22 (2017). https://doi.org/10.15407/pge2017.03.015 15. Kuts, H.O.: Mathematical model of development forecast of heating systems in the country. Probl. Gen. Energy. 3(42), 17–22 (2015). https://doi.org/10.15407/pge2015.03.017 16. Podolets, R.: Energy modeling: foreign experience and guidelines for prospective research in Ukraine. Econ. Forecast. 1, 126−140 (2006). 17. Kasianova, N.V., Levshova, Y.O.: Comprehensive model of energy consumption assessment in the region. Collect. Sci. Pap. Donbass State Eng. Acad. 2, 164–171 (2014). http://www.dgma. donetsk.ua/science_public/science_vesnik/%E2%84%962(14%D0%95)_2014/article/22.pdf 18. Bratkovska, K.O.: About the energy model of sustainable heat consumption. Effic. Econ. 11 (2015). http://www.economy.nayka.com.ua/?op=1&z=4592 19. Blinov, I.V., Parus, E.V., Ivanov, G.A.: Simulation modeling of the banal electricity market taking into account system restrictions on the parameters of the UES of Ukraine. Tech. Electrodyn. 6, 72–79 (2017). https://doi.org/10.15407/techned2017.06.072. http://dspace.nbuv.gov. ua/handle/123456789/158979 20. Kulyk, M.N., Yufa, A.Y., Dunaev, V.N. et al.: Optimization of the republican fuel and energy complex and its branch systems. In: Tonkal, V.E. (eds.) p. 216. Naukova dumka, Kyiv, Ukraine (1992).

84

O. Maliarenko et al.

21. Kulyk, M.M., Horbulin, V.P., Kyrylenko, O.V.: Conceptual approaches to energy development of Ukraine (analytical materials), p. 78. Institute of General Energy of the National Academy of Sciences of Ukraine, Kyiv, Ukraine (2017). 22. Dolinsky, A.A., Basok, V.I., Bazieiev, Y.T.: Heating strategy for settlements of Ukraine. For the discussion of projects of Energy Strategy of Ukraine until 2020, 2030 and 2035, vol. 4, pp. 98−102. Visnyk of the National Academy of Sciences of Ukraine (2015). 23. Karp, I.N., Nikitin, E.E.: Ways to Solve the Problems of Communal Energy, vol. 6, pp. 16−22. Housing and communal economy of Ukraine (2011). 24. Energy Technology Perspectives. Scenarios & Strategies to 2050, p. 710. OECD/International Energy Agency, France (2010) 25. Maliarenko, O.Y., Maistrenko, N.Y.: Energy performance indicators and determining energy saving potential in manufacturing processes. Energy Technol. & Resour. Sav. 3, 18–28 (2015). https://etars-journal.org/index.php/journal/article/view/144/102 26. Energy strategy of Ukraine for the period until 2035. Security, energy efficiency, competitiveness. https://zakon.rada.gov.ua/laws/show/605-2017-%D1%80#Text 27. Dubovsky, S.V.: Energy-Economic Analysis of Interconnected Systems of Electricity and Heat Generation, p. 182. Naukova dumka, Kyiv, Ukraine (2014). 28. Types of thermal power plants. In: Plachkov, I.S. (eds.) Development of heat and hydropower. Book 3. http://energetika.in.ua/en/books/book-3/part-1/section-1/1-2. 29. Zaporozhets, A., Eremenko, V., Serhiienko, R., Ivanov, S.: Methods and hardware for diagnosing thermal power equipment based on smart grid technology. In: Shakhovska, N., Medykovskyy M. (eds.) Advances in Intelligent Systems and Computing III. CSIT 2018. Advances in Intelligent Systems and Computing, vol. 871, pp. 476–489. Springer, Cham, https://doi.org/10.1007/978-3-030-01069-0_34 30. Chakravarty, K., Kumar, S.: Increase in energy efficiency of a steel billet reheating furnace by heat balance study and process improvement. Energy Rep. 6, 343–349 (2020). https://doi.org/ 10.1016/j.egyr.2020.01.014 31. Reuter, M., Patel, M.K., Eichhammer, W., Lapillonne, B., Pollier, K.: A comprehensive indicator set for measuring multiple benefits of energy efficiency. Energy Policy, 139. https://doi. org/10.1016/j.enpol.2020.111284 32. Dolinsky, A.A., Basok, B.I., Bazeev, E.T., Pyrozhenko I.A.: Utilities of Ukraine: state, problems, ways of modernization, vol. 2, pp. 820. Kyiv, Ukraine 33. Klimenko, V.N., Mazur, A.I., Sabashuk, P.P.: Cogeneration systems with heat engines: reference manual. In: 3 parts. Part 1. General issues of cogeneration technologies. CPI ALKON, the National Academy of Sciences of Ukraine, p. 560. Kiyv, Ukraine. 34. Dubovsky, S.V., Dubovskoy, S.V., Levchuk, A.P., Kadenskiy, N.Y.: Increasing of maneuver capabilities of power system due to heat pump controller’s implementation at CHP. Probl. Gen. Energy, 4(35), 16–23 (2013). 35. Shubenko, O.L.: The transfer of a small cogeneration plant to burning local fuel in volumes that ensure its operation in the summer. Energy Sav. & Energy Audit. 4, 17−26 (2014). 36. Artemchuk, V.O., et al.: Theoretical and Applied Bases of Economic, Ecological and Technological Functioning of Energy Objects. TOV «Nash format», Kyiv, Ukraine (2017). https://dsp ace.nau.edu.ua/handle/NAU/37721 37. Maliarenko, V.A.: Cogeneration Technologies in the Energy Sector Based on the Use of Lowpower Steam Turbines: Monograph. Institute of Engineering Problems of the NAS of Ukraine, Kharkov, Ukraine (2014). 38. Kesova, L.O., Horskyi, V.V.: Improving the efficiency of thermal power plants using lowcost technologies. Probl. Gen. Energy, 2(53), 60–64 (2018). https://doi.org/10.15407/pge2018. 02.060 39. Gnidoy, M.V., Maliarenko, O.Y.: Energy Efficiency and Determination of Energy Saving Potential in Oil Refining, p. 182. Naukova Dumka, Kiev, Ukraine (2008). 40. Maliarenko, O. Y., Horskyi, V.V.: An improved approach to the evaluation of the efficiency of energy saving measures and technologies at the TPP. Probl. Gen. Energy, 4(59), 24–31 (2019).

Two-Stage Method for Forecasting Thermal Energy Demand Using …

85

41. Horskyi, V.V., Maliarenko, O.Y.: Estimation of energy saving potential for coal-fired thermal power plants in the implementation of innovative technologies. In: Research Practice Conference “Science, Technology and Technology: Global and Modern Trends.” Prague, Czech Republic. 42. DSTU 2420–94.: Energy Conservation. Terms and Definitions, p. 15. Kyiv, Ukraine (1994). 43. DSTU 3755–98.: Nomenclature of Energy Efficiency Indicators and Procedure for Their Inclusion in Regulatory Documents. Kyiv, Ukraine (1998). 44. DSTU 7674:2014 Energy Saving. The energy intensity of the technological process of generating electrical and thermal energy released by a thermal power plant. Method of determination, p. 34. Kyiv, Ukraine (2014). 45. Kulyk, M.M., Maliarenko, O.Y., Maistrenko, N.Y., Stanytsina, V.V., Kuts, H.O.: Energy Efficiency and Energy Consumption Forecasting at Different Hierarchical Levels of the Economy: Methodology, Forecast Estimates Until 2040, p. 234. Naukova dumka, Kyiv, Ukraine (2021). 46. Energy Technology Perspectives 2010 Scenarios & Strategies to 2050, p. 710. OECD/ International Energy Agency, France (2010). 47. Danylov, M.I., Denysiuk, S.P., Zhovtianskyi, V.A.: Energy Saving Strategy of Ukraine: Analytical Reference Materials, Vol 2, p. 598. Akademperiodyka, Kyiv, Ukraine (2006). 48. KTM 204 Ukraina 244–94.: Norms and Instructions on the Regulation of Fuel and Thermal Energy Consumption for Heating Residential Premises and Public Buildings, as well as for Household Demands in Ukraine, p. 354. VAT «Ukrinzhproekt», Kyiv, Ukraine (2001). 49. KTM 204 Ukraina 244–94.: Guide to Norms and Instructions on the Regulation of Fuel and Thermal Energy Consumption for Heating Residential and Public Buildings, as well as for Household Demands in Ukraine, p. 17. VAT «Ukrinzhproekt», Kyiv, Ukraine (2001). 50. KTM 204 Ukraina 244–94.: Additions to the Norms and Instructions on the Regulation of Fuel and Heat Energy Consumption for Heating Residential and Public Buildings, as well as for Household Demands in Ukraine, p. 265. VAT «Ukrinzhproekt», Kyiv, Ukraine (2001). 51. Libanova, E.M. (ed.): Comprehensive Demographic Forecast of Ukraine until 2050, p. 138. Ukrainian Center for Social Reforms, Kyiv, Ukraine (2006). 52. Pozniak, O.V., Shevchuk, P.E.: Population prospects of Ukraine up to 2060. Demogr. Soc. Econ, 1(21), 72–84 (2014).https://doi.org/10.15407/dse2014.01.072 53. Statistical Yearbook of Ukraine for 2015, p. 575. State statistics service of Ukraine, Kyiv, Ukraine (2016). https://ukrstat.gov.ua/druk/publicat/Arhiv_u/01/Arch_zor_zb.htm 54. Report on Energy supply and consumption of Ukraine. Statistical Information/Economic statistics/Economic activity/Energy. State statistics service of Ukraine, Kiyv, Ukraine (2020). https:/ /www.ukrstat.gov.ua/ 55. Vovchak, V, Teslenko, O., Samchenko, O., Sushkova, D.: The best available technologies for housing and communal services in Ukraine. In: Yermilov, S. (eds.) Technology Selection Guide, p. 134. Polygraph plus, Kiyv, Ukraine (2016).

Optimization of Coal Products Supply for the Power Industry and the Country’s Economy Vitaliy Makarov , Mykola Kaplin , Mykola Perov , Tetiana Bilan , and Olena Maliarenko

Abstract The subject of the study is the directions and volumes of supply of coal products to the economy to ensure the country’s energy balance. The purpose of the article is to substantiate the forecasted economically feasible volumes of production of finished coal products in Ukraine, as well as its supply by import, while guaranteeing the energy security of the state to supply the necessary types of coal products to ensure the energy balance of the country. Research methods: linear programming for the development a model for optimizing the production of coal products, multi-criteria optimization and comparative analysis for the formation of options for the structure of coal fuel, expert evaluations for the formation of information base. Taking into account the potential of coal production by Ukrainian mines, scenarios for the development of the coal industry for the period up to 2040 have been developed, which take into account the factors of occupation of part of the territories of Donbas and optimal strategies for modernization of the industry. Using the model of optimal provision of the electric power industry with coal products, taking into account environmental restrictions, forecast calculations were made to provide the optimal fuel structure of thermal power enterprises of Ukraine for the baseline and pessimistic scenarios of the coal industry development. The calculations are based on the forecast of electricity production developed by NPC “Ukrenergo”. The calculations confirmed the possibility of meeting the needs of thermal power plants with coal products of domestic production until 2040 at the current level of development of the coal industry. Taking into account the forecast of coal consumption in the country’s economy and the potential of its production, the forecast balances of coal products under the baseline and pessimistic scenarios of the development of the coal industry of Ukraine for the period up to 2040 were developed. Keywords Coal industry · Coal products · Electric power industry · Mathematical model structure · Scenarios · Forecast · Balance

V. Makarov · M. Kaplin · M. Perov · T. Bilan (B) · O. Maliarenko General Energy Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_6

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1 Introduction In Ukraine, coal is the only energy resource that has sufficient reserves for hundreds of years, which determines its leading role in ensuring energy security requirements. Various aspects of the efficient functioning of energy systems in ensuring the energy security of the country are discussed in many works [1–17]. However, the current technological state of coal mining and beneficiation is extremely unsatisfactory and needs to be thoroughly updated. At present, the process of providing fuel for thermal power and the country’s economy requires special attention in view of the objectively existing instability of the structure of the coal fuel base in the country, significant uncertainty of the sectors of coal products consumption. The relevance of the work lies in the objective need for an adequate assessment of the role of coal in the fuel balance of Ukraine, due to many factors. First of all, these are the availability and accessibility of deposits, forecasts of the product structure of coal products, increasing the share of renewable energy sources in the fuel balance, infrastructural limitations of the import subsystem, the shortage of certain coal fuel brands in the world coal market. The need for systematic consideration of these factors is a determining prerequisite for the development of qualitative forecasts of technological development of the coal industry. In this regard, it is relevant to create mathematical models and software tools to optimize the technological development of the coal industry, taking into account modern environmental requirements [18–22]. Ukrainian and foreign scientists addressed various methodological issues in the field of coal industry development: Kiyashko (assessment of the efficiency of mines with different options for the use of cleaning equipment) [23], Kulyk and Alaverdyan (optimization of coal industry development) [24, 25], Pavlenko (forecasting the development of the coal industry with limited investments) [26], Yashchenko and Kosarev (technical development of mines) [27], Henderson (a model of supply and demand in the markets of coal products) [28], Suvala (a model of coal industry restructuring) [29] and others. However, these studies did not take into account the connection of the mining fund with processing enterprises and energy facilities, and therefore their results are fragmentary. Studies of this problem by foreign experts relate to the peculiarities of the functioning of the coal mining and processing industry of other countries, and do not take into account the conditions of energy supply of the Ukrainian economy, phased reorganization of the coal industry. Ukraine’s aspiration to integration processes with the countries of Western Europe will inevitably be accompanied by the introduction of more stringent environmental requirements for the operation of enterprises that produce, process and consume coal. Therefore, research on optimization of the structure of coal products of various technological purposes by its quality, taking into account the quality indicators of own reserves and imported coal, current and prospective requirements of consumers

Optimization of Coal Products Supply for the Power Industry …

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of thermal generation, taking into account environmental restrictions, is currently relevant. Providing fuel for the national economy is one of the main factors for forecasting the development of energy in the Energy Strategy of Ukraine for the period up to 2035 [30]. The issues of reliability of supply and efficiency of use of fuel resources at energy facilities are devoted in particular to the work of Krasnyansky [31] and others. Some aspects of the problem of fuel supply were studied in the Council for the Study of Productive Forces of Ukraine of the National Academy of Sciences of Ukraine (Piriashvili) [32, 33], the National Institute for International Security (Pirozhkov) [34], the Institute of Economics and Forecasting of the National Academy of Sciences of Ukraine (Lear) [35–37]. Some results of research carried out at the Institute of General Energy of the National Academy of Sciences of Ukraine, which were the basis for this work, are given in [38]. The expected trends in the provision of fuel for coal-fired thermal power plants and the strengthening of environmental requirements determine, firstly, the need for changes in the technological structure of electricity production; secondly, the improvement of the consumer quality of coal products, which can be ensured by optimizing the volume of coal supplies to coal beneficiation factories. Research on optimization of the structure of coal products of various technological purposes by its quality, taking into account quality indicators, current and future requirements of consumers of thermal generation, taking into account environmental constraints is currently relevant. In particular, the issues of reliability and efficiency of fuel resources use at energy facilities are discussed in [38]. The aim of the work is to substantiate the forecast economically feasible volumes of production of finished coal products in Ukraine, as well as its supply by import, while guaranteeing the energy security of the state to supply the necessary types of coal products to ensure the energy balance of the country. Linear programming methods were used to develop a model for optimizing the production of coal products, multicriteria optimization and comparative analysis for the formation of options for the structure of coal fuel, expert assessments for the formation of an information base.

2 Mathematical Model of Optimization of Finished Coal Production In developing the forecast of coal supply to the country’s economy, a mathematical model for optimizing the provision of high-quality coal products to the country’s economy was used, which takes into account coal products not only for the power industry, but also for other consumers by type of economic activity and population. In contrast to the known ones, the model combines a detailed consideration of the technical and economic indicators of the technological equipment of mines and processing plants with algorithms for coordinating the flows of all types of coal

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products, which made it possible to predict the structure of finished coal products with the provision of the necessary indicators of its quality in the technological chain “mine—beneficiation plant—consumer”. The model of optimization of provision of the country’s economy with coal products is a development of the model of optimal provision of the power industry with coal products [38], to which consumers of non-energy sectors of the economy are added. The target function of the mathematical model for optimizing the production of finished coal products is the sum of the total cost of coal products for the electricity sector S EN and the cost of coal products for other consumers S OSE S = SE N + SOSE .

(1)

The total cost of coal products supplied to the coal-fired thermal power plant (CTPP) is calculated in accordance with the established base price for coal products, taking into account the thermophysical characteristics of its components and transportation costs, which can be expressed by the formula [38]: (L ) G (N  ) (M )  k

S

=

EN

k

k

sikjl · xikjl .

(2)

(k=1) (i=1) ( j=1) (l=1)

The total cost of finished coal products received by consumers of non-energy sectors of the economy is calculated by the formula: G (N  ) (M ) k

S

OSE

=

k

sikj · xikj

(3)

(k=1) (i=1) ( j=1)

with restrictions: – on the weighted average caloric content of fuel reserves of each consumer N  M  k

γ · k

q kp

≥

k

i=1 j=1

N  M  k

qikj

·

xikj

/

k

xikj ≥ δ k · q kp ; k = 1, ..., G,

(4)

i=1 j=1

– on the potential capabilities of suppliers G 

xikj ≤ X i j ; i = 1, ..., Nk ; j = 1, ..., M,

k=1

– on the total volume of needs of each consumer’

(5)

Optimization of Coal Products Supply for the Power Industry … k k (N ) (M )

k

x ikj ≥ X ; k = 1, ..., G,

91

(6)

(i=1) ( j=1)

where G—number of consumers; N k —is the number of suppliers of coal products to the k-th consumer; M k —number of brands of coal products; L k —number of types of coal products; sikjl , sikj —price of 1 t of coal products for CTPP and other consumers respectively, UAH per ton; xikjl , xikj —volumes of coal products for CTPP and other consumers, respectively, tons; q kp —is the design value of the lower heating value of fuel for the k-th consumer, kcal/kg; γ k and δ k —are constant values that set, respectively, the upper and lower limits of fuel caloric content for the k-th consumer; k X —volumes of coal products in terms of fuel equivalent, tons.

3 Scenarios for the Development of the Coal Industry To determine the potential of coal mining in Ukraine, a mathematical model for optimizing the structure of mining capacities of the coal industry was used [20], which, in contrast to the well-known industry-wide balance optimization models that use economic indicators, is focused on increasing the production efficiency of the industry by the criterion of its overall productivity. The model, constructed as a mixed-integer programming problem, allowed us to determine the optimal set of options for the technical re-equipment of mines, which ensure the competitiveness of the industry in the world market and increase the level of energy security of the country, by the criterion of maximizing the volume of own production. Taking into account the potential of coal production, the following scenarios for the development of the coal industry are proposed (Table 1).

4 Optimal Structure of Coal Products for the Power Industry Using the model of optimal provision of the electric power industry with coal products, taking into account environmental restrictions [34], we performed forecast calculations of the optimal fuel structure for thermal power enterprises of Ukraine under the baseline and pessimistic scenarios of the coal industry development. The calculations, the results of which are given in Table 2, accepted the forecast of electricity production developed by NEC “Ukrenergo” [39]. In order to provide the electric power industry with fuel in the required volumes and with acceptable thermal and physical characteristics for the generation of the considered forecast volumes of electricity, it is necessary to modernize mines on the basis of new highly productive coal mining equipment.

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Table 1 Scenarios of coal production in the territories controlled by the Ukrainian authorities, thousand tons Scenarios

Coal grade

2020 (fact)

2025

2030

2035

2040

Optimistic

Coal for energy B

–

1425

1900

2280

2280

G

9032

16,955

19,425

26,260

26,260

DG

13,271

18,670

18,935

14,675

14,675

Total

22,303

37,050

40,260

43,215

43,251

Zh

385

2805

4325

5845

5845

K

6132

7200

10,050

12,140

10,640

Total

6517

10,005

14,375

17,985

16,485

All

28,820

47,055

54,635

61,200

59,700

G

–

16,955

19,425

21,130

21,130

DG

–

18,430

17,270

12,440

12,440

Total

–

35,385

36,695

33,570

33,570

Zh

–

2330

2425

2520

2520

K

–

7200

7200

7200

5700

Total

–

9530

9625

9720

8220

All

–

44,915

46,320

43,290

41,790

G

–

16,055

18,285

19,990

19,990

DG

–

14,915

13,195

8175

8175

Total

–

30,970

31,480

28,165

28,165

Zh

–

2330

2425

2520

2520

K

–

7200

7200

7200

5700

Total

–

9530

9625

9720

8220

All

–

40,500

41,105

37,885

36,385

Coal for coking

Basic

Coal for energy

Coal for coking

Pessimistic

Coal for energy

Coal for coking

In addition, it is extremely important to send the optimal volumes of mined coal to the beneficiation factories, which is a guarantee of providing power generating enterprises with fuel of the required quality with minimal financial costs. To produce 39.4 TWh of electricity in 2025, 18.5 million tons of commercial coal will be needed. In 2040, 14.5 million tons of coal will be needed to produce 34 TWh of electricity. In the period 2025–2040, coal enterprises will produce 21–24 million tons of finished coal products (FCP) for energy purposes under the baseline scenario of the

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Table 2 Optimum provision of the electric power industry with coal products up to 2040 Year

Mined coal of grades G and DG, thousand tons

Volumes of coal products, thousand tons

Generation, TWh

Demand for coal products, thousand tons

Expenditures for coal products, UAH mln

Emission tax, UAH mln

18,541.9

52,719

1138

Baseline scenario of coal industry development 2025

35,385

23,990

39.35

2030

35,460

22,944

38.87

17,272.6

49,727

1080

2035

33,570

21,605

38.87

16,789.5

48,703

1068

2040

33,570

21,268

33.96

14,463.4

41,932

921

54,282

1117

Pessimistic scenario of the coal industry development 2025

30,970

28,793

39.35

18,541.9

2030

30,245

20,178

38.87

17,272.6

49,940

1078

2035

28,165

25,847

38.87

16,789.5

50,862

1066

2040

28,165

19,624

33.96

14,463.4

42,328

943

coal industry development and 20–28 million tons—under the pessimistic scenario. Caloric content of finished coal products will be at the level of 20.6–22.5 mJ/kg.

5 Forecast Balances of Coal Products Coal remains the key fuel for electricity generation from TPPs and partially for electricity and heat supply from CHPs. Ferrous metallurgy enterprises will remain the leaders in coal consumption by industry, in particular in coke production. A significant share of coal is consumed by the cement industry. Substitution of coal burned in boilers with other fuels, introduction of renewable energy sources and heat pump installations will reduce coal consumption in agriculture and industry. Within the framework of the approach proposed in this paper, the coal industry is considered as a multi-product system of interconnected industries, coordinated in terms of volumes and quality indicators, which ensures the fulfillment of energy security conditions in the supply of coal products. Taking into account the forecast of coal consumption in the country’s economy [40, 41] and the potential of its production, the forecast balances of coal products for the basic and pessimistic scenarios of the development of the coal industry of Ukraine for the period up to 2040 were developed (Table 3). As we can see, the consumption forecast includes coal grades that are not mined in the territory controlled by Ukraine, in particular anthracite grades A and P, as well as PS grade, which is necessary for coke production. According to the baseline scenario of the coal industry development, in order to provide the country’s economy with coal products, it will be necessary to import

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Table 3 Forecast balances of coal products for the period up to 2040, thousand tons Indicators

2025

2030

2035

2040

Baseline scenario of coal industry development Amount of coal mined

44,915

46,320

43,290

41,790

Including by grades: G (G, DG)

35,385

36,695

33,570

33,570

A (A, P)

0

0

0

0

K (Zh, K)

9530

9625

9720

8220

PS

0

0

0

0

Coal consumption

37,873.9

40,153

42,203.3

41,864.7

Including by grades: G (G, DG)

27,698.2

28,783.5

29,803.9

28,408.0

A (A, P)

1200

1100

1100

1000

K (Zh, K)

8334.5

9625

9720

9787

PS

641.1

644.4

1579.4

2669.6

Imports

1841.1

1744.4

2579.4

5236.6

Including by grades: A (A, P)

1200

1100

1000

1000

PS

641.1

644.4

1579.4

2669.6

Exports

8882.3

7911.5

3666.1

5162

Including by grades: G (G, DG)

7686.8

7911.5

3666.1

5162

K (Zh, K)

1195.5

K (Zh, K)

1567

Pessimistic scenario of the coal industry development Amount of coal mined

40,500

41,105

37,885

36,385

Including by grades: G (G, DG)

30,970

31,480

28,165

28,165

A (A, P)

0

0

0

0

K (Zh, K)

9530

9625

9720

8220

PS

0

0

0

0

Coal consumption

37,873.9

40,153

42,203.3

41,864.7

Including by grades: G (G, DG)

27,698.2

28,783.5

29,803.9

28,408

A (A, P)

1200

1100

1100

1000

K (Zh, K)

8334.5

9625

9720

9787

PS

641.1

644.4

1579.4

2669.6

Imports

1841.1

1744.4

4318.3

5479.6

1638.9

243 1000

Including by grades: G (G, DG) A (A, P)

1200

1100

1100

PS

641.1

644.4

1579.4

Exports

4467.3

2696.5

Including by grades: G (G, DG)

3271.8

2696.5

K (Zh, K)

1195.5

K (Zh, K)

1567 2669.6

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1–1.2 million tons of anthracite, 0.6–2.7 million tons of coking coal of PS grade and in 2040—1.6 million tons of coking coal of G and K grades. Along with this, there is a possibility to export 3.7–8.8 million tons of gas coal of G and DG grades, as well as 1.2 million tons of coking coal of G and K grades in 2025. According to the pessimistic scenario of the coal industry development, in order to provide the country’s economy with coal products, it will be necessary to import 1–1.2 million tons of anthracite, 0.6–2.7 million tons of coking coal of PS grade, in 2040—1.6 million tons of coking coal of Zh and K grades and in 2035–2040—0.2– 1.6 million tons of G grade coal for the needs of metallurgy. Export opportunities are reduced to 2.7–3.3 million tons of G and DG gas coal in 2025–2030, and 1.2 million tons of Zh and K coking coal in 2025.

6 Conclusions The scenarios of coal industry development are presented, according to which the maximum coal production—61 million tons—will be reached in 2035 under the optimistic scenario. According to the baseline and pessimistic scenarios, the maximum production of 46 and 41 million tons, respectively, will be achieved in 2030. By 2040, due to the depletion of reserves, production will be reduced to 60, 42 and 36 million tons under the optimistic, baseline and pessimistic scenarios, respectively. Calculations of the volumes of provision of thermal energy with coal products of domestic production confirmed the possibility of meeting the needs of power plants until 2040 at the current level of development of the coal industry. To produce electricity in the amount of 39.4 TWh in 2025, 18.5 million tons of commercial coal will be needed. In 2040, 14.5 million tons of coal will be needed to produce 34 TWh of electricity. The developed balances of coal products in the economy of Ukraine show that under the baseline and pessimistic scenarios of the coal industry development it will be necessary to import 1–1.2 million tons of anthracite, 0.6–2.7 million tons of coking coal of PS grade and in 2040—1.6 million tons of coking coal of G and K grades. In addition, according to the pessimistic scenario, in 2035–2040, it will be necessary to import 0.2–1.6 million tons of G grade coal for the needs of metallurgy. At the same time, according to the baseline scenario, there is a possibility to export 3.7–8.8 million tons of G and DG gas-coal, and in 2025—1.2 million tons of coking coal of Zh and K grades, and in 2025—1.2 million tons of coking coal of grades Zh and K. The importance of the results is determined by both the security aspects of the overall energy balance of the country and the objectively existing need for significant changes in the structure of the coal industry, which requires significant investments. The predominant consumption of self-produced coal fuel will significantly increase the level of energy independence of the state, reduce the impact of crisis phenomena of the world economy, geopolitical factors of irresistible force.

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References 1. Bilan, T.R., Kaplin, M.I.: Security aspects of the balance of carbonaceous fuels under conditions of establishing the new fuel supply schemes in Ukraine. Probl. Gen. Energy 4(47), 23–29 (2016). https://doi.org/10.15407/pge2016.04.023 2. Kulyk, M.M., Kyrylenko, O.V.: The state and prospects of hydroenergy of Ukraine. Tech. Electrodyn 4, 56–64 (2019). https://doi.org/10.15407/techned2019.04.056 3. Bilan, T., Kaplin, M., Makarov, V., Perov, M., Novitskii, I., Zaporozhets, A., Havrysh, V., Nitsenko, V.: The balance and optimization model of coal supply in the flow representation of domestic production and imports. Energies 15, 8103 (2022). https://doi.org/10.3390/en1521 8103 4. Nechaieva, T.P.: Modeling ensuring demand-supply balance of the power system in conditions of significant renewable generation. Probl. Gen. Energy 1–2(68–69), 42–99 (2022). https://doi. org/10.15407/pge2022.01-02.042 5. Maliarenko, O.Ye., Maistrenko, N.Yu., Horskyi, V.V.: Forecast of fuel and coal consumption in Ukraine until 2040 by a complex method of forecasting energy consumption. Probl. Gen. Energy 3(66), 28–35 (2021). https://doi.org/10.15407/pge2021.03.028 6. Dubrovskyi, V.V., Shraiber, O.A.: World trends in the development of coal-fired thermal generation and their influence on the energy of Ukraine. Probl. Gen. Energy 2(61), 11–16 (2020). https://doi.org/10.15407/pge2020.02.011 7. Shulzhenko S.V.: Thermal power plant fuel consumption accounting using “negative” member method in linear programming model for optimal generation dispatch. Probl. Gen. Energy 3(58), 4–10 (2019). https://doi.org/10.15407/pge2019.03.004 8. Kostyukovskyi, B.A., Maksimets, A.A., Spitkovskyi, A.I., Sas, D.P., Parasyuk, M.V.: Formation of the coordinated prognoses of economy and energetics development with the use of optimization models. Probl. Gen. Energy 2(18), 21–23 (2008) 9. Nechaieva, T.P.: Model and structure of the long-term development of generating capacities of a power system with regard for the commissioning and decommissioning dynamics of capacities and changing their technical-and-economic indices. Probl. Gen. Energy 3(54), 5–9 (2018). https://doi.org/10.15407/pge2018.03.005 10. Kesova, L.O., Horskyi, V.V.: Enhancement of the efficiency of operation of heat and power plants witch using low-cost technologies. Probl. Gen. Energy 2(53), 60–64 (2018). https://doi. org/10.15407/pge2018.02.060 11. Energy security strategy. Approved by the order of the Cabinet of Ministers of Ukraine dated August 4, 2021 № 907. https://zakon.rada.gov.ua/laws/show/907-2021-%D1%80#Text. Accessed 3 December 2022 12. Diyak, I.V.: Energy security of Ukraine. Kyiv, p. 172 (2001) 13. Geets, V.M.: The concept of economic security of Ukraine. NAS of Ukraine, Institute of Economics. Prognostication, p. 56. Logos, Kyiv (1999) 14. Krykavskyi, E.V., Kosar, N.S., Moroz, L.A.: Marketing of energy supply. Lviv Polytechnic, Lviv, p. 196 (2001) 15. Stogniy, B.S., Kyrylenko, O.V., Denysyuk, S.P.: Energy security of Ukraine. Global and national challenges. From the physical and technical problem of Energy of the National Academy of Sciences of Ukraine. Ukr. encyclopedia knowledge, Kyiv, p. 408 (2006) 16. Kulik, M.M., Gorbulin, V.P., Kirilenko, O.V.: Conceptual approaches to energy development in Ukraine (analytical materials), p. 78. Institute of General Energy of the National Academy of Sciences of Ukraine, Kyiv (2017) 17. Piriashvili, B.Z., Voronchuk, M.M., Halynovskii, E.Y., Chirkyn, B.P., Shchepets, O.Y.: Simulation modeling in the energy sector. In: Danilishin B.M. (ed.), p. 303. Naukova dumka, Kyiv (2008) 18. Kaplin, M.I., Makarov, V.V., Bilan, T.R.: The balance-optimization model of the Ukrainian power sector and fuel industries mutually coordinated operation in the view of European environmental legislation. Probl Gen Energy 1(52), 5–11 (2018). https://doi.org/10.115407/pge 2018.01.005

Optimization of Coal Products Supply for the Power Industry …

97

19. Makarov, V.V., Kaplin, M.I., Perov, M.O.: Consideration of environmental constraints in modeling the development of coal industry. Probl. Gen. Energy 4(59), 36–44 (2019). https:// doi.org/10.115407/pge2019.04.036 20. Makarov, V.: Optimization of technological development of coal mining in Ukraine. Economic system development trends: the experience of countries of Eastern Europe and prospects of Ukraine: monograph, pp. 345–363. Baltija Publishing, Riga, Latvia (2018). https://doi.org/10. 30525/978-9934-571-28-2_18 21. Makarov, V.M.: Forecasting the development of the coal industry taking into account environmental constraints. Economic development: global trends and national peculiarities: collective monograph, pp. 100–118. Baltija Publishing, Kielce, Poland (2020). https://doi.org/10.30525/ 978-9934-588-61-7-8 22. Makarov, V., Kaplin, M., Bilan, T., Perov, M.: Modeling the coal industry technological development considering environmental restrictions. In: Zaporozhets A., Artemchuk V. (eds.) Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, vol. 346, pp. 153–165. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69189-9_9 23. Kiyashko, Yu.I.: Estimation of the efficiency of the mines in various applications of treatment equipment. Coal Ukr 5, 24–26 (2001) 24. Kulyk, M.M.: The role of coal in the formation of fuel and energy balances and optimization of the development of the coal industry in Ukraine. Probl Gen Energy 6, 7–16 (2002) 25. Alaverdian, L.M.: Economic-mathematical model of optimal development of the coal industry of Ukraine, pp. 121–123. Visnyk MNTU, seriia «Ekonomika», 1. (2010) 26. Pavlenko, I.I.: Mechanism of development of the coal industry with limited investments. Ekonomika promyslovosti 1, 93–97 (2007) 27. Yashchenko, Yu.P., Kosarev, I.V.: Scientific and technical solutions for the development of coal mines in Ukraine, p. 361. Logos, Kyiv (2014) 28. Henderson, J.M.: A short-run model for the coal industry. Rev. Econ. Stat. 37(4), 336–346 (1955) 29. Suwala, W.: Models of coal industry in Poland. Gospodarka surowcami mineralnymi 26, 41–52 (2010) 30. Energy strategy of Ukraine for the period until 2035. Approved by the order of the Cabinet of Ministers of Ukraine dated August 18, 2017 № 605. https://zakon.rada.gov.ua/laws/file/text/ 58/f469391n10.pdf. Accessed 3 Dec 2022 31. Krasnyansky, G.L.: Economic aspects of the development of the fuel and energy complex of Russia, p. 128. Publishing house AGN, Moscow (2020) 32. Piriashvili, B.Z., Chirkin, B.P., Galinovskyi, E.I., Chukayeva, I.K.: Ukraine’s energy supply and assessment of its impact on its economic development. Economic space and dynamics of development of productive forces of Ukraine: theoretical and methodological foundations of research: monograph, pp. 182–190. In: Danilishin B.M. (eds.) RVPS of Ukraine of the National Academy of Sciences of Ukraine (2008) 33. Piriashvili, B.Z., Chirkin, B.P.: Energy supply of Ukraine: state of affairs, main problems and ways of improvement. Energy Policy Ukr 6, 181–189 (2004) 34. Pirozhkov, S.I.: Energy security of Ukraine in the context of economic reforms. Strategy of economic development of Ukraine (2001) 35. Lear, V.E.: Analysis and forecasting of the energy balance of Ukraine. Econ. Forecasti. 2, 101–115 (2004) 36. Lear, V.E.: The energy balance of Ukraine is an equation of unknowns. Organizational and methodological aspects of the development of the consolidated energy balance of Ukraine. Energy Policy Ukr. 10(62), 4–9 (2005) 37. Lear, V.E., Podolets, R.Z.: Structural changes in energy markets and Ukraine’s prospective energy balance. In: Economy of Ukraine: strategy and policy of long-term development: monograph. Geets V.M. (eds.). Institute of Economic Forecasting of the National Academy of Sciences of Ukraine, pp. 771–792. Phoenix, Kyiv (2003) 38. Makarov, V., Makortetskyi, M., Perov, M., Bilan, T., Ivanenko, N.: Mathematical model of optimal support of thermal energy with coal products taking into account environmental

98

V. Makarov et al.

constraints. In: Zaporozhets A. (eds) Systems, Decision and Control in Energy III. Studies in Systems, Decision and Control, vol. 399, pp. 75–88. Springer, Cham (2022). https://doi.org/ 10.1007/978-3-030-87675-3_4 39. Report on the assessment of the adequacy (sufficiency) of generating capacities to cover the projected demand for electric energy and ensure the necessary reserve in 2020. https://ua. energy/wp-content/uploads/2021/06/Zvit-z-otsinky-vidpovidnosti-dostatnosti-generuyuchyhpotuzhnostej-dlya-pokryttya-prognozovanogo-popytu-na-elektrychnu-energiyu-ta-zabezpech ennya-neobhidnogo-rezervu-u-2020.pdf. Accessed 9 May 2022 40. Maliarenko O.Ye.: Methodical approach to the determination of forecasting structure of energy consumption on the basis of a complex method. Probl. Gen. Energy 1(52), 24–31 (2018). https:/ /doi.org/10.15407/pge2018.01.024 41. Maliarenko O.Ye.: Methodical approach to determining the predictive structure of the consumption of primary fuel. Probl. Gen. Energy 3(46), 28–39 (2016). https://doi.org/10.15407/pge 2016.03.028

Concept for Using Permutation-Based Three-Pass Cryptographic Protocol in Noisy Channels Emil Faure , Anatoly Shcherba , Mykola Makhynko , Constantine Bazilo , and Iryna Voronenko

Abstract The study develops a concept for providing secure information interaction that will apply the permutation-based three-pass cryptographic protocol under the conditions of strong noise in the communication channel. The research substantiates the procedure based on majority and correlation processing of fragments received from the communication channel having a length equal to the permutation length to synchronize frames (permutations) under the conditions of strong noise. The findings suggest that reliable permutation transmission under the conditions of strong noise in the data communication channel can be ensured by the method that uses circular bit shifts of another permutation with the maximum value of the minimum Hamming distance to all its circular shifts as symbols for the permutation to be transmitted. Constructing an ensemble of messages (permutations) has been achieved by using affine and projective general linear groups. An algorithm for building and storing ensembles of messages has been developed. This approach enabled generating network and session keys during data exchange in addition to providing the required code distance for permutation codelists (arrays). Further analysis has proved that the proposed codewords generation scheme can also be effectively applied for non-separable factorial data coding, for instance, when providing informational interaction of objects with a dynamically changing structure. Keywords Permutation · Factorial coding · Short packet · Permutation code · Frame synchronization · Reliability · Security

E. Faure (B) · A. Shcherba · C. Bazilo Cherkasy State Technological University, Cherkasy, Ukraine e-mail: [email protected] M. Makhynko GoodLabs Studio Inc., Toronto, ON M5H 3E5, Canada I. Voronenko National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_7

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1 Introduction A three-pass cryptographic protocol ensures a secure information exchange that does not require a preliminary cryptographic key exchange [1]. Such prerequisites can arise, for example, during data transfer between the objects, when a preliminary key exchange is impossible, and the communication is to be established in an insecure environment. This is evident in the case of interaction between unmanned aerial vehicles [2, 3] under difficult conditions, data exchange in self-organizing sensor networks [4, 5], machine-type communications [6] with a dynamically changing structure, etc. Operating three-pass protocols under the conditions of strong noise in the communication channel may be complicated by the necessity to involve means able to increase the reliability since the message needs to be transmitted between the participants (Alice and Bob) three times. During each pass when the message is transported it can be corrupted by the channel noise. A recent study investigates a permutation-based three-pass protocol [7]. Unlike other widely used three-pass protocols with exponentiation being the basic operation [8–13], where the cryptographic strength is determined by the complexity of the discrete logarithm, a cryptographic strength of the three-pass protocol based on permutations relies on the complexity of the permutation factorization and the complexity of the transformations inverse to nonlinear operations based on the identical cyclic structure of conjugate permutations. The described three-pass cryptographic protocol uses short messages (for example, for M = 8, the permutation length with uniformly encoded symbols is 24 bits) and thus can be employed in short-packet communication systems [14, 15], in ultra-reliable low-latency communications in particular [16, 17]. Unlike other protocols, a permutation-based three-pass protocol, when used under the conditions of strong noise in the communication channel, requires that Alice and Bob receive permutations at each data transmission stage. Such a feature substantiates a need for methods of reliable permutation transmission. In addition, a prerequisite for the information transfer phase to launch is synchronism establishing procedure, which should be adapted to the conditions of strong noise in the communication channel. Synchronization and information transmission processes become specifically complicated when the bit error probability approaches 0.5. The aim of this work is to ensure the information interaction between the objects under the conditions of strong channel noise by applying a permutation-based threepass cryptographic protocol. To achieve this aim, the following tasks must be solved. – The frame synchronization procedure to be used under the conditions of strong channel noise with the bit error probability p0 → 0.5 must be determined. – The procedure ensuring reliable permutation transmission under the conditions of strong channel noise with the bit error probability p0 → 0.5 must be defined. – The message (permutation) ensemble design procedure must be developed to achieve the desired code size and reliability indicators.

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2 The Influence of Communication Channel Noise on the Possibility of Implementing a Permutation-Based Three-Pass Cryptographic Protocol The approach to building a three-pass cryptographic protocol [7] relies on presenting an information message as a permutation of numbers π of a given length M. This representation would be used in factorial data coding [18–20], as well as in the cryptographic key exchange method [18]. Let permutation elements belong to a finite set of non-negative integers Z M = {0, 1, . . . , M − 1}. Then we note permutation π as a sequence of all elements in Z M . A set of permutations on Z M shall be called a permutation array [22] and denoted by S M . It has been suggested in the study [7] that the permutation-based three-pass protocol includes a sequence of procedures. |n(α) – Alice and Bob know the permutation α = i=1 αi , α ∈ S M . – Alice randomly) generates secret as an n(α)-dimensional vector s = ( s1 , s2 , . . . , sn(α) , where 0 ≤ si ≤ l(αi ) − 1, l(αi ) is the cycle αi order. |n(α) si – Alice generates a key permutation σ A = i=1 αi and inverse permutation σ A−1 . −1 Alice keeps s, σ A , and σ A secret. – Bob randomly ) generates secret as an n(α)-dimensional vector r = ( r1 , r2 , . . . , rn(α) , where 0 ≤ ri ≤ l(αi ) − 1. |n(α) ri – Bob generates a key permutation σ B = i=1 αi and inverse permutation σ B−1 . – Bob randomly generates a permutation χ B ∈ S M where all disjoint cycles in χ B decomposition are of different length. The length of all disjoint cycles in α decomposition is pairwise coprime with the length of disjoint cycles in χ B decomposition. Bob keeps r , σ B , σ B−1 , and χ B secret. – Let permutation π ∈ S M be a plaintext to be transmitted by Alice. Then Alice generates a ciphertext Y1 = σ A · π and sends it to Bob. – Bob encrypts the received message Y1 and calculates Y2 = σ B · Y1 · χ B . Bob sends Y2 to Alice. – Alice calculates Y3 = σ A−1 · Y2 · π −1 and sends Y3 to Bob. – Bob calculates Y4 = σ B−1 ·Y3 and represents Y4 as a product of disjoint cycles Y4 = ) |n(Y4 ) |n(χ B ) ( permutations π from Y4 = k4 . Then, Bob finds k=1 Y j=1( l χ j B possible ( )) ) ( ) |n(χ B ) ( π χ1 j B , π χ2 j B , . . . , π χl (χ j B ) j B based on the known Y4 and χ B . j=1 ) |n(χ ) ( Bob selects a correct permutation π from the possible j=1B l χ j B permutations |n(α) si αi . by calculating Y1 · π −1 = i=1 Figure 1 shows the informational interaction between Alice and Bob. An error in the communication channel affects Y1 , Y2 , and Y3 ciphertexts. If such an error distorts the Y1 , Y2 , or Y3 values in such a way that the receiver recognizes a sequence that is not the permutation, the further protocol implementation will be impossible. If the error distorts the Y1 , Y2 , or Y3 values in such a way that the receiver

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Fig. 1 The scheme of informational interaction

recognizes a sequence as a permutation, although different from the transmitted one, Bob will receive an incorrect message as a result of the protocol implementation. The probability that an error in the communication channel will transform either Y1 or Y2 into another permutation and this transformation will be “corrected” at the next ciphertext transmission stage is almost non-existent.

3 The Procedure for Synchronizing Frames (Permutations) Under the Conditions of Strong Noise The procedure for transferring Y1 , Y2 , and Y3 ciphertexts is performed after the procedure for identifying the frame (permutation) boundaries. The method described in the study [23] enables implementing frame synchronization for short-packet data transmission systems, in particular, those built on non-separable factorial data coding [18, 19]. The investigation conducted in [23] has confirmed the effectiveness of using the developed method in channels with strong noise. The authors have identified the general characteristics and quantitative parameters of the synchronization system for correct synchronization probability Ptr ue ≥ 0.9997 and false synchronization probability P f alse ≤ 3 · 10−4 when the bit error probability in the communication channel p0 ≤ 0.495. The main characteristics of the proposed method are as follows. – The syncword is a permutation π of length μ with the maximum value of the minimum Hamming distance to all of the permutation π circular shifts. – The receiver uses majority processing and correlation processing of the accumulated fragments of μ symbols received from a communication channel. – A predetermined minimum threshold for Ptr ue limits the number of accumulated fragments.

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4 The Procedure for Reliable Permutation Transmission Under the Conditions of Strong Noise Recent research [19] has proposed a method for reliable permutation transmission to be used in communication systems with non-separable factorial coding. As the authors show in the study [20], the codewords in a non-separable factorial code belong to a set Sμ of permutations π . Permutation symbols are encoded with a uniform binary code with the codeword length lr = |log2 μ|. Then the length of the permutation π binary representation equals n = μ · lr = μ · |log2 μ|. The method for reliable permutation transmission is described as follows. – A codeword is a permutation W ∈ S M and is called a word. Each permutation W symbol is called a letter. Letters are chosen from a set of permutation π circular shifts L j , 0 ≤ j ≤ n − 1. The permutation π is chosen so that the minimum Hamming distance to all its circular shifts is maximized. Thus, it is obvious that M ≤ n. – The receiver uses majority processing and correlation processing of the accumulated fragments of M symbols received from a communication channel. – A predetermined minimum threshold for the probability M of receiving a word PW _tr ue without errors limits the number of accumulated fragments. The study [19] has proved the efficiency of the proposed reliable permutation transmission method for channels with high-level noise. In particular, this survey investigates probabilistic indicators of the permutation transfer process for μ = 8 and M = 23 under probability for permutation W to be received correctly PW _tr ue ≥ 0.999. Note that a reliable permutation transmission [19] involves transporting letters, which make up words used in three-pass cryptographic protocol [7]. Therefore, synchronizing the permutations is to be performed both at the letter level and at the word level. Obviously, synchronizing letters is performed prior to synchronizing words. Since permutations are the syncwords for letters and words, the use of the method described in [23] is justified for both procedures. The difference is in the fact that, after majority data processing for letter synchronization, the result of word synchronization will be affected by a letter recognition error instead of being affected by a bit error. It is beyond the scope of this study to examine the probabilistic indicators of the specified two-stage synchronization procedure, although we admit that they present certain interest.

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5 The Procedure for Designing an Ensemble of Messages (Permutations) with the Given Code Size and Code Distance The method for reliable permutation transmission [19] involves creating the word W using either a set of all letters L j , 0 ≤ j ≤ n − 1, or a selection of them. Reliability indicators in the study [19] are determined based on the assumption that the correct recognition for a word W of length M occurs if and only if all M letters are recognized correctly. This situation is typical for an arbitrarily chosen word set S M including that containing all M! permutations. In this case, the Hamming distance hd(σ, τ ) between any words σ, τ ∈ S M , σ /= τ , has a lower bound: hd(σ, τ ) ≥ 2. Hence, it follows that in an arbitrarily designed ensemble S M , there are potential permutations σ, τ with hd(σ, τ ) = 2, whereas errors should be absent in the letters for the word to be recognized correctly. In case when an ensemble of messages S M with a code size significantly smaller than M! should be used for information transmission, the arbitrary choice of the words to design the set S M is not considered most effective in terms of providing for the reliability of the permutation transmission. It follows from the theory of error correcting coding [24, 25] that the transmission reliability of codewords increases together with the distance between them. Let (M, Dmin )-code be generated by the permutation array S M , where hd(σ, τ ) ≥ Dmin for arbitrary σ, τ ∈ S M , σ /= τ , or hd(S M ) ≥ Dmin . The value Dmin is the code distance for (M, Dmin )-code. Then, the probability for the word W to be received correctly [19] is found by the expression PW _tr ue =

|(Dmin −1)/ 2|

E

( )i ( ) M−i i CM , 1 − PL_tr ue PL_tr ue

(1)

i=0

)M ( instead of PW _tr ue = PL_tr ue , where PL_tr ue is the probability for a word W letter to be received correctly. This research does not aim to evaluate probabilistic indicators of the word W transmission reliability. Instead, this research is to define approaches to generating an error correcting permutation code (M, Dmin ) [21]. Note that non-binary permutation codes [27–29] are used as a solution for reliable message delivery in noisy channels such as power lines [30]. According to the authors of the study [22], permutation codes have parameters similar to those for Reed-Solomon codes [23] in some cases. We denote the (M, Dmin )-code size as N (M, Dmin ). Data from several sources [21, 24–32] have identified upper and lower bounds on max(N (M, Dmin )), in particular, Gilbert-Varshamov bounds and their improvement. For instance, the authors in [25] have proved that if M is a prime

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power, max(N (M, M − 1)) = M(M − 1) and max(N (M + 1, M − 1)) = (M + 1)M(M − 1). The study [26] has demonstrated that max(N (11, 8)) = 7920 and max(N (12, 8)) = 95040. To provide max(N (M, Dmin )) lower bounds, other studies consider automorphism groups [21, 27], permutations invariant under isometries [28] (for M ≤ 22), sequential and parallel partition and extension, modified Kronecker product operation [29–31], permutation rational functions [32]. The study [33] develops and implements a statistical method to generate codewords for (M, Dmin )-code. An advantage of the proposed method is the possibility of working with large M, although the method does not ensure achieving potentially maximum indicators for N (M, Dmin ). Further we consider algorithms for generating codes (M, M − 1) and (M + 1, M − 1) with a prime M to achieve N (M, M − 1) = M(M − 1) and N (M + 1, M − 1) = (M + 1)M(M − 1).

5.1 Generating (M, M − 1)-Code with a Prime M and N(M, M − 1) = M(M − 1) Example 1.18 (d) from [25] and the proof in Theorem 1 (ii) from [24] use a linear transformation of the ax + b type in the Galois field G F(M), where a /= 0, a, b, x ∈ G F(M). The study [29] has analyzed a permutation group AG L(1, M) generated by affine linear transformations. AG L(1, M) = { ax + b|a, b, x ∈ G F(M), a /= 0}.

(2)

This permutation group is called an affine general linear group. It is sharply 2transitive and has the order M(M − 1). Since Hamming distance is hd(G) = M − k + 1 for a k -transitive permutation group G on Z M [24], AG L(1, M) has the Hamming distance M − 1. Then, for each pair of elements (a, b), a, b ∈ G F(M), from the array A = {(a, b)i }, i = 1, 2, . . . , M(M − 1), by changing the value x = 0, 1, . . . , M − 1 in (2) we can calculate all symbols for the i -th permutation of (M, M − 1)-code. The number of different permutations in the generated permutation array S M equals N (M, M − 1) = |AG L(1, M)| = M(M − 1). Example 1 Let us construct a permutation code (5, 4). We assume that A = {(1, 0), (1, 1), . . . , (1, 4), (2, 0), (2, 1), . . . , (4, 4)}. All the possible N (5, 4) = 20 permutations for the (5, 4)-code are enumerated in Table 1. Remark 1 The linear function ax + b is basic for the linear congruent method for generating a pseudo-random sequence of numbers [40], which, by varying the a, b parameters, allows building a high-speed permutation generator [41] for any M, not necessarily for a prime power.

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Table 1 Codewords for the (5, 4)-code x a

b

0

1

2

3

4

1

0

0

1

2

3

4

1

1

1

2

3

4

0

1

2

2

3

4

0

1

1

3

3

4

0

1

2

1

4

4

0

1

2

3

2

0

0

2

4

1

3

2

1

1

3

0

2

4

2

2

2

4

1

3

0

2

3

3

0

2

4

1

2

4

4

1

3

0

2

3

0

0

3

1

4

2

3

1

1

4

2

0

3

3

2

2

0

3

1

4

3

3

3

1

4

2

0

3

4

4

2

0

3

1

4

0

0

4

3

2

1

4

1

1

0

4

3

2

4

2

2

1

0

4

3

4

3

3

2

1

0

4

4

4

4

3

2

1

0

Theorem 1 An (M, M − 1)-code created by AG L(1, M) with a prime M can be generated by any element (permutation) of this code, that is, if S M = {ax + b, a, b ∈ Z M , a /= 0} and σ ∈ S M , then {aσx + b, a, b ∈ Z M , a /= 0} = S M . Proof If σ ∈ S M , there are a1 /= 0, b1 : σx = a1 x + b1 , x ∈ Z M . Let the{permutation τ ∈ S M . Then, there are a2 /= 0, b2 : τx = a2 x + b2 , x ∈ Z M . σx = a1 x + b1 , Then while τx = a2 (σx − b1 )/a1 + b2 or τx = a3 σx + b3 , τx = a2 x + b2 ; ( ) where a3 = aa21 , b3 = b2 − aa21 b1 . Since there is a single inverse element a1−1 ∈ Z M , then clearly a3 , b3 ∈ Z M , and the transformation σ → τ is uniquely determined by a1 , a2 , b1 , and b2 values. It can be strictly proved that different pairs (a2 , b2 ) do not allow obtaining the coinciding pair (a3 , b3 ). Thus, an arbitrary permutation σ ∈ S M can be generative for all other permutations { in the array S M = {ax + b, a, b ∈ Z M , a /= 0}. Example 2 Suppose that M = 5. For a1 = 2, b1 = 3, the permutation calculated by σx = 2x + 3 is described by σ = (3 0 2 4 1). For a2 = 4, b2 = 2, the permutation

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calculated by τx = 4x + 2 is noted as τ = (2 1 0 4 3). From the expression σx = 2x +3, it then follows at once that x = (σx − 3)/2. Then τx = 2σx −4 or τx = 2σx +1. Corollary 1 Different permutation arrays S M = {ax + b, a, b ∈ Z M , a /= 0} do not ' '' ' '' contain identical permutations, that is, S M ∩ S M = ∅ i f S M /= S M . Corollary 2 The number of different (M, M − 1)-codes generated by permutation array S M (2) with a prime M (with accuracy to the mutual position of permutations M! in S M ) equals N (M,M−1) = (M − 2)!. Corollary 3 The number of different (M, M − 1)-codes generated by permutation array S M (2) with a prime M (accounting for the mutual position of permutations in S M ) equals M! · (M(M − 1) − 1)!. Example 3 Let M = 5. Then the number of (5, 4)-codes with different content will be 6, and the number of different (5, 4)-codes taking into account the mutual position of permutations in S5 constitutes 5! · 19!.

5.2 Generating (M + 1, M − 1)-Code with a Prime M and N(M + 1, M − 1) = (M + 1)M(M − 1) The study [29] considers projective general linear group P G L(2, M) on Z M ∪ ∞. The elements of this group are generated by the following transformations: { P G L(2, M) =

f (x) =

\ } ax + b \\ a, b, c, d ∈ G F(M), x ∈ G F(M) ∪ ∞, ad / = bc , cx + d \ (3)

, if x ∈ G F(M) and x /= − dc ; f (x) = ∞, if x ∈ G F(M) and where f (x) = ax+b cx+d x = − dc ; f (x) = ac , if x = ∞ and c /= 0; f (x) = ∞, if x = ∞ and c = 0. The permutation group P G L(2, M) is sharply 3-transitive and has the order (M + 1)M(M − 1). By [24], P G L(2, M) has the Hamming distance M − 1. Then, for each set of elements (a, b, c, d) from the array A = {(a, b, c, d)i }, i = 1, 2, . . . , (M + 1)M(M − 1), all characters in the i—th permutation of (M + 1, M − 1)—code can be calculated by changing x = 0, 1, . . . , M − 1, ∞ in (3) and substituting ∞ for M in the f (x) value. The number of different permutations in the generated permutation array S M is equal to N (M + 1, M − 1) = |P G L(2, M)| = (M + 1)M(M − 1). Remark 2 The sets of elements (a, b, c, d)i in A = {(a, b, c, d)i }, i = 1, 2, . . . , (M + 1)M(M − 1) should be selected from M 4 possible combinations in such a way that they generate different permutations during the calculation of (3). While generating the (a, b, c, d)i sets, it is necessary to discard not only degenerate cases with ad = bc, but also combinations that generate identical permutations for i /= j: ai c j = ci a j , ai d j + bi c j = ci b j + di a j , bi d j = di b j .

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) ( a x+b x+bi Proof If acii x+d = c jj x+d jj , then ai c j x 2 + bi c j + ai d j x + bi d j = a j ci x 2 + i ( ) b j ci + a j di x +b j di . Polynomials of degree 2 have the same values for all elements x of a finite field G F(M) of order M ≥ 3 only if these polynomials are equal. { Example 4 Here we build a fragment for permutation code (6, 4). Let there be A = {(0, 1, 1, 1), (0, 1, 2, 1), (1, 1, 2, 1), (2, 1, 2, 2), (3, 1, 4, 2), . . . }. Then, we can sum up the first permutations of the (6, 4)-code in Table 2. To determine the generative permutation for the (M + 1, M − 1)-code, we formulate the following remark. Remark 3 The superposition of non-degenerate linear fractional transformations is a non-degenerate transformation. Proof Let there be non-degenerate linear fractional transformations R(x) = ax+b cx+d \ \ \ \ \a b \ \α β\ αx+β \ \ \ \ /= 0 and \ /= 0. Then R(G(x)) = and G(x) = γ x+δ , where \ c d\ γ δ\ a(αx+β)+b(γ x+δ) c(αx+β)+d(γ x+δ)

or R(G(x)) =

(aα+βγ )x+(aβ+bδ) . (cα+dγ )x+(cβ+dδ)

(

ab cd

(

)

αβ γ δ

)

Consider matrices A = and B = while C = A · B = ) aα + βγ aβ + bδ . In this case |C| = |A · B| = |A| · |B|. |C| /= 0 since |A| /= 0 cα + dγ cβ + dδ and |B| /= 0 whereas the R(G(x)) transformation is non-degenerate. { (

Theorem 2 The (M + 1, M − 1)-code created by P G L(2, M) with a prime M can \ by any element (permutation) of this} code. That is, if S M+1 = { be generated \a, b, c, d ∈ Z M , x ∈ Z M ∪ ∞, ad /= bc and σ ∈ S M+1 , we obtain f (x) = ax+b cx+d \ { } aσx +b \ f (x) = cσx +d \a, b, c, d ∈ Z M , ad /= bc = S M+1 . Proof If σ ∈ S M+1 , then there are a1 , b1 , c1 , d1 , a1 d1 /= b1 c1 : σx = Z M ∪ ∞.

a1 x+b1 , c1 x+d1

x, σx ∈

Table 2 A fragment for a (6, 4)-code’s set of codewords x a

b

c

d

0

1

2

3

4

∞

0

1

1

1

1

3

2

4

5

0

0

1

2

1

1

2

5

3

4

0

1

1

2

1

1

4

5

2

0

3

2

1

2

2

3

2

0

4

5

1

3

1

4

2

3

4

5

0

1

2

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Let there be a permutation τ ∈ S M+1 . Then, there are a2 , b2 , c2 , d2 , a2 d2 /= b2 c2 : x+b2 , x, σx ∈ Z M ∪ ∞. τx = ac22 x+d {2 x+b1 σx = ac11 x+d ; −d1 σx +b1 1 Thus a2 x+b2 It follows from the first expression that x = c1 σx −a1 . Then τx = c2 x+d2 . τx =

−d1 σx +b1 c1 σx −a1 −d1 σx +b1 c2 c σx −a 1 1

a2

+b2 +d2

=

a2 (−d1 σx +b1 )+b2 (c1 σx −a1 ) c2 (−d1 σx +b1 )+d2 (c1 σx −a1 )

or τx =

a3 σx +b3 , where a3 c3 σx +d3

= b2 c1 −a2 d1 ,

b3 = a2 b1 − a1 b2 , c3 = d2 c1 − c2 d1 , d3 = c2 b1 − a1 d2 . The above calculations are correct according to the projective general linear group P G L(2, M) defined by\ the formula \ \ (3). \ \ d1 b1 \ \ −d1 b1 \ \ \ \ \ /= 0, therefore, the linear fractional = \ The determinant is \ c1 −a1 \ c1 a1 \ transformation x =

−d1 σx +b1 c1 σx −a1

is non-degenerate. \ \ \ \ \ a2 b2 \ \ a3 b3 \ \ \ \ \ /= 0. /= 0, it follows that \ Because of Remark 3 and condition \ c2 d2 \ c3 d3 \ Thus, there is a single set of a3 , b3 , c3 , d3 \∈ Z M \ coefficients for the given \ \ +b3 \ a3 b3 \ /= 0. , permutation pair σ and τ such that τx = ac33 σσxx +d 3 \c d \ 3 3 Thus, an arbitrary permutation σ ∈ S M+1 can be generative for all other permutations in the array S M+1 = \ } { \a, b, c, d ∈ Z M , x ∈ Z M ∪ ∞, ad /= bc . { f (x) = ax+b cx+d Example 5 Let M = 5. For a1 = 0, b1 = 1, c1 = 1, and d1 = 1, the permutation 1 is noted as σ = (1 3 2 4 5 0). For a2 = 3, calculated by the expression σx = x+1 3x+1 b2 = 1, c2 = 4, and d2 = 2, the permutation calculated by the expression τx = 4x+2 3σx +3 . is noted as τ = (3 4 5 0 1 2). Then τx = 3σ x +4 Corollary permutation arrays S M+1 = \ 4 Different } { \a, b, c, d ∈ Z M , x ∈ Z M ∪ ∞, ad /= bc do not contain identical f (x) = ax+b cx+d ' '' ' '' permutations, that is, if S M+1 /= S M+1 , then S M+1 ∩ S M+1 = ∅. Corollary 5 The number of different (M + 1, M − 1)-codes, generated by permutation array S M+1 (3) with a prime M (with accuracy to the mutual position of (M+1)! = (M − 2)!. permutations in S M+1 ), equals N (M+1,M−1) Corollary 6 The number of different (M + 1, M − 1)-codes, generated by permutation array S M+1 (3) with a prime M (taking into account the mutual position of permutations in S M+1 ), equals (M + 1)! · ((M + 1)M(M − 1) − 1)!. Example 6 Let M = 5. Then the number of different (6, 4)-codes is 6, and the number of different (6, 4)-codes accounting the mutual position of permutations in S6 is 6! · (6 · 5 · 4 − 1)! = 6! · 119!.

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5.3 Algorithm for Designing and Storing Ensembles of Messages The ensemble of messages or the permutation codelist S M , which is intended to transfer information and is represented a code (M, M − 1) or (M + 1, M − 1) with a prime M, can be created by the participants of the information exchange independently according to the following algorithms. The algorithm to create a codelist S M based on the (M, M − 1)-code includes the following stages. 1. The conversers save the array A = {(a, b)i }, i = 1, 2, . . . , M(M − 1), a, b ∈ Z M , a /= 0. The array A = {(a, b)i } can act as a network key: pair (a, b) numbering in the array A determines the codelist structure. 2. The conversers agree a permutation σ on Z M . This permutation can act as a session key. 3. The conversers create a codelist S M = {aσx + b, a, b ∈ Z M , a /= 0}. 4. The algorithm to create a codelist S M+1 is based on the (M + 1, M − 1)-code. {(a, b, c, d)i }, i 5. The conversers save the array A = = 1, 2, . . . , (M + 1)M(M − 1), a, b, c, d ∈ Z M , ad /= bc. The A = {(a, b, c, d)i } array can also act as a network key. 6. The conversers agree a permutation σ on σx ∈ Z M ∪ ∞. This permutation can act as a session key. \ } { x +b \ a, b, c, d ∈ Z M , ad /= bc . 7. The conversers create a codelist S M+1 = aσ cσx +d \

6 Conclusions In this research, we have determined the mechanism for ensuring information interaction between the conversers under the conditions of strong noise in the data transmission channel, through implementing a permutation-based three-pass cryptographic protocol. In particular, this study has applied an approach based on majority and correlation processing of fragments received from the communication channel to synchronize frames (permutations) under the conditions of strong noise. The fragments’ length is equal to the permutation length. We have concluded that reliable permutation transmission under the conditions of strong noise is ensured by a method that uses circular bit shifts of a permutation where the minimum Hamming distance to all its circular shifts is maximized, as symbols for the permutation to be transmitted. Generating an ensemble of messages (permutations) is performed with applying the affine general linear group AG L(1, M) and projective general linear group P G L(2, M). This approach, in addition to providing M − 1 code distance for the permutation codelists (arrays) S M and S M+1 , enabled generating network and session keys in the process of data exchange.

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Such a scheme for producing codewords based on permutations can also be efficient in non-separable factorial data coding, for example, when serving the information interaction between machine-type communication objects with a dynamically changing structure. Acknowledgements This research was funded from the Ministry of Education and Science of Ukraine under grant 0120U102607.

References 1. Schneier, B.: Applied Cryptography: Protocols, Algorithms, and Source Code in C. Wiley, New York (1996) 2. Mozaffari, M., Saad, W., Bennis, M., Nam, Y.-H., Debbah, M.: A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun. Surv. Tutor. 21, 2334– 2360 (2019). https://doi.org/10.1109/COMST.2019.2902862 3. Fotouhi, A., Qiang, H., Ding, M., Hassan, M., Giordano, L.G., Garcia-Rodriguez, A., Yuan, J.: Survey on UAV cellular communications: practical aspects, standardization advancements, regulation, and security challenges. IEEE Commun. Surv. Tutor. 21, 3417–3442 (2019). https:/ /doi.org/10.1109/COMST.2019.2906228 4. Ibrahim, Q., Qassab, M.: Theory, concepts and future of self organizing networks (SON). Recent Adv. Comput. Sci. Commun. 15, 904–928 (2022). https://doi.org/10.2174/266625581 4666210201102326 5. Norman, M.D., Silvey, P.E., Koehler, M.T.K., Joe, K.C.: Engineering decentralized enterprises: emergent mission accomplishment without centralized command and control. In: Yang Z., von Briesen E. (eds.) Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas. pp. 124–151. Springer International Publishing, Cham (2022) 6. Sharma, S.K., Wang, X.: Toward massive machine type communications in ultra-dense cellular IoT networks: current issues and machine learning-assisted solutions. IEEE Commun. Surv. Tutor. 22, 426–471 (2020). https://doi.org/10.1109/COMST.2019.2916177 7. Shcherba, A., Faure, E., Lavdanska, O.: Three-pass cryptographic protocol based on permutations. In: 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), 281–284. IEEE, Kyiv, Ukraine (2020). https://doi.org/10.1109/ATIT50783.2020.934 9343 8. Oktaviana, B., Utama Siahaan, A.P.: Three-pass protocol implementation in caesar cipher classic cryptography. IOSR J. Comput. Eng. 18, 26–29 (2016). https://doi.org/10.9790/06611804032629 9. Lang, J.: A no-key-exchange secure image sharing scheme based on Shamir’s three-pass cryptography protocol and the multiple-parameter fractional Fourier transform. Opt. Express. 20, 2386 (2012). https://doi.org/10.1364/OE.20.002386 10. Rachmawati, D., Andri Budiman, M., Adib Rikzan, M.: Analysis of file security with three-pass protocol scheme using massey-omura algorithm in android. J. Phys. Conf. Ser. 1235, 012075 (2019). https://doi.org/10.1088/1742-6596/1235/1/012075 11. Nguyen, N.H., Moldovyan, N.A., Shcherbacov, A.V., Nguyen, H.M., Nguyen, D.T.: No-Key protocol for deniable encryption. In: Bhateja V., Nguyen B.L., Nguyen N.G., Satapathy S.C., Le D.-N. (eds.) Information Systems Design and Intelligent Applications, pp. 96–104. Springer Singapore, Singapore (2018) 12. Minh, N.H., Moldovyan, D.N., Moldovyan, N.A., Le, Q.M., Ho, S.T., Nguyen, L.G., Nguyen, H.V., Tran, C.M.: Post-quantum commutative deniable encryption algorithm. In: Solanki V.K., Hoang M.K., Lu Z. (Joan), Pattnaik P.K. (eds.) Intelligent Computing in Engineering, pp. 993– 1005. Springer Singapore, Singapore (2020)

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E. Faure et al.

13. Moldovyan, A., Moldovyan, D., Moldovyan, N.: Post-quantum commutative encryption algorithm. Comput. Sci. J. Mold. 81, 299–317 (2019) 14. Durisi, G., Koch, T., Popovski, P.: Toward massive, ultrareliable, and low-latency wireless communication with short packets. Proc. IEEE. 104, 1711–1726 (2016). https://doi.org/10. 1109/JPROC.2016.2537298 15. Saad, W., Bennis, M., Chen, M.: A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Netw. 34, 134–142 (2020). https://doi.org/10. 1109/MNET.001.1900287 16. Nasrallah, A., Thyagaturu, A.S., Alharbi, Z., Wang, C., Shao, X., Reisslein, M., ElBakoury, H.: Ultra-Low Latency (ULL) networks: the IEEE TSN and IETF DetNet standards and related 5G ULL research. IEEE Commun. Surv. Tutor. 21, 88–145 (2019). https://doi.org/10.1109/ COMST.2018.2869350 17. Sybis, M., Wesolowski, K., Jayasinghe, K., Venkatasubramanian, V., Vukadinovic, V.: Channel Coding for Ultra-Reliable Low-Latency Communication in 5G Systems. In: 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), pp. 1–5. IEEE, Montreal, QC, Canada (2016) 18. Faure, E., Shcherba, A., Vasiliu, Ye., Fesenko, A.: Cryptographic key exchange method for data factorial coding. CEUR Workshop Proc. 2654, 643–653 (2020) 19. Faure, E., Shcherba, A., Stupka, B., Voronenko, I., Baikenov, A.: A method for reliable permutation transmission in short-packet communication systems. In: Faure, E., Danchenko, O., Bondarenko, M., Tryus, Y., Bazilo, C., Zaspa, G. (eds) Information Technology for Education, Science, and Technics. ITEST 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 178. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35467-0_ 12 20. Al-Azzeh, J., Ayyoub, B., Faure, E., Shvydkyi, V., Kharin, O., Lavdanskyi, A.: Telecommunication systems with multiple access based on data factorial coding. Int. J. Commun. Antenna and Propagation. 10(2), 102–113 (2020). https://doi.org/10.15866/irecap.v10i2.17216 21. Smith, D.H., Montemanni, R.: A new table of permutation codes. Des. Codes Cryptogr. 63, 241–253 (2012). https://doi.org/10.1007/s10623-011-9551-8 22. Blake, I.: Permutation codes for discrete channels (Corresp.). IEEE Trans. Inf. Theory. 20, 138–140 (1974). https://doi.org/10.1109/TIT.1974.1055142 23. Reed, I.S., Solomon, G.: Polynomial codes over certain finite fields. J. Soc. Ind. Appl. Math. 8, 300–304 (1960). https://doi.org/10.1137/0108018 24. Frankl, P., Deza, M.: On the maximum number of permutations with given maximal or minimal distance. J. Comb. Theory Ser. A. 22, 352–360 (1977). https://doi.org/10.1016/00973165(77)90009-7 25. Huppert, B.: Endliche Gruppen I. Springer, Berlin Heidelberg, Berlin, Heidelberg (1967) 26. Dixon, J.D., Mortimer, B.: Permutation Groups. Springer, New York, New York, NY (1996) 27. Chu, W., Colbourn, C.J., Dukes, P.: Constructions for permutation codes in powerline communications. Des. Codes Cryptogr. 32, 51–64 (2004). https://doi.org/10.1023/B:DESI.0000029212. 52214.71 28. Janiszczak, I., Lempken, W., Östergård, P.R.J., Staszewski, R.: Permutation codes invariant under isometries. Des. Codes Cryptogr. 75, 497–507 (2015). https://doi.org/10.1007/s10623014-9930-z 29. Mojica de la Vega, L.G.: Permutation Arrays with Large Hamming Distance (2017) 30. Bereg, S., Mojica, L.G., Morales, L., Sudborough, H.: Kronecker product and tiling of permutation arrays for hamming distances. In: 2017 IEEE International Symposium on Information Theory (ISIT), pp. 2198–2202. IEEE, Aachen, Germany (2017) 31. Bereg, S., Mojica, L.G., Morales, L., Sudborough, H.: Constructing permutation arrays using partition and extension. Des. Codes Cryptogr. 88, 311–339 (2020). https://doi.org/10.1007/s10 623-019-00684-z 32. Bereg, S., Malouf, B., Morales, L., Stanley, T., Sudborough, I.H.: Using permutation rational functions to obtain permutation arrays with large hamming distance. Des. Codes Cryptogr. 90, 1659–1677 (2022). https://doi.org/10.1007/s10623-022-01039-x

Concept for Using Permutation-Based Three-Pass Cryptographic …

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33. Faure, E., Shcherba, A., Makhynko, M., Stupka, B., Nikodem, J., Shevchuk, R.: Permutationbased block code for short packet communication systems. Sensors. 22, 5391 (2022). https:// doi.org/10.3390/s22145391

Conformal Mapping of Discontinuous Functions for Inverse Radon Transform Mykola Vinohradov , Oleksandr Ponomarenko , Andrii Moshensky , and Alina Savchenko

Abstract The problem of constructing conformal mappings of piecewise continuous (discontinuous) functions are considered. It is shown that the requirement of differentiability of a function of a complex variable on the set of its values imposes the Cauchy-Riemann condition on the behaviour of the real and imaginary parts of the function. The classical definition of an analytic function is applied, which differs from the applied nature usually accepted in the literature by the requirement of continuity of partial derivatives. It is established that this implies the sufficiency of the condition for the existence of the first differential, i.e. differentiability of any function of several variables. This requirement was introduced taking into account the peculiarities of processing discrete functions as a subclass of discontinuous functions. A conformal mapping of continuous functions and a quasi-conformal mapping of piecewise continuous functions are considered. the specificity of the application of conformal mappings in tomographic methods and algorithms based on the Radon transform is analysed. It is proposed to use discrete Fourier transform algorithms to restore images and determine the coordinates of point sources of radiation as a tool for quasi-conformal mapping of functions subjected to the Radon transform. Thanks to the parametric assignment of functions, a simplified method for calculating derivatives in the expansion in a Taylor series has been developed. The method makes it possible to find the derivative of a function given parametrically, without finding an expression for the direct dependence of the function on the argument. Due to the parametric assignment of functions used in tomography problems, it becomes possible to modify the quasi-conformal mapping. When expanding in a Taylor series, both linear and quadratic terms are taken into account. Numerical calculations show that for a limited scanning sector, the errors of the proposed method are smaller than in the case of a classical quasi-conformal mapping.

M. Vinohradov · A. Moshensky · A. Savchenko National Aviation University, Kyiv, Ukraine O. Ponomarenko (B) General Energy Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_8

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Keywords Piecewise continuous (discontinuous) functions · Cauchy-Riemann condition · Conformal mapping · Parametric function definition · Quasi-conformal mapping

1 Introduction In the theory of functions of a complex variable, continuous functions are most often considered as analytic continuations of functions of a real variable. Specifying a complex function w(z) of a complex variable z = x + j y is equivalent to specifying two real functions of two real variables: w(z) = u(x, y) + jv(x, y). (Here and below, as the symbol of the imaginary unit, we use the notation j, which is most familiar to engineering applications.) In general, the theory of functions of a complex variable is constructed in complete analogy with the theory of functions of a real variable. However, when introducing the concept of differentiability of a function of a complex variable, by analogy with the corresponding concept of the theory of functions of a real variable, differences of a fundamental nature arise. In particular, the requirement that a function of a complex variable be differentiable on the set of its values imposes a very important condition on the behaviour of the real and imaginary parts of the function, known as the Cauchy-Riemann condition. The Cauchy-Riemann relations are used in the study of fundamental properties of the class of analytic functions not only in the theory of functions of a complex variable. They play a key role in solving applied problems in various natural and technical sciences (including, of course, information technology in general and digital signal processing in particular). It should be noted here that we use the classical definition of an analytic function, which differs from the applied one usually accepted in the literature by the requirement of continuity of partial derivatives. This implies the sufficiency of the condition for the existence of the first differential, i.e., differentiability of any function of several variables. This requirement was introduced taking into account the peculiarities of processing discrete functions as a subclass of discontinuous functions. Leaving aside the singularities of discontinuous functions for the time being (this question is considered in Sect. 4), let us turn to the problem of mapping some ε-neighbourhood of a point z 0 —the argument of the function w(z)—onto a υ-neighbourhood of the point w0 = w(z 0 ). The mapping is carried out by an analytical function w(z) with the conservation of angles and the constancy of distances. Such a mapping is conformal. For any conformal mapping, there is some orthogonal grid of curves that transforms into a rectangular Cartesian grid. A typical example of an orthogonal grid of curves is a polar grid. In this case, infinitesimal figures (for example, triangles) with a vertex at a point z 0 are transformed into infinitesimal triangles similar to them with a vertex at a point w0 .

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The transformation of plane performed by the analytic function has the following important properties in the ε-neighbourhood of the point z 0 , for which the derivative is w' (z 0 ) /= 0. The vectors of all directions outgoing from this point: – change (increase or decrease) in length by the same number of times, equal to the modulus w' ; – rotate through the same angle equal to the argument w' . Thus, any figures in an infinitely small area (triangles, rectangles, ellipses, etc.) are transformed into similar ones, i.e. keep their shape. Therefore, such a transformation is called a conformal mapping. Figures of finite dimensions are distorted, although the angles between the tangents to two curves are preserved—there is so-called corner conservatism. In general, the properties of conformal mappings of continuous functions have been studied very well. Conformal mappings are used in electrical and radio engineering, aero- and hydrodynamics, and in other engineering applications. Unfortunately, we cannot say the same about conformal mappings of discontinuous functions. For example, the applied aspects of conformal mappings of such a class of discontinuous functions as discrete signals have not yet been studied enough. These include display errors, computational complexity, etc. This article attempts to fill this gap in relation to the problems of tomographic detection and measurement of the coordinates of point sources of acoustic noise radiation.

2 Related Work 2.1 Retrospective Complex numbers as arguments of functions of a complex variable arose from the internal needs of mathematics proper, in particular, from the theory and practice of solving algebraic equations and related root extraction operations over real numbers. Complex numbers naturally arose as a result of the need to automate calculations when trying to obtain all solutions of quadratic equations, including those that do not belong to the set of real numbers. The fundamental property of complex numbers is the fact that the basic mathematical operations on complex numbers do not take the researcher out of the field of complex numbers, functions of a complex variable and operations on them. Conformal mappings are no exception. In their mathematical basis, conformal mappings are inextricably linked with the theory of analytic functions and the Cauchy–Riemann conditions for differentiable functions of a complex variable. The first works in this direction appeared in the second half of the eighteenth century (d’Alembert 1752; Euler 1777). Work Development of d’Alembert and Euler took place in the first half of the nineteenth

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century (Cauchy 1814—construction of the theory of functions; Riemann 1851— general foundations of the theory of functions.) Therefore, the Cauchy—Riemann conditions are often called the d’Alembert—Euler conditions. Applied aspects of the theory of functions of a complex variable, including the Cauchy-Riemann conditions and the use of conformal mappings of continuous differentiable functions of a complex variable, in particular, in tomography, turned out to be in demand and began to be actively studied only after the advent of computers and digital signal processing tools. It should be noted that tomography itself was also developed as a computer science, although the fundamental foundations of tomography, integral geometry and the Radon transform, originated at the beginning of the twentieth century (Radon 1917). At the same time, computers and digital signal processing tools are fundamentally discrete devices and operate with discrete signals, i.e. with discontinuous functions. Correspondingly, conformal mappings also undergo fundamental changes. Let us consider the state and prospects for the development of methods for displaying discontinuous functions in more detail.

2.2 Regular Theory of Functions of a Complex Variable and Related Problems The theory of functions of a complex variable is described in a large number of monographs and textbooks by domestic and foreign scientists in the field of mathematics and mathematical physics [1–5]. In these papers, special considerations of elementary functions of a complex variable are traditionally carried out. In [6], the presentation of the material is quite close to the traditional one, however, elementary functions of a complex variable are introduced as a direct analytic continuation of elementary functions of a real variable. Theorems on the analytic continuation of relations, transformations, and mappings make it possible to uniformly transfer the known properties of elementary functions of a real variable to the complex domain. First of all, this refers to the general principles of conformal mapping and its practical applications. As for conformal mappings (conformal representations, conformal mappings), the authors of [1, 6, 7] should be included among the scientists who made the greatest contribution to the theory and practice of conformal mappings of continuous functions. For example, in [6], variation principles of conformal mappings and boundary derivatives are considered; the conclusions of the basic formulas of the variation method of the theory of conformal mappings are presented in a new, more rigorous way. In [7–9], methods for analyzing functions of a complex variable are described in relation to practical applications, including some problems of mechanics, computational (computer) mathematics, etc.

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It is necessary to emphasize once again that in the theoretical and applied works mentioned above, continuous functions of a complex variable are considered. Therefore, all the basic properties of conformal mappings are valid, including the constancy of dilations, the conservatism of angles, the Cauchy-Riemann conditions. However, real functions, including, of course, the functions of a complex variable, as a rule, are discontinuous. Conformal mappings have their own specifics and require different approaches to analysis.

2.3 Conformal Mappings for Discontinuous Functions One of the fundamentally new approaches to the analysis of discontinuous functions is the theory of quasi-conformal mappings. A great contribution to the theory and applications of quasi-conformal mappings was made by the Ukrainian scientist L.I. Volkovysk and foreign scientists H. Grötzsch, M. Lavrent’ev, L. Alfors. The main idea of the transition from conformal mappings to quasi-conformal mappings is as follows. We introduce the following assumptions: – the complex plane xOy is transformed into another complex plane uOv; – the mapping of one plane onto another has certain geometric properties in the vicinity of each point of the plane xOy, in particular, the direction of the semi axes and the ratio of the semi axes of two ellipses are given for the point (x, y); – the neighbourhood of the point (x0 , y0 ) is considered small; – the plane xOy to plane uOv mapping is considered as affine. Then, when expanding the function p(u, v) along x − x0 and y − y0 in a series with keeping only the first terms, the ellipses centered at a (x0 , y0 ) point with the main axes parallel to the coordinate axes and the ratio ab = p(x0 , y0 ) of the semi axes on the plane uOv pass into circles centered at the point (u 0 , v0 ). If we consider not the affine transform given by the first terms of the expansion, but the classical affine transform, then the mapping property found will be the more accurate, the smaller the dimensions of the ellipse semi-axes. Under the assumptions made, it can be argued that this property will hold for infinitely small ellipses. In conclusion, we can assert that when a plane is mapped onto a plane, the infinitesimal ellipses of the first family with centers at the points (x, y) are transformed into infinitesimal ellipses of the second family with centers at the points (u, v). In quasi-conformal mappings, in essence, the asymptotic approach is implemented. They represent a powerful mathematical apparatus of the geometric theory of functions of a complex variable. However, in the study of practically realizable discontinuous functions, the results of the analysis of the accuracy of expansion into a series with a limited number of terms in the series [10–14] are of great practical interest. The presented work is devoted to some aspects of this problem.

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3 Problem Statement With the reconstructing the tomographic images of large 3D objects from projections and multisite detection/measurement of coordinates of point (small-sized) objects it is sufficient to obtain sets of layered images with the possibility of determining the coordinates of point sources. The problem is to calculate the discrete Radon transform, which is performed on a function with a finite or countable number of discontinuities. Typical examples are functions on Cartesian or polar discrete coordinate grids (Fig. 1). According to the central section theorem [15, 16], there is a one-to-one correspondence between the Fourier transform and the Radon transform, which, in essence, is the problem of restoring the original function from its integrals along the rays of the polar grid. The properties of the Radon transform and the formulas for its inversion are considered for individual layers, i.e. for the two-dimensional case. Figure 2 shows layers of three-dimensional functions on coordinate grids, and Fig. 3 illustrates the distortion caused by uneven distribution of information in the frequency domain. Thus, the research problem is formulated as follows: – to develop a variant of a quasi-conformal mapping of a discontinuous (piecewisecontinuous) function; – to conduct a comparative analysis of the accuracy of various methods for constructing quasi-conformal mappings.

Fig. 1 Coordinate grids of two-dimensional functions with a finite number of discontinuities. a Cartesian grid: xi = 0, /\x, 2/\x, . . . , N /\x; yi = 0, /\y, 2/\y, . . . , N /\y. /\x = /\y, N < ∞. b Polar grid: ϕi = 0, /\ϕ, 2/\ϕ, . . . , N /\ϕ; ρi = 0, /\ρ, 2/\ρ, . . . , N /\ρ. N < ∞

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Fig. 2 Examples of 3D functions with a finite number of discontinuities

Fig. 3 Illustration of image distortions on a 3D spherical coordinate grid

4 Construction of a Quasi-conformal Mapping Let’s consider the simplest piecewise-continuous function, which is a collection of points on a circle. This function is typical for the most tomographic problems. Moreover, it’s very important problem from the point of view of accuracy of tomographic imaging and detection/measurement.

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The points are located at equal angular distances from each other. Figure 4 shows the sectors of the circle. Obviously, the smaller the central angle of the sectors, the smaller the conformal mapping errors. Figure 5 shows graphs of the dependence of internal and external angles, which are of interest for the analysis of conformal mapping errors, the number of partitions of the circle into sectors. Consider the equation of a circle with a radius ρ centered at a point {x0 , y0 }. We write the equation in parametric form:

External angle

Internal (central) angle

Fig. 4 Part of a circle divided into sectors with the same central angles

Angles 180 150 120 90 60 30 0

3

6

9

12

15 Internal

18

21

24

27

30

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36 n

External

Fig. 5 Graphs of the dependence of internal and external angles on the number of partitions of the circle

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{

x = x0 + ρ cos ϕ ; y = y0 + ρ sin ϕ,

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(1)

where ϕ is the angle formed by the rotating radius ρ with the positive axis Ox direction. The function f (ρ, ϕ) has continuous partial derivatives of all orders in the vicinity of the point {x0 , y0 } and satisfies the Cauchy-Riemann conditions. Let us write in general terms the expression of the Taylor series for a function of two variables {ρ, ϕ }: | | ∂ f (ρ0 , ϕ0 ) 1 ∂ f (ρ0 , ϕ0 ) (ρ − ρ0 ) + (ϕ − ϕ0 ) + 1! ∂ρ ∂ϕ ⎡ 2 ⎤ 2 ∂ f (ρ0 , ϕ0 ) ∂ f (ρ0 , ϕ0 ) 2 + 2 − ρ − ϕ − ρ (ρ (ρ 0) 0 )(ϕ 0 )⎥ 1⎢ ∂ρ 2 ∂ρ∂ϕ ⎥ + ... + ⎢ ⎦ 2! ⎣ ∂ 2 f (ρ0 , ϕ0 ) 2 + − ϕ (ϕ ) 0 ∂ϕ 2

f (ρ, ϕ) = f (ρ0 , ϕ0 ) +

As applied to the problem under consideration, the circle equation takes the following form: { ) ( x0 + ρ cos ϕi ; ϕi = 0, /\ϕ, 2/\ϕ, . . . , n/\ϕ. f ϕi |ρ=const ∼ (2) y0 + ρ sin ϕi , Thus, assuming a constant parameter, we)pass to a function of one (discrete) variable ( ϕi . We consider the function f ϕi |ρ=const to be discontinuous with the discretization interval ϕi+1 − ϕi = /\ϕ. Taking into account the previously introduced assumptions about the properties of the transformation of the complex plane xOy into another complex plane uOv, we expand the functions (2) in a Taylor series with retention of two terms. We call it modified quasi-conformal mapping. In order to avoid sophisticated and bulky transformations, we use the parametric definition of function (2). The series will look like this: ) ( 1 d f (ϕ0 ) 1 d 2 f (ϕ0 ) f ϕi |ρ=const = f (ϕ0 ) + (/\ϕ − ϕ0 )2 . (/\ϕ − ϕ0 ) + 1! dϕ 2! dϕ 2

(3)

Now you need to calculate the first and second derivatives of the function. When defining a function parametrically, you can use universal formulas. y ' (x) =

yϕ' xϕ'

;

(4)

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yx''x

=

( ' )' yx ϕ xϕ'

( ') =

yϕ xϕ'

xϕ'

=

yϕ'' · xϕ' − xϕ'' · yϕ' . ( )3 xϕ'

(5)

However, there is an easier way. In order to find the second derivative y '' (x) of a given function {x = cos ϕ; y = sin ϕ }, we first find its first derivative y ' (x): yx' =

(sin ϕ)'ϕ

(cos ϕ)'ϕ

=

cos ϕ = −ctgϕ. − sin ϕ

(6)

The second derivative yx''x is formally the first derivative of y ' (x), therefore, it can be taken by a formula similar to (4): yx''x

=

( ' )' yx t xt'

1 (−ctgt)'t 2 = sin t = − 3 . − sin t (cos t)'t sin t 1

=

(7)

Specifying in expression (3) the functions of the first and second derivatives (6, 7), we obtain the final expression for the Taylor series with retention of terms up to quadratic inclusive: ( ) f ϕi |ρ=const = f (ϕ0 ) − ctgϕ × (/\ϕ − ϕ0 ) −

1 (/\ϕ − ϕ0 )2 . 2 sin3 ϕ

(8)

Using expressions (3–8), the normalized errors of the quasi-conformal mapping were calculated. Figure 6 shows graphs of the exact values of the circle Eq. (2) and approximation by a Taylor series with retention only linear term of expansion. Figure 7 shows plots of normalized errors of the quasi-conformal mapping during approximation by a Taylor series with retention of only the linear expansion term and with retention of the expansion terms up to including the quadratic one. The errors of the quasi-conformal mapping depend on the value of the central angle of the sector (and, accordingly, on the number of sectors of the partition of the circle). For example, for a sector with a central angle of 90° (a quadrant), the approximation error will tend to infinity. However, with a reasonable choice of the angular size of the sector, the errors of the quasi-conformal mapping with approximation by the Taylor series with the retention of the terms of the expansion up to the quadratic inclusive will always be smaller. Finally, the using modified quasi-conformal mapping gives us substantial improvement of accuracy.

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Fig. 6 Graphs of the exact values of the equation and approximation of the Taylor series with a linear expansion term

Fig. 7 Plots of normalized errors of a quasi-conformal mapping with one and two expansion terms

5 Conclusions This study is devoted to the problem of conformal mapping of discontinuous (piecewise-continuous) functions when calculating the inverse Radon transform by successively applying the discrete Fourier transform. For any conformal mapping, there is some orthogonal grid of curves that transforms into a rectangular Cartesian grid. A typical example of an orthogonal grid of curves is a polar grid. The difficulties that arise when replacing the Radon transform with the Fourier transform can be successfully overcome by applying a quasi-conformal mapping. With fan-shaped scanning, we work in a limited angular range of directions of sighting of a point source of radiation. Then it turns out to be possible to use quasi-conformal

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mappings and approximation by a Taylor series with retention of terms of the series up to and including the quadratic one. By applying the parametric assignment of functions that describe the equations of the circular sector, it is possible to significantly simplify the calculation of the derivatives that make up the Taylor series. The comparative analysis of conformal mapping errors and, consequently, the accuracy of calculation results using Fourier transform algorithms is simplified. In the future, it is planned to investigate the issues of stability and sensitivity of the Radon transform method as an inverse problem of mathematical physics.

References 1. Martynenko, M.A.: Theory of the function of complex change. Operational calculus. Martynenko, M.A., Yurik, I.I.– K.: Publishing House "Word" (2013), p. 296 2. Caratheodory C.: Theory of Functions of a Complex Variable, 2nd ed, vol. 1, p. 301. Chelsea Pub. Co (2001) 3. Lavrentiev, M.A., Shabat, B.V.: Methods of the Theory of Functions of a Complex Variable, 4th ed, p. 749. Nauka, M. (1973) 4. Sveshnikov, A.G., Tikhonov, A.N.: Theory of Functions of a Complex Variable, 6th ed, p. 336. Fizmatlit, M. (2005) 5. Heinonen, J.: What is ... a quasiconformal mapping? Notices Am. Math. Soc. Nr. 53(11), 1334–1335 (2006) 6. MR 2268390, Zbl 1142.30322 7. Papadopoulos, A.: Handbook of Teichmüller Theory, vol. VII, p. 626. EMS Press (2020) 8. Lavrent’ev, M.A.: Conformal Mappings with Applications to Some Problems in Mechanics, p. 166. Ripol Classic (2014) 9. Harris, F.E.: Mathematics for Physical Science and Engineering, p. 944. Academic Press (2014) 10. Ponomarenko, O.: Tomographic application-specific integrated circuits for fast radon transformation. In: Ponomarenko O., Bulakovskaya A., Skripnichenko A. et al. (eds.) Proceedings of the International Workshop on Cyber Hygiene (CybHyg-2019) co-located with 1st International Conference on Cyber Hygiene and Conflict Management in Global Information Networks (CyberConf 2019), pp. 339–351. Kyiv, Ukraine, November 30 (2019) 11. Zeng, W.: Computing quasiconformal maps using an auxiliary metric and discrete curvature flow. Zeng W., Lui L.M., Luo F., Chan T.F.C., Yau S.T., Gu D.X. (eds.) Numerische Mathematik, vol. 121, pp. 671–703 (2012) 12. Zhang, D.: A unifying framework for n-dimensional quasi-conformal mappings. Zhang D., Choi G.P.T., Zhang J., Lui L.M. (eds.) SIAM J. Imag. Sci. 15(2), pp. 960–988 (2022) 13. Huang, M.: On the quasisymmetry of quasiconformal mappings and its applications. Huang M., Ponnusamy S., Rasila A., Wang X. (eds.). arXiv:1207.4360 [math.CV] (2013). https://doi. org/10.48550/arXiv.1207.4360 14. Farroni, F., Giova, R.: Change of variables for A_\infty weights by means of quasiconformal mappings. Annales Academiae Scientiarum Fennicae Mathematica 38, 785–796 (2013) 15. Ram, M. (ed.): Mathematics in Engineering Sciences: Novel Theories, Technologies, and Applications, p. 384. CRC Press, Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300. Boca Raton, FL 33487–2742 (2020) 16. Herman, G.T.: Fundamentals of Computerized Tomography: Image Reconstruction from Projections, 2nd ed, p. 312. Springer-Verlag London Ltd (2010)

Electric Power Engineering

Information Support for Identification of the Technical State of Electric Power Facilities Vitalii Babak , Artur Zaporozhets , Svitlana Kovtun , Mykhailo Myslovych , Yurii Kuts , and Leonid Scherbak

Abstract This chapter deals with the actual scientific and applied task of identifying the state of operating electric power objects, which have worked out more than 80% of their regulatory resource to date. Proposed information support for such identification based on the use of the information resource of the noise signal, which is formed during the operation of the object. The noise signal, which is a stochastic spatial noise field, most integrally reflects the state of the electric power object. For practical use, information support for identification is proposed, a mathematical model of the noise signal is substantiated in the form of a linear stationary random process as a put in component of the studied noise field. Such a model describes the physical process of the noise signal of an object and is a color stochastic noise obtained by converting the current stationary white noise by a linear filter. Color noise is characterized by infinitely separated distribution laws, including Gaussian and Poisson laws. It is proposed to use the density of the noise signal distribution law as characteristics of the object state identification, the statistical evaluation of which is carried out to process the realizations of a linear stationary process with using a system of Pearson curves. Algorithmic software for statistical estimation of empirical noise signal densities of the system of Pearson curves for practical use has been developed. Keywords Electric power object · Condition identification · Object noise signal · Information resource · Linear stationary process · Pearson curve system · Algorithmic software

V. Babak · A. Zaporozhets (B) · S. Kovtun · L. Scherbak General Energy Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] M. Myslovych Institute of Electrodynamics of NAS of Ukraine, Kyiv, Ukraine Y. Kuts National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_9

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1 Introduction From a significant number of works based on the results of studies of electric power facilities, we will consider the following [1–4]. Thus, the work [1–12] is devoted to the issues of preliminary preparation of experimental data of electric power facilities before their further processing by computing instruments, including with the help of information-measuring systems (IMS) of monitoring and diagnostics. In turn, data preparation performed according to certain algorithms discussed in this paper makes it possible to reduce their volume for further processing, which is especially important with using Smart Grid technologies. The work [2] is devoted to the issues of ensuring the two-way exchange of information between different levels of electric power facilities. This paper discusses the practical application of wireless sensors compatible with the IEEE 802.154 standard in various electric power facilities. The paper presents the results of a comprehensive experimental study to determine the statistical characteristics of a wireless information exchange channel between objects at an electric power substation with a voltage of 500 kV. The conducted studies have shown the promise of using two-way sensors of the specified standard in IMS creating with using Smart Grid technologies. In Secic et al. [3], the issues of applying methods for monitoring the state of individual nodes of a power transformer based on the use of information diagnostic signals are considered. Significant interest is the classification of a certain number of published works carried out in the paper [3], which discusses the application of methods (frequency, time, etc.) of vibroacoustic diagnostics to determine the technical condition of the transformer, and also notes the features of the authors’ approaches and the results obtained during such diagnostics. In this work [3], attention is also drawn to the need to store in the memory of the monitoring and diagnostics IMS, and organization of quick access to diagnostic data—training sets (TS) on the technical condition of the studied transformers, which are formed in the process of training such IMS. In turn, quick access to the emergency data stored during the training process in combination with the appropriate software makes it possible to implement an intelligent approach to assessing the state of the studied electrical equipment. The issues of practical application of the intelligent system D5000 for the dispatching control of networks are considered in Zhou et al. [4]. The use of this platform in combination with the system architecture and appropriate software allows to conduct real-time monitoring and storage the data about the technical condition of electrical equipment. The papers [5–7] described the features of designing multi-level systems for diagnosing equipment of electric power facilities with taking into account the Smart Grid concept [5, 8], and on this basis a generalized structure of a multi-level system for monitoring the state and technical diagnostics of such objects was developed. In turn, the use of distributed computing resources and taking into account the degree of criticality of defects in various units of electrical equipment in the IMS, proposed

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in [5, 7], reduces the cost of the IMS itself while maintaining high accuracy and probability of detecting defects with its help. The current state of the application of various technologies in the electric power industry of the developed countries of the world is based on the results of solving a significant number of scientific, technical and industrial production problems, which makes it possible to provide: • high level of reliability and automation of the functioning of the entire technological chain: from electricity generation to final power consumption devices; • use of significant information and computing resources for active interaction, information exchange between objects of all levels of the hierarchical structure of modern intelligent multi-level electric power systems; • effective use of up-to-date information about the state of each facility, the appearance of defects, failures at all levels of the structure of electric power systems, formed by hardware and software systems for monitoring, identifying and diagnosing electric power systems. The development and creation of modern systems in developed countries takes place using various technologies, including the use of intelligent network technologies Smart Grid [5, 9]. In García et al. [1], it is emphasized that due to the complexity of the structure of the internal components and assemblies of the machine, it is difficult to obtain an accurate forecast of the state of such a machine, that is, it is full of uncertainty. Moreover, even for a single component, the representation of the characteristics of the received conditional monitoring signal may be different due to the different location of the sensors and the environment, which causes difficulties in the selection of characteristics and uncertainty in the identification of faults. In order to increase the reliability of the technical condition of the studied object, the authors [1] proposed a hybrid approach associated with ensemble learning of measurement results, diagnostic signals, carried out by multisensor monitoring systems using the organization of training of this system and concept of the so-called mixing autoencoder [1]. In addition, the six extracted features are combined using the two-stage SAE method proposed in García et al. [1]. According to this method, the measurement carried out by the appropriate sensor is identified with the measurement of the appropriate feature. A composite feature combined in a feature dimension is treated as a complex representation of the appropriate component. Finally, the combined features containing a complex representation of different components are used to predict the technical state of the machine by an ensemble of several deep belief classifiers. In García et al. [1], the effectiveness of the proposed method is illustrated by two examples - a wind turbine gearbox and a port industrial crane. Experimental results show that the proposed ensemble learning approach is exceed other traditional deep learning approaches in terms of prediction accuracy and prediction stability during working with a combination of multi-sensor features and accurate fault identification of heavy industrial machines.

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Let’s move on to the actual problems of the electric power industry of Ukraine [10]. Today, the electric power industry of Ukraine needs to solve a number of problems, one of the main ones is the continuation of the operation of existing electric power facilities, which, on average, have worked out more than 80% of their regulatory resource. Partially, this problem can be solved by creating modern hardware and software systems for monitoring, identifying and diagnosing operating objects. At the same time, it should take into account the world experience in creating such systems by developed countries, the timeframe for the solution, the economic and financial resources of Ukraine, and a number of other restrictions. One of the scientific technical problems is the identification of the actual state of the operating object of the electric power industry, which in most cases is reduced to the identification of the physical signal generated during the operation of this object. This chapter considers the identification of vibronoise objects of the electric power industry as an information resource of the actual state of the object using the results of these publications [11–16]. This chapter solves the problem of: developing information support to determine the actual state of energy facilities based on the identification of a noise signal generated during the operation of the facility and which is an appropriate information resource for identification, substantiation of the mathematical model and determining its characteristics of identification and developing of appropriate algorithmic software for practical use.

2 Main Part Let’s move to the substantiation of the physical noise signal generated during the operation of electric power facilities [13–15]. 1. The noise signal is the integral sum of the action of a large set of elementary impulse mechanical vibrations that propagate in the space of the studied object and form a space–time noise field. 2. The generated noise field is converted into appropriate sensory measuring instruments, as a rule, into electrical voltage (current) in limited volumes of space and time. 3. During forming arrays of TS (arrays for the implementation of noise signals), it is necessary to ensure the same conditions for conducting measurement experiments, while observing the stable mode of the electric power facility, stationary conditions for conducting research. TS of vibronoise signals play an important role for the effective use of systems for monitoring, identifying and diagnosing objects, including: • obtaining more probable (averaging over an ensemble of implementations) statistical estimates of the characteristics of identification of vibronoise signals;

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• as TS array, for conducting the learning process of artificial intelligence tools—instruments of intelligent systems for monitoring, identification and diagnostics; • saving the retrospective information, as well as current information on the operation of an electric power facility, that is, saving the profiles in the form of vibration and noise signals at past and current stages of its life cycle. 4. For statistical processing of the noise signal identification characteristics, it is necessary to develop and use appropriate algorithmic software based on a reasonable noise signal model. 5. It is important during conducting experimental studies to choose the spatial coordinates of fixation of sensory instruments using the well-known criterion for the maximum value of the signal-to-noise ratio. 6. The noise signal is one of the many information resources used to determine the actual state of the electric power industry object, but the noise signal of information resource itself is the most integrally powerful for assessing the actual state of the object. 7. Models and characteristics of noise signals can be used simultaneously as models and features of the operation of electric power facilities. Let’s consider an example of the structure of a multi-level electric power facility. Example 1 The structure of the levels of electrical equipment of a typical electric power plant. Let’s consider the breakdown at the level of electrical equipment using the example of an electric power plant. Figure 1 shows a stylistic representation of the structure of such levels of electrical equipment of a typical electrical power plant. At constructing this hierarchy, the goal was not to take into account all possible types of electrical machines and their components. Therefore, the above structure is not exhaustive, but only conditionally illustrates the principle of distribution of power plant equipment by levels in order to develop appropriate technical systems for monitoring, identification and diagnostics. At the first level there are structural elements of the main components of the power plant equipment. It is this level that determines what defects are possible in the studies object. A deep study of the elements located at the first level of the hierarchy provides all the necessary information about the types and causes of defects. The second level consists of equipment units that are structurally a single whole. This includes the rotor and stator windings of rotating machines, magnetic circuits, bearing assemblies, housing, frame, base, cooling system. The third level consists of the electrical equipment of the power plant: generators, auxiliary motors, transformers, circuit breakers, disconnectors, insulators, pumps, etc. The fourth level of the hierarchy is the level of the power plant as a whole. Let’s give the following example of a multilevel structure of a monitoring, identification and diagnostics system.

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Fig. 1 Schematic representation of the structure of the levels of electrical equipment of a typical electric power plant

Example 2 The structure of the means levels of monitoring, identification and diagnostics of electric power plant. Such structure consists of a number of modules, each of which is designed to select and pre-process measurement information about the technical condition of a specific unit (generator, circulation pump, transformer, etc.) and one central module that collects and summarizes information from all local modules, and also, if necessary, generalizes the data and transfers them to a higher level of the hierarchy (Fig. 2). Such a system should measure the values of signals that carry information about the actual state of the testing equipment. It is necessary to include sensors of measured physical quantities into the system. Depending on the object of identification, the system may include, for example, thermocouples or thermistors—for measuring

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Fig. 2 Schematic representation of the structure of the levels of the monitoring, identification and diagnostics system of the power plant

temperature, accelerometers—for measuring vibration intensity, measuring microphones—for determining the level of acoustic noise, acoustic emission sensors, various sensors of electrical quantities: measuring transformers, shunts, meters—for determining the electrical parameters and electric power accounting.

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Let’s describe in more detail the schematic representation of Fig. 2. The distribution of functions between these levels of system tools is proposed to be organized as follows: • I level—primary selection and preparation of research signals (measurement, amplification, analog filtering, digitization); • II level—preliminary mathematical processing and making intermediate decisions (simple algorithms, the implementation of which does not require significant computing resources, separation of information according to the degree of criticality of defects); information transfer to a higher level in the case the defects presence; accumulation of insignificant volumes of measuring information and its transfer to a higher level (upon request); • III level—accumulation, full processing and deep analysis of data, quick response to alarms from a lower level, decision-making for the facility as a whole, archiving of statistical data, reliability forecasting and assessment of the residual life of equipment, planning of repair work. Let’s consider more general issues of creating modern systems for monitoring, identifying and diagnosing electric power facilities [7, 16–18]. Almost all systems are built on the basis of digital computing tools (microcontrollers, personal computers, industrial workstations). Thus, the signals during measurements must be converted into digital form for the purpose of subsequent processing by computer means. To do this, the signals from the primary converters must undergo a certain primary statistical processing, be reduced to a certain unified voltage level, as well as to the selected frequency band. In some cases (mainly to ensure compatibility with the means already installed at the power facility), it may be rational to separate functions between different devices: data selection and pre-processing can be organized directly at the measurement points, and digitization can be done in a single unit with switching analog channels. At the same time, with developing new systems based on modern components, it is advisable to combine all of the above functions in one functionally complete device with a digital interface for two-way information exchange. At the input, the device must accept control commands, and at the output it must supply the implementation of the studied signal in some standardized form. This external interface can be either wired (based on RS232, Ethernet, USB, etc.) or wireless (based on BlueTooth, Wi-Fi, ZigBee). For further analysis of decision-making information, the measured and digitized signals are transmitted to the computing core of the system, which, depending on the specific need, can be in the form of a low-power microcontroller, a powerful modern computer or a cluster server system. The final stage of information processing within the system and diagnostics of power plant equipment is the display of results to users. To do this, it is necessary to include appropriate tools in the system structure, which, in particular, should ensure the authorization of system users, separation of access rights, and information protection.

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In complex objects, a significant number of studied signals can be measured, which leads to the exchange of huge amounts of information between the components of the system. To reduce the load on communication channels (this issue can be especially important when wireless data transmission channels are used), it is necessary to decentralize computing resources. The reduction in the amount of information transmitted between the system components can be achieved as follows: the measured implementation of the studies signal is not transferred to the computing core of the system immediately after digitization, but is subjected to simplified processing in the module responsible for its measurement. Further, depending on the results of such an intermediate analysis, this module decides what information to provide to the computational core. In this case, the following options are possible: • do not transmit any information at all—if no deviations from the normal state of the equipment were detected; • give a warning signal—if minor deviations were detected; • provide the measured implementation in the computing core for a complete analysis—if the identified deviations can be considered significant; • issue an alarm for immediate response—if critical deviations are detected. The functions discussed above are implemented in the module for measuring and processing the studied signals using specialized algorithmic support. It should be noted that taking into account the degree of criticality of defects at the stage of system development makes it possible to simplify its structure, reduce the amount of information processed in the system and transmitted between its levels, and ultimately reduce the cost of the system while maintaining its functions at a sufficient level.

2.1 Mathematical Models The solution of various problems of studying stochastic noise signals is based on the use of mathematical models. Despite the fact that they do not reflect all the properties and characteristics of real objects, models play a fundamental role in theoretical, modeling and experimental studies. The mathematical model generalizes the physical model to a greater extent. That is why it is used in most cases. The same mathematical model is applicable to describe objects of different physical nature. Based on the mathematical model, information support is created for modeling and statistical evaluation of the characteristics of noise signals based on the obtained experimental data. Based on the mathematical model, the characteristics and their statistical evaluations are determined, according to which the objects of research are identified. This fully applies to mathematical models of stochastic noise processes. Let’s formulate some definitions. Definition 1 A mathematical model of a noise signal is created on the basis of a set of knowledge, hypotheses, initial and limiting conditions formed from a priori data

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of noise signal studies, written using mathematical objects, terms and symbols in the form of a logically consistent, consistent structure that reflects the dynamics in space and time, properties, values and characteristics of the noise signal, and their quantitative assessment is carried out by processing experimental measurement and research data. The mathematical model in the process of conducting research has three stages of its life cycle. At the first stage, the model, based on a priori research results, is formed and becomes a research tool. At the second stage, the model is used in the relevant subject areas, a posteriori results of its application are accumulated. At the third stage, the analysis of the obtained a posteriori results is carried out and one of three possible decisions is made: • the model can be used in further research; • the model requires additional adjustments taking into account the results obtained, and can be recommended for use; • the model is not recommended to be used and a new model needs to be developed. Based on the obtained results of the study of noise signals, we present the following definition. Definition 2 The mathematical model of the noise field is a multidimensional random function ξ(ω, r, t), ω ∈ u, r = (x, y, z) ∈ R 3 , t ∈ T ; D(ξ ) = u × R 3 × T , E(ξ ) = R, with different distribution laws. The realizations of such model form an ensemble of multidimensional deterministic functions {

} u i (r, t), i = 1, n ;

D(u) = R 3 × T , E(u) = R. Based on the processing of the ensemble of realizations, statistical estimates of the space–time characteristics of the field are determined. We can formulate the following statistical hypothesis based on the results of the central limit theorem for the sum of independent random variables studied in probability theory [19–23]: the distribution laws of the noise field as a multidimensional random function of spatial and temporal variables are infinitely divisible. To date, this hypothesis has been confirmed: • for a random noise field—by the results of theoretical studies; • for processes with independent increments—by the results of theoretical and experimental studies.

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In practical studies of the noise field, more limited information is used in modeling and experimental studies of electric power facilities. We can form the following definition. Definition 3 The mathematical model put in the studied space–time noise field of the power industry object is a random vector process of the form. { } En (ω, t) = ξi (ω, t), i = 1, n , ω ∈ u, t ∈ T , with D(E) = u × T , E(E) = R. Realizations of a vector random process form an ensemble of deterministic functions { } u i j (t), i = 1, n, j = 1, m , with D(U ) = T , E(U ) = R. In the problems of studying noise signals and fields, both continuous and discrete models are used. During conducting modeling and experimental studies, discrete models are mainly used, which are a special case of continuous models. Depending on the research conditions, various combinations of continuous and discrete areas are used to define multidimensional models. In the aggregate of such models, there are: continuous models with continuous domains of definition; • discrete models with discrete domains of definition, that is, given on the corresponding discrete lattices of variables; • discrete–continuous or continuous-discrete models, if the general domain of definition of the model is a combination of continuous and discrete domains of definition of variables; thus, for a random space–time field ξ(ω, r, t), the number of combinations of continuous and discrete domains of definition of variables is equal to 25 − 2 = 30.

2.2 Measures of Stochastic Noise Signals The multidimensional random functions {ξ (ω, r, t), En (ω, t)} have • by variable ω ∈ u—a probabilistic measure, which is explicitly given by the probability distribution functions (the integral law of the probability distribution) of the corresponding functions;

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• by spatial variables r = (x, y, z) ∈ R 3 and observation time interval t ∈ T —the corresponding measures of physical quantities, that is, the physical quantities of spatial variables x ∈ R, y ∈ R, z ∈ R and time t. The given set of continuous and discrete mathematical models of stochastic noise signals is written in a general form. Therefore, an urgent task is to use constructive mathematical models, which to a greater extent reflect the specifics and basic properties of stochastic noise signals for identifying research objects.

2.3 Constructive Mathematical Models Such models of noise signals are investigated in two directions: • in the theory of random functions—random processes and fields with independent increments, infinitely divisible probability distribution laws, including Gaussian and Poisson, generalized random processes of white noise, random processes of color noise; • applied methods of mathematical physics, statistical radiophysics and statistical radio engineering—methods of shaping linear filters, white noise, generating and recovering random processes, stochastic integral representations. In this work, taking into account its practical orientation, a constructive model of the noise signal of an electric power plant will be used, namely, a linear stationary random process of the form {∞ ξ (ω, t) =

ϕ(t − τ )dη(ω, τ ), −∞

where ϕ(τ )—deterministic square-integrated function having a physical interpretation of the impulse transient function of a linear shaping filter; η(ω, τ )—homogeneous random process with independent increments, whose generalized derivative η' (ω, τ ) is a random stationary white noise process with independent values. It is known [19] that the characteristic function of a homogeneous random process η(ω, τ ) is described by an infinitely divisible distribution law with finite variance in the form of the canonical Kolmogorov formula: ⎫ ⎧ ⎛ ⎞ {∞ ⎬ ⎨ iux f η (u, t) = exp |t|⎝iua + eiux − 1 − 2 ⎠d K (x) , ⎭ ⎩ x −∞

where a—parameter, K(x)—Poisson spectral function in the Kolmogorov form (spectral function of Poisson jumps).

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Let’s present with using the results of the work [19, 20] the characteristics of a linear stationary random process—the information characteristics of the identification of the noise signal of the electric power object: • one-dimensional characteristic function ⎧ ⎫ ) {∞ {∞ ( ⎨ ⎬ iuxϕ(τ ) d eiuxϕ(τ ) − 1 − f ξ (u) = exp iua + d K x) , (τ, τ x ⎩ ⎭ x2 −∞ −∞

• cumulant function of the m-th order, m > 1 {∞ κm [ξ(ω, t1 ), ξ(ω, t2 ), ..., ξ(ω, tm )] = cm

ϕ(t1 − τ )ϕ(t2 − τ ) · ... · ϕ(tm − τ )dτ ; −∞

{∞ cm =

x m−2 d K (x), −∞

• expected value {∞ Mξ(ω, t) = a

ϕ(τ )dτ ,

−∞

• correlation function {∞ R(τ ) = K 2

ϕ(t)ϕ(t + τ )dt,

−∞

• spectral power density {∞ S( f ) = 4

R(τ ) cos(2π f τ )dτ. 0

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2.4 Identification of Noise Signals of Electric Power Facilities Based on the System of Pearson Curves The correlation (energy theory) of random processes limits the study of noise signals of electric power facilities within the framework of the first two moment functions (expected value, variance, correlation function, spectral density and function). The use of a linear random process as a model of a noise signal makes it possible to study such signals using the moments of functions and higher orders. This is not the only rationale for choosing a system of Pearson curves for identifying noise signals. So, on the basis of the characteristic function of a linear process using the FourierStieltjes transform, one can investigate the laws of the probability distribution of such a process, and, accordingly, evaluate the distribution laws of the noise signal. In addition, for the system of Pearson curves, algorithmic software for the statistical evaluation of the characteristics of the noise signal has been developed. Let’s consider the main ratios and algorithms that formed the basis of the software for histogram analysis of the noise signals of the power industry object of the system of Pearson curves. The famous English scientist Karl Pearson in his original works at the end of the nineteenth-twentieth centuries studied distributions that have skewness and kurtosis (associated with the third and fourth moments), that is, different from the Gaussian (normal) distribution. In order for the theoretical curve to describe the experimental distribution with sufficient accuracy, it must have the same numerical values of the first four moments as the experimental curve. Based on the difference equation, which satisfies the hypergeometric distribution, and introducing some additional assumptions, Pearson studied a differential equation of the form x −a dp(x) = p(x), dx b0 + b1 x + b2 x 2

(1)

where p(x)—the distribution density of the studied noise signal, which is described by a linear stationary random process. Solutions to this equation are called Pearson curves. The a, b0 , b1 and b2 parameters, included in (1), can be expressed in terms of the first four moments of the distribution for which the analytical expression is selected, namely, under the condition that the distribution is centered a = b1 , b0 = where

c0 c1 c2 , b1 = , b2 = , d d d

(2)

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( ) ⎧ 2 c0 = −μ2 4μ 2 μ4 − 3μ ⎪ 3 ; ⎪ ( ) ⎨ c1 = −μ3 μ4 + 3μ22 ; ⎪ c = −2μ2 μ4 + 6μ32 + 3μ23 ; ⎪ ⎩ 2 d = 10μ2 μ4 − 18μ32 − 12μ23 .

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(3)

where in μn , n = 2, 3, 4—distribution central moments. As is known, the general solution of (1) has the form p(x) = Ce

φ(x)

{ , φ(x) =

x − a0 d x. b0 + b1 x + b2 x 2

(4)

The constant C, included in (4), is selected from the condition that the density of any distribution must satisfy, namely: {∞ p(x)d x = 1.

(5)

−∞

The character of the curve φ(x) can be very different depending on the roots of the characteristic equation b0 + b1 x + b2 x 2 = 0.

(6)

In [20], the next value was introduced κ=

b12 , 4b0 b2

(7)

the value of which determines the roots of Eq. (6), and then the character of the distribution curve. The value (7) was called “Pearson’s criterion” or “Pearson’s kappa”. Pearson’s kappa is the main criterion for choosing the type of approximating curve. In total, he singled out 12 types of curves, among which 3 are the main ones, while others are their limiting or special cases. The main types occur at the following values of Pearson’s kappa: type I—at κ < 0, type IV—at 0 < κ < 1, type VI—at κ > 1. Some authors additionally consider the Gaussian (normal) distribution as XIII type of Pearson curves. The shape of any curve from the Pearson system is determined by two parameters depending on the values of the first three central moments: β1 =

μ˜ 4 μ˜ 23 , β2 = 2 , μ˜ 32 μ˜ 2

where μ˜ k —estimation of k-th central moment, k = 2, 3, 4.

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Therefore, each specific distribution curve can be uniquely associated with a certain point on the plane (β1 , β2 ). The β1 and β2 parameters determine how asymmetric and deformed the curve is along the vertical axis relative to the normal Gaussian curve (point G in the diagram). Let’s consider expressions for the three main types of Pearson curves. We introduce auxiliary quantities: ( / ) l1 = 5β2 − 6β1 − 9, l2 = 3β1 − 2β2 + 6, S = 2 l1 l2 + 1 , w = β1 (S + 2)2 + 16(S + 1). Pearson’s kappa due to β1 and β2 values is expressed as follows: κ=

−β1 (S + 2)2 . 16(S + 1)

Type I corresponds to the distribution | | | | | x ||q2 x ||q1 || | p(x) = p0 |1 + | |1 − | , −a1 < x < a2 , a1 a2

(8)

where a1 + a2 > 0, q1 > −1, q2 > −1, p0 —normalizing factor. The parameters, included in (8), are determined through the estimates of the moments as follows: q1 =

1 1 [(S − 2) + ν], q2 = [(S − 2) − ν]; 2 2

a1 = p0 =

/ 1 1 / c μ˜ 2 w, a2 = (1 − c) μ˜ 2 w; 2 2

|a1 |q1 |a2 |q2 |(q1 + q2 + 2) , (a1 + a2 )q1 +q2 +1 |(q1 + 1)|(q2 + 1)

where / / 2ν˜ 1 q1 + 1 −√ , ν = −sign(μ˜ 3 )S(S + 2) β1 |w|; c = S β2 w and ν˜ 1 – estimate of expected value (first sample moment). With a certain change of variables in (8), it transforms into the known expression of the beta distribution. Type IV corresponds to the expression ( )q x2 x p(x) = p0 1 + 2 e−ν·ar ctg a , −∞ < x < ∞, a

(9)

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where −∞ < ν < ∞, a > 0, q < −2.5, p0 , – normalizing factor. The parameters, included in (9), are determined in terms of sample moments as follows: / 1/ −μ˜ 2 w, p0 = [a · F(−S, ν)]−1 , q = l1 l2 , a = 4 where {π / / − νπ 2 ν = −sign(μ˜ 3 )S(S + 2) β1 |w|; F(S, ν) = e sin S (x)eνx d x. 0

Type VI corresponds to the expression p(x) = p0

( x )q1 ( x a

a

)q2 − 1 , x / a > 1,

(10)

where a /= 0, q1 < −4, q2 > −1, p0 —normalizing factor. The parameters included in (10) are determined in terms of sample moments as follows: q1 =

/ 1 1 1 [(S − 2) + ν], q2 = [(S − 2) − ν]; a = sign(μ˜ 3 ) μ˜ 2 w; 2 2 2 p0 =

|(−q1 ) , |a||(−q1 − q2 − 1)|(q2 + 1)

where / / ν = −S(S + 2) β1 |w|. Distribution (10) is specified on the interval −∞ < x < a at μ˜ 3 < 0 and on the interval a < x < ∞ at μ˜ 3 > 0. The other types are special cases of the above expressions: • for specific values of β1 and β2 , which corresponds to one point on the (β1 , β2 ) plane—types X and G; • with certain ratios between β1 and β2 , which corresponds to lines on the (β1 , β2 ) plane—types II, III, V, VII, VIII, IX, XI, XII. Figure 3 shows areas, lines, and points corresponding to particular types of Pearson curves. Detailed information about the formulas of all Pearson curves can be found in his original papers, as well as in a number of later papers by other authors, for example, in [5, 7, 20]. Any empirical distribution corresponds to a certain Pearson distribution curve, since having a finite implementation, one always obtains finite values of the marks

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Fig. 3 Diagram of Pearson curves in the plane (β1 , β2 )

of the first four moments. Based on these values, a well-defined value of the Pearson criterion is calculated, and, consequently, a well-defined type of distribution. It is obvious that the curves obtained by the Pearson method do not necessarily fully correspond to the true empirical distribution. Their moments of order higher than 4 may differ from the moments of the empirical distribution. But in most practical cases, the approximation of the distribution of the closest curve from the Pearson system fully satisfies the requirements of the research problems.

2.5 Algorithmic Software for Identifying Noise Signals Based on the System of Pearson Curves Let’s consider an algorithm for selecting a specific curve from the Pearson system that optimally describes the empirical data. This task is not trivial, as will be shown below. Perhaps one of the first programs for smoothing empirical histograms using a system of Pearson curves was written in the 70 s of the XX century for “Mir 1” and “Mir 2” machines. This program used the original algorithm for selecting the most appropriate type of Pearson curve. Further, this algorithm was improved, and in 1986, based on the results of developments, a package of applied programs for statistical processing of time ergodic series was transferred to the State Fund of Algorithms and Programs of the Ukrainian SSR.

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Fig. 4 Scheme of the Pearson curve fitting algorithm

A diagram of the Pearson curve fitting algorithm generated as a result of lengthy studies is shown in Fig. 4. A feature of this algorithm is that, as a result of the selection, not one type of curve is proposed, but several types, that are closest to the empirical distribution. The algorithm, shown in Fig. 4, in fact, determines in which area the (β1 , β2 ) point, obtained as a result of the experiment, lies. This gives Pearson’s basic type: I, IV, or VI. Next, it is determined to which of the lines (or points) of transition types the experimental point lies fairly close. These types are offered as alternatives to the basic types because their expressions are simpler to write and contain less parameters than the basic ones. Therefore, it is easier to work with them during further statistical analysis of experimental data. This approach is, in fact, justified, since the values β1 and β2 obtained as a result of processing the measurement results are random variables and therefore have a certain scatter. Thus, it is quite possible that the theoretical values of these parameters lie just on one of the lines of transitional types. But at the same time, in the considered algorithm, the parameters of transition types are determined for the experimentally obtained values (β1 and β2 ), which in some cases leads to incorrect expressions, since formulas are used that are formally not valid for such parameter values. In practice,

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Fig. 5 Fragment of the diagram of Pearson curves in the (β1 , β2 ) plane

this can lead to the fact that the distribution curve will take on negative values for some abscissas, or that the area of the curve will not be equal to 1. Let’s consider an improvement of the Pearson curve selection algorithm, which allows to avoid the above problems. Figure 5 shows a fragment of the diagram in the (β1 , β2 ) plane in the area of small values of ( β1 and) β2 . The point β¯ 1 , β¯ 2 corresponding to the empirical distribution will be denoted as A. In the example shown, it lies in the area of the main type I between the lines of the transitional types II, III and IX. In addition, it is quite close to the G point, which corresponds to the normal distribution. With a small number of measurements, the scatter of estimates of the β1 and β2 parameters can be quite large. The area in which, at a certain level of confidence probability, the (β1 , β2 ) point corresponding to the “real” distribution could be located is conventionally shown in Fig. 5 part of an ellipse. It seems quite possible that the “real” distribution of the measurand does not belong to the basic type I, but to one of the transitional types II, III, IX or G. Thus, expressions of all five listed types can be used to smooth the empirical diagram. The algorithm considered above does not answer the question which of these expressions describes the experimental data better than others. Thus, expressions of all five listed types can be used to smooth the empirical diagram. The algorithm considered above does not answer the question which of these expressions describes the experimental data better than others. In addition, as already mentioned, the substitution of empirical (β1 , β2 ) in expressions of types II, III, IX, or G is incorrect, since they are not formally valid for such values. To find points that really belong to the transition types of curves, we draw perpendiculars from point A to the corresponding lines and find the points of intersection.

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It is known [5] that a line, corresponding to type III, satisfies the equation 2β2 − 32β1 −6 = 0, that is, a straight line. The line corresponding to type II is also a straight line—a fragment of the y-axis from the point β2 = 1.8 to the point β2 = 3.0. The distance from a point to a straight line on a plane is found according to the classical expression known from the course of analytic geometry. A line of type IX satisfies the equation β1 (8β2 − 9β1 − 12)(β2 + 3)2 = (4β2 − 3β1 )(10β2 − 12β1 − 18)2 , that is, not a straight line. from point A to this line can be found by minimizing the expression /(The distance )2 ( )2 ( ) β1 − β 1 + β2 − β 2 over the entire type IX curve, where β 1 , β 2 are the coordinates of point A. The point at which the minimum is reached will be considered a point of type IX corresponding to the empirical distribution. Further, according to the χ 2 − criterion, at a given level of confidence, the statistical hypothesis is tested that the theoretical distribution corresponds to the empirical one [21]. This is true for all types: II, III, IX and G. As a result, some types may be rejected as not corresponding to the empirical distribution with a given confidence level. The rest of the types can be sorted by the size of the corresponding χ 2 statistic and thus suggest the best descriptive empirical distribution. Based on the analytical ratios proposed by Pearson and considered in the previous subsection, the PEARSON program for PC was developed, designed to obtain quantitative estimates of diagnostic features based on the analysis of the distribution of vibronoise signals. The main window of the PIRSON program is shown in Fig. 6. It is divided into several meaningful parts. It can be also selected a data file for analysis using the button located on the left directly below the main menu area of the program. The name of the selected file is displayed in the box to the right of this button.

Fig. 6 PIRSON main window

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Fig. 7 Type diagram

The input data for the PIRSON program is generated as a text file (in ASCII code). The left part of the window contains control elements for selecting a part of the sample, centering and normalizing the time series, as well as a button for starting calculations. After clicking on it, numerical values of the calculated parameters are displayed in the lower left corner of the program window, and graphical or tabular results are displayed in the right part of the window, the choice between which is carried out using tabs. The program allows you to display the following results: • a histogram with a graph of the selected curve superimposed on it; • a diagram of types of Pearson curves (Fig. 7); • a report on the results of calculations in text form, containing the value of all calculated parameters, as well as tables that can be used to build graphs in a third-party program (Fig. 8). The report contains the following items: • • • •

type of histogram: centered, normalized or not; sample size, number of intervals in the histogram; estimates of initial moments; estimation of variance, standard deviation, histogram interval, smallest and largest observation; • estimates of central moments, coefficients of skewness and kurtosis, Pearson’s kappa; • type of smoothing curve and its parameters (more than one curve, if possible);

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Fig. 8 Report about the results of calculations with buttons for additional operations

• tables of values of the smoothing curve and histograms, their corresponding graphs. The tab that displays the calculation results report in text form has additional buttons that allow to selectively write some tabular results to a text file. With the possibility of selecting several types of Pearson curves at the same time, it becomes possible to select only those of them that are of interest to the researcher, while hiding graphs that are not of interest to him. To do this, the buttons from “I” to “XIII”, as well as “Any” are introduced at the top of the right part of the main program window. The “Any” button is intended for experienced users who deeply understand the principles of the program and the theoretical foundations for selecting distribution curves using the Pearson method.

3 Conclusions 1. The information support for identifying the state of the electric power object using the information resource of the noise signal, generated during the operation of the object, is proposed. 2. In the general case, the generated noise signal is a stochastic space–time noise field. 3. In modeling and experimental studies of the stochastic noise field of an electric power object, technical means of measurement (monitoring) are used in limited volumes of space and finite time intervals.

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4. In this chapter, for practical implementation, a mathematical model of the noise signal of an electric power object is used in the form of a linear stationary random process as a put-in component of a stochastic noise field formed by a linear filter with an input random process of white noise. 5. In the algorithms for identifying the state of an electric power object, statistical study of implementations of a stationary linear random process is used using a system of Pearson curves as the corresponding empirical laws of probability distribution with statistical estimates of asymmetry and kurtosis coefficients. 6. Algorithmic software for statistical processing of experimental data of a noise signal for identifying the state of an electric power object is presented.

References 1. García, S., Luengo, J., Herrera, F.: Data preprocessing in Data Mining, vol. 72, p. 320. Springer International Publishing, Cham, Switzerland (2015). https://doi.org/10.1007/978-3-319-102 47-4 2. Gungor, V.C., Lu, B., Hancke, G.P.: Opportunities and challenges of wireless sensor networks in smart grid. IEEE Trans. Indust. Electron. 57(10), 3557–3564 (2010). https://doi.org/10.1109/ TIE.2009.2039455 3. Secic, A., Krpan, M., Kuzle, I.: Vibro-acoustic methods in the condition assessment of power transformers: a survey. IEEE Access 7, 83915–83931 (2019). https://doi.org/10.1109/ACC ESS.2019.2923809 4. Zhou, G.P., Luo, H.H., Ge, W.C., Ma, Y.L., Qiu, S., Fu, L.N.: Design and application of condition monitoring for power transmission and transformation equipment based on smart grid dispatching control system. J. Eng. 2019(16), 2817–2821 (2019). https://doi.org/10.1049/ joe.2018.8456 5. Myslovych, M.V., Sysak, R.M.: About some features of construction of intellectual multilevel systems of technical diagnostics of electric power objects. Tekhnichna Elektrodynamika 1, 78–85 (2015) 6. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Principles of construction of systems for diagnosing the energy equipment. In Diagnostic Systems for Energy Equipments, pp. 1–22. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-444 43-3_1 7. Myslovych, M.V.: Models of forms of representation of learning sets for multilevel systems of diagnosis of electrical equipment assemblies. Tekhnichna Elektrodynamika 2021(3), 65–73 (2021). https://doi.org/10.15407/techned2021.03.065 8. Zhou, Y., Wang, J., Wang, Z.: Multisensor-based heavy machine faulty identification using sparse autoencoder-based feature fusion and deep belief network-based ensemble learning. J Sens (2022). https://doi.org/10.1155/2022/5796505 9. Wu, J., Su, Y., Cheng, Y., Shao, X., Deng, C., Liu, C.: Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system. Appl. Soft Comput. 68, 13–23 (2018). https://doi.org/10.1016/j.asoc.2018.03.043 10. Liu, M., Yao, X., Zhang, J., Chen, W., Jing, X., Wang, K.: Multi-sensor data fusion for remaining useful life prediction of machining tools by IABC-BPNN in dry milling operations. Sensors 20(17), 4657 (2020). https://doi.org/10.3390/s20174657 11. Yan, H., Liu, K., Zhang, X., Shi, J.: Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions. IEEE Trans. Reliab. 65(3), 1416–1426 (2016). https://doi.org/10.1109/TR.2016.2575449

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12. Banerjee, T.P., Das, S.: Multi-sensor data fusion using support vector machine for motor fault detection. Inf. Sci. 217, 96–107 (2012). https://doi.org/10.1016/j.ins.2012.06.016 13. Babak, V., Zaporozhets, A., Zvaritch, V., Scherbak, L., Myslovych, M., Kuts, Y.: Models and measures in theory and practice of manufacturing processes. IFAC-PapersOnLine 55(10), 1956–1961 (2022). https://doi.org/10.1016/j.ifacol.2022.09.685 14. Stohnii, B., Kyrylenko, O., Butkevych, O., Sopel, M.: Information support of problems of electric power systems control. Energetyka: ekonomika, tehnologii, ekologia, 1, 13–22 (2012). https://doi.org/10.20535/1813-5420.1.2012.160112 15. Babak, V., Scherbak, L., Kuts, Y., Zaporozhets, A.: Information and measurement technologies for solving problems of energy informatics. In: The 1st International Workshop on Information Technologies: Theoretical and Applied Problems 2021. CEUR Workshop Proceedings, vol. 3039, pp. 24–31 (2021). http://ceur-ws.org/Vol-3039/short20.pdf 16. Babak, V.P., Babak, S.V., Eremenko, V.S., Kuts, Y.V., Myslovych, M.V., Scherbak, L.M., Zaporozhets, A.O.: Problems and features of measurements. In Models and Measures in Measurements and Monitoring, pp. 1–31. Springer, Cham (2021). https://doi.org/10.1007/9783-030-70783-5_1 17. Babak, V.P., Babak, S.V., Eremenko, V.S., Kuts, Y.V., Myslovych, M.V., Scherbak, L.M., Zaporozhets, A.O. (2021). Models and measures for the diagnosis of electric power equipment. In: Models and Measures in Measurements and Monitoring, pp. 99–126. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70783-5_4 18. Babak, V., Zaporozhets, A., Kuts, Y., Scherbak, L., Eremenko, V.: Application of material measure in measurements: theoretical aspects. In: Systems, Decision and Control in Energy II, pp. 261–269. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69189-9_15 19. Marchenko, B.H.: Method of statistical integral representations and its application in radio engineering. Naukova dumka, p. 191 (1973) 20. Marchenko, B.H., Myslovych, M.V.: Bearings vibrodiagnostics in digital machines. Naukova dumka, p. 196 (1992) 21. Stuart, A., Ord, K.: Kendall’s advanced theory of statistics, distribution theory, vol. 1. John Wiley & Sons (2010) 22. Lévy, P.: Processus stochastiques et mouvement brownien, vol 1948. Paris (1948) 23. Loeve, M.: Probability Theory. Courier Dover Publications (2017)

Comparison of the Energy Efficiency of Synchronous Power Generator with Spark Ignition Engine Using Different Types of Fuels Stefan Zaichenko

and Denys Derevianko

Abstract This chapter presents experimental studies to determine the energy performance of the generator using gasoline and liquefied petroleum gas. The following are selected as energy indicators using liquefied petroleum gas and gasoline: specific fuel consumption and energy efficiency factor of the power plant. The highest specific fuel consumption is observed at the lowest and heaviest loads. It has been found that the highest values of efficiency for different fuels are observed at different loads. The established values of the distribution of energy indicators allow to predict the optimal parameters of autonomous installations using different types of fuels at different load levels. An analytical model describing a test system for diagnosing an autonomous generator based on a single-cylinder four-stroke internal combustion engine makes it possible to determine energy efficiency. The mathematical model developed allows to determine the technical condition of the autonomous generator by the main parameter of the cylinder piston group of the internal combustion engine—the true compression ratio. The basis for determining an autonomous power generator condition is a test diagnosis system, which is based on the analysis of the starter motor generators current in different modes without fuel supply. As a new criterion for determining condition of an autonomous power generator based on the internal combustion engine, ratio of the average value of starter motor current when scrolling the depressed system and in the compressor, state is proposed. Setting a true compression ratio allows to determine the main parameters of the systems energy efficiency. Keywords Electric generator · Liquefied petroleum gas · Gasoline · Spark ignition engine · Autonomous power source · Technical condition · Internal combustion engine · Starting current

S. Zaichenko (B) · D. Derevianko National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_10

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1 Efficiency of the Synchronous Electric Generator with the Engine with Spark Ignition Operating on Various Types of Gasoline Fuel World’s energy strategy supports the course of rational “minimization” of energy use. In particular, in Ukraine a significant increase in the share of renewable energy sources and off-balance energy resources is planned to reach 57.73 million toe at the level of 2030 compared to 15.51 million toe used in 2005 (an increase of 3.72 times). According to the forecasts, growth of electricity production by renewable energy sources power plants in Ukraine (excluding electricity production at small hydropower plants and biofuels in the baseline scenario) was expected to the level of 50 million kWh in 2010; 800—in 2015; 1500—in 2020; 2000 million kWh—in 2030. At the same time, electricity production by industrial and municipal power units was expected to increase to 9.85 billion kWh in 2010; 10.8—in 2015; 11.4—in 2020; 13.5 billion kWh in 2030 [1, 2]. Analysis of the dynamics and structure of Ukraine’s electricity production sector over the past two years (2019–2020) shows a steady increase in the share of autonomous power generation in total electricity production from 1 to 2%, respectively 2372 million kWh and 5 357 million kWh. This feature is explained by the development of autonomous power supply systems, which not only complement stationary power plants, but in many cases provide solutions to important technical problems of power supply in distant areas. Autonomous energy units allow consumers to be independent from the centralized energy supply and to use the technologies, optimal for these conditions in accordance to the Smart Grid concept [3]. In different modes of the power supply system operation, electric generators usually operate with nominal load. This is due to the need for a reserve to provide electricity to variable amount of energy consumers. Analysis of the autonomous energy sources market, shows a fairly narrow range of generators [4]. The most common variant of an autonomous energy source (more than 50% of all units) are gasoline generators with spark ignition with a synchronous electric generator of 2.5–3.5 kW rated capacity. The actual efficiency of the generator unit in case of underload depends on the efficiency of the engine and the efficiency of the synchronous generator operating at different loads. There is a number of methods for determination of these parameters for both internal combustion engines (ISO 15550: 2016) and for synchronous generators (DSTU IEC 60,034-2-1: 2019). However, the application of these methods for autonomous generators is complicated due to the need of examination of the individual components, the internal combustion engine and the generator, which in some cases is impossible to define due to their design. That is why consideration of the system states, as a whole is needed to facilitate the process of setting the parameters of engine- generator system performance. To use the specified approach, it is necessary to modernize the existing methods of research of autonomous electrical units at different load levels, which are generally aimed at determining the power supply quality indicators in nominal operating modes or other to nominal (overload) conditions.

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A number of works, which can be divided into two main groups, are devoted to the analysis of energy efficiency parameters of units based on internal combustion engines. The first group considers the process of determining the energy parameters by studying mainly the characteristics of the internal combustion engine. In this case, as a characteristic of energy efficiency the ratio of fuel consumption rate to engine’s capacity—brake-specific fuel consumption is used. This approach is based on the fact that the main losses of units fall on the internal combustion engine [5]. The second group of studies is devoted to the analyses of power unit as a whole, in some cases considering the energy performance of system’s components. Energy efficiency in this case is assessed by the ratio of generated power and fuel consumption. A characteristic which is common for these studies is the determination of energy efficiency indicators for units with load variation. Also in both research groups the internal combustion engine is connected to the electromechanical load—an electric brake or the generator. Among the total scope of papers of the first group part of the papers are devoted to the use of mixtures of alternative fuels with mineral fuels. These studies mainly experimentally investigate the influence of various factors (the ratio of biofuels to mineral fuels, gas and liquid fuels, speed, angle of advance of the spray or ignition) on energy performance indicators of the units. The main purpose of aforementioned research papers is to confirm the possible reliable and safe for the environment use of alternative fuels. A number of studies note the independence of specific consumption from the power plant type [6]. In the paper [7] on operation optimization of the power unit which use a mixture of fuels (hydrogen and gasoline) the concentration of fuels and the speed of the shaft of generators with internal combustion engine are used as a main factors. The established pattern is the optimal efficiency value change at different concentrations of the fuel mixture and the load, which allows to optimize the generation process by using different mixtures. To determine the conditions that correspond to the optimal value of the efficiency ratio, most experiments were conducted on the minimum power values ( ϕmax , than

(7)

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ϕij (t + 1) = ϕmax .

(8)

ϕij (t + 1) < ϕmin ,

(9)

ϕij (t + 1) = ϕmin .

(10)

Reciprocally, if

than

For to get the shortest route is true (11): | | f (x+ (t)) = |x+ (t)|.

(11)

7. Condition check. Steps 4–7 are repeated until one of the following conditions for terminating the algorithm is met: • passed a certain number of iterations of the algorithm; • the resulting route does not change during a certain number of iterations of the algorithm; • if a function that evaluates the quality of the received path is greater than the minimum set value that determines the quality of the route. • obtaining the arrival path of the special services unit to the places of emergency situations. Let’s show how the method for calculating the arrival path of the special services unit to the sites of emergency occurrence works on a test example. Input data are startpoint—1, endpoint—1, forbidden sections of the path—5. Figure 2 shows the results of the method after 150 iterations of the algorithm. Figure 3 shows the results of the method after 350 iterations of the algorithm. Figures 2 and 3 show that the path does not pass through the forbidden sections, the change is performed only at the turnpoints and it is visually seen that the path is the shortest. The total number and size of the forbidden section of the path can be set during the operation of the algorithm. Figure 4 shows the results of calculating the arrival path of the special services unit to the sites of emergency occurrence from the starting point (Kharkiv city) to the final point (Chkalovske, Kharkiv region) on a real map. Red color shows the result of the method for calculating the arrival path of the special services unit to the sites of emergency occurrence, provided there are 4 forbidden sections of the path. Green color shows the result of the method with the addition of one more forbidden sections of the path near the city of Chuhuiv.

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Fig. 2 The result of the method for calculating the arrival path of the special services unit to the sites of emergency occurrence after 150 iterations

The path is laid in blue color by a known method. It does not take into account forbidden sections of the path and road junction. Calculation using this method is only suitable for special services units on air transport (for example, helicopters).

4 Conclusions Today in Ukraine the problem is very urgent providing assistance to units of special services, such as ambulance, rescuers, firefighters, public utilities, etc. Vehicles of these services need to pave the way based on the availability of streaming information about the state of roads and events taking place in the area. The chapter presents a typical approach to the response of special services units to an emergency. The calculation of the time of arrival of special services units to places of emergency situations is described. The dispatcher of special services units uses both real maps of the area and special applications to determine the optimal route of movement of the vehicles of the special services unit to the sites of emergency occurrence. The main disadvantage of the considered services is that their developer and owner is the russian federation. And as a result, these search and information services are completely controlled by the aggressor country.

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Fig. 3 The result of the method for calculating the arrival path of the special services unit to the sites of emergency occurrence after 350 iterations

Fig. 4 The results of calculating the arrival path of the special services unit to the sites of emergency occurrence from Kharkiv to Chkalovske on real map by the proposed method (red and green color) and the known method (blue color)

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Features for determining the optimal route for the movement of vehicles of the special services unit to the places of emergency situations are given. It is proposed to use a modification of the ant colony algorithm, namely, the Max–Min algorithm to calculate the arrival path of a special services unit to emergency situations. This choice was associated with the ability to take into account the presence of forbidden sections of the path when calculating the arrival path of a special services unit to the sites of emergency occurrence. The work of the method for calculating the path of arrival of a special services unit to the places of emergency situations on a test example is shown. The work presents the results of calculating the path of arrival of the special services unit to the places of occurrence of emergency situations from one starting point (Kharkiv) to one end point (Chkalovske, Kharkiv region) on a real map. It is possible to choose a different number of start and end points other than 1. The path laid using the proposed method does not pass through the forbidden sections, changes direction only at turning points. It is visually visible that this path is the shortest. The total number and size of the forbidden sections of the path can be set during the algorithm’s operation. The path laid by the proposed known method does not take into account prohibited areas, and is only suitable for air transport. The reliability of the obtained results of the method for calculating the arrival path of the special services unit to the sites of emergency occurrence was verified by comparing these results with the results of solving the problem by the exhaustive enumeration method. Area for further research is to develop the information technology of calculating the arrival path of the special services unit to the sites of emergency occurrence. The basis for it may be information technology [22].

References 1. Russia invaded Ukraine. Why does this matter to the world? https://war.ukraine.ua. Accessed every day 2. Ukraine in maps: tracking the war with Russia. https://www.bbc.com/news/world-europe-605 06682. Accessed 12 May 2022 3. Ukraine’s State Emergency Service—The backbone of the country’s emergency response. https://www.undp.org/ukraine/news/ukraine’s-state-emergency-service-backbonecountry’s-emergency-responseAccessed 13 April 2022 4. State Emergency Service of Ukraine (SESU). The main tasks of the State Emergency Service of Ukraine. https://www.devex.com/organizations/state-emergency-service-of-ukraine-sesu126814. Accessed 23 April 2022 5. Ukraine emergency—UNHCR. https://www.unhcr.org/ukraine-emergency.html. Accessed every day 6. Smallwood, C.A.H., Perehinets, I., Meyer, J.S., Nitzan, D.: Who’s emergency response framework: a case study for health emergency governance architecture. Eurohealth 27(1), 20–25 (2021) 7. National incident management system. Homeland Security (2008). 170 p. https://www.fema. gov/pdf/emergency/nims/NIMS_core.pdf. Accessed 25 April 2022

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8. Khudov, H., Tyurina, V., Ovod, Y., Kozyr, M., Chala, A., Khizhnyak, I.: The ways of psychological and pedagogical barriers overcoming between teachers and students during COVID-19 pandemic. Systematic Rev. Pharm. 11(11), 373–379 (2020). https://doi.org/10.31838/srp.2020. 11.55 9. Khudov, H., Makoveichuk, O., Khizhnyak, I., Shamrai, N., Glukhov, S., Lunov, O., Lohachov, S., Chervotoka, O., Halosa, A.: The method for determining informative zones on images from on-board surveillance systems. Int. J. Emerging Technol. Adv. Eng. 12(08), 61–69 (2022). https://doi.org/10.46338/ijetae0822_08 10. Aamodt, E., Meraner, C., Brandt, A.: Review of efficient manual fire extinguishing methods and equipment for the fire service. FRIC P4.1: Fire Extinguishment. FRIC—Fire Research and Innovation Centre (2021) 11. Khorram-Manesh, A.: Handbook of Disaster and Emergency Management (1st edn). Disaster Medicine. ISBN: 978–91–639–3200–7. (2017) 12. Emergency Traffic Control and Scene Management Guidelines. WisDOT Emergency Traffic Control and Scene Management Guidelines. November 2014. 98 p. 13. Tait, G.: Implementing geoportals: applications of distributed GIS. Comput. Environ. Urban Syst. 29, 33–47 (2005) 14. Maguire, J., Longley, A.: The emergence of geoportals and their role in spatial data infrastructures. Comput. Environ. Urban Syst. 29, 3–14 (2005) 15. Lobanov, G.V., Moskalenko, O.: Geoportals as sources of information about the territory. Graphicon. (2020). https://doi.org/10.51130/graphicon-2020-2-4-22 16. Vasserman, S., Feidman, M., Hassidim, A.: Implementing the wisdom of waze. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, p. 24 (2015) 17. Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006). https://doi.org/10.1109/MCI.2006.329691 18. Dorigo, M.: Swarm Intelligence, Ant Algorithms and Ant Colony Optimization. Reader for CEU Summer University Course “Complex System”, Budapest, Central European University, pp. 1–38 (2001) 19. Khudov, H., Oleksenko, O., Lukianchuk, V., Herasymenko, V., Yaroshenko, Y., Ishchenko, O., Ikaiev, D., Golovchenko, O., Volobuiev, A., Drob, Y., Solomonenko, Y., Khizhnyak, I.: The determining the flight routes of unmanned aerial vehicles groups based on improved ant colony algorithms. Int. J. Emerging Technol. Adv. Eng. 11(9), 23–32 (2021). https://doi.org/10.46338/ ijetae0921_03 20. Stützle, T., Hoos, H.: The max-min ANT system and local search for combinatorial optimization problems. Future Gener. Comput. Syst. 16 (2000) 21. Min Max Algorithm in AI: Components, Properties, Advantages & Limitations. https://www. upgrad.com/blog/min-max-algorithm-in-ai/. Accessed 05 May 2022 22. Khudov, H., Baranik O., Kovalenko O., Yakovenko Y., Chahan Y. The information technology for determining vehicle route based on ant colony algorithms. Int. J. Emerging Technol. Adv. Eng. 12(12), 117–128 (2022). https://doi.org/10.46338/ijetae1222_13

Ship Refrigeration System Operating Cycle Efficiency Assessment and Identification of Ways to Reduce Energy Consumption of Maritime Transport Oleg Onishchenko , Andrii Bukaros , Oleksiy Melnyk , Vladimir Yarovenko , Andrii Voloshyn , and Oleh Lohinov

Abstract The constituents of perspective and existing models of maritime transport means, these are ships of different target purpose, quite actively use various systems of artificial cooling and air conditioning as well as high-frequency microprocessor equipment and power electronics. However, the existing centralized ship cooling and conditioning systems are imperfect, they have low energy indices, produce noises and vibrations, use outdated principles of parameters control and stabilization, which does not always ensure necessary cooling modes and makes it impossible to use them under severe operational conditions. Industry demands new conceptual solutions aimed at improving performance, energy efficiency, reliability of operation. Summarizing practical experience, it becomes obvious that improvement of technical characteristics of existing various ship-cooling systems, which are based on principles of use of decentralized, energy-efficient and fully controllable artificial cooling systems, adaptable to uncertain external conditions of operation is of significant theoretical interest. Keywords Ship refrigeration system · Shipping · Ship operation · Vaporcompressor refrigeration systems · Ship power plants · Energy-efficient cooling systems · Ship refrigeration units · Energy efficiency · Operation cycle · Maritime transportation · Efficiency assessment

A. Bukaros · O. Melnyk (B) · V. Yarovenko · A. Voloshyn · O. Lohinov Odesa National Maritime University, Odesa, Ukraine e-mail: [email protected] O. Onishchenko National University “Odesa Maritime Academy”, Odesa, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_36

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1 Introduction The intensive development of modern technologies requires that the size of systems with a high density of electronic equipment be maintained. Therefore, there is a problem of significant excess heat generation, which requires the use of special, energy-efficient cooling systems. There is a contradiction and a demand from practice: improving the technical characteristics of advanced models of transport systems while ensuring that they fulfill their main technological task, safe use and environmental friendliness, which is almost impossible while simultaneously increasing energy efficiency and reliability, reducing noise and vibration. The solution to this problem is further complicated by the fact that existing cooling systems do not always provide the required quality of stabilization of the parameters of cooled objects, due to insufficient knowledge of their dynamic properties, mathematical models, calculation methods and the principles of cooling and control used. Thus analysis of opportunities for increasing energy indicators of asynchronous electric motors of propulsive complexes of autonomous surface ships, vessel’s model parameters and mathematical models application studied in [1–3, 28]. Modernization of Luenberger observer for control system of hermetic compressor electric drive, simulation modeling of vapor compression refrigeration unit temperature modes researched in [4, 5]. Main problems of creating energy-efficient positioning systems for multipurpose sea vessels, improvement of the operation for electromechanical system under non-permanent loading considered in [6, 7]. In [8–10] Experimental energy and exergy analyses of ship refrigeration system operated by frequency inverter at varying sea water temperatures, thermo-economic and ecological analyses of combined cold supply system for ship and waste heat source profiles for marine application. Analysis of methods to increase the efficiency of ship refrigeration plants, performance of a vapour compression refrigeration cycle and adsorption refrigeration technologies studied in [11–13]. Simulation of the resistance moment of single-piston compressor of ship refrigeration unit examined in [14]. Ship operational condition evaluation during transportation examined in [16, 17]. Safety of maritime transportation and insurance of ship navigation safety issues and matters related to general safety of shipping proposed in [15, 18–22, 26]. Study of environmental efficiency of ship operation in terms of freight transportation effectiveness provision and ship energy efficiency issues in [23, 24]. Ship information security and associated risks and energy-effcient operation mode investigated in [25, 27]. Issues of management of operations, increasing the efficiency of project decisions and management on water transport proposed in [29, 31–35]. Marine diesel engines operating cycle simulation for diagnostics issues, features of the fastest pressure growth point during compression stroke proposed in [36, 37]. The purpose of this study is to present the author’s view on the conceptual basis for creating energy-efficient vapor-compressor refrigeration systems (VCRS), capable of ensuring invariance to the effects of various complex operating conditions and operational tasks assigned to high-tech samples of transport systems, provide temperature modes of special electronic equipment of ships and thereby improve their energy and

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overall performance parameters. Therefore, research of processes of adaptive energyefficient control and transformation of energy in the VCRS of special electronic equipment of sea-going ships is the actual task.

2 Materials and Methods The concept of creating energy-efficient fully controlled cooling systems for transport systems is based on: . development and use of simulation models of individual VCRS nodes in control loops, which take into account the variable dynamic properties of both cooling systems and cooling objects with distributed parameters, the effect of external disturbances and the interconnections of nodes with each other; . application of the principles of adaptive performance control and algorithms for assessing the energy efficiency of VCRS in real time with full-order state observers under uncertain operating conditions; . application of the principle of multichannel performance control of VCRS based on the criterion of minimum energy losses; . construction of VCRS control systems based on the criterion of minimum hardware redundancy; (e) application of the idea of VCRS “digital twins” for predictive operation, maintenance, and remote monitoring in real time. Assessing the perfection of the operating cycle and identifying ways to reduce energy costs to achieve the required result is the goal of thermodynamic analysis of any power plant, including ship refrigeration systems (SRS). When studying refrigeration systems, three groups can be distinguished based on energy, exergy and entropy-statistical methods of thermodynamic analysis. The energy analysis of the refrigeration cycle is based on the first law of thermodynamics, which is the law of energy conservation and transformation. Therefore, the results of its application can give an idea of the magnitude and distribution of energy losses. As a result, this method is widely used in refrigeration engineering to assess the energy efficiency of existing refrigeration units. Despite the fact that the exergy method is used in the thermodynamic analysis of refrigeration systems, it is more applicable to the analysis of installations whose purpose is to generate work. In addition, it should be noted that exergy is not a parameter of the state of the working substance, as it depends on the environmental parameters and characteristics of the energy conversion process. All this complicates the exergy analysis of refrigeration systems. In turn, the entropy-statistical method of analysis is based on the second law of thermodynamics and the concept of entropy, which is a unambiguous, continuous and finite function of the parameters of the working substance state. However, this method has one significant drawback, namely, the need to use statistical information, which excludes its use for a “quick” assessment of the technical condition of a ship refrigeration system.

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In view of the above, the energy method of ship refrigeration system is further considered. The result of the energy analysis is to find the actual refrigeration coefficient, which is a criterion for assessing the energy efficiency of the refrigeration unit. The basis for the analysis is the energy balance equation. Since the mass of the refrigerant (CA) remains unchanged during the operation of the refrigeration system, it is convenient to use the specific values of the relevant parameters of the refrigeration cycle. Taking this into account, as well as the fact that no external work is performed when the refrigerant is throttled, the energy balance Eq. (1) will be transformed to the form: qk = q0 + wc ,

(1)

where q0 —is the specific amount of heat that is removed from the cooled object in the coolant, W/kg; qk —the specific amount of heat that is removed by the condenser to the environment, W/kg; wc —specific compression work, J/kg. All components of Eq. (1) can be determined through the parameters of the actual refrigeration cycle of a SRS, in particular, through the difference in enthalpies at the characteristic points of (1). The specific cooling capacity of a SCS is defined as the difference in enthalpies at the boiling line: q0 = h1 − h7 ,

(2)

where h1 —enthalpy of CA at the end of the boiling process (at the evaporator outlet), W/kg; h7 —enthalpy of CA at the beginning of the boiling process (at the evaporator inlet), W/kg. The specific heat transferred to the environment is equal to the difference in enthalpies across the condensation line: qκ = h5 − h4 ,

(3)

where h5 —enthalpy of RA at the end of the condensation process (at the condenser outlet), W/kg; h4 —enthalpy of CA at the beginning of the condensation process (at the condenser inlet), W/kg. The compressor’s specific compression work is equal to the difference in enthalpies across the compression line: wc = h3 − h2 ,

(4)

where: h3 —is the enthalpy of CA at the end of the compression process (on the discharge line), W/kg; h2 —enthalpy of CA at the beginning of the compression process (at the suction line), W/kg. The specific cooling coefficient is determined by the ratio:

Ship Refrigeration System Operating Cycle Efficiency Assessment …

ε=

h1 − h7 q0 = . wc h3 − h2

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(5)

The obtained coefficient cannot serve as a criterion for assessing the energy efficiency of SRS, since it does not give an idea of the degree of thermodynamic perfection of the refrigeration unit. With the help of the specific refrigeration coefficient, it is only possible to determine the degree of deviation of the actual refrigeration cycle of a cold storage system from the theoretical one, which may be one of the symptoms of malfunctions during operation.

3 Results and Discussions There are various performance indicators for the energy assessment of refrigeration systems based on different standards or principles. Most of these performance indicators are used exclusively for the certification of components such as compressors or heat exchangers under standardized test conditions in laboratories. Some well-known examples of different performance indicators are: – – – – –

COP (Coefficient of Performance); Energy Efficiency Ratio (EER); European Seasonal Energy Efficiency Ratio (ESEER); Seasonal performance factor SPF (Seasonal Performance Factor); Integrated Part Load Value (IPLV) and others.

All of these metrics have certain drawbacks when applied to SRS. For example, the COP and EER coefficients actually represent an instantaneous assessment of energy efficiency by finding the ratio of the amount of cold produced per unit of time to the electrical power consumed by the compressor. The power of other components of the system is not taken into account. These coefficients are calculated at 100% of the refrigeration unit’s capacity and at a fixed ambient temperature, which does not correspond to the operating conditions of the SRS. The seasonal ESEER and SPF coefficients are more integral indicators, as they take into account seasonal fluctuations in ambient temperature and the degree of thermal load of the refrigeration unit. However, the amount of time the equipment operates at partial load that is included in these indicators differs depending on the standard used. The use of the IPLV indicator further complicates the procedure for determining energy efficiency. It is proposed to use the following indicators to assess the energy efficiency of SRS: – TCOP (Total Coefficient of Performance), which can be used for a “quick” assessment of energy efficiency P0 TCOP = E , Pe

(6)

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Fig. 1 Typical points of the actual refrigeration cycle of SRS

where: P0 —amount of cold produced per unit time, W; power consumption by all SRS elements, W.

E

Pe —total electrical

Total energy efficiency factor TEPF (Total Energy Performance Factor), which can be seasonal depending on the selected time period Q0 , TEPF = E We

(7)

E We —is the total electrical energy where Q0 —amount of cold produced, J; consumed by all elements of the storage system, J. If in the last expression we move to the specific values, which can be easily determined from the diagram lgp-h (Fig. 1), the following can be obtained: {τ TEPF = { τ0 0

(mxa · q0 )dt . (pκM + pκ )dt

(8)

where pkm —instantaneous active power consumed by the compressor from the power grid, W; pk —instantaneous active power consumed by the electrical equipment of the capacitor, W. Values pkm and pk are determined by the measured values of current, voltage, and power factor of the corresponding electrical equipment. The mass flow rate of CA, taking into account (Fig. 1), can be defined as mxa =

Nc . Wc

(9)

Thus, in addition to measuring and calculating refrigeration cycle parameters and electrical parameters, determining the energy efficiency of SRS also requires knowledge of the compressor’s compression power N c , which involves analyzing the compressor’s operation dynamics. The construction of a system for diagnostics, monitoring the technical condition and assessing the energy efficiency of SRS is impossible without obtaining the necessary input data, which are used to determine the operating parameters, energy

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Fig. 2 The actual refrigeration cycle

efficiency coefficients and fault symptoms. The thermodynamic and energy analysis of SRS operation shows that such input data can be CA parameters—temperatures, pressures, enthalpies at the nodal points of a real refrigeration cycle, plotted in p–h coordinates (Fig. 2). Most SRSs, especially those with small and medium cooling capacity, do not provide for the possibility of connecting external pressure devices, and depressurization of the system leads to the release of CA into the atmosphere. The proposed methodology considers the option of building a cycle with the possibility of installing only four temperature sensors without using pressure sensors. Thus, to calculate the cycle parameters, determine the technical condition of the SRS, and, most importantly, determine the refrigeration coefficient, which is used to ensure the energy efficiency of the SRS, it is necessary to determine: the condensation temperature T k and boiling temperature T 0 of CA; the suction temperature T 2 of the compressor; the temperature of supercooled liquid CA behind the condenser before the thermostatic control valve T 6 ; the brand of CA used and the model of the compressor. Conventionally, the connection points of temperature sensors are shown in Fig. 3. To determine the parameters of the CA in the single-phase region, it is possible to use special tables or thermal diagrams “pressure–enthalpy” and “temperature– entropy”, but they are not always available. In addition, the use of heat diagrams Fig. 3 Block diagram of the ship’s refrigeration unit

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or tables reduces the accuracy of calculations, since it is necessary to use graphical or numerical interpolation methods. In connection with the above, it is proposed to use a set of simple equations based on experimental data and the developed table of coefficients A, B, C, D, the known equations of state of CA. These equations provide sufficient accuracy in calculating the enthalpies of h depending on pressure p and temperature T. Based on the measured values of T k and T 0 the condensation pressure pk and boiling pressure p0 are determining by expression: p = eD0 +D1 ·T +D2 /T .

(10)

Pressure in points 1, 2, 7, 7a is accepted as equal p0 , and at points 3, 3a, 4, 5, 6 equal to pk (Fig. 2). Temperature in points 1, 7, 7a is accepted equal to T 0 , and in points 4, 5 equal to T k . Determine the enthalpies at the points 1, 2, 4: ( h = C0 T + 50C · o2 + C1 + p

) 3B3 2 , − B o 2 o2

(11)

where o = T/100. Determine the enthalpies at the points 5, 6, 7a: h = A0 + A1 · o + A2 · o5 .

(12)

Determining the dryness of steam CA at the point 7: x7 =

h6 − h7a . h1 − h7a

(13)

The enthalpy at point 7 is calculated, taking into account the CA parameters on the condensation and boiling lines: h7 = h7a + x7 (h1 − h7a ).

(14)

The entropy at the point 2: s = ln

) ( p 2B3 T C0 + C · o + C + − 2B o − B 2 2 1 . pR 100 o3

(15)

The entropy at point 3a is assumed to be equal to s2 . The value of the compressor discharge temperature at point 3a is determined as the root of Eq. (15) with the substitution p3a and s3a . The enthalpy at point 3a is determined by the expression (11) with substitution p3a and T 3a . The enthalpy value at point 3 is calculated:

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h3a − h2 , ηad

h3 = h2 +

649

(16)

where ηad —average adiabatic efficiency of the compressor. A comparison of the results obtained and those obtained using different methods shows that the maximum error of the proposed methodology does not exceed 1%. As noted earlier, one of the main indicators of the efficiency and reliability of SRS operation is the cooling capacity factor TCOP and the energy efficiency factor TEPF. They characterize the degree of sophistication of the CM and determine the energy consumption for cold production. Therefore, the SRS diagnostics and technical condition monitoring system should be able to determine these coefficients in real time and, if possible, use a minimum of technical means for this purpose. Based on expressions (7) and (8), we can conclude that the algorithm for determining TCOP and TEPF can be divided into two parts: thermodynamic and electromechanical. Consider first the “thermodynamic” part of the algorithm, namely, the determination of the amount of cold produced Q0 SRS for the given time perion τ. (1) According to the measured values T k , T 0 , T 2 , T 6 determine the parameters (temperatures, pressures, enthalpies) of the refrigeration cycle at the nodal points by expressions (10)–(16). (2) Specific cooling capacity of SRS is determined q0 and compressor compression performance wc . (3) The compression power is determined by the previously calculated mechanical power on the shaft N km , taking into account the efficiency ηkm for a given compressor model and load. Compressor efficiency ηk is generally determined by geometric dimensions and indicator diagram and can be determined. (4) The mass flow rate of CA through the compressor is determined by expression (9). The amount of cold produced per unit of time for the TCOP calculation is calculated as P0 = mxa · q0 .

(17)

(5) The amount of cold produced over a period of time τ for calculating TEPF is calculated by integration: { Q0 =

τ

mxa · q0 .

(18)

0

The value of the integration time interval τ is determined depending on the dynamic properties of the SRS. The “electromechanical” part of the algorithm for determining the energy efficiency of a SRS involves real-time calculation of the consumed electricity W e and mechanical power on the compressor shaft N km for a given time period τ e .

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4 Conclusions Thus, in this study, algorithms for diagnosing and assessing the energy efficiency of vapor-compressor refrigeration systems of ship refrigeration units and special cooling systems were developed and improved. Criterion-based approaches to the justification of the multichannel structure of vapor-compressor refrigeration systems control systems in the implementation of projects to improve their efficiency and technical characteristics under the influence of external disturbances have been formulated. Universal algorithms and models of the components of vapor-compression and thermoelectric vapor-compressor refrigeration systems are proposed, which can be used to determine the technical characteristics, operational and economic indicators in projects for the development, improvement of the efficiency of operation and modernization of vapor-compressor refrigeration systems.

References 1. Volyanskaya, Y., Volyanskiy, S., Nikul, S., Onishchenko, O.: Analysis of opportunities for increasing energy indicators of asynchronous electric motors of propulsive complexes of autonomous floating vehicle. East.-Eur. J. Enterp. Technol. 2/8(92), 25–32 (2018). https:// doi.org/10.15587/1729-4061.2018.126144 2. Melnyk, O., Onishchenko, O., Onyshchenko, S., Voloshyn, A., Kalinichenko, Y., Rossomakha, O., Naleva, G., Rossomakha, O.: Autonomous ships concept and mathematical models application in their steering process control. TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 16(3), 553–559 (2022) 3. Golikov, V.A., Golikov, V.V., Volyanskaya, Y., Mazur, O., Onishchenko, O.: A simple technique for identifying vessel model parameters. In: IOP Conference Series: Earth and Environmental Science, vol. 172(1) (2018), art. no. 012010. https://doi.org/10.1088/1755-1315/172/1/012010 4. Bukaros, A.Y., Onyshchenko, O.A., Montik, P.N., Malyshev, V.L., Bukaros, V.N.: Modernization of Luenberger observer for control system of hermetic compressor electric drive. Radio Electron., Comput. Sci., Control. 1(48), 230–237 (2019). https://doi.org/10.15588/1607-32742019-1-21. 5. Bukaros, A., Onishchenko, O., Herega, A., Trushkov, H., Konkov, K.: Simulation modeling of vapor compression refrigeration unit temperature modes. systems, decision and control in energy IV. Stud. Syst., Decis. Control., Springer, 454 (2023). https://doi.org/10.1007/978-3031-22464-5_14 6. Budashko, V., Obniavko, T., Onishchenko, O., Dovidenko„ Y., Ungarov, D.: Main Problems of Creating Energy-efficient Positioning Systems for Multipurpose Sea Vessels. In: 2020 IEEE 6th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), Kyiv, pp. 106–109 (2020). https://doi.org/10.1109/MSNMC50359.2020.9255514 7. Budashko, V., Hvozdeva, I., Onishchenko, O., Shevchenko, V., Kudelkin, R.: Improvement of the operation for electromechanical system under non-permanent loading. In: 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv- Slavske, pp. 35-39 (2020). https://doi.org/10.1109/ TCSET49122.2020.235588. 8. Yilmaz, O., Bayar, H, Ba¸shan, V., Kenan, Y.: Experimental energy and exergy analyses of ship refrigeration system operated by frequency inverter at varying sea water temperatures. J. Braz. Soc. Mech. Sci. Eng.. 44, 133 (2022). https://doi.org/10.1007/s40430-022-03439-5

Ship Refrigeration System Operating Cycle Efficiency Assessment …

651

9. Wajs, J., Mrozek, M., Fornalik-Wajs, E.: Combined cold supply system for ship application based on low GWP refrigerants - Thermo-economic and ecological analyses. Energy Convers. Manage. 258, 115518 (2022). https://doi.org/10.1016/j.enconman.2022.115518 10. Ng, C., Tam, I., Wetenhall, B.: Waste heat source profiles for marine application of organic rankine cycle. J. Mar. Sci. Eng. 10(8), 1122 (2022). https://doi.org/10.3390/jmse10081122 11. Ovcharenko, I., Yenivatov, V., Vyngra, A.: Analysis of methods to increase the efficiency of ship refrigeration plants. In: MATEC Web of Conferences, vol. 239, p. 04017 (2018). https:// doi.org/10.1051/matecconf/201823904017 12. Memet, F.: Effects of vaporization temperature and sub cooling variation on the performance of a vapour compression refrigeration cycle working with r134a, met on newer ships. Int. J. Mod. Manuf. Technol. 14, 152–157 (2022). https://doi.org/10.54684/ijmmt.2022.14.3.152 13. Wang, J.: Comprehensive analysis for state-of-the-art adsorption refrigeration technologies. Highlights Sci., Eng. Technol. 16, 344–351 (2022). https://doi.org/10.54097/hset.v16i.2559 14. Bukaros, A., Bukaros, V., Onishchenko, O.: Simulation of the resistance moment of singlepiston compressor of ship refrigeration unit. Technol. Audit. Prod. Reserv. 4, 46 (2015). https:/ /doi.org/10.15587/2312-8372.2015.47765 15. Burmaka, I., Vorokhobin, I., Melnyk, O., Burmaka, O., Sagin, S.: Method of prompt evasive manuever selection to alter ship’s course or speed. Trans. Marit. Sci. 11(1), 1–9 (2022). https:/ /doi.org/10.7225/toms.v11.n01.w01 16. Onyshchenko, S., Melnyk, O.: Probabilistic assessment method of hydrometeorological conditions and their impact on the efficiency of ship operation. J. Eng. Sci. Technol. Rev. 14(6), 132–136 (2021) 17. Onyshchenko, S., Melnyk, O.: Efficiency of ship operation in transportation of oversized and heavy cargo by optimizing the speed mode considering the impact of weather conditions. Transp. Telecommun. 23(1), 73–80 (2022) 18. Melnyk, O., Bychkovsky, Y., Voloshyn, A.: Maritime situational awareness as a key measure for safe ship operation. Sci. J. Sil. Univ. Technology. Ser. Transp. 114, 91–101 (2022) 19. Melnyk, O., Onyshchenko, S.: Ensuring safety of navigation in the aspect of reducing environmental impact. In: ISEM 2021, vol. 463. LNNS, pp. 1–9 (2022) 20. Eliopoulou, E., Papanikolaou, A., Voulgarellis, M.: Statistical analysis of ship accidents and review of safety level. Saf. Sci. 85, 282–292 (2016). https://doi.org/10.1016/j.ssci.2016.02.001 21. Melnyk, O., Onyshchenko, S., Koryakin, K.: Nature and origin of major security concerns and potential threats to the shipping industry. Sci. J. Sil.N Univ. Technology. Ser. Transp. 113, 145–153 (2021) 22. Eliopoulou, E., Papanikolaou, A.: Statistical analysis of accidents and review of safety level of passenger ships. J. Mar. Sci. Eng. (2023). https://doi.org/10.3390/jmse11020410 23. Onishchenko, O., Golikov, V., Melnyk, O., Onyshchenko, S., Obertiur, K.: Technical and operational measures to reduce greenhouse gas emissions and improve the environmental and energy efficiency of ships. Sci. J. Sil. Univ. Technology. Ser. Transp. 116, 223–235 (2022) 24. Melnyk, O., Onishchenko, O., Onyshchenko, S., Golikov, V., Sapiha, V., Shcherbina, O., Andrievska, V.: Study of environmental efficiency of ship operation in terms of freight transportation effectiveness provision. TransNav 16(4), 723–729 (2022). https://doi.org/10.12716/ 1001.16.04.14 25. Melnyk, O., Onyshchenko, S., Onishchenko, O., Shumylo, O., Voloshyn, A., Koskina, Y., Volianska, Y.: Review of ship information security risks and safety of maritime transportation issues. TransNav 16(4), 717–722 (2022). https://doi.org/10.12716/1001.16.04.13 26. Melnyk, O., Onyshchenko, S., Onishchenko, O., Lohinov, O., Ocheretna, V., Dovidenko, Y.: Basic aspects ensuring shipping safety. Sci. J. Sil. Univ. Technology. Ser. Transp., 117, 139-149 (2022). https://doi.org/10.20858/sjsutst.2022.117.10 27. Onishchenko, O., Shumilova, K., Volyanskyy, S., Volyanskaya, Y., Volianskyi, Y.: Ensuring cyber resilience of ship information systems. TransNav 16(1), 43–50 (2022). https://doi.org/ 10.12716/1001.16.01.04 28. Volyanskaya, Y., Volyanskiy, S., Volkov, A., Onishchenko, O.: Determining energy-efficient operation modes of the propulsion electrical motor of an autonomous swimming apparatus.

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30.

31.

32.

33.

34.

35.

36.

37.

O. Onishchenko et al. East.-Eur. J. Enterp. Technol. 6(8–90), 11-16 (2017). https://doi.org/10.15587/1729-4061. 2017.118984 Sherstyuk, O., Olekh, T., & Kolesnikova, K.: The research on role differentiation as a method of forming the project team. East.-Eur. J. Enterp. Technol. 2(3(80)), 63–68 (2016). https://doi. org/10.15587/1729-4061.2016.65681 Varbanets, R., Fomin, O., Píštˇek, V., Klymenko, V., Minchev, D., Khrulev, A., Zalozh, V., Kuˇcera, P.: Acoustic Method for Estimation of Marine Low- Speed Engine Turbocharger Parameters. J. Mar. Sci. Eng.. 9(3), 321 (2021). https://doi.org/10.3390/jmse9030321 Bondar, A., Bushuyev, S., Bushuieva, V., Onyshchenko, S.: Complementary strategic model for managing entropy of the organization. In: Proceedings of the 2nd International Workshop IT Project Management (ITPM 2021), Slavsko, Lviv region, Ukraine, February 16–18, 2021, CEUR Workshop Proceedings, vol. 2851, pp. 293–302 (2021) Bushuyev, S., Bushuieva, V., Onyshchenko, S., Bondar, A.: Modeling the dynamics of information panic in society. In: COVID-19 case. CEUR Workshop Proceedings, vol. 2864, pp. 400–408 (2021) Bondar, A., Onyshchenko, S., Vishnevska, O., Vishnevskyi, D., Glovatska, S., Zelenskyi, A.: Constructing and investigating a model of the energy entropy dynamics of organizations. East.Eur. J. Enterp. Technol. 3 (3–105), 50-56 (2020). https://doi.org/10.15587/1729-4061.2020. 206254 Lapkina, I., Malaksiano, M. Elaboration of the equipment replacement terms taking into account wear and tear and obsolescence. East.-Eur. J. Enterp. Technol. 3(3(93)), 30-39 2018. https:// doi.org/10.15587/1729-4061.2018.133690 Lapkina, I., Malaksiano, M.: Estimation of fluctuations in the performance indicators of equipment that operates under conditions of unstable loading. East.-Eur. J. Enterp. Technol. 1(3(91)), 22–29 (2018). https://doi.org/10.15587/1729-4061.2018.123367 Minchev, D., Varbanets, R., Aleksandrovskaya, N., Pisintsaly, L.: Marine diesel engines operating cycle simulation for diagnostics issues. Acta Polytech. 3(61), 428–440 (2021). https:// doi.org/10.14311/AP.2021.61.0435 Yeryganov, O., Varbanets, R.: Features of the fastest pressure growth point during compression stroke. Diagnostyka 19(2), 71–76 (2018). https://doi.org/10.29354/diag/89729

Simulation-Based Method for Predicting Changes in the Ship’s Seaworthy Condition Under Impact of Various Factors Oleksiy Melnyk , Svitlana Onyshchenko , Oleg Onishchenko , Olha Shcherbina , and Nadiia Vasalatii

Abstract Adverse changes in the ship’s seaworthiness, or the loss of it, occurring in the process of cargo carriage under the influence of many random events, are a matter of great concern. A significant attention must be given to the problem of negative factors impact on the ship seaworthiness and her technical condition as well as cargo safety. In this connection measures aimed at ensuring the ship’s safety, stable functionality according to her purpose, and reliability as technical object among priority tasks which are extremely important in the process of maritime transportation. In the course of this study, the decomposition of the ship’s basic conditions under the influence of various factors is defined. The simulation model of change of a vessel’s operational condition is developed, allowing to establish interrelation with the set conditions which is revealed and formalized in the form of homogeneous Markov process model. Experimental research for various initial conditions is conducted and the most probable changes of ship’s seaworthiness through the specified time range are determined. Keywords Ship operation · Safety of navigation · Cargo carriage · Maritime transportation · Markov chain · Seaworthiness · Ship safety management

1 Introduction Many studies show that the relationship between seaworthiness and crew competence as a guarantee of safe navigation, taking into account many factors, is fundamental. Seaworthiness of a vessel is one of the main criteria, since it is seaworthiness that often guarantees safe performance of the voyage and safety of the freight transportation. O. Melnyk (B) · S. Onyshchenko · O. Shcherbina · N. Vasalatii Odesa National Maritime University, Odesa, Ukraine e-mail: [email protected] O. Onishchenko National University “Odesa Maritime Academy”, Odesa, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_37

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The concept of “seaworthiness” is the most actual in the process of investigation and evaluation of emergencies at sea or ships accidents. Thus, the ship seaworthy condition is an aggregate of its properties, ensuring operation as designed in accordance with standards, criteria and requirements established by the classification society, which exercises technical supervision over the vessel. A vessel’s seaworthiness includes such properties as stability, strength, watertightness, unsinkability, buoyancy and steerability. It should be noted that its content can be interpreted contextually; in fact, it is the carrier’s obligation to bring the ship into seaworthiness before the voyage and to maintain this condition throughout the voyage. It is also stipulated that a carrier is to ensure that a vessel is technically in good order, properly equipped and furnished with all necessary provisions, crew manning, as well as bringing the holds and all other vessel’s compartments, which carry the cargo in a condition, which ensures its proper handling, stowage and transportation. As practice shows, a vessel can be declared unseaworthy if it does not meet all the above criteria and thus is technically unfit for navigation. One aspect that is more significant is that if during the voyage a vessel comes to an unseaworthy condition, the carrier bears responsibility, therefore development of methods and models of forecasting changes in a condition of various objects in the course of their operation is always of scientific interest. Ensuring the safe operation of a vessel is an important task, the focus of both ship owners and ship operators. Possible hull or cargo damage as a result of poor operating conditions, weather conditions or insufficient stability, failure of the vessel’s strength, reliability of its equipment and facilities—this is by no means a complete list of scenarios which a vessel can get into while in operation. In this connection the measures on providing the safety of the ship operation include the whole complex in which the basic aspects should properly consider the technical and operational as well as the external factors, which have a direct impact on the ship seaworthy condition, respectfully crew and cargo during the sea passage. Environmental conditions and human factor are undoubtedly the most important ones at making management decisions, but subjective factors have no less influence on the safe operation and technological safety of ships. Simulation modeling plays a significant role in solving problems related to the safety of maritime transport, which allows based on statistical data and expert evaluations to predict the status of the vessel at a particular point in time. Probabilistic estimation of conditions of ship passage between ports has been considered in works [1, 3, 4]. In particular, the work [19, 20] evaluated the efficiency of ship operation, taking into account the negative impact of various factors. Issues on safety in transportation: a review of the concept, its context, safety preservation and improvement effective, safety modeling of port, shipping and ship traffic and port operation, environmental and navigation safety reviewed in [2, 5, 13, 18, 21–23, 26–29]. Safety of navigation with respect to collision avoidance measures and course control systems application reviewed in [24, 25, 31]. The legal aspects of seaworthiness and the effect of vessel seaworthiness and crew’s competence on marine safety in [17, 30].

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The change of ship’s condition can be described using the apparatus of Markov processes [16]. Certain theoretical and practical experience has been gained to ensure the shipping safety in solving such problems by using the apparatus of Markov processes theory, including in transport sphere [14, 15], i.e. stochastic model possessing Markov property—a specific type of random processes. In particular, on the application of Markov processes [8–10] shipping safety simulation and determining distribution of probabilistic process of marine accidents resulting from collision of ships is performed. The hidden Markov model approach for determining ship activity from monitoring system data and studying other dynamical states was studied in [6, 11, 12]. Nonetheless, the research results do not fully represent the specificity of ship operation and offer safety tools for the ensuring the safety of ship under influence of various factors. In this regard, ensuring the functional reliability of the vessel as a technical object is one of the priority tasks.

2 Methodology of the Research A ship’s operational condition can be represented as the set of two entities “shipcargo”. Ultimate operational condition of a ship in the process of transportation is formed by taking into account various events associated with the ship itself (for example, technical systems failure) or with the cargo, its shifting. Ship-cargo system passes from one state to another under the influence of many random factors, such as weather conditions, “human error”, condition of vessel at voyage beginning, quantity and quality of deck cargo lashings, etc., while voyage is in progress. For this reason, there are reasons and preconditions for identification of ship operational condition alteration process during the voyage. To identify the main conditions of the “ship-cargo” entity under study, it is suggested to carry out decomposition of the specified condition into two variants (“in good condition”, “having problems”) by three components: “ship’s seaworthiness”, “technical condition”, “cargo condition”. Such approach is determined, above all, the specifics of ship operation, which is manifested, for example, in the peculiarities of cargo stowage and securing on board. Accordingly, this can lead to certain violations both during the loading process on the ship and breach of integrity of the object “ship-cargo” during transportation. Both positive (“normal”-1) and negative (“problems present”-0) assessments of each component form the following classification of the main variants of ship’s condition (Table 1). There are two theoretically possible options: satisfactory condition of the cargo with unsatisfactory technical and seaworthiness of the vessel; loss of seaworthiness and unsatisfactory condition of the cargo with normal technical condition have no practical sense. In the following stage of the study, the type of Markov process is determined. As a number of considered states is a countable set, there is a discrete random process. As the vessel status is recorded at certain moments of time, there is a Markov process

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Table 1 Ship’s basic operational conditions Ship’s state

Seaworthiness

Technical condition

Condition of cargo

Description

S1

1

1

1

Satisfactory operating conditions

S2

1

1

0

Shifting and/or loss of cargo

S3

1

0

0

Ship hull or structural elements damages

S4

1

0

1

Ship hull or structural elements damages due to the cargo shifting

S5

0

1

1

Dangerous heeling, deterioration/loss of stability due to the cargo shifting

S6

0

0

0

Inadequate operating or emergency condition

with discrete time t = 0, 1, 2, 3, …. We will assume that the considered Markov chain is homogeneous, since the probabilities of transition from condition to condition do not depend on time, but depend solely on a set of random factors of influence. The time moments can be an hour, a day or even a decade, but considering the specifics of the dynamics of changes in weather conditions and the situation on board the ship, we will take a day within the voyage time as t = 0, 1, 2, 3, …. Diagram of conditions and transitions of the studied object is presented in Fig. 1. Satisfactory seaworthiness of the vessel S1 is a reversible condition, as the vessel can return to it, for example, as a result of normalization of weather conditions, even with the previous impact of negative factors. The transition probability matrix:

Fig. 1 Markov chain transition diagram

Simulation-Based Method for Predicting Changes in the Ship’s …

⎡

p11 ⎢ 0 ⎢   ⎢ ⎢ 0 P = pi j = ⎢ ⎢ p41 ⎢ ⎣ p51 0

p12 p22 0 0 0 0

0 p23 p33 p43 0 0

p14 0 0 p44 0 0

p15 0 0 p45 p55 0

657

⎤ p16 p26 ⎥ ⎥ ⎥ p36 ⎥ ⎥, p46 ⎥ ⎥ p56 ⎦ 1

(1)

where 0 ≤ pi j ≤ 1, i = 1, 6, j = 1, 6.

(2)

Note that matrix (1) already has zero probability values for impossible state transitions according to the diagram in Fig. 1. In this case, the state S6 is absorbing, that is, the ship does not leave this state, so the corresponding probability p66 = 1. This means that the considered Markov chain does not have the ergodic property. By the properties of the Markov process, the elements of the matrix satisfy the condition: 6

pi j = 1, i = 1, 6.

(3)

j=1

Initial probabilities are required for the Markov process p1 (0), p2 (0), p3 (0), p4 (0), p5 (0), p6 (0) position of the given system “ship-cargo” at the initial moment of time t = 0: p1 (0) + p2 (0) + p3 (0) + p4 (0) + p5 (0) + p6 (0) = 1.

(4)

The subject moment of time (t = 0) is the ship’s departure from the port of loading. In essence, the initial probabilities estimate the impact of many random factors in the process of ship loading and on the ship’s condition before loading. Thus, the initial state of the “ship-cargo” object, forming the initial ship’s operational condition on a given voyage, p1 (0), p2 (0), p3 (0), p4 (0), p5 (0), p6 (0), is determined based on the analysis of the possible impact of various factors for a specific ship. This is done with due regard to its characteristics and peculiarities of previous operation, the specific cargo of the port of loading (according to expert opinion or statistics). Given that this process does not have ergodic property, it does not have a steady state. Thus, after a significant period of time, the probabilities of conditions do not tend to limit values which do not depend on the initial conditions and the current moment of time. Thus, the Markov process—the ship-cargo object in the process of transportation process is started from a certain initial point p1 (0), p2 (0), p3 (0), p4 (0), p5 (0), p6 (0), on which depends the probability of further ship’s presence in a particular condition during the voyage. The Kolmogorov-Chapman equation (5) can be used to define the probabilities of the ship’s operational conditions at any given time.

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p j (k) =

n

p j (k − 1) · pi j , j = 1, n, k = 1, 2, 3, . . .

(5)

i=1

Therefore, the study of the dynamics of the ship’s operational condition is carried out on the proposed Markov chain.

3 Results and Discussions An experimental study of the proposed simulation model (the transition matrix and the condition diagram form the simulation), which displays the change in the ship’s operational condition, is carried out using the following initial data: – matrix of transition probabilities: ⎡

⎤ 0.99 0.0092 0 0.0004 0.0003 0.0001 ⎢ 0 0.7 0.27 0 0 0.03 ⎥ ⎢ ⎥ ⎢ ⎥   ⎢ 0 0 0.7 0 0 0.3 ⎥ p = pi j = ⎢ ⎥; ⎢ 0.1 0 0.2 0.5 0.1 0.1 ⎥ ⎢ ⎥ ⎣ 0.1 0 0 0 0.5 0.4 ⎦ 0 0 0 0 0 1

(6)

– at different values of probabilities of ship operational conditions considered at the initial moment of time t = 0: (1) P1 (0) = 0.99; P2 (0) = 0.01; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0); (2) P1 (0) = 0.95; P2 (0) = 0.05; P3(0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0); (3) P1 (0) = 0.90; P2 (0) = 0.10; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0); (4) P1 (0) = 0.85; P2 (0) = 0.15; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0);

(7)

Four options for the initial operational condition of the vessel are determined by the characteristics of cargo operations and the condition of the vessel prior to the voyage. The first variant is the most favorable of the considered ones, and the fourth one sets, essentially, 15% for possible errors in the loading procedure and not quite satisfactory condition of the vessel before the commencement of cargo operations. Thus, the port, the cargo and the vessel define the probabilities of the ship’s operational state (“ship-cargo” object).

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For all considered options of probabilities of the vessel condition at the moment of time t = 0, relevant computations were carried out according to the formula (5), which allowed to form the following regularities (Figs. 2, and 3). In Fig. 1 it is shown that the probability of the ship condition S1 decreases almost linearly from any initial point, and, for example, for the 4th variant of initial

Fig. 2 Evolution of probabilities of S1 condition for various initial probabilities (time t = 0)

Fig. 3 Evolution of probabilities of S6 condition for various initial probabilities (time t = 0)

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conditions: P1 (0) = 0.85; P2 (0) = 0.15; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0), for the 11th time step reaches almost 0.75. This is due, first of all, to the specificity of matrix (6). At the same time, for the first initial condition: P1 (0) = 0.99; P2 (0) = 0.01; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0), the probability of S1 condition is almost equal to 0.9 at the same 11th time step. Figure 2 shows the evolution of the worst-case probability of the ship S6. At the 11th time step for the first initial state P1 (0) = 0.99; P2 (0) = 0.01; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0) probability of this condition is about 0.06, that can be accepted as an admissible situation. In this case, for the fourth initial state P1 (0) = 0.85; P2 (0) = 0.15; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0), the probability of state S6 approaches 0.18, that cannot be considered admissible. During the next stage of experimental research, the transient probability matrix was changed to a more “optimistic” version: ⎡

⎤ 0.99 0.00925 0 0.00045 0.0003 0.00001 ⎢ 0 0.85 0.149 0 0 0.001 ⎥ ⎢ ⎥ ⎢ ⎥   ⎢ 0 0 0.95 0 0 0.3 ⎥ p = pi j = ⎢ ⎥. ⎢ 0.1 0 0.17 0.7 0.02 0.1 ⎥ ⎢ ⎥ ⎣ 0.1 0 0 0 0.6 0.3 ⎦ 0 0 0 0 0 1

(8)

The probability of transition to the S6 condition in this matrix is much lower than for the previous option. Besides, the probabilities of the vessel staying in 2, 3, 5 conditions have increased. It means that some fixed ship condition is more likely to be provided. Corresponding diagrams of changes in the probabilities of ship conditions S1, S6 are shown in Figs. 4, 5. Note that the dynamics of the probabilities of S1 condition remained the same, which is explained by the absence of changes in the transition probability matrix for this condition. However, changes in (8) affected all other states of the vessel. In particular, the dynamics for S6 is more “optimistic” and, particularly, at the 11th time step the probability of S6 condition for the first variant of initial probabilities: P1 (0) = 0.99; P2 (0) = 0.01; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0) is slightly more than 0.01 (compared to 0.06 in the preceding situation). Moreover, at the 11th time step, the probability of the state S6 for the fourth variant of the initial probabilities: P1 (0) = 0.85; P2 (0) = 0.15; P3 (0) = 0; P4 (0) = 0; P5 (0) = 0; P6 (0) amounts to slightly less than 0.04 (compared to 0.18 in the previous situation). Thus, a simulation model of changes in the operational state of the vessel has been developed, which allows carrying out experimental studies for different initial

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Fig. 4 Evolution of probabilities of S1 condition for various initial probabilities (time t = 0)

Fig. 5 Evolution of probabilities of S6 condition for various initial probabilities (time t = 0)

conditions and determining the most likely changes in the operational state of the vessel. Practical application of the Markov chain model has been demonstrated by experimental studies. The model describes the change in the operational state of the vessel. It was concluded that the proposed approach is adequate to the actual processes of ship operation and reliability of the obtained results.

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Thus, depending on many factors, the initial probabilities for the six selected operational states of the vessel are formed. The interrelation of these states is identified and formalized in the form of a model of a homogeneous Markov process with discrete time, the presence of an irreversible state S6 (i.e. with the absence of the ergodicity property).

4 Summary and Conclusion Seaworthiness along safety are terms widely used in relation to shipping and it is important to understand their meaning, relevance and importance. In addition, safety has become an important aspect as it includes the operational and structural integrity of the vessel, the cargo and most importantly the safety of the crew. On this basis, the main operational changes of the ship condition during operation are revealed. A simulation model of changes in the operational state of the vessel is developed and the relationship of these states is identified and formalized as a model of a homogeneous Markov process with discrete time, the presence of an irreversible state (i.e., with the absence of the ergodicity property). The offered model allows to carry out the experimental researches for various initial conditions and to define the most probable variations in the operational state of the vessel through the given number of time steps. This makes it possible to evaluate risks and make decisions to ensure the required operating condition of the vessel in the process of cargo transportation in the form of a theoretical basis.

References 1. Onyshchenko, S., Koskina, Y., Savelieva, I.:Developing a logit model for the provision of the process of managing the conclusion of voyage chartering transactions. East.-Eur. J. Enterp. Technol., 6, 26–31. https://doi.org/10.15587/1729-4061.2016.85233. 2. Szczukowski, M.: Safety in transportation: a review of the concept, its context, safety preservation and improvement effectiveness. Sci. J. Sil.N Univ. Technology. Ser. Transp., 95, 197–212 (2017). https://doi.org/10.20858/sjsutst.2017.95.18. 3. Drozhzhyn, O., Koskina, Y., Tykhonina I.: “Liner shipping”: the evolution of the concept. Pomorstvo 35, 365–371 (2021). https://doi.org/10.31217/p.35.2.18. 4. Drozhzhyn, O., Koskina, Y.: The model of container feeder line organization focused on the nature and parameters of external container flows. Commun.—Scientific Lett. Univ. Zilina, 23, A94–A102 (2021). https://doi.org/10.26552/com.C.2021.2.A94-A102. 5. Schneider, T., Armingaud F., Tabarre M. (1999). Statistical Analysis of Accident Data Associated with Sea Transport (Data from 1994–1997), Rep. CEPN-IPSN, NTE/99/02, Institut de Protection et de Sûreté Nucléaire, Fontenay-aux-Roses (1999). 6. Ovchinnikova, N.: A simple semi-Markov model of functioning of agricultural cleaning and transport system. J. Phys.: Conf. Ser., 1333 (2019). https://doi.org/10.1088/1742-6596/1333/ 3/032061

Simulation-Based Method for Predicting Changes in the Ship’s …

663

7. Girtler, J.: Application of theory of semi-Markov processes to determining distribution of probabilistic process of marine accidents resulting from collision of ships. Pol. Marit. Res., 1(81), 21, 9–13 (2014). https://doi.org/10.2478/pomr-2014-0002. 8. Guze, S., Smolarek, L.: Semi-Markov approach to the shipping safety modelling. Arch. Transp. 23, 475–488 (2012). https://doi.org/10.2478/v10174-011-0032-7 9. Sujatha, M., Muthusamy, M., Parvathy, A.: Reliability analysis of converter using Markov approach. AIP Conference Proceedings 2519, 050034 (2022). https://doi.org/10.1063/5.012 0112 10. Chen, X., Bai, S., Wei, Y., Zhao, Y., Yan, P., Jiang, H.: Passenger engagement dynamics in ride-hailing services: A heterogeneous hidden Markov approach (2022). 11. Hussain, S., Langley, J., Seitz, A., Peters, M., Hu, X.: A novel hidden Markov approach to studying dynamic functional connectivity states in human neuroimaging (2022). https://doi. org/10.1101/2022.02.02.478844 12. Peel, D., Good, N.: A hidden Markov model approach for determining vessel activity from vessel monitoring system data. Canadian Journal of Fisheries and Aquatic Sciences 68, 1252– 1264 (2011). https://doi.org/10.1139/f2011-055 13. Guze S., Kołowrocki K.: Safety modeling of port, shipping and ship traffic and port operation information critical infrastructure join network related to its operation process, 115–115 (2017). https://doi.org/10.1201/9781315210469-98 14. Mageirou, E.: Stochastic optimal positioning of tramp vessels: a Markovian approach (2012) 15. Korolyuk, V., Brodi, S., Turbin, A.: Semi-markov processes and their applications. J Math Sci 4, 244–280 (1975). https://doi.org/10.1007/BF01097184 16. Onishchenko, S., Melnyk O.: Modelling of changes in ship’s operational condition during transportation of oversized and heavy cargo. Technol. Audit. Prod. Reserv. 6/2(56), 66–70 (2020) 17. Ahmad, H.K.: The legal aspects of seaworthiness: current law and development. Doctor of Philosophy of Law Thesis. Swansea University (2006) 18. Melnyk O., Onyshchenko S., Koryakin, K.: Nature and origin of major security concerns and potential threats to the shipping industry. Sci. J. Sil. Univ. Technology. Ser. Transp. 113, 145–153 (2021). https://doi.org/10.20858/sjsutst.2021.113.11. 19. Onyshchenko, S., Melnyk, O.: Probabilistic Assessment Method of Hydrometeorological Conditions and their Impact on the Efficiency of Ship Operation. J. Eng. Sci. Technol. Rev. 14(6), 132–136 (2021). https://doi.org https://doi.org/10.25103/jestr.146.15. 20. Onyshchenko, S., Melnyk, O.: Efficiency of Ship Operation in Transportation of Oversized and Heavy Cargo by Optimizing the Speed Mode Considering the Impact of Weather Conditions. Transp. Telecommun. J. 23(1), 73–80 (2022). https://doi.org/10.2478/ttj-2022-0007 21. Melnyk, O., Onyshchenko, S.: Ensuring safety of navigation in the aspect of reducing environmental impact. ISEM 2021. LNNS 463, 1–9 (2022). https://doi.org/10.1007/978-3-031-038 77-8_9 22. Burmaka I., Vorokhobin I., Melnyk O., Burmaka O., Sagin S.: Method of prompt evasive maneuver selection to alter ship’s course or speed. Trans. Marit. Sci. 11(1) (2022). https:// doi.org https://doi.org/10.7225/toms.v11.n01.w01. 23. Onishchenko, O., Golikov, V., Melnyk, O., Onyshchenko, S., Obertiur, K.: Technical and operational measures to reduce greenhouse gas emissions and improve the environmental and energy efficiency of ships. Sci. J. Sil. Univ. Technology. Ser. Transp. 116, 223–235 (2022). https://doi. org/10.20858/sjsutst.2022.116.14. 24. Melnyk, O., Volianska, Y., Onishchenko, O., Onyshchenko, S., Kononova, O., Vasalatii, N.: Development of computer-based remote technologies and course control systems for autonomous surface ships. Int. J. Comput. Sci. Netw. Secur. 22 (09), 183–188 (2022). https:// doi.org/10.22937/IJCSNS.2022.22.9.27. 25. Melnyk, O., Onishchenko, O., Onyshchenko, S., Voloshyn, A., Kalinichenko, Y., Rossomakha, O., Naleva, G., Rossomakha, O.: Autonomous ships concept and mathematical models application in their steering process control. Trans., Int. J. Mar. Navig. Saf. Sea Transp. 16(3), 553–559 (2022). https://doi.org/10.12716/1001.16.03.18.

664

O. Melnyk et al.

26. Kong, M.-C., Roh, M.-I., Kim, K.-S., Lee, J., Kim, J., Lee, G.: Object detection method for ship safety plans using deep learning. Ocean Eng. 246, 110587 (2022). https://doi.org/10.1016/ j.oceaneng.2022.110587 27. Yang, C., Liu, H., Zhan, X., Zheng, H.: Construction of ship safety management performance evaluation system (2010). https://doi.org/10.1109/ICMSS.2010.5577607 28. Ciuic˘a, O., Dragomir, C., Pus, c˘a, B.: Safety culture model in military aviation organisation. Sci. J. Sil. Univ. Technology. Ser. Transp. 108, 15–25 (2020). https://doi.org/10.20858/sjsutst. 2020.108.2. 29. Hadi, S., Heru, S.: Marine safety: case study on safety equipment fulfillment towards ship safety. Res., Soc. Dev. 8, 158101338 (2019). https://doi.org/10.33448/rsd-v8i10.1338 30. Wiweko, A., Thamrin, M., Wahyu, E.D.: The Effect of vessel seaworthiness and crew’s competence on marine safety. J. Manaj. Transp. Logist. (JMTranslog) 02(3), 287–296 (2015) 31. Melnyk, O., Bychkovsky, Y., Shumylo, O., Onyshchenko, S., Onishchenko, O., Voloshyn, A., Cheredarchuk, N.: Study of the risk assessment quality dependence on the ships accidents analysis. Sci. Bull. Nav. Acad. 25, 136–146 (2022). https://doi.org/10.21279/1454-864X-22I1-015

Comprehensive Study and Evaluation of Ship Energy Efficiency and Environmental Safety Management Measures Oleksiy Melnyk , Oleg Onishchenko , Svitlana Onyshchenko , Andrii Voloshyn , and Valentyna Ocheretna

Abstract The growth of freight operations has always been a major factor in the growth of energy consumption in transport. The uneven dynamics of energy consumption is partly determined by the uneven dynamics of the transport work of different modes of transport. Energy efficiency of maritime transport, calculated in terms of energy consumption per unit of transported cargo, is very high compared to other types of transport, but the adoption of recent policies to improve the energy efficiency of transport, include a number of measures aimed at improving the energy efficiency of ships, primarily by reducing the volume of carbon dioxide emissions into the atmosphere. Therefore, this work is devoted to the review of means and methods of ensuring the energy efficiency of ships, taking into account the growing modern requirements for ensuring the environmental safety of transport, which remains the central object of research in the modern theory and practice of maritime transport. The issues of improvement of universal methods of energy efficiency and development of tools for economic analysis of energy efficiency of the shipping are also relevant. Keywords Ship energy efficiency · Design index · Ship management plan · Environment pollution · Transport work · Voyage planning improvement · Fleet operation

O. Melnyk (B) · S. Onyshchenko · A. Voloshyn · V. Ocheretna Odesa National Maritime University, Odesa, Ukraine e-mail: [email protected] O. Onishchenko National University “Odesa Maritime Academy”, Odesa, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_38

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1 Introduction Maritime transport remains, without exaggeration, the main mode of transport for the vast majority of cargoes consisting of grain, raw materials, dangerous and unique cargoes, such as oversized and heavy cargoes, as the cost of their transportation remains competitive and acceptable to all market participants. But the emissions in the shipping industry from more than 100 thousand ships account for almost 3% of the total greenhouse gases in the world, causing climate change such as global warming and acidification, so shipping plays a significant role in the problem of climate change. Much of this is the result of inefficient ship design, as well as lack of planning and optimal use of resources. With the development of the transport sector, the pressure on the environmental component of the industry continues to grow proportionally. As a result, the unstable level of fuel costs, the increased need for environmental regulations and the tendency to reduce fuel consumption are the main factors of the need to implement energy efficiency measures on ships, so the concepts and needs for their application in shipping have been embodied in the realities of the daily functioning of the maritime complex. Therefore, this work is devoted to the review of means and methods of ensuring the energy efficiency of ships, taking into account the growing modern requirements for ensuring the environmental safety of transport, which remains the central object of research in the modern theory and practice of maritime transport. The issues of improvement of universal methods of energy efficiency and development of tools for economic analysis of energy efficiency of the shipping fleet are also relevant. Leading scientists in the field of maritime transport, as well as international bodies and foundations always closely monitor the issues of energy efficiency, as well as enhancing the environmental safety of seagoing ships. For instance, aspects of the alternative fuels application considered in works [1–3]. Tools and methods of ship energy efficiency management investigated in [4–6]. The basic principles and measures to improve energy efficiency on ships are proposed in [7, 8]. Recommendations on the use of innovative energy efficiency technologies to calculate and verify the achieved indicators for ships in adverse conditions and regulatory requirements from regulatory organizations presented in [9–11, 27]. In [12–14, 18], the operational indicators of energy efficiency of the vessel are analyzed, considering the influence of the navigation conditions and optimization of the vessel’s arrival time in port. Probabilistic assessment method of hydrometeorological conditions and their impact on the efficiency of ship operation and general issues of navigation safety in [19, 39, 40]. Development of technical and operational measures to reduce greenhouse gas emissions and improve the environmental and energy efficiency of ships in [20]. Ensuring the safety of navigation in the aspect of reducing environmental impact and autonomous ship concept, study of the effect of sea passage parameters considered in [21–23, 26]. Issues related to the impact of maritime transport emissions on coastal air quality and marine pollution [25, 27–31]. Specific evaluation of emissions from shipping, investigation of marine pollution caused by ship operations studied

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in works [32, 33]. Other issues relating to problem of pollution from ships, ports and air pollution by particulate substances reviewed in [34–38]. Despite a wide range of works on the subject of research, further search for ways to reduce emissions of harmful substances from ships and the issues of improving the energy efficiency of ships through the introduction of various operational methods is characterized by a high degree of relevance. Therefore, in this paper it is proposed to analyze the main instruments of ship energy efficiency management, their interconnection and dependence on the initiatives of the IMO (International Maritime Organization), on ways to economize ship energy, comprehensive assessment and forecasting of the efficiency of their operation and reducing the carbon footprint in the environment. In addition, an analysis of the international regulatory framework is necessary to identify solutions to the problem of ship emissions by means of practical measures both by crew members to reduce fuel consumption and by persons working ashore and dealing with environmental protection and climate change issues.

2 Materials and Methods Transport work of all transport can be expressed by the sum of indicators of freight turnover and passenger turnover. A similar indicator can also be used for all modes of transport, land, water and air transport. For example, the railroad uses an indicator expressed in thousand tons-km gross, which reflects the work of both freight and passenger transport. For pipeline transport, the indicator given by statistics of freight turnover is used. The different value characterizing changes in transport work indicators is reflected by the assessment of the contribution of the shifting factor. For some period there were shifts mainly in the direction of more energy-intensive modes of transport, which led to a rapid growth in demand for energy. The contribution of this factor was particularly significant due to the relative stability of energy consumption by road transport (Fig. 1). Each sector of the economy contributes to greenhouse gas emissions, with energy production being the most significant. Each source of carbon emissions to the atmosphere is the result of specific processes and technologies that cannot be addressed collectively. For example, carbon emissions from fossil fuel-based electricity production (coal or gas) cannot be reduced in the same way as carbon emissions associated with cement production. Transportation accounts for about 20% of greenhouse gas emissions, with road transportation accounting for three-quarters of that share. Air transport and maritime transport account for 11%, respectively. Each of the major modes of transportation requires its own emission reduction strategy [38]. Maritime transport emits about 940 million tons of CO2 annually and is responsible for about 2.5% of global greenhouse gas (GHG) emissions. According to forecasts, these emissions will increase significantly if the industry remains unchanged. Therefore, in 2018, IMO approved a greenhouse gas strategy that aims to reduce carbon intensity by 40% by the end of 2030 and 50% by the end of 2050. In this

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Fig. 1 Greenhouse gas emissions by types of transport (Source International Energy Association: IEA and ICPCC)

regard, already by 2023, the IMO is achieving a combination of various technical and operational measures (Fig. 2).

Fig. 2 Carbon intensity reduction stepway (Source Shipnerd)

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The trajectory of specific energy consumption in transport fluctuated around a constant level. Only the energy intensity of rail transport decreased relatively steadily. For automobile and pipeline transport, the energy intensity dynamics was unstable. For maritime transport, energy intensity was steadily on the rise, and then stabilized, declining slightly during periods of crisis for the global economy. The growth was due to a reduction of land transport load and an increase in the share of sea and air transport. As for specific energy consumption in land transport, it is calculated per vehicle, so a decrease in transportation by road transport steadily leads to a decrease in specific fuel consumption. Therefore, the energy efficiency index for transport is used to identify the resulting factor for the main modes of transport and the contribution of each of them to changes in energy intensity due to improvements in technology. Its dynamics depend on the energy intensity of transport and with its growth in the last decade, say 3%, the energy efficiency index decreased by 8%, i.e. decreased on average by 0.9% per year, indicating that more energy-efficient transport technologies and equipment were used in transport.

3 Results and Discussion The International Convention for the Prevention of Pollution from Ships (MARPOL) is another important convention that protects the marine environment from pollution from ships and is considered an effective tool by the International Maritime Organization (IMO) in the field of environmental safety and protection. Constant technical development and innovation constitute crucial areas for improving the energy efficiency of ships. During a certain period, monitoring of the ships’ energy efficiency was on a voluntary basis and was expected that shipowners were aware of their responsibility for the energy efficiency of their fleet. However, due to the growing concern about the increase in greenhouse gas emissions and fuel consumption, the maritime industry’s regulatory body MEPC (Marine Environment Protection Committee) of the IMO introduced a number of steps to reduce greenhouse gas emissions from ships, namely, the adoption of Chap. 4 of Annex VI of the MARPOL Convention, the energy efficiency rules for ships. The key task was to introduce two mandatory mechanisms—Energy Efficiency Design Index (EEDI) and Ship Energy Efficiency Management Plan (SEEMP). As is known, the EEDI index is necessary to track the amount of CO2 (carbon dioxide) and pollutant emissions from ships and is a means of supporting and stimulating the development of energy efficiency standards. The idea behind its application is to achieve a reduction in CO2 emissions by improving the hull design and optimizing the operation of ship technical systems and equipment, thereby increasing the overall efficiency of the ship. The level of CO2 emissions is calculated on the basis of fuel consumption taking into account the carbon content. In turn, the level of fuel consumption is based on the power used for propulsion and auxiliary power, which is measured under certain design conditions. The transportation work of the vessel is estimated as the design power of its propulsion system multiplied by the speed

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Fig. 3 Main tools for ship energy efficiency management

measured at summer draught with maximum load and at 75 percent of the nominal installed power. Ship Energy Efficiency Management Plan is a specific instrument developed by IMO to manage and control greenhouse gas (GHG) emissions from ships. The SEEMP’s main objective is not only to reduce the amount of pollutant emissions from ships, but also to enhance their operational efficiency and reduce fuel consumption. Both SEEMP and EEDI instruments for proper control of pollution from ships are being implemented for all new ships built after 2013. Nevertheless, a SEEMP should be developed and implemented by the ship owner or operator to potentially reduce the vessel’s operating costs, which ultimately aims to reduce overall fuel consumption, including emissions in the long term. The best ways to optimize and maintain a ship’s energy efficiency are planned and implemented through a Ship Energy Efficiency Management Plan. The SEEMP outlines all the best practices to be applied on board the ship and in the shipowner’s office to ensure maximum efficiency of the ship’s voyage.

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Fig. 4 Concept of interaction of energy efficiency management tools [3]

A critical analysis of the shipboard measures and the quality of their implementation can be made using the Energy Efficiency Operation Index (EEOI). The main difference between EEDI and EEOI lies in the fact that the first measures how efficiently the ship was built, while the second measures how efficiently it is operated. Therefore, IMO has introduced an operational energy efficiency indicator (EEOI) in order to use it as a performance indicator for EEDI monitoring in combination with SEEMP. Figure 3 shows the concept of how the above tools work together, covering both ship design and operation processes (Fig. 4). High emissions are a consequence of inefficient ship design and lack of planning and optimal use of resources, therefore the main objective of the IMO’s innovations was to standardize and coordinate efforts and, by introducing management tools, to achieve stimulation and development of technological processes on board ships towards more energy efficient performance standards. Thus, EEDI is a technical standard applicable to new ships. Designers and shipbuilders can freely choose technologies to meet EEDI requirements in each specific ship project. Over time, the EEDI level will be reduced, which will gradually lead to more energy-efficient ships, bringing technologies and operations to improve energy efficiency in the maritime sector and accelerating the shipping sector towards a cleaner and greener future. This becomes also relevant for regions that are particularly vulnerable to the effects of climate change and the potential to promote energy efficiency technologies and operations in the maritime industry. The Global Maritime Energy Efficiency Partnership (GloMEEP) is one of the IMO’s largest initiatives to successfully implement these programs. The aim of GloMEEP is to increase the awareness and technical capabilities of shipping companies around the world to move the maritime world towards a low-carbon future. The

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European Union-funded MTCC Global Networking Project is another initiative that aims to connect developing and underdeveloped countries involved in international shipping to more technologically advanced countries, which can help them benefit from the latter’s technical advances. As for new ships, within the framework of the energy efficiency program, many advanced changes are planned to be implemented, such as optimization of ship speed, improvement of voyage planning and port cargo operations, upgrading technical systems, ship hull in terms of shape and size, improvement of paint coating and other methods, such as trim optimization, ballast condition, etc. However, for existing and older ships, the issue remains unsolved and not sufficiently developed, some aspects of which lie on the surface. Speed decrease is certainly an important way to reduce fuel consumption; however, it increases the voyage time of the ship and has a negative impact on the ship’s commercial performance and the efficiency of the ship’s propulsion system, resulting in significant fuel consumption for subject distance. Moreover, the level of energy efficiency of the ship’s power plant depends not only on the operating condition of the main engine and the condition of the cargo, but also on changes in the navigation situation. Stages of SEEMP development: planning, introduction; monitoring; selfevaluation and improvement. The shipboard SEEMP is developed by the company for each vessel using the Energy Efficiency Operating Index (EEOI) in accordance with the recommendations of MERS.1/Circ.684 of 17.08.2009 and Resolution MEPC 203. The main recommendations that can be included in the SEEMP are listed below: 1. Fuel-efficient operation of ships and improved voyage planning can be achieved by developing an optimal route using software to solve various problems, including navigational, during the sea passage, ship routing in the most favorable ways, taking into account the specific hydrometeorological conditions. 2. Compliance with voyage timing for planning of joint port and ship operations, on-time ship’s berthing and cargo handling. 3. It should be noted that speed optimization does not mean minimum speed. In fact, sailing below the optimum speed will result in higher fuel consumption, increased vibration at low speeds and problems with soot fouling in combustion chambers and exhaust systems. When negotiating charter conditions, efforts should be made to encourage the vessel to operate at optimum speed to maximize energy efficiency. 4. Optimized shaft power - recommended operation of the ship at a constant shaft speed. 5. Optimized trim - The trim of a vessel in cargo or in ballast condition has a large effect on the ship’s resistance in the water, and optimized trim can provide significant fuel savings. Ship design or safety factors can hinder the full application of trim optimization. 6. Optimal ballasting of the vessel. Ballast should be adjusted to meet the requirements for optimum trim and ship steering conditions as well as optimum ballast conditions provided through adequate cargo operations planning. Conditions

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and measures for ballast management are outlined in the Ship’s Ballast Water Management Plan. Optimization of propeller run-on flow. Improving propeller run-on water flow with devices such as steerable rudders or directional nozzles can increase effective propulsive power and therefore reduce fuel consumption. Energy efficiency can be significantly improved by cleaning and polishing the propeller or by changing its coating. Optimal use of the steering device and the ship’s course control system (autopilot) - achieved by improving course control by less frequent and minor rudder changes and minimizing energy losses due to rudder resistance. When approaching ports and pilot stations, the autopilot is less efficient because manual rudder control provides a quicker response to the commands given. Hull maintenance, removal of biofouling and roughness. Hull resistance can be reduced by coating with new technology, possibly combined with shorter cleaning intervals. There should be regular checks of the condition of the hull afloat. Smoother the surface of the ship’s hull improved fuel efficiency. Alternative fuels for the ship’s power plant: – liquefied natural gas (LNG); – methanol, higher aliphatic alcohols; – hydrogen (raw material for hydrogen production is inexhaustible—water, the product of hydrogen combustion is water); – hydrogen sulfide, dissolved in seawater, is a potential threat, but on the other hand, it is an energy carrier with a high reserve factor; – water-fuel, alcohol-fuel emulsions designed to save hydrocarbon fuel, reduce emission of harmful toxic components with the exhaust gases of the power plant; – fuel and lubricant additives allow solving the following problems: decreasing soot concentration in the exhaust gases of a power plant, improving tribotechnical properties of lubricants, decreasing friction coefficient and increasing time between repairs of machinery and shipboard equipment.

11. Heat utilization of the exhaust gases of the power plant. Existing shipboard technologies for utilization of the recycling of the exhaust heat have a number of drawbacks: – low exhaust heat utilization factor; – high labour input and hazards for the crew, conditioned by frequent operations on cleaning of the exhaust heat recovery boiler tube assemblies from soot; – soot emissions with the blowdown gases into the atmosphere. 12. Rational use of tonnage can be ensured by improving the fleet operation planning and, in particular, by reducing the periods of ballast passage, in which all parties to the cargo transportation process are interested.

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13. Monitor and calculate the expediency of maintaining aged ships in order to identify the benefits and losses from the necessary repairs of worn-out ships and keep them in good technical and seaworthy condition. 14. Ships trading area. In areas corresponding to its class according to the Classification Certificate. Ways to choose the most efficient combination of navigation area and cargo to be transported are within the competence and responsibility of the shipping company. 15. Compatibility of measures. In the process of maritime transportation, various interested parties are involved, and in this connection, it is important to have well-established and improved ways of interaction to carry out successful transportation of goods. It is necessary to note, that the offered recommendations are of a general character, therefore for each specific vessel project it is necessary to consider the specific character of a vessel, characteristics of transported cargo, navigation planning of a crossing, time of a year and date of departure/arrival to a port when elaborating SEEMP. Set of technological solutions for increasing energy efficiency of ships is shown in Fig. 5. Ship design plays an important role in ensuring the ship’s energy efficiency, as it is the foundation of operational efficiency. The technical measures to reduce fuel consumption include the use of high-efficiency marine engines and propulsion systems, optimized hull contours, rudder and propeller geometry, and innovations such as the bulbous bow. Further fuel-saving potential comes from heat recovery and the use of LNG (liquefied natural gas), which is the fuel of choice when it comes to replacing traditional fuels in shipping as it has comparatively lower sulphur and nitrogen oxide emissions. However, the large space required for LNG storage hinders its widespread use at present. The principles of a ship energy efficiency management plan are shown in Fig. 6.

Fig. 5 Design, operational and economic solutions by IMO

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Fig. 6 Principles of functioning of ship’s energy efficiency tools [7]

A successful and sustainable reduction in fuel consumption on board ships and an overall increase in operational efficiency can be achieved by the introduction of various operational techniques. They include scheduling operations at low speeds, better use of capacity and resources, and constant liaison between shipping operators for efficient route planning. Reducing turnaround time in ports is another way to improve the efficiency of ships. Research [18] has shown that the two largest sources of unproductive time in port are berth waiting time when the port is closed and berth waiting time due to early arrival. With a reduction of one to four hours per port call, the energy efficiency potential is 2–8% (Fig. 7). Thus, the emphasis is placed on the completion of short-term measures and proposals for the introduction and implementation of medium-term measures where two short-term approaches are being considered, a technical one in which EEDI for existing ships is expressed as Energy Efficiency Existing Ship Index (EEXI) as retroactive requirements applied to existing ships. In addition, operational in the form of an expanded SEEMP whose mandatory goal is to reduce operational emissions. As for the medium-term proposals expected to come into force after 2023, these are the formation of the International Marine Research and Development Board (IMRB), implementation of requirements and guidelines for quantification of greenhouse gases and carbon footprint, methane emission control; EEDI Phase 4. To technical measures including ship design, Energy Efficiency Indicator for Existing Ships (EEXI)–EEDI applies to existing ships. Operational measures are an advanced SEEMP with a mandatory carbon intensity indicator (CII) rating scheme (A–E). All of these measures are combined into a single package that achieves a finely balanced political compromise. The Marine Environment Protection Committee (MEPC 75) has approved a draft of new mandatory regulations for reducing the

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Fig. 7 Ship energy efficiency plan implementation

carbon intensity of existing ships. These build on existing mandatory energy efficiency requirements for the continued reduction of greenhouse gas emissions from shipping.

4 Summary and Conclusion Despite the growing demands, the energy efficiency of maritime transport, in general, at a higher rate in contrast to other modes of transport, which determines the consistent growth and further stimulation and development of measures to improve the energy efficiency of ships. Therefore, the conducted study of the main tools for managing the energy efficiency of ships demonstrates the fact of implementing effective methods. A separate place in the issue of improving the energy efficiency of the fleet is occupied by the cost of introducing new and more efficient technologies for upgrading existing ships in order to improve their environmental and economic performance, so these measures require the development of more advanced and cost-effective means and methods to improve energy efficiency. Among the measures are improvement of transport energy consumption statistics and methods of its analysis, which will allow to obtain more reliable estimates of the use of energy-efficient transport equipment in restraining the growth of energy consumption in transport. Development of operative measures on increase of ship operation effectiveness is undoubtedly an extremely

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urgent task requiring additional research and implementation of the latest technologies, one of which could be the use of integrated decision support systems aimed at reduction of both fuel consumption and emissions of harmful substances. Today, a set of technologies for improving ship energy efficiency has already been developed and successfully implemented, where the focus is on ship hull design, energy recovery, fuel quality and level, and operational measures on the ship, but these tools and methods still need to be improved with each area to achieve the maximum level of efficiency.

References 1. Khasanov, I., Gimaeva, A.: Features of fuel bunkering for liquefied natural gas vessels. Transp. Storage Pet. Prod. Hydrocarb. 3, 19–22 (2017) 2. Karpenko, A., Koptseva, E.: Prospects of conversion of marine and river transport vessels to alternative fuels. Transp. Bus. 3, 63–66 (2017) 3. Bezyukov, O., Zhukov, V., Yashchenko, O.: Gas-engine fuel on water transport. Bull. Admiral S. O. Makarov State Mar. Eng. Univ. 6(28):31–39 (2014) 4. Managing energy efficiency. Marine transport. [Electron resource]. Retrieved from: http:// www.morvesti.ru/analitika/1692/23595/. Accessed 10 Oct 2022 5. Energy Efficiency Measures. https://www.imo.org/en/OurWork/Environment/Pages/Techni cal-and-Operational-Measures.aspx. Accessed 20 Oct 2022 6. Hüffmeier, J., Johanson, M.: State-of-the-Art Methods to Improve Energy Efficiency of Ships. J. Mar. Sci. Eng. 9, 447 (2021). https://doi.org/10.3390/jmse9040447 7. Capt Rajeev Jassal (2018) Ship energy efficiency: here is all you need to know. https://www. myseatime.com/blog/detail/ship-energy-efficiency. Accessed 10 Oct 2022 8. Energy efficiency in shipping - why it matters! (2018) Maritime Cyprus. https://maritimec yprus.com/2018/04/03/energy-efficiency-in-shipping-why-it-matters. Accessed 15 Oct 2022 9. IMO Train the Trainer (TTT) Course on Energy Efficient Ship Operation. Module 2 – Ship Energy Efficiency. Regulations and Related Guidelines. London (2016) 45 p 10. MEPC.1/Circ.684, “Guidelines for voluntary use of the ship EEOI”, MEPC.1/Circ.684, 17 August 2009 11. MEPC.1/Circ.815: 2013 Guidance on treatment of innovative energy efficiency technologies for calculation and verification of the attained EEDI for ships in adverse conditions 12. Yuan, Y., Li, Z., Malekian, R., Yan, X.: Analysis of the operational ship energy efficiency considering navigation environmental impacts. J. Mar. Eng. Technol. 16(3), 150–159 (2017). https://doi.org/10.1080/20464177.2017.1307716 13. Energy efficiency technologies information portal. https://glomeep.imo.org/resources/ene rgy-efficiency-techologies-information-portal/#:~:text=These%20include%20measures%20s uch%20as,hydrodynamic%20performance%20of%20the%20vessel 14. Sargam, S.: Ship energy efficiency. http://themarineexpress.com/ship-energy-efficiency/. Accessed 4 Oct 2022 15. Melnyk, O., Bychkovsky, Yu., Shumylo, O., Onyshchenko, S., Onishchenko, O., Voloshyn, A., Cheredarchuk, N.: Study of the risk assessment quality dependence on the ships accidents analysis. Sci. Bull. Nav. Acad. 25, 136–146 (2022). https://doi.org/10.21279/1454-864X-22I1-015 16. Onishchenko, O., Shumilova, K., Volyanskyy, S., Ya, V., Volianskyi, S.: Ensuring Cyber Resilience of Ship Information Systems. TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 16, 43–50 (2022)

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O. Melnyk et al.

17. Melnyk, O., Onyshchenko, S., Koryakin, K.: Nature and origin of major security concerns and potential threats to the shipping industry. Sci. J. Sil.N Univ. Technology. Ser. Transp. 113, pp. 145–153 (2021). ISSN: 0209–3324. DOI:https://doi.org/10.20858/sjsutst.2021.113.11. 18. Onyshchenko, S., Melnyk, O.: Probabilistic assessment method of hydrometeorological conditions and their impact on the efficiency of ship operation. J. Eng. Sci. Technol. Rev. 14(6), 132–136 (2021). https://doi.org/10.25103/jestr.146.15 19. Onyshchenko, S., Melnyk, O.: Efficiency of ship operation in transportation of oversized and heavy cargo by optimizing the speed mode considering the impact of weather conditions. Transp. Telecommun. J. 23(1), 73–80 (2022). https://doi.org/10.2478/ttj-2022-0007 20. Onishchenko, O., Golikov, V., Melnyk, O., Onyshchenko, S., Obertiur, K.: Technical and operational measures to reduce greenhouse gas emissions and improve the environmental and energy efficiency of ships. Sci. J. Sil. Univ. Technology. Ser. Transp. 116, 223–235 (2022). https://doi. org/10.20858/sjsutst.2022.116.14 21. Golikov, V., Golikov, V., Volyanskaya, Y., Mazur, O., Onishchenko, O.: A simple technique for identifying vessel model parameters. In: Proceedings of IOP Conference Series: Earth and Environmental Science, 172, 012010 (2018) 22. Melnyk, O., Onishchenko, O., Onyshchenko, S., Voloshyn, A., Kalinichenko, Y., Rossomakha, O., Naleva, G., Rossomakha, O.: Autonomous ships concept and mathematical models application in their steering process control. TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 16(3), 553–559 (2022). https://doi.org/10.12716/1001.16.03.18 23. Melnyk, O., Onyshchenko, S.: Ensuring safety of navigation in the aspect of reducing environmental impact, ISEM 2021. LNNS 463, 1–9 (2021) 24. Hannes, J., Styhre, L.: Increased energy efficiency in short sea shipping through decreased time in port. Transp. Res. Part A-Policy Pract. 71, 167–178 (2015) 25. Implementing Energy Efficiency Design Index (EEDI) Indian register of shipipping. https:// www.irclass.org/media/1393/energy-efficiency-design-index.pdf. 26. Leonov, V., Timoshenko, V.: Study of influence of sea crossing parameters on operational coefficient of energy efficiency of a vessel. Bulletin of Admiral Makarov State University of Maritime and River Fleet 10(2), 390–401 (2018). https://doi.org/10.21821/2309-5180-201810-2-390-401 27. Pongpiachan, S., Thumanu, K., Chantharakhon, C., Phoomalee, C., Tharasawatpipat, C., Apiratikul, R., Poshyachinda, S.: Applying synchrotron radiation-based attenuated total reflection-Fourier transform infrared to evaluate the effects of shipping emissions on fluctuations of PM10-bound organic functional groups and ionic species. Atmos. Pollut. Res. 13, 101517 (2022). https://doi.org/10.1016/j.apr.2022.101517 28. Marine Environment. https://www.imo.org/en/OurWork/Environment/Pages/Default.aspx. Accessed 25 Oct 2021 29. Review of maritime transport – 2020. Available at:https://unctad.org/system/files/official-doc ument/rmt2020_en.pdf (Accessed: 20.10.2021). 30. Viana, M., Hammingh, P., Colette, A., Querol, X., Degraeuwe, B., Vlieger, I., Aardenne, J.: Impact of maritime transport emissions on coastal air quality in Europe. Atmos. Environ. 90, 96–105 (2014). https://doi.org/10.1016/j.atmosenv.2014.03.046 31. Corbett, J., Winebrake, J., Green, E., Kasibhatla, P., Eyring, V., Lauer, A.: Mortality from ship emissions: a global assessment. Environ. Sci. Technol. 41, 8512–8518 (2007) 32. Campling, P., Janssen, L., Vanherle, K., Cofala, J., Heyes, C., Sander, R.: Specific evaluation of emissions from shipping including assessment for the establishment of possible new emission control areas in European Seas. Flem. Inst. Technol. Res. (VITO), Mol, BE, 395 (2013). 33. Özdemir O., Yılmaz H., Ba¸sar E.: Investigation of marine pollution caused by ship operations with DEMATEL Method. TransNav, Int. J. Mar. Navig. Saf. Sea Transp., 315-320 (2016). https://doi.org/10.12716/1001.10.02.14. 34. Mazzoccoli, M., Altosole, M., Vigna, V., Bosio, B., Arato, E.: Marine Pollution Mitigation by Waste Oils Recycling Onboard Ships: Technical Feasibility and Need for New Policy and Regulations. Front. Mar. Sci. 7, 566363 (2020). https://doi.org/10.3389/fmars.2020.566363

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35. European Maritime Safety Agency. Preventing Pollution from Ships. Lisboa: European Maritime Safety Agency, 2008. 36. Eslam, M. (2019). Recycling marine used oil using green ship conversion technique. In Proceedings of the SPE Gas & Oil Technology Showcase and Conference, Dubai. 37. Mueller D., Uibel S., Takemura M. (2011). Ships, ports and particulate air pollution - an analysis of recent studies. J Occup Med Toxicol, 6 (31). DOI:https://doi.org/10.1186/1745-6673-6-31. 38. The Geography of Transport Systems. Global Greenhouse Gas Emissions by the Transportation Sector. Available at: https://transportgeography.org/contents/chapter4/transportation-andenvironment/greenhouse-gas-emissions-transportation/. (Accessed on 20.10.22). 39. Melnyk, O., Onyshchenko, S., Pavlova, N., Kravchenko, O., Borovyk, S.: Integrated Ship Cybersecurity Management as a Part of Maritime Safety and Security System. International Journal of Computer Science and Network Security 22(03), 135–140 (2022). https://doi.org/ 10.22937/IJCSNS.2022.22.3.18 40. Burmaka, I., Vorokhobin I., Melnyk, O., Burmaka, O., Sagin, S. (2022) Method of Prompt Evasive Maneuver Selection to alter Ship’s Course or Speed, Transactions on Maritime Science, 11(1). DOI:https://doi.org/10.7225/toms.v11.n01.w01.

Modern Aspects of Ship Ballast Water Management and Measures to Enhance the Ecological Safety of Shipping Oleksiy Melnyk , Oleksandr Sagaydak , Oleksandr Shumylo , and Oleh Lohinov

Abstract The problem of ballast water transfer has long gone beyond the ecological, bringing economic consequences in the form of destruction of marine ecosystems, reduction of seafood stocks and threat to human life. This paper offers an analysis of the implementation stages of the ballast water management convention, as well as an overview of the basic ways to improve the efficiency of ballast water management on board ships, the requirements of regulatory documents for existing ships and those under construction. Practical recommendations for systematic control and monitoring of the process of compliance of ship documentation with the current requirements are given and the process of introducing ballast water treatment systems is considered. The algorithmic model allowing planning the process of ballast change taking into account the compliance with the existing requirements and rules is developed. Keywords Ballast water management · Ballast exchange procedure · Ecological safety · Marine environment · Port state control inspection · Sediments and pathogenic organisms · Ballast treatment systems

1 Introduction The processes of globalization, despite the problems of environmental protection observed in the last decade, have caused changes in almost all spheres of human activity. The shipping and shipbuilding industries are not an exclusion. As a result of the implementation of environmental protection projects, ships using alternative types of energy began to appear, new models of ship equipment are being developed, the latest technologies for the utilization of both the ships themselves and the associated waste from their production activities, including ballast water, are being mastered. Such changes are connected not so much with the desire to maximize economic efficiency from the use of ships, but rather with the need to meet the strict O. Melnyk (B) · O. Sagaydak · O. Shumylo · O. Lohinov Odesa National Maritime University, Odesa, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_39

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requirements of the International Conventions on water transport and its operation in order to achieve environmental friendliness of transport as a global perspective idea. It is clear that all these and other measures taken should lead to an increase in the level of environmental safety, or at least not worsen it. However, as practice shows, during the development and implementation of some projects aimed at preserving the environment, enough issues require additional study and research. Sufficient research on various aspects of marine transportation has been studied with a focus on marine ecosystem protection, dealing with such topics as liner transportation, container feeder line organization, ballast water treatment, green vessel concepts, environmental impact of ballast water, ship tests of ballast water treatment systems and emerging risks in ballast water treatment. In addition, there are studies on optimization of ship-cargo interaction, situational awareness at sea, maritime safety, optimal composition of technical facilities at marine grain terminals, and statistical analysis of marine accidents. Some articles use models such as logit models, semiMarkovian processes, and Markovian approaches to analyze and evaluate various aspects of maritime operations. The study [1] focuses on developing a logit model for managing the conclusion of voyage chartering transactions, aiming to optimize chartering decisions. In [2] research examines the environmental efficiency of ship operations in terms of freight transportation effectiveness provision. It analyzes the impact of ship operations on the environment and suggests measures for improving efficiency. The authors in [3] present a model for organizing container feeder lines based on the nature and parameters of external container flows. Article [4] discusses practical methods of ballast water treatment, addressing the issue of invasive species and environmental contamination associated with ballast water discharge. The paper [5] explores green ship concepts, highlighting environmentally friendly practices and technologies for reducing emissions and improving the sustainability of shipping operations. The study [6] examines the ecological impacts of ballast water loading and discharge, specifically focusing on the toxicity and accumulation of disinfection by-products. This study [7] discusses ships’ ballast water replacement monitoring at sea based on microcontroller units (MCU). The paper [8] presents shipboard trials of an ozonebased ballast water treatment system. Publication [9], from the Federal Institute for Risk Assessment, focuses on emerging risks from ballast water treatment. The study [10] discusses the selection of ballast water management systems and associated considerations. In [11] authors discusses chemical and physical treatment options to kill toxic dinoflagellate cysts in ships’ ballast water, aiming to mitigate the spread of harmful organisms, highlighing ballast water as a serious problem, emphasizing its potential negative impact on marine ecosystems in [12]. Study [13] explores how infection by invasive parasites can increase the susceptibility of native hosts to secondary infection through modulation of cellular immunity. International Convention for the Control and Management of Ships’ Ballast Water and Sediments, an international agreement aimed at preventing the spread of harmful aquatic organisms through ballast water [14, 15]. The authors in [16] propose a new approach for the localization of invasive species in ballast water of seagoing vessels. The authors investigate in [17–20] the nature and origin of major security concerns and

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potential threats to the shipping industry and presents a probabilistic assessment method for evaluating hydrometeorological conditions and their impact on the efficiency of ship operations. They focuses in research as well on the efficiency of ship operations in transporting oversized and heavy cargo by optimizing speed modes while considering the impact of weather conditions. They also discussed the technical and operational measures to reduce greenhouse gas emissions and improve the environmental and energy efficiency of ships. Work [21] discusses basic aspects of ensuring shipping safety. The article [22] focuses on the ship-cargo interface and proposes the concept of optimization using risk assessment methods and network data exchanging technologies. In [23] explored the means of ensuring the safety of navigation while reducing environmental impact. The articles [24, 25] highlights maritime situational awareness as a key measure for safe ship operation and presents a multicriteria approach to determining the optimal composition of technical means in the design of sea grain terminals. In [26] authors examined the prevention of negative consequences related to ballast water. The works [27–29] considered diagnostics in ballast water management, focus on determining ballast water discharge profiles for effective ballast water management and environmental studies, and consider ballast water treatment technologies. The works [30, 31] presented development measures to enhance the ecological safety of ships and reduce operational pollution to the environment and proposing an integral approach to vulnerability assessment of a ship’s critical equipment and systems. The papers [32, 33] discussed the simulation of marine diesel engines’ operating cycle for diagnostic purposes, examined features related to the fastest pressure growth point during the compression stroke. The analysis undertaken covers various topics related to ballast water treatment, invasive species, environmental and energy efficiency, safety issues, and the impact of weather conditions on ship operations and prompts the need to revisit this relevant topic for research.

2 Methodology of the Research The impact of invasive species worldwide is a significant ecological and economic concern. The introduction and establishment of invasive species pose a significant problem due to their ability to disrupt ecosystems and cause harm to native species, biodiversity, and ecosystem functioning. Invasive species often outcompete native species for resources, prey upon them, or introduce diseases, leading to population declines or even extinction of native species. This loss of biodiversity can have cascading effects on ecosystem stability and resilience. Additionally, invasive species can have detrimental economic impacts, such as damaging agricultural crops, causing infrastructure damage, and increasing the cost of eradication and control efforts. The spread of invasive species also poses challenges for human health, as some species may transmit diseases or cause allergic reactions. The problem of invasive species highlights the need for effective prevention, early detection, and management strategies to mitigate their negative impact as non-native

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Fig. 1 Impact of invasive species worldwide (Source Clear Seas)

organisms that establish themselves in new habitats and can negatively affect local biodiversity, ecosystem functioning, and human activities (Fig. 1). The ship’s ballast system serves to ensure the stability of the ship, to modify ship’s list, trim and draft by filling or emptying special compartments or tanks. According to the International Convention for the Control and Management of Ships’ Ballast Water and Sediments, innovative systems for ballast water treatment, disposal or neutralization, to prevent the discharge or intake of pathogenic organisms in sediments and ballast water should be installed on ships. Thus, the implementation of the requirements of the International Ballast Water Management Convention, as well as the implementation of a ballast water management plan and ballast water treatment systems on board ships become of great importance. Although ballast water remains the only tool to ensure the stability of a ship and its safety, reliability and efficient management, it has been scientifically researched and proven by expert bodies that ballast water is a pathway for the transfer of significant volumes of harmful aquatic organisms and other pathogens that cause serious environmental, economic and health problems. When released into sea basins or watercourses, these organisms can endanger the environment, human health, property or resources, degrade biodiversity or interfere with other uses of these areas. Convention on the Management of Ships’ Ballast Water and Sediments, 2004 (entered into force in September 2017). It should be noted that Ukraine is still not a Party to this Convention, despite the fact that the issue of transfer of invasive species with ballast water has been known here since the eighties (the first requirements for the change of ballast water appeared in 1988. The fact that Ukraine is not a Party to the Convention does not exempt ships flying its flag from the need to comply with the requirements of the Convention. Main provisions: there is a grace period until 2024, during which all ships must install on board special ballast handling equipment that must comply with the requirements of the D-2 Convention ballast handling standard. The terms of compliance are set for each vessel separately depending on the need to update the International Oil Pollution Prevention Certificate (IOPP). That is, during this interim

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Fig. 2 Terms of convention compliance (Source Gard.no)

Table 1 IMO D2 standards for discharged ballast water [26]

Organism category

Regulation

Plankton, >50 µm in minimum dimension

αcr2 ) as the permissible levels of NOx emissions are reached.

O. Siryi (B) · M. Abdulin · Y. Bietin · O. Kobylianska · A. Magera National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine e-mail: [email protected] O. Siryi Thermal Energy Technology Institute of NAS of Ukraine, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_43

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Keywords Flame stabilizer · Burner device · Flue gases · Nitrogen oxides · Carbon monoxide · Fuel distribution parameters · Excess air coefficient · Specified parameters · Mathematical planning of the experiment

1 Introduction Fossil fuel combustion is always associated with the emission of pollutants. Modern research in the field of technical combustion is motivated not only by increasing the level of efficiency, but also by the problem of reducing pollutant emissions into the atmosphere. This is achieved by appropriate modifications of the working process of burner devices. Among a number of toxic substances, such as sulfur oxides, polycyclic aromatic compounds, fine inorganic aerosols and greenhouse gases, nitrogen oxides (NOx ) deserve special attention due to their wide range of effects on the environment and living organisms [1]. It should be noted that today there are a number of both active and passive methods of NOx suppression, which are effectively used in practice [2–5]. Currently, the introduction of biomass as an energy resource in district heating facilities, industry and the “private” sector is spreading [6]. In this regard, of particular concern is the increased emission of substances such as carbon monoxide (CO), organic gaseous compounds and particulate matter. Compliance with permissible norms in this case is associated with the proper organization of the furnace process of gas combustion facilities (GCF). In general, technology of combustion process should include the following successive stages: effective regulation of the fuel and oxidant ratio in the combustion zone, effective control of mixing process fuel-oxidizing gases, organization of the conditions for their chemical reaction, maximum use of waste gas heat [7]. The study of the process of carbon monoxide formation during the combustion of natural gas is not a priority task, but the control of their emissions plays an important role in the performance of thermal debugging of gas combustion systems. The construction and analysis of the “combined” emission characteristics of nitrogen and carbon oxides is an important stage in the environmental and thermal commissioning of GCF, including the environmental audit of burner devices [8]. The burning device determines the mechanism of the flame stabilization and the reliability of the burner system operating process and provides an appropriate range of thermal loads of GCF regulation [9]. Thus, the combustors operating on the basis of a bluff-body stabilize elements (angles, cones, cavities, etc.) have a number of advantages in comparison with the most common burners with flow rotation [8, 10]. In addition, studies of the kinetic combustion process of a pre-prepared mixture are often performed using burners based on bluff-body [11–13]. The stabilization mechanism in bluff-body premixed flames condition occurs by igniting a fresh combustible mixture that enters the combustion zone and contacts with highly heated products in the recirculation zone. This scheme allows to neglect complex physics of mixing and simplify the phenomenon under study when performing mathematical modeling.

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The “combustion technology” of the fuel has a decisive influence on the emission indices of GCF. This concept is understood as a full cycle of chemical energy use of fuel starting from the process of its mixing with an oxidizer and ending with the removal of oxides of combustible components, particular matters and gases from the gas path. For boiler units, combustion technology involves the organization of the furnace process [14]. An additional set of technical measures for the implementation of combustion technology is the division of the process into separate stages with the additional introduction of flue gases into the combustion zone. Such technical measures have a decisive influence on the working process of high-capacity steam boilers [15, 16]. The peculiarity of the technology of combustion of gaseous fuels is outlined, including the problem of rational mixing of fuel and oxidizer, which largely determines the reliability of the flame stabilization process [8, 17]. Such principles of combustion technology as: rational distribution of fuel in the oxidizer flow; stable regulated structure of the flow of fuel, oxidizer and combustion products; self-regulation of the composition of the fuel mixture in the flame stabilization zone; high combustion intensity, process stability in the operating range of heat output change provide high efficiency and environmental friendliness of fuel use. One of the promising developments of the Kyiv Polytechnic Institute, which is based on the above principles, is the jet-niche combustion technology (JNT) [18]. As of 2022, significant experience in the energy and environmental modernization of boiler equipment with a capacity of 0.5–125 MW has been gained on the basis of JNT (Fig. 1). The following canonical principles of minimizing the concentrations of harmful NOx are considered as an important aspect of reducing toxic oxides: pre-mixing, staged combustion and direct-flow aerodynamic flow scheme. In terms of implementation of these principles, burner devices developed in Igor Sikorsky Kyiv Polytechnic Institute are considered promising [18]. Regarding JNT, the principles of minimizing nitrogen oxides are achieved due to the successful constructive placement of the jet-niche system (JNS) [19] on an autonomous collector, which consists of the burner (Fig. 2). A wide industrial implementation of JNT technology achieved on the natural gas burning systems. But, the above features of JNT technology provide the prospect of

a

b

c

Fig. 1 Jet-niche technology of fuel combustion: burner (a), open gas flare (b), layout of burners on gas-consuming equipment (c)

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Fig. 2 Schematic of location of JNS on a flat stabilizer: W A —air velocity, W G —fuel velocity, L 1 —distance of fuel openings from detachment edge of the niche, S—step of openings location, d—diameters of openings, L—length of niche cavity, B—thickness of stabilizer

its use in the combustion of gas fuels of a wide range of origin. The main results presented in the work will concern the possibility of adapting the tested burner design to the combustion of propane–butane mixture. This fuel is considered a promising alternative gas with a developed infrastructure and sufficient production volume for its use as a backup fuel at industrial facilities, including municipal energy facilities. The realization of this goal can be achieved by appropriate changes in the fuel distribution system to combustion of gas with a stoichiometry different from natural gas. Also, the task of the research includes the task of choosing both geometric and operating parameters of the burner system at which the most efficient operation of the stabilizer is ensured while maintaining the permissible emission indicators. The characteristic dependences of emission indices of nitrogen and carbon oxides in the jet-niche flame stabilizer are shown in Fig. 3. The ecological and thermal characteristics of the JNS determine the efficiency of the working process of the industrial burning devices (BD) of the same name, which implement the promising jet-niche technology of fuel combustion [20]. The combination of CO and NOx emissions characteristics allows analyze the presence of two characteristic critical points. These points correspond to the extremes of the corresponding curves. Taking into account the location of αkr1 and αkr2 , three regions can be distinguished for the general analysis of the areas in the range of the studied modes. The first of which corresponds to the interval (α > αkr2 , α → max) and in which the emission of nitrogen oxides decreases, but the carbon oxides emission increases extremely. In the direction of reducing the excess air In the second zone, which corresponds to (αkr1 ≥ α ≥ αkr2 ), there is a clear increase in the studied emission characteristics. In the third region (α < αkr1 ), in the direction of decreasing excess air (α → 1.0), an ambiguous behavior of the characteristics is also observed: CCO increases extremely with a simultaneous decrease in the concentration of nitrogen oxides CNOx . The critical values of the excess air coefficient are the main criterion for the selection of BD in accordance with the technological conditions of the combustion process at the GCF: furnaces of power and hot water boilers, gas turbine combustion chambers, dryers, furnaces, etc. The influence of α on the emission level depends

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Fig. 3 Influence of excess air on the nitrogen oxides concentration and carbon monoxide during the combustion of propane–butane mixture in JNS with parameters: diameter of gas supply holes d = 3 mm, relative spacing of holes = 5.0, height of the air channel H ch = 36 mm, air velocity in the channel, thermal capacity N = 110 kW

mainly on the design of the burner, the method of mixture formation and the intensity of heat and mass transfer processes in the combustion zone. The fact of the opposite behavior of the studied flame stabilizer emission indicators becomes obvious from the considered dependences. That is, the improvement of the combustion process leads to almost complete oxidation of carbon (αkr2 , CCO → 0) and a simultaneous nitrogen oxides concentration increase to the maximum value (αkr1 , CNOx → max). This feature is mainly due to the predominance of atmospheric nitrogen thermal process of oxidation, which occurs at high combustion temperatures. Thus, there is a certain ambiguity in the choice of the stabilizer operating mode in terms of ensuring minimal emissions of these oxides and depends on the quality of the BD mixing.

2 Influence of Fuel Distribution Geometrical Parameters on Emission Qualities of JNS at Combustion of Propane–Butane Mixture Figure 4 presents the results of gas analysis of combustion products during the combustion of liquefied gas in JNS. The studies were performed at the atmospheric pressure, cold air with a temperature of 15–20 °C was used as an oxidant. The air velocity was kept constant W air = 20 m/s, which corresponds to the average velocity in front of the burners of fire equipment. The characteristics are generally similar, but the parameters of the pitch and diameters of the fuel holes have some influence on the carbon monoxide concentrations. The αkr2 depending on the geometry is in the range of 1.6–1.4 and the minimum values of underburning correspond to the situation with a relative pitch = 5.0 (when entering the mode αkr2 , the value of underburning was CCO = 0 ppm). With a decrease

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Fig. 4 Influence of excess air on the carbon monoxide concentration during the combustion of liquefied propane–butane mixture in JNS with different relative steps of fuel holes location: 1—S = 3.0, d = 3 mm; 2—S = 4.5, d = 3 mm; 3—S = 5.0, d = 3 mm; 4—S = 3.0, d = 2 mm

in the value of the fuel holes pitch, the underburn increased and the highest concentration value corresponded to the fuel distribution geometry with =5.0 and d = 2 mm. The maximum possible depth of fuel burnout in the region of critical values was not possible to reach in the operating mode (α → 1.0). At the system operating modes in the region of α < 1.5, the flame break phenomena is prevails and the combustion process stabilizes in the refractory area. The breakdown occurs due to the violation of the basic principle of JNS associated with the organization of a stable vortex structure in the area of flame stabilization, which obviously cannot exist in the studied geometric conditions. “S” reducing to the values of 4.5 allows organize the necessary flow structure. At the same time, the flare is reliably stabilized in the α = 1.15 mode without disruptions and pulsations. Further reduction of the pitch leads to a local enrichment of the fuel mixture with fuel and does not allow minimizing the underburning to the sampling site as much as possible. It should be noted that there were no multiple breakdowns during the passage of αkr2 in all other studied modules. Increasing the diameter of the fuel holes provides deeper fuel burnout (in the region of α < 3.0). The opposite picture is observed on the starting modes, the studied modules with a larger diameter reduce the completeness of carbon burnout. Figure 5 shows the results of nitrogen oxides measurements in the geometric conditions described above. The characteristics of NOx = f(α) are physically similar and are consistent with the general concepts of NOx emissions during the combustion of hydrocarbon fuels. In the conditions of working modules, where the minimum values of CO concentrations were achieved (which indirectly indicates complete combustion and high temperature), the emission of nitrogen oxides is maximum and reaches values of about 100 ppm in operating modes. Such concentrations in accordance with modern

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Fig. 5 Influence of excess air on the nitrogen oxides concentration during the combustion of liquefied propane–butane mixture in JNS with different relative steps of fuel holes location: 1—S = 3.0, d = 3 mm; 2—S = 4.5, d = 3 mm; 3—S = 5.0, d = 3 mm; 4—S = 3.0, d = 2 mm

environmental standards require the use of technological methods to reduce emissions. For modules with the parameter S = 3.0, the indicators in the corresponding modes of excess air are 2 times less and are no more than 46 ppm. In the range of starting modes corresponding to the values of excess air α > 4.0, the fuel supply geometry has an insignificant impact on the emission qualities of JNS.

3 Comparative Results of Combustion Products Gas Analysis at Combustion of Natural and Liquefied Gases The combined emission characteristics under the same geometric conditions are shown in Fig. 6. The chosen geometry of fuel distribution allowed to reach the nominal fuel consumption without breakdown and pulsations, which for the two gases under study corresponds to an excess of air of 1.07 and 1.2 for natural and liquefied gases, respectively. The analysis of the results indicates that in the range of changes in the excess air coefficient, the CO indicator is significantly dependent not only on the operating mode of the system, but also on the type of fuel. From the point of view of the organization of fuel combustion in “stoichiometric conditions”, the best performance is achieved when using natural gas, and in the case of combustion of lean mixtures, which corresponds to the mode α > 2.0, the combustion of liquefied gas is a higher priority. Particular attention is drawn to the excitation of the underburning characteristics in the region of α ≈ 2.3 for natural gas, where the CO concentration is about 35% higher compared to liquefied fuel. The nitrogen oxides emission is almost independent of the studied factors and has similar characteristics in the entire studied range of JNS operating modes.

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Fig. 6 Combined emission characteristics of JNS for nitrogen oxides (1, 2) and carbon oxides (3, 4): d = 2.0 mm, S = 3.0, Wair = 5 m/s; 1, 3—natural gas, 2, 4—mix of propane with butane (50/ 50%)

Thus, the results for the presented geometry (Fig. 6) show that the concept of universal fuel distribution geometry for both natural and liquefied gases can be implemented by selecting the appropriate burner operating mode. In fact, burners designed for natural gas combustion are suitable for the efficient use of propane– butane mixture only in a rather narrow region of lean fuel mixtures (α ≈ 1.5). Also noteworthy is the absence of a characteristic region αkr1 for liquefied gas, apparently due to the local over enrichment flame stabilization zone and the impossibility of working in the region close to stoichiometric conditions. The effect of fuel hole diameters is shown in Fig. 7. Increasing the diameters of the gas holes has a significant impact on the emissions of pollutant oxides only in the area corresponding to the range of values α < αkr1 ≈ 1.5. With increasing diameter (d = 3.0 mm) there is an increase in underburning, which leads to a slight decrease in NOx emissions. Attention is drawn to the similarity of characteristics in the rest of the range of changes in operating modes and confirmation of the presence of a maximum of CO values in the region of α ≈ 2.3.

Fig. 7 The diameter size of the fuel holes influence on the nitrogen oxides concentration (1, 2) and carbon monoxide (3, 4) during the combustion of natural gas in JNS with parameters: S = 3, Hch = 36 mm, Wair = 5 m/s, 1, 3—d = 2.0 mm, 2, 4—d = 3.0 mm

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The obtained results confirm the general ideas about emission processes during the combustion of hydrocarbon fuels. The results also emphasize the decisive influence of mixing processes on the quality of fuel carbon burning. In general, the organization of fuel distribution according to the scheme used in the JNS allows to rationally organize the mixing of reaction components. The αkr1 presence is determined by the location of αkr2 and is determined by the geometric characteristics of the fuel distribution. In the conditions of the laboratory bench, for all the studied variants of jet-niche modules, αkr2 corresponds to an excess of air α ≈ 1.5–1.2 and depends on the possibility of fuel distribution to ensure the stoichiometric level of fuel in the field of combustion stabilization.

4 Air Flow Rate Influence Analysis of the results in terms of the influence of air flow rate allows us to conclude that this factor is relatively insignificant in relation to the formation of carbon monoxide. Attention is drawn only to the fact that the similarity of CO characteristics regardless of the flow rate is manifested only in the case of an appropriate choice of the relative step value: 3.0–3.5 for natural gas and 4.5–5.0 for propane–butane mixture. Regarding the characteristics of NOx , it should be noted that the influence of flow rate has no qualitative effect, but quantitatively it is quite significant. It is clear that the direct influence of the oxidant flow rate determines the heat intensity of the working area of the experimental site. Increasing the air flow rate under conditions close to stoichiometric combustion will require an increase in fuel consumption and, accordingly, will increase the heat intensity of the working area. From a practical point of view, it is necessary to take into account, in addition to heat stress, other technological influences on the emission performance of burners. The main influencing factors on NOx emission are: pressure in the working area, air temperature, degree of oxidant dilution (in the presence of recirculation of combustion products into the combustion zone), etc. Thus, the paper presents a methodology for constructing the so-called “reduced emission characteristics” of burner modules [7]. The construction of the reduced emission characteristics of nitrogen oxides allows take into account the influence of the main technological factors, which is a prerequisite for conducting an environmental audit of various types of gas-burning equipment in the conditions of a wide range of water sources.

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5 Analysis of the Specified Emission Characteristics of Burner Modules Based on JNS The analysis of emission indicators of burner modules is based on generalizations arising from the theory of formation of thermal NOx (Zeldovich), as well as works performed in the problem combustion laboratory of Igor Sikorsky Kyiv Polytechnic Institute by Lyubchyk [8]. The need for such an approach is explained by the need to take into account the influence of interdependent factors on the formation of nitrogen oxides, the main of which are: temperature and oxygen content in the oxidizer, specific thermal stress and pressure in the combustion zone, as well as the excess air coefficient. In addition, these characteristics are more convenient in the processing and analysis of experimental results compared to the analysis of direct dependences of NOx emission. When processing the experimental data, it is proposed to use the following dependence: NOsp x

| | NOx −E ef , = || 0.5 = k0 exp RT Ki

(1)

where NOx sp is the||total concentration of nitrogen oxides (NO and NO2 ) reduced to nitrogen dioxide; K i is the multiplication of the influence coefficients K i , among which k 0 is the pre-exponential factor, E is the activation energy of the resulting NO formation reaction, R and T are the gas constant and combustion temperature, respectively, /

/ K 1 = (1 − ψ) · ψ, 2

K2 =

P Ps.cond.

,

K3 =

Ts.cond. , T0

/ K4 =

(α − 1) . (2) α

Coefficient K 1 specifies the oxygen concentration in the oxidizer, coefficients K 2 and K 3 specify the pressure and initial temperature; the coefficient K 4 gives the specify of the oxidizer excess. The coefficient of influence of the residence time in the combustion zone K 5 = K τ gives specifies in the residence time τres , it in turn depends on the heat of fuel combustion (Qlow , MJ/kg), the excess oxidant coefficient (α), the temperature of the beginning (T 0 ) and the end (T ) of the combustion process, the specific heat density (qv , W/(m3 Pa)), the stoichiometric coefficient (L 0 , kg/kg) and the technical gas constant R = 287 kJ/(kg K) and is determined from the ratio: K τ = qv−n ,

(3)

where n = 0.5, and the value of qv is determined by the formula: qv =

ρg · Vg · Q low . Vb.ch · Pb.ch

(4)

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Fig. 8 Linearized emission characteristics of JNS depending on the excess air coefficient; 1—d = 3 mm, S = 3.0 (natural gas), 2—d = 3 mm, S = 3.0 (propane), 3—d = 3 mm, S = 5.0 (propane), 4—d = 3 mm, S = 4.5 (propane)

In this case, the effective concentration of nitric oxide is described by the ratio: NO =

k0 · [NO] · τNO

(5)

Obtained results are shown in Fig. 8 (the processing of experimental data from Fig. 5 is shown). These results correlate with general ideas about the relationship between the processes of nitrogen oxides formation and the quality of fuel burning in the flame (Fig. 4). Thus, the module with a relative pitch of the fuel holes S = 4.5 has the highest levels of the parameter lnNOx sp in the entire studied range of system operating modes, while this pitch provides the best indicators of the main operating parameters for liquefied gas, such as combustion stability and fuel burnout quality. Reducing the pitch for propane–butane is accompanied by a decrease in NOx emissions due to over-enrichment of the combustible mixture with fuel and. At the same time, the formation of CO products of incomplete combustion increases. This is confirmed by the results in Fig. 4, where the minimum carbon monoxide emission is provided by a relative step of S = 4.5 and 5.0 and is 0–5 ppm, and at S = 3.0 in the region of αkr2 values it is not less than CCO > 150 ppm. To compare the level of emission of nitrogen oxides during the combustion of liquefied gas, the concentrations of NOx in combustion products for natural gas in JNS with the appropriate choice of fuel distribution parameters are given (Fig. 8). In the region of αkr2 the lowest NOx emission level is observed for natural gas combustion products, and in the region of α < αkr2 —for propane–butane combustion products. This is confirmed by the presence of a significant increase in chemical underburning. When choosing a rational step of location of fuel holes for liquefied fuel, the level of nitrogen oxide emissions increases significantly and is about 205 mg/m3 . These figures are 2.2 times higher than when burning natural gas under conditions of oxygen concentration in the combustion products, which corresponds to (α ≈ 1.14). The results confirm the fact that the improvement of the mixing process leads to more efficient fuel combustion with minimization of chemical underburning. On the other hand, it leads to a significant of the environmental performance deterioration of the burner modules.

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Table 1 Results of statistical analysis of emission characteristics when changing fuel distribution parameters in JNS with parameters L 1 = 10 mm, H ch = 36 mm, L/H = 40/10 mm №

Parameters fuel distribution

1

Fuel

ANOx

Degree exponent, n

Approximation reliability coefficient, R2

d = 3.0 mm, S = Natural gas 3.0

9868.1

−3.3

0.91

2

d = 3.0 mm, S = Mixture pr–but 3.0

4569

−1.48

0.71

3

d = 3.0 mm, S = Mixture pr–but 5.0

8888

−1.88

0.79

4

d = 3.0 mm, S = Mixture pr–but 4.5

14,616

−1.93

0.79

5

d = 2.0 mm, S = Mixture pr–but 3.0

5723

−3.24

0.73

According to the above material, the results can be represented by the following dependence: NOx = ANOx · α n · K ψ · K P · K T · K α · K τ .

(6)

NOx is the concentration of nitrogen oxides reduced to α = 1.0, mg/m3 ; ANOx is the coefficient determined individually for each module based on the measurement results; α is the coefficient of excess air. In accordance with the results shown in Fig. 8 results, Table 1 presents the values of the ANOx coefficient, the degree index at α, as well as the value of the reliability coefficient of approximation of the proposed dependencies. The use of the obtained dependences is proposed in the design of gas-consumption equipment with the possibility of burning liquefied mixture of propane–butane. Correctness of data is possible for the following values of parameters: α = 1.01– 7.5; W air = 1–25 m/s, W gas = 1–70 m/s; T a = 5–15 °C; NOx = 1–100 ppm; qv = 13–50 for natural and 5–30 for liquefied gas W/(m3 ·Pa); ψ = 0.21; pressure in the combustion zone is near atmospheric.

6 Analysis of Emission Characteristics of JNS Burner Modules Using Mathematical Planning of the Experiment To determine the influence of the main parameters of the JNS on its emission indicators, it is advisable to use the procedure of mathematical planning of the experiment. This approach allows to simultaneously study the influence of several factors and, along with the quantitative accounting of each factor, to establish the existence of inter-factor interactions in the system and evaluate their effects [21]. In this case,

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the mathematical model of the system is represented as a polynomial of the second degree: Y = b0 +

n E

bi X i +

i=1

n E

bi j X i X j +

i, l=1

n E

bil X i2 ,

(7)

i=1

where b0 , bi , bij , bil —polynomial coefficients. In the process of experimental and industrial studies of burners, the NOx = f(α) and CO = f(α) characteristics can be realized in two ways. The first is by changing the air flow rate at a constant fuel flow rate. The second way is to change the fuel consumption at constant air velocities. In this case, other parameters remain constant. The influence of the following parameters is determined, these are the diameter of the gas supply holes (d), the relative spacing of the holes (S) and the excess air coefficient (α). The conditions of uniqueness in the study of burners include: type of BD, volume and configuration of the furnace space, heat exchange conditions, etc. The ranges of factors variation for the study of the characteristic CO = f(d, S, α) are given in Table 2. As a result, a mathematical interpretation of the studied processes of emission of nitrogen and carbon oxides depending on three main factors was obtained. The surface of the studied oxides behavior at α = 1.45 is shown in Fig. 9, the dependences of CO and NOx emissions are given below, ppm. CO = 1620 · α − 373 · S + 1142 · d − 53 · d 2 − 7.4 · S 2 · 1387 − 612 · α · d + 252 · α · S;

(8) NOx = 212.3 · α + 15 · S − 8.14 · d − 74 · α 2 − 136.4.

(9)

The adequacy of the presented results was checked using Fisher’s criterion and for both polynomials were respectively F pCO = 4.95 and F pNOx = 4.21, with the critical value of the criterion Fκ p = 5.05 [22–24]. Table 2 Intervals of variation of the studied factors in JNS Coded scale

Zero level

0

Natural scale x1

x2

x3

d

S

α

2.5

4

1.5

Interval of variation

–

0.5

1

0.15

Upper level

1

3

5

1.65

Lower level

−1

2

3

1.35

Star points

1.682

3.34

5.68

1.75

−1.682

1.66

2.32

1.25

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Fig. 9 Surfaces for JNS emission characteristics at α = 1.45

Table 3 Results of the search for minima of response functions

Area of minimum values of response functions d

α

S

CO

1.66

1.2

5.7

NOx

3.34

1.05

2.3

According to the results of the study of the reduced functions for the presence of an extremum, the areas of the factor space that provide the minimum levels of emission indicators of the system are determined (Table 3). The burner operating mode should be selected by plotting the combined emission characteristics of the system, taking into account the permissible operating concentration of nitrogen oxides and carbon monoxide in the system.

7 Conclusions The possibility of using rototable central compositional planning of the experiment for mathematical interpretation of the behavior of emission parameters of gas-burning equipment on the example of jet-niche flame stabilizer is proved. From the list of three studied factors, their relevance in terms of influence on emission indicators was determined. Thus, the hole diameter has almost no influence on NOx emission, while α has a decisive influence. For the response function for CO emissions, it should be noted that each of the selected factors is influential. The fuel and oxidizer feed rates have mainly quantitative influence on the emission performance of the JNS, qualitatively the characteristics are similar. The efficiency of application of the procedure of construction of the specified emission characteristics of fuel combustion devices when performing their environmental audit in any technical test conditions and for any fuel is shown. The optimization procedure for the obtained dependencies gave a predictable result: the area of the factor space that provides a minimum for nitrogen oxide is, respectively, the area that provides the maximum value of the function for CO. Thus,

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the regulated ranges of burner operation are selected in the zone of minimum CO emissions (zone αkr1 ), possibly with some shift towards the lean fuel mixture to ensure permissible levels of nitrogen oxide emissions. The choice of fuel distribution geometry for propane–butane combustion (d, S) should be performed in terms of minimum CO concentration, in order to ensure maximum carbon burnout, taking into account the requirements for sustainable combustion in a wide range of operating modes of the BD. From the results of experimental studies, two concepts for the implementation of a dual-fuel burner follow. The first is based on the use of geometry adapted to the combustion of natural gas, which requires a clear choice of operating mode when switching to a liquefied propane–butane mixture. The second approach is more versatile and does not require a strict limitation of operating ranges, but provides for a moderate deterioration of the stabilizer performance and is implemented by choosing an “average” relative pitch of the holes (~3.8–4.0) with a decrease in their values from the range of research.

References 1. Boningari, T., Smirniotis, P.: Impact of nitrogen oxides on the environment and human health: Mn-based materials for the NOx abatement. Chem. Eng. 13, 133–141 (2016). https://doi.org/ 10.1016/j.coche.2016.09.004 2. Gholamia, F., Tomasa, M., Gholami, Z., Vakilic, M.: Technologies for the nitrogen oxides reduction from flue gas: a review. Sci. Total Environ. 714 (2020). https://doi.org/10.1016/j.sci totenv.2020.136712 3. Kobzar, S.H., Khalatov, A.A.: Znyzhennia vykydiv oksydiv azotu u hazovykh kotlakh metodom retsyrkuliatsii dymovykh haziv. Promyslova Teplotekhnika 31(4), 5–11 (2009). ISSN 02043602 (in Russian) 4. Kim, H.K., Yongmo, K., Sang, M.L., Kook, Y.A.: NO reduction in 0.03–0.2 MW oxy-fuel combustor using flue gas recirculation technology. In: Proceedings of the Combustion Institute, vol. 31, pp. 3377–3384 (2007). https://doi.org/10.1016/j.proci.2006.08.083 5. Navrodska, R., Fialko, N., Presich, G., Gnedash, G., Alioshko, S., Shevcuk, S.: Reducing nitrogen oxide emissions in boilers at moistening of blowing air in heat recovery systems. In: E3S Web of Conferences, p. 100 (2019). https://doi.org/10.1051/e3sconf/2019100000 6. Horvat, I., Dovic, D., Filipovic, P.: Numerical and experimental methods in development of the novel biomass combustion system concept for wood and agro pellets. Energy 231 (2021). https://doi.org/10.1016/j.energy.2021.120929 7. Böhler, L., Görtlerb, G., Krailb, J., Kozeka, M.: Carbon monoxide emission models for smallscale biomass combustion of wooden pellets. Appl. Energy 254 (2019). https://doi.org/10.1016/ j.apenergy.2019.113668 8. Lyubchik, G.N, Govdyak, R.M., Mikulin, G.A., Shelkovsky, B.I., Zaritsky, A.A.: Environmental audit of gas-burning devices based on the application of the method of basic characteristics of NOx and CO. In Ecotechnologies and resource saving, vol. 4, pp. 64–70 (2007) (in Ukrainian) 9. Jianlong, W., Haibo, Z.: Experimental study on blow-off limit of a preheated and flame holderstabilized laminar premixed flame. Chem. Eng. Sci. 223, 115754 (2020). https://doi.org/10. 1016/j.ces.2020.115754 10. Abdulin, M.Z., Siryi, O.A.: Research of hydrodynamic flame stabilizer with cross fuel feed characteristics. Sci. J. Riga Tech. Univ. Ser.: Power Electr. Eng. 32, 12–18 (2014)

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O. Siryi et al.

11. Marissa, K.G., Cal, J.R., Anthony, J.M., Kareem, A.A.: Turbulent flame-vortex dynamics of bluff-body premixed flames. Combust. Flame 223, 28–41 (2021). https://doi.org/10.1016/j. combustflame.2020.09.023 12. Shanbhogue, S.J., Husain, S., Lieuwen, T.: Lean blowoff of bluff body stabilized flames: scaling and dynamics. Prog. Energy Combust. Sci. 35, 98–120 (2009) 13. Barlow, R.S., Dunn, M.J., Sweeney, M.S., Hochgreb, S.: Effects of preferential transport in turbulent bluff-body-stabilized lean premixed CH4 /air flames. Combust. Flame 159, 2563–2575 (2012) 14. Chenglong, Z., Chengtang, L., Li, X., et al.: A novel clean combustion technology for solid fuels to efficiently reduce gaseous and particulate emissions. J. Clean. Prod. 320 (2021). https:/ /doi.org/10.1016/j.jclepro.2021.128864 15. Hu, L., Lei, Z., Qianqian, L., Hua, Z., Lei, D., Yinhe, L., Defu, C.: Effect of FGR position on the characteristics of combustion, emission and flue gas temperature deviation in a 1000 MW tower-type double-reheat boiler with deep-air-staging. Fuel 246, 285–294 (2019). https://doi. org/10.1016/j.fuel.2019.02.119 16. Chunlong, L., Zhengqi, L., et al.: Gas/particle two-phase flow characteristics of a down-fired 350 MWe supercritical utility boiler at different tertiary air ratios. Energy 102, 54–64 (2016). https://doi.org/10.1016/j.energy.2016.02.016 17. Jianping, J., Zhengqi, L., Qunyi, Z., Zhichao, C., Lin, W., Lizhe, C.: Influence of the outer secondary air vane angle on the gas/particle flow, characteristics near the double swirl flow burner region. Energy 36, 258–267 (2011). https://doi.org/10.1016/j.energy.2010.10.043 18. Abdulin, M.Z., Siryi, O.A., Tkachenko, O.M., Kunyk, A.A.: Boilers modernization due to energy-ecological improvement technology of burning. Bulg. Chem. Commun. 52, 14–19 (2020). https://doi.org/10.34049/bcc.52.F.0002 19. Siryi, O., Zhuchenko, A., Abdulin, A.: Improvement of reliability of fire engineering equipment based on a jet-niche technology. East.-Eur. J. Enterp. Technol. 2/8(92), 12–19 (2018). https:// doi.org/10.15587/1729-4061.2018.126917 20. Abdulin, M.Z., Siryi, O.A., Sheleshei, T.V.: Energy and ecological assessment of gas burning boiler equipment In: 2022 IEEE 8th International Conference on Energy Smart Systems, ESS 2022—Proceedings, pp. 102–107 (2022). https://doi.org/10.1109/ESS57819.2022.9969254 21. Marcin, D., Mario, D., Terese, L.: Application of a central composite design for the study of NOx emission performance of a low NOx Burner. Energies 8(5), 3606–3627 (2015). https:// doi.org/10.3390/en8053606 22. Montgomery, D.C.: Design and Analysis of Experiments, p. 456. Wiley, Hoboken, NJ, USA (2001) 23. Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery, pp. 47, 455. Wiley-Interscience, Hoboken, NJ, USA (2005) 24. Andersson, O.: Experiment: Planning, Implementing and Interpreting, p. 164. Wiley, Chichester, UK (2012)

Peculiarities of Using Ammonium Reagents in Technologies of Semi-dry Desulfurization of Flue Gas Igor Volchyn , Serhii Horyanoi , Serhii Mezin , Wlodzimierz Przybylski , and Andrii Yasynetskyi

Abstract The use of ammonium reagents in the processes of semi-dry desulfurization of flue gas is promising, since the product of desulfurization is ammonium sulfate, which can be used as a mineral fertilizer. Due to the toxicity of ammonia and its solutions, their use in technological processes is inextricably linked to the increase of production safety measures that must be implemented during transportation, storage and use of the reagent. In this regard, it is proposed to use a solution of urea, which in the process of hydrolysis at boiling temperature acts as a source of gaseous ammonia. The formed gaseous ammonia is introduced into flue gas, where it effectively binds sulfur dioxide in the process of gas-phase reactions. In these studies, the efficiency of gas-phase sulfur dioxide binding reactions reached 80% when using a 30% urea solution for the generation of gaseous ammonia. Keywords Flue gas desulfurization · Ammonia · Urea · Hydrolysis

1 Introduction The mankind’s energy needs are closely related to the generation of a large number of different emissions, including toxic gases. Among such gases, we can distinguish sulfur dioxide (SO2 ), which is a product of the oxidation of sulfur contained in fuel or raw materials that undergo heat treatment. The share of sulfur dioxide emissions from combustion plants and thermal power stations is almost 2/3 of the total world anthropogenic SO2 emissions [1]. I. Volchyn · S. Horyanoi · S. Mezin · A. Yasynetskyi (B) Thermal Energy Technology Institute of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] S. Horyanoi · A. Yasynetskyi National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine I. Volchyn · W. Przybylski National University of Food Technologies, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_44

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Fig. 1 Distribution of measures to control emissions of sulfur oxides in coal-fired power generation in individual countries and throughout the world

Today, methods of combating the emission of sulfur dioxide are widely implemented in coal-fired power generation. Figure 1 shows the spread of measures to control emissions of sulfur oxides in coal-fired power generation in individual countries (a–g) and throughout the world (h) [2]. Flue gas desulfurization (FGD) is one of the most effective measures to combat SO2 emissions, as it does not require intervention in the main technological process and has many modifications, which makes it possible to implement it for a specific case. According to technological features, flue gas desulfurization technologies are divided into wet, dry and semi-dry [3]. The FGD process in semi-dry desulphurization technologies is implemented by introducing process water into the flue gas, which must evaporate completely and a dry desulfurization product is formed at the output. Compounds containing calcium, for example, lime, have become widely used as reagents in semi-dry technologies. As a result of the desulfurization process, mainly calcium sulfite CaSO3 is formed. This product is not widely used commercially and most of it is disposed of as landfills and backfilling of mines, etc, [4]. An alternative to the use of calcium reagents in semi-dry FGD technologies can be the use of an ammonia solution. The chemistry of sulfur dioxide absorption will be as follows [5]: SO2 + 2NH3 + H2 O ↔ (NH4 )2 SO3 ; SO2 + NH3 + H2 O ↔ NH4 HSO3 . In the presence of oxygen, the formed sulfite and hydrosulfite are partially oxidized in a moist environment [6]: (NH4 )2 SO3 + 1/2 O2 ↔ (NH4 )2 SO4 ;

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Fig. 2 Volumes of world production of ammonium sulfate from 2009 to 2020

2NH4 HSO3 + 1/2O2 ↔ (NH4 )2 SO4 + SO2 + H2 O. As can be seen from the chemical equations, the product of cleaning flue gas from sulfur dioxide when using an ammonia solution will be ammonium sulfate. Ammonium sulfate is a mineral fertilizer that is widely used. The need for ammonium sulfate can be estimated by the volume of its world production (Fig. 2) [7].

2 Features of the Use of Ammonia and Urea in Semi-dry Flue Gas Desulfurization Technologies The technology that uses an ammonia solution as a reagent in the process of semi-dry purification of flue gas from sulfur dioxide is the technology of semi-dry ammonium desulfurization of flue gas (Fig. 2), proposed by specialists of the Thermal energy technology institute of National Academy of Sciences of Ukraine and PJSC TEHENERGO, in which a solution is used as a sorbent ammonia, the product of desulfurization is a dry powder of ammonium sulfate, which is a mineral fertilizer [8] (Fig. 3). The main feature and difference when using an ammonia solution in comparison with the use of other sorbents in semi-dry FGD technologies is the desorption of ammonia from the solution into the gas phase. The process of absorption of sulfur dioxide will take place both in solution drops and in the gas phase in which gaseous ammonia interacts with sulfur dioxide in wet flue gas [9]. The expediency of using ammonia in semi-dry desulfurization technologies is due to its high solubility in water, which at 20 °C is 52.9 g per 100 g of water, while the

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Fig. 3 Scheme of the semi-dry ammonium FGD

solubility of sulfur dioxide is 11.28 g [10]. This allows reducing losses on own needs due to the lack of recirculation compared to the use of calcium reagents. However, the use of ammonia in an aqueous solution as a reagent in FGD systems requires a significant increase in production safety measures. This is primarily due to the high toxicity of ammonia and its aqueous solutions [11]. This feature imposes strict safety requirements during transportation, storage and use of this reagent, which significantly reduces the attractiveness of using this reagent. An alternative in this case is the use of a solution of urea ((NH2 )2 CO), which can act as a source of ammonia. In the practice of gas cleaning of flue gas, a urea solution, like an ammonia solution, is used in the technologies of selective non-catalytic reduction of nitrogen oxides [12–14].

3 Research on the Use of Ammonia Solution and Urea Solution in Semi-dry Flue Gas Desulfurization Technologies To study the process of binding sulfur dioxide with ammonia solution and urea solution, experimental studies were carried out on a semi-dry gas cleaning installation, the scheme of which is shown in Fig. 4. The experimental installation for the study of the process of cleaning flue gas from sulfur dioxide is a vertically located cylindrical reactor with a height of 2.6 m and an internal diameter of 0.32 m. A smoke extractor is connected to the outlet of the reactor, which creates the movement of air through the reactor into the stack. The air speed in the reactor is regulated by changing the cross section of the channel. Air intake is carried out directly from the room where the reactor is located, emission– into the exhaust pipe and further into the stack. The air is heated using an electric

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Fig. 4 Scheme of the experimental installation of semi-dry desulfurization: 1—air flow regulator, 2—differential pressure gauge, 3—electric furnace for air heating, 4—reagent solution supply nozzle, T1—T3—thermocouples

furnace, which is a ceramic pipe in which heaters are located–carbide-silicon rods connected in parallel. The reactor and the hot air supply path have thermal insulation from the outside. Sulfur dioxide is supplied to the air path from the cylinder after the electric furnace. The reagent solution is fed into the reaction volume using a mechanical nozzle installed in the upper part of the reactor. During each experiment, the following parameters were measured: air flow through the reactor, gas flow temperature after the electric furnace (T1), temperature in the reactor medium (T2), reactor outlet temperature (T3) and SO2 concentration at the reactor outlet using a Testo 350 gas analyzer.

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To compare the efficiency of sulfur dioxide binding, experiments were conducted using ammonia solution and urea solution as reagent (Table 1). Concentrations of reagent solutions were selected taking into account that 2 mol of ammonia are formed during the decomposition of 1 mol of urea. As a result of the experiments, the value of the output concentration of sulfur dioxide was obtained (Fig. 5). It can be seen in Fig. 5 that when using a urea solution as a reagent, binding of sulfur dioxide practically does not occur, in contrast to an ammonia solution, when up to 98% of SO2 is absorbed under the same temperature conditions. This result is related to the complete evaporation of the water in the solution, which led to the impossibility of the hydrolysis of the urea solution with the formation of ammonia and carbon dioxide [15, 16]: (NH2 )2 CO + H2 O = 2NH3 + CO2 . With the complete evaporation of water, the process of crystallization of urea took place. The process of destruction of crystalline urea occurs according to the following mechanism: (NH2 )2 CO → NH3 + HNCO ; HNCO + H2 O → NH3 + CO2 . The intense flow of these reactions with the formation of NH3 from crystalline urea occurs at temperatures above 250 °C, which does not correspond to the conditions of the semi-dry process of flue gas desulfurization, when the inlet temperature of flue gas is in the range of 140…180 °C [17]. Table 1 Parameters of experimental studies Parameter

Ammonia

Air consumption through the reactor, l/min

2000

SO2 consumption, l/min

4

Concentration of SO2 at the entrance to the reactor, ppm

2000

Temperature at the entrance to the reaction zone (T1), °C

160

Urea

Reagent solution consumption, ml/min

85…100

Volume of the injected solution, ml

1000

Mass concentration of the reagent in the solution, g/l

56.5

100

Molar mass of the reagent, g/mol

17

60

Molar concentration of the reagent in the solution, mol/l

3.32

1.66

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Fig. 5 Dependencies of the output concentration of SO2 and temperature on the length of the experiment when: a—urea solution, b—ammonia solution are introduced as reagents

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4 Absorption of Sulfur Dioxide by Ammonia Formed by Hydrolysis of Urea Solution Thea results of using a urea solution as a sorbent posed the task of creating conditions under which a urea solution can act as a source of gaseous ammonia. Such conditions are possible at the boiling temperature of an aqueous solution of urea, when the process of its hydrolysis occurs with the formation of ammonia and carbon dioxide [18–21]. In view of this, the preliminary hydrolysis of the urea solution at the boiling temperature with the subsequent supply of the formed gaseous ammonia to the reaction zone, where the gas-phase reactions of binding sulfur dioxide will take place, is proposed. To study the process of binding sulfur dioxide with ammonia obtained by hydrolysis of urea solution, an experimental setup was developed, the scheme of which is presented in Fig. 6. The basis of the installation is a reactor in which the reaction between gaseous sulfur dioxide and ammonia. Sulfur dioxide is supplied to the reactor by introducing a SO2 –N2 gas mixture from a cylinder. The consumption of the mixture is regulated by a tap and measured by a rotameter. The source of ammonia is a urea solution of a known concentration. The solution is heated in a heat-resistant flask mounted on a heater. The temperature in the flask is controlled using a thermocouple (T1*). When the boiling temperature of the solution is reached, the process of hydrolysis occurs with the formation of ammonia. In order to introduce the formed ammonia

Fig. 6 Scheme of the experimental setup: 1—cylinder with SO2 –N2 mixture; 2, 5—regulating taps; 3, 6—rotameters; 4—cylinder with N2 ; 7—heat-resistant bulb; 8—heater; 9—gas cooling system; 10—reactor; 11—laboratory autotransformer; 12—voltmeter; T1*—T3*—thermocouples

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into the reaction volume, technical nitrogen is supplied from a cylinder, the flow rate of which is regulated by a tap and measured by a rotameter. The nitrogen supplied to the flask together with the formed ammonia and water vapor is supplied to the cooling system, where condensation of the water vapor takes place. Condensed water vapor from the cooling system is returned to the flask with the urea solution. Since ammonia is very soluble in water, in the process of condensation, ammonia is absorbed by water and returned to the flask, where the process of its desorption to the gas phase takes place. Thus, in the process of decomposition of urea, sorption and desorption of ammonia, a stationary mode is established with a certain concentration of ammonia at the entrance to the reaction zone. After introducing a mixture of nitrogen and sulfur dioxide into the reactor, as well as a mixture of technical nitrogen, ammonia and water vapor, the process of binding SO2 with ammonia in the gas phase in the presence of water vapor occurs. The reactor is a cylindrical glass heated by an electric heater, the voltage on the heater is regulated by a laboratory autotransformer and measured by a voltmeter. The temperature in the reactor is measured using thermocouples installed in the lower (T2*) and central (T3*) parts of the reactor. After the reactor, the gas flow with the reaction products is fed to the gas analyzer to determine the concentration of sulfur dioxide, and the excess is removed to the hood. At this installation, three experiments were conducted in which the concentration of urea in the solution changed. The main parameters of the experiments are given in Table 2. In Fig. 7 shows the temperature values of the urea solution in the flask at different concentrations over the duration of the experiment. As a result of the experiments, the values of the concentration of sulfur dioxide at the exit from the reaction zone were obtained for the duration of the experiment at different concentrations of the urea solution in the flask. (Fig. 8). These curves can be conditionally divided into 3 parts. The first part is the registration at the exit of the reaction zone of a sulfur dioxide concentration of about 900 ppm, which indicates the absence of the urea solution hydrolysis process with the release of ammonia, and therefore the SO2 binding reaction. The second part is a gradual decrease in the concentration of SO2 , which indicates the hydrolysis of the Table 2 Main input parameters of the experiment

Parameter

Value

Consumption of SO2 -N2 mixture, l/min

1

Concentration of SO2 in the mixture, ppm

1800

Consumption of technical nitrogen, l/min

1

Concentration of urea in the solution, wt%

10; 20; 30

Temperature at the entrance to the reactor (T2*), °C

95…110

Temperature in the middle of the reaction zone (T3*), °C

130…140

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Fig. 7 Change in the temperature of the urea solution in the flask (T1*) over the duration of the experiment

Fig. 8 Change in the output concentration of sulfur dioxide over the duration of the experiment at different values of the concentration of the urea solution in the flask

urea solution with the release of ammonia, as well as the process of ammonia absorption in the cooling system and its desorption when the ammonia solution enters the flask. From Figs. 7 and 8 shows that the thermal decomposition of the urea solution occurs at temperatures close to the boiling point of the solution (98…104 °C). The third part is the stabilization of SO2 concentration values, which indicates the equilibrium of the processes of decomposition of the urea solution in the flask and absorption and desorption of ammonia in the cooling system. Taking into account the obtained data, we can conclude that during the preliminary hydrolysis of the urea solution at the boiling temperature, ammonia can be obtained, which will effectively bind sulfur dioxide. Experimental data established that the efficiency of gas-phase binding of sulfur dioxide is 18–20% when using a 10% urea solution, 50–55% when using a 20% solution, and about 80% when using a 30% solution.

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5 Conclusions • The use of ammonium reagents in FGD technologies makes it possible to obtain a useful commercial product - ammonium sulfate, which is a mineral fertilizer. • The use of urea solution instead of ammonia solution in semi-dry flue gas desulfurization technologies will make it possible to significantly simplify the use of this technology in terms of safety and reagent transportation. • The use of urea solution in semi-dry FGD technologies by introducing it into the flue gas will not lead to a decrease in the level of sulfur dioxide at the exit from the reaction zone. This happens due to the complete evaporation of water from the solution, which makes the process of hydrolysis of the urea solution impossible. The crystalline urea formed in this process cannot form ammonia due to the low temperature level in this process. • The use of urea solution for effective binding of sulfur dioxide requires its preliminary hydrolysis. In the process of hydrolysis of urea at the boiling temperature, gaseous ammonia will be formed, which in the process of gas-phase reaction can bind up to 80% of SO2 at the initial concentration of urea in the solution of 30%.

References 1. Lecomte, T., Ferrería de la Fuente, J. F., Neuwahl, F., Canova, M., Pinasseau, A., Jankov, I., Brinkmann, T., Roudier, S., Delgado Sancho, L.: Best available techniques (BAT) reference document for large combustion plants. EUR 28836 EN. https://doi.org/10.2760/949 2. van Ewijk, S., McDowall, W.: Diffusion of flue gas desulfurization reveals barriers and opportunities for carbon capture and storage. Nat Commun 11, 4298 (2020). https://doi.org/10.1038/ s41467-020-18107-2 3. Ladwig, K.J., Blythe, G.M.: Flue-gas desulfurization products and other air emissions controls. Coal Combust Prod (CCP’s) 67–95 (2017). https://doi.org/10.1016/B978-0-08-100945-1.000 03-4 4. Production and utilisation of coal combustion products in 2016 in Europe. https://www.ecoba. com/evjm,media/ccps/ECO_stat_2016_EU15_tab.pdf 5. Xiaowen, L.: Progress of desulfurization and denitration technology of flue gas in China. In IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/ 242/4/042010; 6. Chun, T., Long, H., Di, Z., Zhang, X., Wu, X., Qian, L.: Novel technology of reducing SO2 emission in the iron ore sintering. Proc. Saf. Environ. Protect. 105, 297–302 (2017). https:// doi.org/10.1016/j.psep.2016.11.012 7. Production volume of ammonium sulfate worldwide from 2009 to 2020. https://www.statista. com/statistics/1287045/global-ammonium-sulfate-production/#statisticContainer 8. Volchyn, I.A., Dunayevska, N.I., Haponych, L.S., Topal, O.I., Chernyavskyi, M.V., Zasyadko, Y.I.: Prospects for the Introduction of Clean Coal Technologies in the Energy Industry of Ukraine, P. 308. Kyiv (2012) 9. Speight, J.G., ed.: Lange’s Handbook of Chemistry. 17th edn. McGraw-Hill Education, New York. https://www.accessengineeringlibrary.com/content/book/9781259586095 10. Ammonia Solution, Ammonia, Anhydrous: Lung Damaging Agent. CAS #: 7664–41–7. National Institute for Occupational Safety and Health. https://www.cdc.gov/niosh/ershdb/eme rgencyresponsecard_29750013.html

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I. Volchyn et al.

11. Volchyn, I., Kryvosheiev, S., Yasynetskyi, A., Zaitsev, A., Samchenko, O.: Selective noncatalytic reduction of nitrogen oxides in the production of iron ore pellets. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 1, 88–94 (2022). https://doi.org/10.33271/nvngu/ 2022-1/088 12. Wypiór, T., Krzy˙zy´nska, R.: Influence of ammonia on wet-limestone flue gas desulfurization process from coal-based power plant. E3S Web Conf. 116, 00101 (2019). https://doi.org/10. 1051/e3sconf/201911600101 13. Blejchaˇr, T., Konviˇcka, J., von der Heide, B., Malý, R., Maier, M.: High temperature modification of SNCR technology and its impact on NOx removal process. EPJ Web of Conf. 180, 02009 (2018). https://doi.org/10.1051/epjconf/201818002009 14. Lieber, C., Koch, R., Bauer, H-J.: Spray evaporation of urea–water solution: Experiments and modelling. Exp. Therm. Fluid Sci. 116, 110108. https://doi.org/10.1016/j.expthermflusci.2020. 110108 15. Tang, T., Zhang, J., Shuai, S., Cao, D.: Urea decomposition at low temperature in SCR systems for diesel engines. SAE Technical Paper 2014–01–2808 (2014). https://doi.org/10.4271/201401-2808 16. Denghui, W., Ning, D., Yanqing, N., Shien, H.: A review of urea pyrolysis to produce NH3 used for NOx Removal. J. Chem. (2019). https://doi.org/10.1155/2019/6853638 17. Zhu, F., Gao, J., Chen, X., Tong, M., Zhou, Y., Jun, L.: Hydrolysis of urea for ammonia-based wet flue gas desulfurization. Indus. Eng. Chem. Res. 54(37) (2015). https://doi.org/10.1021/ acs.iecr.5b02041 18. Alexandrova, A., Jorgensen, W.: Why urea eliminates ammonia rather than hydrolyzes in aqueous solution. J. Phys. Chem. B 111(4), 720–730. https://doi.org/10.1021/jp066478s 19. Wang, D., Hui, S., Liu, C., Zhuang, H.: Effect of oxygen and additives on thermal decomposition of aqueous urea solution. Fuel 180, 34–40 (2016). https://doi.org/10.1016/j.fuel.2016.04.018 20. Korchuganova, E.N., Tantsyura, E.V., Abuzarova, K.R., Prihorodov, P.V.: Investigation of the kinetics of urea hydrolysis under the conditions of precipitation of metal hydroxides and salts. Eastern Eur. J. Enterprise Technol. 5/6(77), 53–57 (2015). https://doi.org/10.15587/1729-4061. 2015.51057 21. Volchyn, I.A., Mezin, S.V., Yasynetskyi, A.O.: Study of the absorption of sulfur dioxide by ammonia in the gas phase in the presence of water vapor. Ekologichni Nauky [Environmental sciences.] 1(20), 104–109. http://ecoj.dea.kiev.ua/archives/2018/1/part_1/21.pdf

Peculiarities of Specialized Software Tools Used for Consequences Assessment of Accidents at Chemically Hazardous Facilities Oleksandr Popov , Taras Ivaschenko , Liudmyla Markina , Teodoziia Yatsyshyn , Andrii Iatsyshyn , and Olha Lytvynenko

Abstract An effective response to emergencies at chemically hazardous facilities under various circumstances is only possible with software modeling tools. They allow for determining the affected area, forecasting changes in its scale, and assessing risks to public health due to such events’ occurrence. The article critically analyzes the existing specialized software tools used in various countries to determine accident consequences of technogenic objects. Such objects are potential sources of emergencies associated with significant environmental pollution. The work results of these software tools are given, and their strengths and limitations in their application in solving practical problems are shown. Keywords Accident · Chemically hazardous objects · Software tools · Modeling

O. Popov (B) · T. Yatsyshyn · A. Iatsyshyn Center for Information-Analytical and Technical Support of Nuclear Power Facilities Monitoring of the National Academy of Sciences of Ukraine, Kyiv, Ukraine e-mail: [email protected] O. Popov · A. Iatsyshyn G.E. Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, Kyiv, Ukraine O. Popov Interregional Academy of Personnel Management, Kyiv, Ukraine T. Ivaschenko · L. Markina State Ecology Academy of Postgraduate Education and Management, Kyiv, Ukraine T. Yatsyshyn Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine O. Lytvynenko National University of Civil Defence of Ukraine, Kharkiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_45

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1 Introduction The increase in capacities of technological complexes, vehicles, and energy systems leads to the accumulation of combustible, explosive, and poisonous substances in chemical industry enterprises [1, 2]. Accidents at chemically hazardous facilities are often accompanied by releasing gaseous chemically hazardous substances (CHS) into the environment. Their spread threatens the life and health of the service personnel of enterprises and the population in the surrounding area—several injured and dead, maybe thousands in particularly unfavorable cases [3–5]. Safety measures during the storage, transportation, and processing of such CHS and actions of units dealing with the localization of accidents at chemically hazardous facilities are regulated by several regulatory documents. But, the frequency of emergencies caused by chemical accidents remains the same despite the measures taken. As a result, environmental pollution occurs, and severe destruction is possible over a large area due to a chemical explosion during such accidents. Furthermore, there is a danger to all living things in the contaminated area (death of people, and animals, destruction of crops) [6–10]. Effective solution for emergency, rescue and other urgent works in the emergency zones requires the use of methodical, mathematical, and software for the rapid adoption of appropriate management decisions at all stages of the development of such situations. Therefore, we will consider the existing software used by different world countries while solving the above-described problems.

2 The Research Results Response effectiveness to emergencies is achieved with the help of unique means and the use of various methods of forecasting such cases in the world’s developed countries, such as the USA, Canada, Japan, and the countries of the European Union. Let’s consider some software tools of the developed countries of the world which are used to assess the consequences of chemical accidents. EFFECTS The EFFECTS software [11] is developed by the Dutch organization TNO. It allows the user to predict, calculate and present the physical consequences of any accident scenario with toxic and flammable chemicals. This software offers more than 50 calculation models for emission, evaporation, fire, explosion, dispersion, and damage. Critically features of EFFECTS are: – availability of a comprehensive chemical database containing toxic, flammable, and thermodynamic properties of more than 2000 chemical substances. The builtin editor is also available for detailed definitions of own chemicals; – models can transfer the results of their calculations as input data to other models using the “binding” method;

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– EFFECTS offers combined models in a fully automatic process in addition to the method of (manual) linking of models; – GIS tool is included in the software. It allows you to overlay results on GIS drawings and Google Earth screenshots easily. Strong sides of EFFECTS: – easy to use. The user interface is suitable for experts and casual users; – results are presented in graphs, special reports, and contours on background maps, facilitating management steps [12]. ARCHIE ARCHIE is an automated resource for evaluating chemical hazard accidents. The US Department of Transportation developed this software in partnership with the Federal Emergency Management Association (FEMA) and the USEPA. It is an atmospheric emission and dispersion assessment tool that can be used to assess vapor dispersion, fire, and explosion effects associated with episodic releases of hazardous materials into the environment. In addition, the software can estimate emissions and duration of liquid/gas releases from tanks, pipelines, and corresponding ambient concentrations downwind of those releases [13]. ARCHIE’s strong sides: – model is straightforward to use; – it contributes to a better understanding of the nature and sequence of events that can occur after the accident and the resulting consequences. The software has a limitation. It takes approximately 10–15 min to run the program, including the time required to answer all questions on a typical problem (www.eng.utolefo.edu). BREEZE The Environmental Protection Agency, the US Military, and Coast Guard in 1987 developed this complex. It includes toxic gas dispersion, thermal radiation fire, and explosion models. BREEZE functions include 3D Analyst in its arsenal for visualization of results and export to Google Earth. In addition, it can simulate multiple sources simultaneously. The user can define this receptor/target location n and draw a base mipmap (AutoCAD DXF, ESRII Shapefile, or bitmap) using a mouse for precise placement (www.chempute.com). There is a reduced model setup and execution time with an intuitive ribbon panel interface and script templates. There is a built-in chemical database [14]. Strong sides of BREEZE: – easy to use and quick to work; – the intuitive interface will help the user enter the necessary and additional data related to the potential release of chemical substances (for example, the size and position of the tank rupture, the shape of the storage tank, the volume of the spill, and the presence of a reservoir) and also select the appropriate algorithms;

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– results are provided in tabular and graphical formats, including 2D contour, 3D volume, and time series plot; – operator errors are greatly minimized due to a clear list of model execution warnings. CHARM This software allows air transport and dispersion calculations, vapor cloud explosions, boiling liquid expanding bursts (BLEVE), jet fire, and pool fire radiation. Two versions of CHARM software are available: one for a single source on flat terrain and another for multiple origins on rough terrain. Features of CHARM include the effects of nuclear radiation. It was added to the complex version of the landscape. In addition, an intricate understanding of the terrain uses a 3D grid for modeling [15]. This software is designed for the following solutions: – movement and concentration of air plumes from released chemicals; – mechanical overpressure from pressure tanks and overpressure explosion from steam cloud ignition; – traces of thermal radiation associated with jet fires, pool fires, and BLEVEs; – impact on the population associated with any of the traces described above. CHARM allows you to define and save a base map automatically displayed each time you start CHARM. It is done to save time in emergencies. CHARM is a Gaussian air movement model that treats any emission as a series of air flows. The distribution description calculated by the Source Term module is required information for the Transport/Dispersion module. Calculated distribution description data includes the following [16]: – – – – – – – –

position X, Y, Z; chemical mass fraction (vapor and liquid phases); temperature; a mass of air; a mass of water vapor; exit direction and speed; dimensions; hidden energy.

The emission type determines the calculations used in the Source Term module. CHARM models the following types of emissions: container/surface description and evaporation pool/lagoon, defined by the user. The flat terrain version of CHARM is designed to simulate release from a single source [17] (Fig. 1). Release description, meteorological parameters, and grid are the inputs required to run the scenario in the complex terrain version. This version does not require a grid. The version with rugged terrain is much more complete and can work with several sources of different types (Fig. 2).

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Fig. 1 Visualization of the flat terrain version [17]

Fig. 2 Visualization of the complex terrain version [18]

Both versions include the following features: – – – – – –

editor for adding/editing chemicals; editor for adding maps; the rate of emissions changing over time; two-phase emissions; shroud emissions, evaporation pools, and user-defined velocity; meteorology changing over time;

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– real-time meteorology is available if there is a data collection system; – automatic map import from the Internet. The main features list of the complex terrain version: – the influence of 3-dimensional relief on the wind field and dispersion over the area; – averaging time is included in all concentration results; – all results can be displayed in 3-D. This includes fires, explosions, and concentration effects; – vertical sections can be displayed for the region along any line drawn by the user; – effects “Probability of fatal outcome” (if probit parameters are provided), “Dose,” “Toxic load,” and “Thermal dose” can be displayed as any other effect; – the editor can define grids by importing DEM and land use data available over the Internet from the USGS, the Globe Project, and other sources. Various file formats can be read, including DEM and GeoTiff; – the height of the receptor for the plan view can be the MSL height or the height above the surface; – maps can be superimposed on 3-D terrain; – source location can be defined as a location on a track defined by x (longitude), y (latitude), z, and time; – graphics program is available to display terrain, hits, and maps using DirectX for a more realistic display; – calculation of advection/dispersion is performed on a three-dimensional grid using a finite-difference solution; – script and its calculations can be saved in a separate set of files, so recalculation is not required; – several release locations (each with different views) can be simulated in one run; – each release can be at a different time in the same scenario; – several meteorological sites; – liquid emissions can be allowed to flow with the terrain during the formation of basins; – source can be particles with a user-defined size distribution. Concentrations and amounts of precipitation can be displayed; – particles can be coagulated, evaporated/condensed, and formed as a result of chemical reactions and fell to the ground; – convective heat transfer from the surface to the torch is considered; – chemical reactions can be calculated. The reaction rate can vary depending on temperature, solar radiation, particle size, or depends on air and water vapor; – user can edit chemical reactions; – three-dimensional calculation grid can contain nested grids; – nested grids can be defined by the user or automatically created by CHARM; – 3D grid can represent rooms, pipes, or other objects not attached to the ground; – the ability to export grid information to ASCII and BREEZE files; – the ability to create Google Earth files (kml/kmz) of buildings/objects, 2D and 3D effects;

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– the impact of nuclear radiation from nuclides in emissions; – possibility to specify the nuclide and mass composition of the emission; – radiation level and dose are calculated and displayed in 2D, 3D, and tables. So, let’s summarize the main strong sides of CHARM software: – it can be used inside and around buildings in addition to taking into account the influence of the terrain; – results are provided in 2D, 3D and tabular format; – it can calculate particle dynamics; – the model can automatically set nested grids around the source to provide more detailed calculations [19]. CANARY Quest’s CANARY software is designed to assess the potential consequences of the hazardous liquid release [20]. It includes application-specific hazard models for vapor dispersion, fire emission, and vapor cloud explosions. CANARY has supporting models to generate the source terms required for consequence models: multicomponent thermodynamic calculation, liquid evaporation calculation, and release rate calculation. CANARY allows the user to define hazard endpoints (e.g., gas concentration, radiation flux, overpressure) that determine the extent of the toxic or flammable gas cloud, radiation from multiple types of fire, or overpressure from an explosion. It provides thermodynamic calculations for mixtures of up to 10 components. In addition, it has a database of over 250 members. The component database and thermodynamic model allow CANARY to process many mixtures and materials, including light hydrocarbons, heavy hydrocarbons, aromatics, toxins, refrigerants, and liquefied gases. The complete list of these substances is presented in [21]. CANARY integrates multicomponent thermodynamics into time-varying fluid release simulations. These simulations account for two-phase flow, flash evaporation, aerosol formation, and liquid rain. In addition, evaporation from liquid pools takes into account pool dispersion, heat transfer effects, and neutralization. The information generated by these models forms the input term(s) for hazard models. CANARY includes application-specific hazard models: vapor dispersion, fire radiation, and vapor cloud explosions. In addition, CANARY allows users to define hazard endpoints: gas concentration, radiation flux, overpressure, and hazard of flammable and toxic vapor clouds as a result of emissions: pressurized gases, liquefied gases, superheated liquids, supercooled liquids, and volatile liquids. An example of modeling in the software is presented in Fig. 3. Strong sides of CANARY: – It accepts a wide range of user input. – It has a complex character, valid for any project that requires the calculation of process hazards.

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Fig. 3 Example of modeling in the CANARY software

Analysis of the consequences begins with calculating the liquid release rate and its thermodynamic state after depressurization into the atmosphere. The amount of material spilled into the ground or became a vapor or aerosol is determined if the release starts as a liquid. Dispersion of the released material in the atmosphere is predicted for the vapor clouds hazard. The release model provides information to fire radiation models to determine thermal radiation effects if the ignition of flammable material is assumed vapor cloud explosion (VCE) model can be used to assess the impact of overpressure if a combustible vapor cloud ignites. Several other models of consequences for specific purposes are also used. These include using publicly available consequence models (e.g., DEGADIS or LNGFIRE for LNG), computational fluid dynamics models to solve complex problems, or models built to solve specific consequences analysis tasks [22].

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The models used in this software are taken from open scientific sources (published scientific articles), and individual data included in the models are obtained from internal tests conducted by Quest. Hazardous liquids were released into the atmosphere during these tests. At the same time, most of the received data are unavailable. PHAST PHAST is a comprehensive hazard analysis software for all process design and operation phases. PHAST contains models developed for hazard analysis of scenarios: emissions and dispersion, jet fires, pool fires, fireballs, and toxic emission hazards, including indoor harmful dose calculations. Its features include the use of a geographic information system (GIS) to display the results of consequences on maps and site plans (Figs. 4, 5, 6 and 7) [23]. In addition, this software may conduct sensitivity studies to assess the need for mitigation measures such as design, operational, or response changes. Strong sides of PHAST: – Ease of use is achieved due to the predefined relationship of emission, dispersion, pool, flammability, and toxicity calculations. – Quick and accurate results. – Comprehensive reports and charts for the easy and intuitive display of results. – It considers emissions from leaks, pipe breaks, safety devices, vessel breaks, and ventilation.

Fig. 4 Thermal radiation contours

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Fig. 5 List of scenarios for simulation

Fig. 6 Results of modeling on an artificial object

KAMELEON FIREX This software tool was developed by Comput IT together with partners Statoil, ENIgroup, ConocoPhillips, Gaz de France, Ruhrgas, and Sandia National Laboratories. KAMELEON FIREEX is an advanced Computational Fluid Dynamics (CFD) tool for 3D transient modeling of flares, gas dispersion, fire development, and fire mitigation. Features of this tool include efficient and user-friendly pre- and post-processing capabilities. In addition, it has options for animating simulation results and “moving cameras” through the simulation [24].

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Fig. 7 Pool fire modeled by Phast CFD extension

This software has CAD import capabilities where CAD geometries are automatically converted to solid structures or surface/volume porosity used by the KFX calculation model. In addition, its models can pass the results of their calculations as input data to other models. Strong sides of KAMELEON FIREX: it gives quick and accurate results; a clear list of model launch warnings greatly minimizes operator error. FLACS This is a CFD tool developed by GEXCON. It is widely used in the explosion and atmospheric dispersion modeling [25]. It has special modules for modeling gas, dust, and explosions using chemical explosives such as TNT. Its features include integrated explosion and dispersion modeling capabilities. This allows modeling consequences in Full-3D with an assessment of mitigation effects and preventive measures. In addition, it has a porosity distribution model for obstacles at a small subgrid-scale, a semi-automatic process for generating complex flow geometries, and a simple Cartesian grid that allows fast simulations compared to other general-purpose CFD codes (Figs. 8, 9) [26]. This software allows you to choose modules for individual needs [27]: – FLACS-Dispersion can simulate natural, forced ventilation and loss of tightness due to leaks and dispersion of hazardous substances; – FLACS-GasEx can simulate steam cloud explosions; – FLACS-Fire module has jet and basin fire modeling; – FLACS-Hydrogen is a tool for assessing the risk associated with the use of hydrogen as an energy carrier; – FLACS-Blast simulates the propagation of blast waves arising from the detonation of condensed-phase explosives;

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Fig. 8 Modeling the consequences

Fig. 9 Simulation of various geometry and texture

– FLACS-Risk can model and visualize 3D CFD risks. Strong sides of FLACS: – it has various options for viewing results in 2D, 3D, animation, and text files. It provides a better understanding of phenomena and results; – atmospheric conditions at the entrance can be determined by taking into account the roughness of the surface and stability of the atmosphere; – the wide application can perform both dispersion and explosion with the same installation. Limitations of FLACS: – many of the validation studies reported that FLACS do not provide enough detail to give confidence in the reproducibility of the results;

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– often, results are shown for only one grid, and grid sensitivity tests are not reported; – FLACS uses a one-block Cartesian grid. This can lead to unreliable predictions of dense gas scattering over sloping or undulating terrain due to too high momentum dissipation from the stepped surface simulation. Modeling such flows requires a curvilinear, unstructured grid or a cut-cell approach. The single-block Cartesian grid also introduces limitations to the approach that can be used to model gas jets. Grids cannot contain vast numbers of cells. Code is not yet parallelized; – FLACS has no submodel selection. Only one turbulence model is provided, which is well established. But has some limitations, and the code lacks a Lagrangian or inhomogeneous Euler model for splash or particle modeling. TRACE TRACE is an engineering analysis tool for dispersion modeling [28]. It provides simulations of accidental releases of toxic substances caused by pipe/flange leaks, water spills, hydrogen fluoride, acid spills, pipe emissions, or elevated dense gas emissions [29]. In addition, simulation capabilities for scenarios such as vapor cloud explosions, solid explosives, pool fires, and flares are updated. Its key features include a comprehensive chemical database of over 600 pure constituents of substances as well as liquid mixtures. The sequential process prompts the user and leads to a set of models to solve the problem. WHAZAN Technica International Ltd., in collaboration with the World Bank, develops WHAZAN (World Bank Hazard Analysis). It is an impact analysis package. This package calculates consequences and hazard zones resulting from incidents involving toxic and flammable chemicals. It contains models of the dispersion of harmful substances, fire, and explosion [30]. WHAZAN has an extensive chemical library that the user can expand. The software can run models separately or link two or three models so that the output of one model can be automatically used as input to another model [31]. All models included in AST are semi-empirical and subject to constraints. The accuracy of the results depends on the assumptions made by the model and the accuracy of the input data. Strong sides of WHAZEN: – product is easy to use and quick to work; – the user interface is designed to minimize operator errors. SCIA SCIA software [32] is designed to assess and analyze all possible hazards from industrial accidents. It includes models for fire, explosion, and toxic release assessment. These models can be used to study radiation, overpressure, and toxic spill hazards in various scenarios. SCIA features include GIS for scenario screening/evaluation. It contains MSDS.

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The strong sides of SCIA are: – Simulation quickly and reliably assesses the consequences of possible accidents. – A convenient and effective tool for assessing the consequences of significant chemical accident decision-making processes for land use planning. – Results can be saved in various formats, exported to Microsoft Excel, and then plotted using Microsoft Excel or VB. – A clear list of model execution warnings greatly minimizes operator error. HAZDIG HAZDIG (HAZardous Dispersion of Gases) is a tool for studying the accidental release of hazardous chemicals and their consequences [33]. HAZDIG consists of five main modules for data, emission scenario generation, dispersion, characterization, and graphics. In addition, it includes the latest models for estimating atmospheric stability and distribution. HAZDIG has unique features. The database contains various proportionality constants, and complex empirical data is built into the system. Moreover, it has a modular structure that ensures fast data processing and calculation of the result. Strong sides of HAZDIG: – graphic module provides a presentation of the results in an easy-to-understand and visually attractive form; – output data of the software is formatted in such a way that it can be directly used for reporting results without the need for editing; – broader applicability: it includes more models than existing accident simulation packages to handle more situations with minimal input data; – higher accuracy: it contains more accurate and newer models than those processed by existing packages; – convenience for the user. MAXCRED MAXCRED software allows you to simulate an accident and assess potential damage. This software includes various explosion and fire models: confined vapor cloud explosion, unconfined vapor cloud explosion, BLEVE, pool fires, flares, and fireballs. A unique feature of MAXCRED is that it can handle the dispersion of heavier-thanair and lighter-than-air gases. In addition, the software can check the plausibility of the proposed scenario [34]. The strong sides of MAXCRED are the following: – it is capable of simulating accidents of the second and higher order; – broader applicability: it includes more models than existing accident simulation packages to handle more situations with a minimum amount of data; – it contains more accurate and newer models than those processed by existing packages;

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– convenience for the user. ALOHA ALOHA (Areal Locations of Hazardous Atmospheres) is a computer program designed specifically for use by emergency responders for emergency planning and training [35, 36]. ALOHA models the critical hazards—toxicity, flammability, thermal radiation (heat), and overpressure (explosive force). These issues are associated with chemical releases that result in toxic gas releases, fires, and explosions. In versions before 5.4, ALOHA only models a harmful threat: how the cloud of poisonous gas may dissipate in the atmosphere after an accidental chemical release. This software dashes on small computers that are easy to transport and affordable to most users [37]. This program’s chemical library contains information on the physical properties of approximately 1,000 common hazardous chemicals. Calculations performed by the program represent a compromise between accuracy and speed; ALOHA was designed to get results quickly and minimize operator error. The program checks the information you enter and warns you of mistakes. ALOHA was developed jointly by the National Oceanic and Atmospheric Administration (NOAA) and the Environmental Protection Agency (EPA). ALHOA models three hazard categories: toxic gas release, fire, and explosion. This program uses several different models, including the air dispersion model. This model estimates the movement and dispersion of chemical gas clouds. ALOHA can estimate toxic gas dispersion, overpressure values from a vapor cloud explosion, or flammable vapor cloud regions using the given model. ALOHA uses additional models to assess hazards associated with other fires and explosions. As a result, ALOHA can quickly solve problems and provide results in a graphical and easy-touse format. ALOHA allows the modeling of chemical emissions from four sources (Direct, Spill, Tank, and Gas pipe) (Table 1). ALOHA can model the dispersion of pollutant gas clouds in the atmosphere. It also displays a diagram showing a top view of regions or threat zones where it predicts exceeding key hazard levels (LOCs). ALOHA uses the Gaussian model to predict how gases with roughly the same buoyancy as air disperse in the atmosphere (Fig. 10). This software uses weather data that can be entered by the user or directly from a weather station. Heavy gas dispersion calculations used in ALOHA are based on the same as in the DEGADIS model. The last is one of several known heavy gas models. This model was chosen due to its general acceptance and extensive testing by the authors. ALOHA allows simulating fire and explosion scenarios and toxic gas dispersion scenarios starting with version 5.4. ALOHA has an easy-to-use graphical interface and display. It also includes a mapping program called MARPLOT that allows you to customize overlays showing local features and vulnerable populations.

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Table 1 ALOHA sources and scenarios Source

Poisoning scenarios

Fire scenarios

Explosion scenarios

Cloud of toxic vapor

Flammable zone (fire flare)

Vapor cloud explosion

Cloud of toxic vapor

Flammable zone (fire flare)

Vapor cloud explosion

Direct Direct emission Spill Evaporation Burning (spill fire)

Spill fire

Tank Not flammable

Cloud of toxic vapor

Flammable zone (fire flare)

Combustion

Jet fire, spill fire

The explosion of boiling, expanding liquid

The explosion of expanding boiling liquid (fireball and spill fire)

Vapor cloud explosion

Gas pipe Not flammable

Cloud of toxic vapor

Combustion

Flammable zone (fire flare)

Vapor cloud explosion

Jet fire

Fig. 10 Gaussian distribution

ALOHA system has a limitation–e input should be corrected, and a new hazard zone chart created if any atmospheric conditions (such as wind speed) change significantly during the response. In another case, the old chart may need to be revised even if you can provide accurate data. ALOHA results can be unreliable under certain conditions. There are some effects that ALOHA does not a model at all. ALOHA results may be unpredictable under the following conditions: – deficient wind speed;

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– very stable atmospheric conditions; – wind movement and terrain effects; – concentration unevenness, especially near the emission source. ALOHA does not consider the accumulation of high gas concentrations in lowlying areas. ALOHA allows you to enter only individual wind speed and wind direction values. The wind speed and direction are assumed to remain constant (at any given height) throughout the area downwind of the chemical release. ALOHA does not consider terrain, the presence of various structures, or other obstacles (Fig. 11). Wind creates vortices and changes direction and speed in cities with the flow around large buildings. As a result, it significantly changes the shape and movement of the cloud, as shown in Fig. 12. Streets bordering large buildings can create a street canyon pattern that intercepts and directs dissipating clouds. ALOHA ignores these effects when making a threat zone graph; the threat area will appear to pass over or through obstacles such as buildings. ALOHA’s performance is limited because wind can change direction and speed both over distance and over time. As a result, ALOHA will not make predictions more than an hour after the start of the release of more than 10 km (6.2 miles) from the release point (it truncates threat zones longer than 10 km). ALOHA does not take into account the following issues during simulation: – – – – –

by-products of fires, explosions, or chemical reactions; shares; chemical mixtures; topography of the area; dangerous fragments.

Fig. 11 Change in wind movement depending on the topography of the area

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Fig. 12 Small-scale fluctuations in wind direction

ALOHA assumes that dissipating chemical cloud does not react with the atmosphere gases (oxygen and water vapor). Therefore, ALOHA does not consider processes that affect the dispersion of particles (including radioactive particles). ALOHA is designed to simulate the release of pure chemicals and some chemical solutions. ALOHA is not intended for use with radioactive chemical releases. This software does not consider the accumulation of discharge in depressions or liquid flow down the slope. ALOHA does not simulate the trajectories of dangerous fragments that can occur in explosions.

3 Conclusions Accidents at chemically hazardous facilities are often accompanied by releasing dangerous chemicals into the environment. Their spread threatens the life and health of the service personnel of enterprises and the surrounding area population. Methodical software is used to quickly make appropriate management decisions at all stages of such situations to assess the consequences of accidents at chemically hazardous facilities and to effectively solve emergency rescue and other urgent work in emergency zones. It was established that the following software tools are the most effective due to their advantages and features: EFFECTS, ARCHIE, CHARM, CANARY, PHAST, KAMELEON FIREX, FLACS, TRACE, WHAZAN, SCIA, HAZDIG, MAXCRED, and ALOHA. Such an assumption was made due to a critical analysis of specialized computer systems for assessing the consequences of accidents at chemically hazardous facilities. Their use allows simulation of results of the release (spill) of

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dangerous substances during accidents using geoinformation technologies; investigation of the influence characteristics of input factors on the process of distribution of hazardous chemicals in the environment; increasing visibility of the forecasting process of consequences of accidents at chemically hazardous objects and transport by visualizing the calculation process and forecasting results; forming skills and abilities to systematize information about chemically dangerous substances, methods of protection and procedures for dealing with them.

References 1. Popov, O.O., et al.: Approaches to assessing consequences of accidents during transportation of hazardous substances by road. In: Studies in Systems, Decision and Control. Systems, Decision and Control in Energy IV (2023, in press) 2. Anpilova, Y., Yakovliev, Y., Trofymchuk, O., Myrontsov, M., Karpenko, O.: Environmental hazards of the Donbas hydrosphere at the final stage of the coal mines flooding. In: Systems, decision and control in energy III. Studies in systems. Decision and Control. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87675-3_19 3. Yatsyshyn, T., Shkitsa, L., Popov, O., Liakh, M.: Development of mathematical models of gas leakage and its propagation in atmospheric air at an emergency gas well gushing. East. Eur. J. Enterp. Technol. 5/10(101), 49–59 (2019). https://doi.org/10.15587/1729-4061.2019.179097 4. Kotsiuba, I.G., Skyba, G.V., Skuratovskaya, I.A., Lyko, S.M.: Ecological monitoring of small water systems: algorithm, software package, the results of application to the uzh river basin (Ukraine). Methods Objects Chem. Anal. 14(4), 200–207 (2019). https://doi.org/10.17721/ moca.2019.200-207 5. Skurativska, I., Skurativskyi, S., Popov, O., Mykhliuk, E., Dement, M.: Complex oxygen regimes of water objects under the anthropogenic loading. In: Studies in Systems, Decision and Control. Systems, Decision and Control in Energy III, vol. 399. pp. 317–334. (2022). https:// doi.org/10.1007/978-3-030-87675-3_20 6. Popov, O.O., et al.: Software and modeling tools for assessment of environmental consequences of open flowing of oil wells. In: Studies in Systems, Decision and Control. Systems, Decision and Control in Energy IV (2023, in press). 7. Semerikov, S., et al.: Our sustainable pandemic future. E3S Web Conf. 280, 00001 (2021). https://doi.org/10.1051/e3sconf/202128000001 8. Semerikov S.O., et al.: 3rd International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters. In: IOP Conference Series: Earth and Environmental Science, vol. 1049, p. 011001 (2022). https://doi.org/10.1088/1755-1315/1049/1/ 011001 9. Myrontsov, M., Karpenko, O., Trofymchuk, O., Okhariev, V., Anpilova, Y.: Increasing vertical resolution in electrometry of oil and gas wells. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, vol. 346. pp. 101–117 (2021). https://doi.org/10. 1007/978-3-030-69189-9_6 10. Hovalenkov, S.S.: Emergency prevention caused by man-made emissions of hazardous light gaseous chemicals. National University of Civil Defence of Ukraine, State Emergency Service of Ukraine, Kharkiv (2020) 11. TNO: Effects Advanced, easy-to-use consequence analysis: Software that streamlines safety analysis. (2017). http://resolver.tudelft.nl/uuid:0dac0288-8862-4d9e-91d3-38a8fcd42589 12. Suganya, R., Abbasi, S.A.: A critical assessment of available software for forecasting the impact of accidents in chemical process industry. Int. J. Eng. Sci. Math. 6(7), 269–289 (2017) 13. Early, I.I., Livingston, M.C, Newsom, D.E.: A pilot program for introduction of (ARCHIE) automated resource for chemical hazard incident evaluation. United States (1990)

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14. SAFETY & QUALITY (2022). https://www.chempute.com/safety.html 15. CHARM® Software–When Realism Counts (2022). https://www.charmmodel.com/ 16. Eltgroth, M.W.: Complex hazardous air release model (CHARM®) technical reference manual. https://www.charmmodel.com/doc/TechRef_09.pdf 17. CHARM Trial–Flat Terrain (2022). https://www.charmmodel.com/charmtrial-flatterrain.aspx 18. CHARM Trial–Complex Terrain (2022). https://www.charmmodel.com/charmtrial-complexte rrain.aspx 19. CHARM User’s Manual CHARM User’s Manual. Mark Eltgroth LLC, Lometa, Texas. https:/ /www.charmmodel.com/doc/UserMan_09.pdf 20. CANARY by Quest—Consequence Analysis Software (2022). https://www.questconsult.com/ software/canary/ 21. CANARY Materials (2022). https://www.questconsult.com/pdf/CANARY_Materials.pdf 22. Consequence Analysis (2022). https://www.questconsult.com/services/consequence-analysis/ 23. Control your hazards by knowing the consequences (2022). https://www.dnv.com/software/ser vices/plant/consequence-analysis-phast.html#Software 24. Fire simulation software–CFD simulation - Kameleon FireEx–KFX (2022). https://www.dnv. com/services/fire-simulation-software-cfd-simulation-kameleon-fireex-kfx-110598 25. FLACS-CFD (2022). https://www.gexcon.com/software/flacs-cfd/ 26. FLACS-CFD Features & Benefits (2022). https://www.gexcon.com/software/flacs-cfd/fea tures-benefits/ 27. FLACS-CFD Solvers (2022). https://www.gexcon.com/software/flacs-cfd/solvers/ 28. SAFER Systems Safer Trace assessment tool for toxic chemicals (2022). https://www.thebig redguide.com/safer-systems-safer-trace-software-technical-details.html#more-info 29. Popov, O.O., et al.: The use of specialized software for liquid radioactive material spills simulation to teach students and postgraduate students. CEUR Workshop Proc. 3085, 306–322 (2022). http://ceur-ws.org/Vol-3085/paper05.pdf 30. Kiranoudis, C., Kourniotis, S.P., Christolis, M.N., Markatos, N.C.: An operational centre for managing major chemical industrial accidents. J. Hazard. Mater. 289(2–3), 141–161 (2002). https://doi.org/10.1016/S0304-3894(01)00299-0 31. Kumar, A., Agarwal, S.: Whazan: A software for chemical hazards analysis. Environ. Prog. 8(3), A6–A7 (1989). https://doi.org/10.1002/EP.3300080305 32. SCIA ENGINEER (2022). https://www.scia.net/en/scia-engineer 33. Khan, F.I., Abbasi, S.: HAZDIG: a new software package for assessing the risks of accidental release of toxic chemicals. J. Loss Prev. Process Ind. 12(2), 167–181 (1999). https://doi.org/ 10.1016/s0950-4230(97)00043-0 34. Khan, F.I., Abbasi, S.A.: MAXCRED—a new software package for rapid risk assessment in chemical process industries. Environ. Model. Softw. 14(1), 11–25 (1998). https://doi.org/10. 1016/S1364-8152(97)00031-5 35. ALOHA Software (2021). https://www.epa.gov/cameo/aloha-software 36. Zinovieva, I.S., et al.: The use of GIS in renewable energy specialist’s learning. CEUR Workshop Proc. (2023, in press) 37. ALOHA.: USER’S MANUAL. February 2007. National Oceanic and Atmospheric Administration (NOAA) and the Environmental Protection Agency (EPA), Washington (2007). https:/ /nepis.epa.gov/Exe/ZyPDF.cgi/P1003UZB.PDF?Dockey=P1003UZB.PDF

Method for Detecting Natural and Anthropogenic Changes That Filled with Water in Landscapes Using Radar Satellite Imagery Oleksandr Trofymchuk , Yevheniia Anpilova , Oleksandr Hordiienko , Mykyta Myrontsov , and Oleksiy Karpenko

Abstract This chapter analyzes the area where vertical displacements caused by anthropogenic factors occur. Karst sinkholes are formed in the study area. In the study area, these sinkholes appear due to the anthropogenic factor and the closure of mining enterprises. Traditional methods of research of such phenomena are interferometric methods. They are more complicated in technique and understanding. They allow you to accurately determine the magnitude of the displacement. Due to the use of Google Earth Engine cloud technology in this work, this method had to be abandoned. A Random Forest machine learning algorithm was developed to detect local vertical displacements of the Earth’s surface. Binary classification was used. Its advantage over other algorithms is that there is no need to process Single Look Complex data– this is used traditionally, but not in the Google Earth Engine database. The algorithm works based on the classification of the earth’s surface and will work where the landform displacements are filled with water. The result is a time series of images that O. Trofymchuk (B) · Y. Anpilova · O. Hordiienko · M. Myrontsov Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine, Kyiv, Ukraine e-mail: [email protected] Y. Anpilova e-mail: [email protected] O. Hordiienko e-mail: [email protected] M. Myrontsov e-mail: [email protected] M. Myrontsov Center for Information-Analytical and Technical Support of Nuclear Power Facilities Monitoring of the National Academy of Sciences of Ukraine, Kyiv, Ukraine Y. Anpilova Helmholtz Centre for Environmental Research, Leipzig, Germany O. Karpenko Taras Shevchenko National University of Kyiv, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_46

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show a local increase in the water surface, which corresponds to an increase in land failure. It can be concluded that the presented method displays relief displacement in the given territory and can be used in other territories with vertical relief displacement, which are filled with water. It should be noted that the land dips are filled with water in those cases where the groundwater level is higher than the dip level of the sinkhole. This method should be used in such cases. It allows you to watch exactly the changes in the water surface, which filled the gaps in the ground. Keywords Remote sensing · SAR · Terrain deformation · Natural and man-made changes · Monitoring · Environmental protection

1 Introduction The Sentinel-1a and -1b satellite platforms with synthetic aperture radar (SAR) with a spatial resolution of 10–20 m depending on the mode, with a combined revisit time of about six days and complete independence from sunlight and cloud cover, provide an attractive source of Earth observation data for terrain change detection tasks. Given the above, they have a distinct advantage over optical methods for detecting natural and man-made changes in the hypsometric structure of the relief. Google Earth Engine (GEE) includes Sentinel-1 data in dual polarization in its extensive and up-to-date archive: . . . .

vertical transmit and vertical receive (VV); horizontal transmit and horizontal receive (HH); vertical transmit and horizontal receive (VV + VH); horizontal transmit and vertical receive (HH + HV) [1].

GEE provides not only near-real-time data access, but also a very powerful application programming interface (API) for data processing and visualization [2]. The GEE API is currently written in JavaScript for direct interaction with the GEE web code editor and in Python for data analysis outside the GEE web environment. Since complex single-lookup complex (SLC) are not included in the archive for technical reasons, widely used interferometric coherence methods are not available. In their original publication on the analysis of polarimetric SAR data [3], they presented a change detection procedure for SAR data with multiple reviews, including test statistics on the equality of polarimetric covariance matrices, which are assumed to obey a complex Wishart distribution. This procedure is capable of determining, on a pixel-by-pixel basis, the changes that have occurred at any given level of significance in the two SAR images. Alternative approaches to polarimetric detection of SAR change have also been published in [43, 45]. In [5], Whishart-based change detection, with an emphasis on double polarization, described the advantages of an algorithm for long time series of SAR images in the GEE archive using the GEE Python API. This algorithm was tested repeatedly in this work in the area of interest, namely in the study of mining landforms (Solotvyno, Ukraine).

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An algorithm based on machine learning embedded in GEE by Random Forest (RF) was also developed. Research topics of the Institute of Telecommunications and Global Information Space are very diverse. One of the key research areas is the use of geoinformation technologies (GIS) and remote sensing technologies [31, 42], environmental studies of technogenic disturbed areas [28, 29, 44].

2 Detecting Changes in Bi-Temporal Series of Images Let’s take a look at one way to detect changes in SAR that is presented on the GEE website. In it, we explored the ability to automatically detect landform shifts using Sentinel-1 satellites. Our area of interest (AOI) are two zones, which cover the village. Solotvyno and fields of flooded salt mines with active development of karst-dammed forms [6–9], in which there are vertical shifts of relief within the first AOI. In order to filter the collection of images, we used the Sentinel-1 satellite, consisting of images acquired in June and August 2021. Since we are interested in detecting hypsometric changes in topography, it is important for satellite [10] images with an active sensor in this case that the angles of inclination of the satellite over the horizon are the same in both images. You must also specify an ascending (ASC) or descending (DESC) orbit and an orbit number [11]. For our study, we used the ASC and relative orbit number 131. Seven images were obtained for the studied area Table 1. To calculate the mean and variance of an image, you can use the standard methods built into GEE which allow you to build graphs and charts [12]. In the case of working with the GEE database outside the Code Editor, it is possible to build graphs in Python using numpy and matpotlib [13]. Here is a graph of the normal histogram Fig. 1, we using the second method. The distribution is determined by: pγ ;α;β (x) =

Table 1 Initial data for bitemporal analysis

1

x α−1 e− β , mean(x) = αβ, var (x) = αβ 2 , x

β α |(α)

Shooting date

Orbit

Polarization

07.09.2021

ASCENDING, orbit 131

VV

07.15.2021

ASCENDING, orbit 131

VV

07.21.2021

ASCENDING, orbit 131

VV

07.27.2021

ASCENDING, orbit 131

VV

08.02.2021

ASCENDING, orbit 131

VV

08.08.2021

ASCENDING, orbit 131

VV

08.14.2021

ASCENDING, orbit 131

VV

(1)

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Fig. 1 Graph of the normalized histogram

where α is the number of images β–μ/α; μ is the estimated mean value, which is calculated by the Reducer mean method [14]. In our case, the parameter m = 7. We can check this by superimposing the distribution on the histogram, and dividing the histogram by 1000 Fig. 2, because its area width is 0.001. It turns out an approximate match within the permissible value of the difference between the two parameters is 0.3125. Also to assess the optimality of our given AOI, it is advisable to conduct a comparative analysis Fig. 3 between the two images in the stack, which shows small changes, and the difference in time interval is equal to 6 days. But even over these six days, there have been changes in topography that are useful to us [15]. Bright pixels can be seen in the image, which are due to the hypsometric changes in the relief. Of interest to us is the area in the center of the settlement, where vertical relief shifts occur [16]. It is the brightest in the image. The rest of the brightest pixels may be caused by the peculiarity of studying such areas with radar data, namely the amount and density of biomass, changes in the agricultural fields, deformation influence, movement of machinesp [17], and other. Fig. 2 Gamma distribution histogram of the probability density function with data for the first series

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Fig. 3 Difference (divide) the first two images from the collection

This map shows us the changes in the relief hypsometry, but not all changes here are statistically significant [18], for a more detailed and correct analysis we can use the percentage distribution, that is, take into account the statistically important changes in pixels. To do this, we need to know whether we deviate from a given value in each pixel [19]. To do this, we can assume that the significant deviation in pixel value must be greater than 0.005. Map = img1 × 0 wher e q1.lt 0.0005, q1 =

img2 img1 , q2 = , img2 img1

(2)

where img1 – first image in the collection; img2 – second image from the collection; Map – obtained map. On the resulting map Fig. 4 more clearly visible areas where there are vertical changes in the topography. At the same time, more significant changes appeared along the road in the settlement, these changes are in the residential area along the highway. In this regard, we can assume that a structure or tree has been demolished there, or it is caused by car traffic. Regarding our AOI, a few pixels within the former mines where vertical movement occurs show significant changes. It may appear that

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Fig. 4 Significant changes in the two images (left). Significant changes in the area of interest (right)

the pixels do not lie in the place where the changes occur, but this is normal and is due to the spatial resolution of the satellite image, and the distortion can also be caused by the coordinate system.

3 Detecting Changes in Multi-temporal Series of Images Following the bitemporal change detections for Sentenel-1, it is advisable to perform multitemporal area studies as well. For this purpose, we chose slightly different data. For our region of interest there were 2 orbits, which completely covered it–the orbit with the number 7 and the orbit with the number 80. The second orbit was chosen because it met our requirements better, it always completely covered the AOI. We received 23 Table 2 images which we took from the GEE collection. From the last three images for the visual representation of the data is created RGB-composite Fig. 5. For this purpose, as the analysis has shown, it is sufficient to use the data, which are represented as decibels. In the image the structures are well distinguished–they are the brightest pixels, the darkest pixels are water [20]. For further study of the area, we will use the entire obtained collection of 23 images. As writes [21] bitemporal analysis is less statistically independent, that is, the probability of not getting a false positive result is less significant in contrast to multitemporal analysis. Solving expression (3) for our number of images, we get a fairly large value [22] of 19.8%. aT = 1 − (1 − α)k−1 , where α is 0.01;

(3)

Method for Detecting Natural and Anthropogenic Changes That Filled … Table 2 Initial data for multitemporal analysis

805

Shooting date

Orbit

Polarization

01.04.2021

DESCENDING orbit,80

VV,VH

07.04.2021

DESCENDING orbit,80

VV,VH

13.04.2021

DESCENDING orbit,80

VV,VH

19.04.2021

DESCENDING orbit,80

VV,VH

25.04.2021

DESCENDING orbit,80

VV,VH

01.05.2021

DESCENDING orbit,80

VV,VH

07.05.2021

DESCENDING orbit,80

VV,VH

13.05.2021

DESCENDING orbit,80

VV,VH

19.05.2021

DESCENDING orbit,80

VV,VH

25.05.2021

DESCENDING orbit,80

VV,VH

31.05.2021

DESCENDING orbit,80

VV,VH

06.06.2021

DESCENDING orbit,80

VV,VH

12.06.2021

DESCENDING orbit,80

VV,VH

18.06.2021

DESCENDING orbit,80

VV,VH

24.06.2021

DESCENDING orbit,80

VV,VH

30.06.2021

DESCENDING orbit,80

VV,VH

06.07.2021

DESCENDING orbit,80

VV,VH

12.07.2021

DESCENDING orbit,80

VV,VH

18.07.2021

DESCENDING orbit,80

VV,VH

24.07.2021

DESCENDING orbit,80

VV,VH

30.07.2021

DESCENDING orbit,80

VV,VH

05.08.2021

DESCENDING orbit,80

VV,VH

17.08.2021

DESCENDING orbit,80

VV,VH

k is the number of images. For further analysis, we can use Wilkis’ theorem Formula 4, according to which there should be an approximate distribution with k–1. | −2 log Q k = k log k +

E i

log si − k log

E

| si (−2m),

(4)

i

where k is the number of images m–5. Also from the images above, we can establish where vertical displacements and local areas of water filling are already occurring. In this regard, we will highlight this area to reduce our AOI so that the calculations take less time [23]. Next, we need to compare pixels in our collection with the chi-square distribution [24] Fig. 6. We select the whole obtained collection of images and compare by Formula 1, in this case the number of images equals 23. Now we can build a map of Fig. 7, which will more accurately show the multitemporal data on our AOI. To do this, we use the entire series of images from April

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Fig. 5 RGB-composite of the last three images in the AOI (left). RGB-composite of the last three images in the AOI for the area where changes were detected (right) Fig. 6 Gamma distribution histogram of the probability density function with data for the second series

2021 to August 2021. On the map, red indicates the areas where changes occurred over a six-month period. Automatically we have identified where these changes are occurring and now show their spatial extent using the map. Those red pixels that occur on water can be attributed to a significant difference in their electromagnetic and reflective properties [25]. Then a map of changes according to multitemporal data was built Fig. 8, which shows exactly when changes took place. It shows how early (left) and how much (right) changes occurred, black indicates no change, then from the dark pixels to the light pixels the values increase. This means that the VV and VH reflectance intensities are caused mainly by relief shifts [26]. For clarity, these maps have a basic Open Street Map (OSM), on which the existing karst sinkholes [27] are marked and it

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Fig. 7 Surface changes from multitemporal data

is in them that most of the changes in the hypsometric structure of the relief occur. Without reliable verification on the ground, we cannot claim that the maps of changes we have built can be useful for damage assessment [28], but their potential usefulness is quite obvious.

Fig. 8 Multitemporal changes in time interval (left), same changes in quantitative value

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4 Detecting Changes Using the Method Random Forest Studying these maps, we can assume that the changes are mostly filled with water and the area of the water surface increases. To investigate this phenomenon, it is possible to use GEE as follows. For this problem it is necessary to solve the problem of binary classification [29] of the surface: water surface and everything else (ground, structures, vegetation). In our AOI, the above two classes were created for RF machine learning, number of trees 30. Sixteen classes were created to define the water surface and 3 large classes that covered the surface, this method proved to be the best. The entire collection currently available from the first image from December 2014 to August 2021 was used. With GEE, 27 images were obtained Fig. 9. The images are in chronological order, from left to right, from top to bottom. The first image, and all subsequent images, are the three-month median image. Examining the obtained maps, you can see how the area of water increases and, consequently, the area of relief displacements increases, as they are filled with water. To obtain such a time series, all images from the collection were used and the median value function was applied to them for every three months, to reduce the number of queries to the GEE database, as it has a limit on one query and does not allow to make more than 5000 queries at a time. Due to the lack of SLC data in the GEE database, which allows interferometry, this method, under certain conditions, can suggest where and how much, how often vertical relief shifts occur. According to the study in this area there are displacements, mines which are located in those places where the water surface increases.

5 Result To detect vertical relief movements, an alternative way of detecting them was used. Unfortunately, GEE lacks SLC, whose data allow standard methods to investigate such displacements [16, 30]. Therefore, based on the RF machine learning library built into GEE, a model was built that shows itself well. The locations where the shifts occurred were also found using the Sentinel-1 dataset which is inside GEE. Comparison of the maps created from the images at the beginning of the collection and at the end show serious changes, according to previous studies in our AOI are three mines, which are causing vertical shifts of the topography. Using a comparison of the latest available images in August 2021, changes in the VV channel were found to coincide with the location of these three mines. A multitemporal analysis of the six-monthly images was then used to identify and map changes that show how changes are occurring quantitatively and temporally. The final step of the study was to develop an algorithm that uses the entire available imagery collection and maps were created using satellite radar imagery visualization. Also on the basis of GEE JavaScript API a web application was created with the help of which it is possible to conduct monitoring of relief changes in the explored territory. If we compare the

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Fig. 9 Satellite imagery time series

first and the last maps Fig. 10, it is clearly visible how the relief changed under the influence of the man-made factor. Those places inside the settlement where new areas appear, which are flooded by water, this process can be traced from first receiving the data from the satellite to the present day. On the left the first image is a winter image, and perhaps not all of the water on the river, which runs along the southwestern border of the settlement, got into the analysis, which can be explained by the poor recognition of it when the water is frozen. There are also small flecks in the left image, which are also due to the presence of snow, such flecks are seen in all winter images. The right image clearly shows areas where the ground surface displacement is developing due to anthropogenic factor. The whole series of images can be used to estimate the speed of this movement.

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Fig. 10 Comparison of the first (left) and last (right) satellite image

6 Conclusions Thus, the above methods will allow: . to increase the accuracy of the assessment of dangerous zones of surface deformations (landscapes) in the area where residential, industrial, recreational facilities, and important critical infrastructure facilities are located; . to identify at an early stage the threat of development and activation of mining deformations of the relief; . to identify anthropogenically disturbed areas that are potentially subject to flooding; . to quantitatively identify changes in the relief that are filled with water; . traditionally SAR data perform better in cloudy weather than inactive satellites.

References 1. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore R.: Google earth engine: Planetary-scale geospatial analysis for everyone. https://www.sciencedirect.com/sci ence/article/pii/S0034425717302900?via%3Dihub (2017) 2. Mutanga, O., Kumar, L.: Google earth engine applications. Remote Sens. 11(5), 591 (2019). https://doi.org/10.3390/rs11050591 3. Conradsen, K., Nielsen, A.A., Schou, J., Skriver, H.: A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 41(1), 4–19 (2003) 4. Akbari, V., Anfinsen, S.N., Doulgeris, A.P., Eltoft, T., Moser, G., Serpico, S.B.: Polarimetric SAR change detection with the complex Hotelling-Lawley trace statistic. IEEE Trans. Geosci. Remote Sens. 54(7), 3953–3966 (2016)

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5. Canty, M.J., Nielsen, A.A., Conradsen, K., Skriver, H.: Statistical analysis of changes in Sentinel-1 time series on the google earth engine. Remote Sensing 12(1), 46 (2019). https:// doi.org/10.3390/rs12010046 6. Raney, R.K.: Hybrid dual-polarization synthetic aperture radar. Remote Sens. 11(13), 1521– 1539 (2019). https://doi.org/10.3390/rs11131521 7. Mehdizadeh, S., Maghsoudi, Y., Salehi, M.: The extended sub-look analysis in polarimetric SAR data for ship detection. J. Geospat. Inform. Technol. 8, 1–22 (2020). https://doi.org/10. 29252/jgit.8.1.1 8. Gabriel, A.K., Goldstein, R.M., Zebker, H.A.: Mapping small elevation changes over large areas: differential radar interferometry. J. Geophys. Res. 94, 9183–9191 (1989) 9. Hagberg, J.O., Ulander, L.M., Askne, J.I.: Repeat-pass SAR interferometry over forested terrain. IEEE Trans. Geosci. Remote Sens. 33, 331–340 (1995) 10. Martin-del-Campo-Becerra, G.D., Reigber, A., Nannini, M., Hensley, S.: Single-look SAR tomography of urban areas. Remote Sens. (2020). https://doi.org/10.3390/rs12162555 11. Frey, O., Santoro, M., Werner, C.L., Wegmüller, U.: DEM-based SAR pixel-area estimation for enhanced geocoding refinement and radiometric normalization. IEEE Geosci. Remote Sens. Lett. 10, 48–52 (2013) 12. Lee, H., Liu, J.G.: Analysis of topographic decorrelation in SAR interferometry using ratio coherence imagery. IEEE Trans. Geosci. Remote. Sens. 39, 223–232 (2001) 13. Raney, R. K.: Radar fundamentals: Technical perspective. Ch 2 in Manual of Remote Sensing. In: Henderson, F. M., Lewis, A. J. (eds.) Principles and Applications of Imaging Radar, 3rd ed., vol. 2, pp. 9–130 ASPRS, New York: John Wiley and Sons (1998) 14. Chen, F., Guo, H., Tapete, D., Cigna, F., Piro, S., Lasaponara, R., Masini, N.: The role of imaging radar in cultural heritage: From technologies to applications. International J. Appl. Earth Observ. Geoinform. 112, 102907. ISSN 1569–8432. https://doi.org/10.1016/j.jag.2022. 102907 15. Bovenga, F.: Special issue “Synthetic aperture radar (SAR) techniques and applications” (2020). https://doi.org/10.3390/s20071851 16. Canisius, F., Brisco, B., Murnaghan, K., Kooij, M., Keizer, E.: SAR backscatter and InSAR coherence for monitoring wetland extent, flood pulse and vegetation: a study of the amazon lowland. (2019). https://doi.org/10.3390/rs11060720 17. Monti-Guarnieri, A., Manzoni, M., Giudici, D., Recchia, A., Tebaldini, S.: Vegetated target decorrelation in SAR and interferometry: models, simulation, and performance evaluation (2020). https://doi.org/10.3390/rs12162545 18. Zhao, J., Yu, A., Zhang, Y., Zhu, X., Dong, Z.: Spatial baseline optimization for spaceborne multistatic SAR tomography systems. (2019). https://doi.org/10.3390/s19092106 19. Morton, C.: Image Analysis, Classification, and Change Detection in Remote Sensing, 4th edn, Boca Raton, BR (2019) 20. Kumar, A., Das, A., Panigrahi, R.K.: Hybrid-pol decomposition methods: A comparative evaluation and a new entropy-based approach. IETE Techn. Rev. 37(3), 296–213 (2020). https:// doi.org/10.1080/02564602 21. Conradsen, K., Nielsen, A.A., Skriver, H.: Determining the points of change in time series of polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 54(5), 3007–3024 (2016). https:// doi.org/10.1109/TGRS.2015.2510160 22. Deng, X., López-Martínez, C., Varona, E.M.: A physical analysis of polarimetric sar data statistical models. IEEE Trans. Geosci. Remote Sens. 54, 1–14 (2016). https://doi.org/10.1109/ TGRS.2015.2510399 23. Iglesias, R., Aguasca, A., Fabregas, X., Mallorqui, J., Monells, D., López-Martínez, C., Pipia, L.: Ground-based polarimetric SAR interferometry for the monitoring of terrain displacement phenomena–part I: Theoretical description. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 8 (2015). https://doi.org/10.1109/JSTARS.2014.2360040 24. Oliver, C., Quegan, S.: Understanding synthetic aperture radar images. SciTech Publishing (2004)

812

O. Trofymchuk et al.

25. Akbari, V., Anfinsen, S., Doulgeris, A., Eltoft, T., Moser, G., Serpico, S.: Polarimetric SAR change detection with the complex Hotelling-Lawley trace statistic. https://ieeexplore.ieee.org/ abstract/document/1183688 26. Pipia, L., Fabregas, X., Aguasca, A., Lopez-Martinez, C.: Polarimetric temporal analysis of urban environments with a ground-based SAR. IEEE Trans. Geosci. Remote Sens. 51(4), 2343–2360 (2013) 27. Carter, L.M., Campbell, D.B., and Campbell, B.A.: Geologic studies of planetary surfaces using radar polarimetric imaging. Proc. IEEE 99(5), 770–782 (2011). https://doi.org/10.1109/ JPROC.2010.2099090 28. Trofymchuk, A.N.: Unsteady oscillations of a liquid-saturated poroelastic soil layer. Int. J. Fluid Mech. Res. 29(1), 124–133 (2002). https://doi.org/10.1615/InterJFluidMechRes.v29.i1.80 29. Trofymchuk, O., Kaliukh, I., Silchenko, K., Polevetskiy, V., Berchun, V., Kalyukh, T.: Use accelerogram of real earthquakes in the evaluation of the stress-strain state of landslide slopes in seismically active regions of Ukraine. In: Engineering Geology for Society and Territory, vol. 2. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-09057-3_236 30. Lee, J.-S., Pottier, E.: Polarimetric Radar Imaging: From Basics to Applications, 1st edn. CRC Press (2009). https://doi.org/10.1201/9781420054989 31. Trofymchuk, O., Yakovliev, Y., Anpilova, Y., Myrontsov, M., Okhariev, V.: Ecological situation of post-mining regions in Ukraine. Decis. Control Energy II 293–306 (2021). https://doi.org/ 10.1007/978-3-030-69189-9_17 32. Gade, M., Alpers, W., Huhnerfuss, H., Masuko, H., Kobayashi, T.: Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/multipolarization SIR-C/X-SAR. J. Geophys. Res. 103(C9), 18851–18866 (1998) 33. Canty, M.J.: Image analysis, classification and change detection in remote sensing: With algorithms for python, 4th edn. CRC Press (2019). https://doi.org/10.1201/9780429464348 34. Silva-Perez, C., Marino, A., Lopez-Sanchez, J.M., Cameron, I.: Multitemporal polarimetric SAR change detection for crop monitoring and crop type classification. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 12361–12374 (2021). https://doi.org/10.1109/JSTARS. 2021.3130186 35. Migliaccio, M., Gambardella, A., Tranfaglia, M.: SAR polarimetry to observe oil spills. IEEE Trans. Geosci. Remote Sensing 45(2), 506–511 (2007) 36. Dabboor, M., et al.: Comparison of compact and fully polarimetric SAR for multitemporal wetland monitoring. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 12(5), 1417–1430 (2019). https://doi.org/10.1109/JSTARS.2019.2909437 37. Mahdianpari, M., Granger, J.E., Mohammadimanesh, F., Salehi, B., Brisco, B., Homayouni, S., Gill, E., Huberty, B., Lang, M.: Meta-analysis of wetland classification using remote sensing: A systematic review of a 40-year trend in North America. Remote Sens. 12, 1882 (2020). https:/ /doi.org/10.3390/rs12111882 38. Liang, S., Wang, J.: Advansed Remote Sensing, 2nd edn, Beijing, BJ (2019) 39. Deng, X., López-Martínez, C., Varona, E.M.: A physical analysis of polarimetric SAR data statistical models. IEEE Trans. Geosci. Remote Sens. 54, 3035–3048 (2016) 40. Akbari, V., Anfinsen, S., Doulgeris, A., Eltoft, T., Moser, G., Serpico, S.: Polarimetric SAR change detection with the complex Hotelling-Lawley trace statistic. IEEE Trans. 3953–3966 (2016). https://doi.org/10.1109/TGRS.2016.2532320 41. Marowka, A., Stpiczy´nski, P., Szalachowski, P.: Special section on parallel programming. (2018). https://doi.org/10.1007/s11227-018-2278-9 42. Anpilova, Y., Yakovliev, Y., Hordiienko, O.: An integrated method for predicting technogenic flooding in groundwater–dominated catchments in Kherson region. In: 20th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2021) 43. De Grandi, G., Lee, J.-S., Schuler, D.: Target detection and texture segmentation in polarimetric SAR images using a wavelet frame: Theoretical Aspects. IEEE Trans. Geosci. and Remote Sens. 45(11), 3437–3453 (2007) 44. Trofymchuk, O., Kaliukh, I., Klymenkov, O.: TXT-tool 2.380–1.1: Monitoring and early warning system of the building constructions of the livadia palace, Ukraine. In: Landslide

Method for Detecting Natural and Anthropogenic Changes That Filled …

813

Dynamics: ISDR-ICL Landslide Interactive Teaching Tools. Springer, Cham (2018). https:// doi.org/10.1007/978-3-319-57774-6_37 45. De Grandi, G.F., Mayaux, P., Malingreau, J.P., Rosenqvist, A., Saatchi, S., Simard, M.: New perspectives on global ecosystems from wide-area radar mosaics: Flooded forest mapping in the tropics. Int. J. Remote Sens. 21(6/7), 1235–1250 (2000)

Simulation of the Reagent-Free Process of Demanganation Through Aeration with Atmospheric Oxygen Without pH Correction and Using Artificial Catalysts Yuriy Zabulonov , Dmytro Charnyi , Serhii Marysyk , Mykhaylo Rudoman , Volodymyr Komarov , and Oleksandr Puhach

Abstract Taking into account the peculiarities of the hydrochemical composition of groundwater in most regions of Ukraine (more than 70% of water intake wells supply water with an excess of iron and hydrogen sulfide, and of this 70%, more than 15% of the wells have an increased content of soluble manganese) and if water de-ironing does not require high costs and in most cases, it can take place according to the classic scheme: oxidation of Fe2+ by atmospheric oxygen followed by hydrolysis of Fe3+ + 3OH− → Fe(OH)3 with the formation of slightly soluble Fe(OH)3 hydroxide, followed by its removal by filtering on granular filters. According to established ideas, to convert soluble Mn2+ into insoluble forms by oxidation with atmospheric oxygen, it is necessary to increase the pH significantly above the normative 8.5 or to use artificial filter loadings with catalytic properties. At the same time, in natural conditions, processes of transition of soluble forms of iron and manganese to insoluble forms with the formation of, for example, iron-manganese concretions or crusts occur all the time. Several physical and chemical features of natural groundwater can explain this. Based on the experiments of Hem and Listova, the hypothesis was put forward that with a sufficient concentration of Fe(OH)2 + hydroxo complexes, which act as oxidizing catalysts for the transition of Mn2+ to higher valence levels, it is possible to overcome the energy barrier when atmospheric oxygen is used as an oxidant oxygen To test this hypothesis, thermodynamic modeling of the groundwater aeration process was carried out. Modeling was carried out using the accurate hydrochemical composition of underground waters of the water intake of the city of Uzyn, Bilotserkiv Y. Zabulonov · D. Charnyi (B) · O. Puhach State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”, Kyiv, Ukraine e-mail: [email protected] S. Marysyk · M. Rudoman Institute of Water Problems and Land Reclamation of the National Academy of Agrarian Sciences of Ukraine, Kyiv, Ukraine V. Komarov Central Scientific Research Institute of Armament and Military Equipment of Armed Forces of Ukraine, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Zaporozhets (ed.), Systems, Decision and Control in Energy V, Studies in Systems, Decision and Control 481, https://doi.org/10.1007/978-3-031-35088-7_47

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district, Kyiv region, in the GEMS3. The conducted modeling made it possible to establish that a significant transition of soluble Fe(II) to insoluble Fe(III) occurs at a relatively insignificant oxygen concentration of −0.941 mg/dm3 . At the same time, ferrihydrite–Fe(OH)3 is formed. Formation begins at an oxygen concentration of −0.876 mg/dm3 and ends at an oxygen concentration of ~1.117 mg/dm3 . A significant transition of soluble Mn2+ to insoluble forms begins at an oxygen concentration of ~1.126 mg/dm3 . At the same time, pyrolusite–MnO2 is formed. Formation begins at an oxygen concentration of −1.174 mg/dm3 and ends at an oxygen concentration of ~ 1.357 mg/dm3 . It can also be seen that the range of oxygen concentrations, where the existence of unhydrolyzed Fe(OH)2+ is possible, is relatively narrow and is determined by tenths and hundredths of mg, approximately in the range of 0.241 mg, between oxygen concentrations ~0.876 and ~1.117 mg/dm3 . This corresponds to the phase with a change in Fe(II) concentrations during the transition of Fe(II) to Fe(III). With a further increase in the oxygen concentration, there is already a significant amount of Fe(III) compounds in the amorphous phase, mainly Fe(OH)3 , and the transition of soluble Mn2+ to insoluble forms begins. Accordingly, the modeling confirms that there are no thermodynamic prohibitions in the oxidation of Mn2+ by atmospheric O2 dissolved in the source water. Keywords Demanganization · Aeration · Reagentless · Modeling · Thermodynamics · GEMS3

1 Actuality Groundwater is widely used in water supply systems. The application for drinking water supply is explained by the fact that they are mainly protected from pollution by sewage from the surface of the earth, and therefore when they are in the case of unnecessary roads and complex water treatment. Predicted resources, explored operational reserves and use of underground water indicate great potential opportunities for expanding their use in almost all regions of Ukraine, especially for small water consumers with a need for drinking water up to 30–50 thousand m3 /day. In part of the territory of Ukraine, owned by forest and forest-steppe climatic zones, a widespread gley geoczonesal situation in the zone of hypergenesis. The groundwater of the hypergenesis zone with the glee situation is characterized by an increased concentration of total iron, including iron-containing ligands based on organic (fulvates, humates) or inorganic (orthosilicic and polysilicon acids), or joint-organic, and inorganic complexes and high compounds manganese, nitrogen compounds, and hydrogen sulfide. Comparatively common complexes of manganese compounds with bicarbonates, sulfates, silicate ions, and organic matter, such as humic acids [1]. For subsurface water intakes in central and northern Ukraine, exploiting aquifers of the Cenozoic (and in some places also of the Upper Cretaceous), above-standard

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iron content in the range from 0.7 to 3 mg/dm3 is characteristic. However, there are water intake wells with an iron content of more than 17 mg/dm3 . Groundwater with elevated manganese content is characterized by its concentration ranging from 0.2 to 0.9 mg/dm3 [2]. The hydrogen sulfide concentration in the groundwater of water intake wells most often ranges from 0.4 to 45.8 mg/dm3 . More than 50% of water intake wells in Ukraine take water with excessive soluble iron and hydrogen sulfide content. About 15% of water intake wells also have an increased range of soluble manganese [3–5]. And suppose the removal of soluble iron compounds in most cases does not require complex processes and most often uses the oxidation of Fe2+ followed by the hydrolysis of Fe3+ + 3OH− → Fe(OH)3 with the formation of slightly soluble hydroxide Fe(OH)3(s) followed by its removal using filtering on granular filters. In that case, atmospheric oxygen is most often used as an oxidizer. At the same time, excess hydrogen sulfide is blown into the atmosphere during the aeration process [6]. At the same time, the removal of soluble manganese compounds according to established views [2, 7–10] requires the use, in the case of oxidation using the aeration process or raising pH pf the treated water above 8.5, or the use of artificial catalysts that help bypass energy barrier of this process.

2 Hypothesis At the same time, in natural conditions, the processes of the transition of soluble forms of iron and manganese to insoluble forms with the formation of, for example, iron-manganese nodules or crusts [11–14]. Also, in the now classic works of Hem and Lystova [15–17], the oxidation processes of soluble manganese compounds in various physical and chemical conditions were considered, and the kinetics of this process was deduced. Thus, with a slight mineralization of these solutions and a neutral or slightly alkaline pH, the energy limitations are so significant that oxidation occurs exceptionally. According to Ham’s calculations, in the “autocatalytic reaction” [15], the oxidation of manganese by oxygen occurs according to Eq. (1).     2  −d Mn 2+ = k0 Mn 2+ + k1 [Mn O2 ][O2 ] O H − . dt

(1)

According to this equation, at pH = 8.5, it takes about 1 million years to form a former of manganese oxide 0.1 mm thick. It was experimentally shown that only at pH > 8,5, high Eh (pO2 ~ 1 atm) and high concentrations of Mn2+ > 25 mg/kg(450 µmol) oxidation of Mn(II) is possible within a period of several weeks to months [12]. The heterogeneous catalytic process: the formed portion of Mn(IV) oxide and hydroxide absorbs Mn2+ and catalyzes its oxidation. But it is quite possible that manganese precipitation can begin without primary adsorption only due to the difference in phases: it has been experimentally

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proven that the oxidation of Mn(II) is catalyzed by the surfaces of iron oxides and silicates and some other solid steps [18]. At the same time, in the same works, it was shown that the kinetics of the Mn(II) oxidation process significantly accelerates with an increase in heterogeneous mineral compounds. One of the possible catalysts of this process, according to the works of J. D. Hem, can be iron compounds. According to J. Hem’s thermodynamic calculations, if Mn2+ i Fe2+ are simultaneously dissolved in microgram (5–6 µg/dm3 ) amounts In aerated water, they will be oxidized and hydrolyzed according to the following reactions [16]: 4FeOH+ + O2 (hydr.) + 6H2 O = 4Fe(OH)3 + 4H+ , KFe 4      = H+ / FeOH+ O2 (hydr.) = 104909 ;

(2)

6Mn2+ + O2 (hydr.) + 6H2 O = 2Mn3 O4 + 12H+ ,    2+ 6   KMn = H+ Mn O2 (hydr.) = 1036.54 .

(3)

Since the constant KFe > KMn , the oxidation and hydrolysis of iron proceeds much faster than that of manganese. At the intermediate stage of the Fe(II) hydrolysis process, a certain amount of incompletely hydrolyzed Fe(OH)2 + ion is formed. According to [15], the Fe(OH)2 + hydroxo complex can act as an oxidant for Mn2+ according to reactions (4). 2+ 2 Fe(OH)+ + 2H2 O = Mn3 O4 + 2Fe(OH)+ + 6H+ . 2 + 3 Mn

(4)

Fe(III) catalyzes the slow process of oxidation of Mn2+ . As noted by J. Hem, “to obtain a significant catalytic effect, the recycling mechanism, in which FeOH+ is converted to Fe(OH)2 + by oxygen and returned to FeOH+ by reaction with Mn2+ , should be relatively fast, if only small concentrations of dissolved iron [14]”. Considering the peculiarities of the hydrochemistry of our underground waters, which are characterized by the simultaneous presence of active concentrations of soluble Mn2+ , significantly higher concentrations of soluble Fe2+ . According to, it is hypothetically possible to allow the possibility of autocatalytic oxidation of Mn2+ to Mn3 O4 in the process of aeration of water due to the formation of appropriate concentrations of Fe(OH)2 + .

3 Justification of the Use of Thermodynamic Modeling In the conditions of the modern development of geochemistry, it is possible to verify this hypothesis by conducting thermodynamic modeling of aeration of real water in water intake wells and thus establishing the presence of Fe(OH)2 + .

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In our case, the simulation was carried out in an isobaric-isothermal, multicomponent system using the GEMS3 http://gems.web.psi.ch. GEMS3–Gibbs Energy Minimization Software for Geochemical Modeling. Developers: Swiss research center–Laboratory for Waste Management (LES) and Paul Scherrer institute–PSI, the largest research institute in the field of natural and technical sciences in Switzerland.

4 Model Description In this system, for solid crystalline phases of constant composition, traditional equations of state are applied, which ensure the calculation of the Gibbs free energy of compound formation based on the following input data–Gibbs free energy of formation, entropy, and molar volume under standard conditions (25 °C, 1 bar), as well as values of empirical coefficients in the equation of the dependence of heat capacity (Cp ) on temperature. An equation of state based on the modified Helgeson-Kirkham-Flowers model was used for the phase of the aqueous solution of the electrolyte in PC GEMS, in which the H2 O equation of sate according to Kestin was implemented. The calculation of the activity coefficients of water-dissolved compounds from the composition of the solution was considered according to the extended Debye–Huckel equation in the form of the 2nd approximation. We carried out modeling of aeration of the groundwater of the water intake Uzyn, Bilotserkiv district, Kyiv region. The gross composition of the system corresponds to the natural composition of the underground water of the Uzyn water intake: 1000 g H2 O + dissolved components. In the process of simulation, portions of oxygen were successively added to the source water (about 0,0,031,986 mg at each step). A total of 2501 calculations were performed until the oxygen concentration of 7,9997 mg in the system was reached. The composition of the source water is given in Table 1. Table 1 Chemical composition of water intake in the city of Uzyn, Kyiv region General stiffness, mg-eq/dm3

pH

Eh, mV

Oxidation of O2 mg/dm3

Fetotal. , mg/dm3

Mn total. , mg/dm3

Na+ , mg/ dm3

K+ , mg/ dm3

6.9

7.45

85

4

2.1

0.5

21

2

Mg2+ , mg/ dm3

NH4 + , mg/dm3

Cl− , mg/ dm3

SO4 2− , mg/ dm3

NO3 − , mg/dm3

NO2 − , mg/dm3

HCO3 − , mg/dm3

F, mg/ dm3

15.2

1.5

7.4

28.8

1.2

0.1

372.2

0.18

General mineralization, mg/dm3

Ca2+ , mg/dm3

tot. SiO2 , mg/dm3

538.4

82.8

16.13

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The modeling consists of the calculation of balances in the system “natural underground water of the Uzyn water intake- atmospheric oxygen” depending on the amount of atmospheric oxygen. Calculations carried out by the Gibbs free energy minimization method using the GEMS3 software complex [19, 20]. Inputs: variable inputs, temperature: 25 °C (298,15 K), pressure: 1 bar (105 Pa). The composition of the model is determined, on the one band, by the elemental chemical composition of water, and on the other hand, by the metastable states of some system components in hypergenic conditions. This feature of hypergenic processes can be taken into account in the thermodynamic model by using the principle of partial equilibrium of P. Barton [21, 22], according to which equilibrium van be achieved for a part of the reactions that occur most rapidly in an unbalanced system as a whole.

5 Results of Modelling and the Discussion The gross composition of the system (vector b) corresponds to the natural composition of the underground water of the Uzyn water intake: 1000 g H2 O + dissolved components (analytical data, Table 1, which were converted to the content of salt components—Tables 2 and 3 to exclude charge imbalance due to analytical inaccuracies). The chemical description of the model system (multisystem) consists of independent components (hereinafter IC) and dependent components (hereinafter DC) and phases. ICs include C, Ca, Cl, Fe, H, K, Mg, Mn, N, Na, O, S, Si, and Zz (charge). And DC are given in the Table 4. Table 2 Content of salt components, units pH, mg/ dm3

pH

7.45

Cl−

7.4

(SO4 )2−

28.8

HCO3 −

372.2

NO3

−

1.2

Ca2+

82.8

Mg2+

15.2

Na+

21

K+

2

NH4 +

1.5

Si

16.13

Fe3+

2.1

NO2 −

0.1

Mn2+

0.5

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Table 3 Elimination of charge imbalance, unit mol/dm3 NaCl

0.000209

KHCO3

0.0000512

NaHCO3

0.000704

Mg(HCO3 )2

0.000625

Ca(HCO3 )2

0.001766

CaSO4

0.0003

(NH4 )NO3

0.0000194

(NH4 )NO2

0.00000217

(NH4 )HCO3

0.00006163

SiO2

0.000574

CO2

0.00050117

Fe(OH)2

0.0000376

MnO

9.10117E-06

Table 4 List of dependent components Phase (name)

Dependent components (formulas)

Water solution

CaCO3 o , CaHCO3 + , CaSO4 o , Ca2+ , CaOH+ , CaHSiO3 + , CaSiO3 o FeCO3 o , FeHCO3 + , FeHSO4 + , FeSO4 o , Fe2+ , FeCl+ , FeOH+ , FeHSO4 2+ , FeSO4 + , FeSO4 2− , Fe3+ , Fe2 (OH)2 4+ , Fe3 (OH)4 5+ , FeCl2+ , FeCl2 + , FeCl3 o , Fe(OH)2 + , Fe(OH)4 − , Fe(OH)3 o , FeOH2+ , FeHSiO3 2+ KSO4 − , K+ , KOHo MgCO3 o , MgHCO3 + , Mg2+ , MgOH+ , MgSO4 o , MgHSiO3 + , MgSiO3 o MnCO3 o , MnHCO3 + , MnSO4 o , Mn2+ , MnCl+ , MnCl2 o , MnCl3 − , Mn(OH)4 2− , Mn(OH)3 − , Mn(OH)2 o , MnOH+ , Mn3+ , Mn(OH)8 2− , Mn(OH)7 − NaCO3 − , NaHCO3 o , NaSO4 − , Na+ , NaOHo H3 SiO4 − , H4 SiO4 o , H2 SiO4 2− CO2 o , CO3 −2 , HCO3 − , CH4 o ClO4 − , Cl− NO2 − , NO3 − , NH3 o , NH4 + , N2 o

Water solution

S2 O3 2− , HSO3 − , SO3 2− , HSO4 − , SO4 2− , H2 So , HS− , S2− OH− , H+ , H2 Oo , O2 o , H2 o

Gas mixture

O2 , H2 , CO2 , CH4 , H2 S

Graphite

C

Aragonite

CaCO3 (continued)

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Table 4 (continued) Phase (name)

Dependent components (formulas)

Calcite

CaCO3

Disordered dolomite

CaMg(CO3 )2

Gypsum

CaSO4 ·2H2 O

Iron carbonate

FeCO3

Iron (III) oxide-hydroxide (amorphous)

Fe(OH)3

Pyrite

FeS2

Magnesite

Mg(CO)3

Rhodochrosite

Mn(CO)3

Manganosite

MnO

Pyrolusite

MnO2

Alabandin

MnS

Amorphous silica

SiO2

In the first step, the equilibrium was calculated for the system, the composition of which corresponds to the analytical data in the Table 1 and Table 2 using the System PC GEMS3. In the second step of the simulation, the Process procedure was used, with the help of which the initial composition of the system was automatically formed by sequentially adding a portion of oxygen-at each step ~ 0.0031986 mg. A total of 2501 calculations were performed up to 7.9997 mg of oxygen in the system. As a result of the calculation of each system, the following are obtained: By solid phases: list and equilibrium quantities of solid (mineral) phases. In the gas phase: equilibrium partial pressures of the gas mixture components. In the aqueous solution phase: – Equilibrium values of pH, Eh, ionic strength. – Equilibrium gross concentrations of each IC, incl. Fe, Mn, and Ca, the concentrations of which are determined by the solubility of the equilibrium mineral phases. – Concentrations (>10–16 mol/kg of water) of all water-soluble compounds (ions and complexes). Some of the obtained results are presented in graphs, the raw data for the construction of which tabular from were generated using a special PC procedure GEMS3 (Figs. 1, 2. 3, 4, 5 and 6). A relatively small, but sharp decrease in pH value is explained by the hydrolysis of the formed iron (III), according to reaction (5) [23]: 4Fe2+ + 10H2 O + O2 → 4Fe(OH)3 + 8H+ .

(5)

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Fig. 1 Change in the amount of equilibrium mineral (solid) compounds of iron and manganese depending on the amount of oxygen added to 1 L of the aqueous solution, the composition of which corresponds to the analytical data

Modeling of aeration of source water containing iron (II) and manganese (II) ions and ammonium nitrogen showed that the value of Eh increases and stabilizes after the end of their oxidation process (Figs. 4 and 5). The beginning of the second “shelf” on the pH graph corresponds to the end of oxidation of the soluble phase of manganese. This happens according to reaction (6). Mn2+ + 2H2 O → Mn(OH)2 + 2H+ .

(6)

In fact, we see that a relatively sharp decrease in pH is associated with the transition of soluble Fe(II) i Mn(II) to an insoluble state. Reaching a stable pH plateau corresponds to the end of NO2 oxidation and stabilization of the amount of NO3 (Fig. 5). Figure 1 shows that the significant transition of soluble Fe(II) to insoluble Fe(III) occurs at a rather insignificant oxygen concentration of ~0,941 mg/dm3 . At the same time, Iron(III) oxide-hydroxide -Fe(OH)3 is formed. Formation begins at an oxygen concentration of ~0,876 mg/dm3 , ends at an oxygen concentration of ~1,117 mg/dm3 . A significant transition of soluble Mn2+ to insoluble forms begins to manifest itself at an oxygen concentration of ~1,126 mg/dm3 . At the same time, pyrolusite -MnO2 . is formed. Formation begins at an oxygen concentration of ~1,174 mg/dm3 , ends at an oxygen concentration of ~1,357 mg/dm3 . It can also be seen that the range of oxygen

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Fig. 2 Change in equilibrium concentrations of dissolved ions and iron complexes depending on the amount of oxygen added to 1 L of the aqueous solution

concentrations, where the existence of under hydrolyzed Fe(OH)2 + , is possible, is quite narrow and is determined by tenths and hundredths of mg, approximately in the range of 0.241 mg, between oxygen concentrations ~0,876 mg/dm3 and ~1,117 mg/ dm3 . This corresponds to the phase with a change in Fe(II) concentrations during the transition of Fe(II) to Fe(III). With a further increase in oxygen concentration, there is already a significant amount of Fe(III) compounds in the amorphous phase (Fig. 2), mainly Fe(OH)3 and the process of transition of soluble Mn2+ to insoluble forms begins. Accordingly, the modeling confirms that there are no thermodynamic prohibitions in the oxidation of Mn2+ by atmospheric O2 , dissolved in the source water. At the same time, first in the process of oxidation, the transition of soluble Fe(II) to insoluble Fe(III), and with the subsequent increase in oxygen, the transition of Mn2+ to Mn4+ . occurs. At the same time, pH decreases minimally, practically does not change, and Eh increases (Fig. 6). The growth of Eh occurs in several jumps: the first jump corresponds to the transition of Fe(II) to Fe(III), the second Mn(II) → Mn(IV) and the third to the further saturation of the system with oxygen up to 5,7 mg/dm3 . Also, Eh may be influenced by a change in the concentration of nitrogen compounds (Fig. 5). The drop in the concentration of NH4 + completely coincides with the first jump of Eh, and the beginning of the formation of NO3 − coincides with the second jump of Eh. (Figs. 2 and 3) shows the forms of iron and manganese compounds formed in the process of aeration of the source water.

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Fig. 3 Change in equilibrium concentrations of dissolved ions and manganese complexes depending on the amount of oxygen added to 1 L of aqueous solution

Fig. 4 Change in the equilibrium values of the total concentration of dissolved iron and manganese, the concentration of dissolved oxygen, pH and Eh depending on the amount of oxygen added to 1 L of aqueous solution

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Fig. 5 Change in equilibrium concentrations of dissolved nitrogen compounds depending on the amount of oxygen added to 1 L of aqueous solution

Fig. 6 Change in the equilibrium values of the total concentration of dissolved iron, manganese and pH depending on the amount of oxygen added to 1 L of aqueous solution

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6 Conclusions The main conclusion of this work is the theoretical confirmation of the hypothesis put forward by us about the possibility, using only natural elements (aeration of groundwater with atmospheric oxygen), to carry out effective purification of groundwater from excess iron, manganese and hydrogen sulfide without additional introduction of artificial reagents. The possibility of formation of compounds in the process of aeration of this water capable of catalyzing the process of oxidation of Mn2+ by soluble O2 , primarily due to incompletely hydrolyzed Fe(OH)2 + , and already formed amorphous Fe(OH)3, colloids, which act as possible Mn2+ adsorption centers, has been established. The conducted modeling confirmed the formation of these catalytic compounds in the process of aeration. The actual absence of a demanganization effect during the de-ironing of underground waters of the Uzyn water intake at the classic de-ironing station only in dictates that these technologies: filtering of aerated water on pressure filters with sand filter loading does not provide the conditions necessary for the contact of Mn2+ ions with incompletely hydrolyzed Fe(OH)2 + ions. That is, the reconstruction of the iron removal-demanganization process is necessary, which will allow creating areas with increased concentrations of iron compounds in steel treatment plants where the process: Fe2+ + O2 + H2 O → Fe(OH)2 → Fe(OH)3 + H+ is constantly taking place.

References 1. Lykhatska, O.A., et al: State of Underground Waters of Ukraine, Yearbook. State Information Geological Fund of Ukraine, Kyiv (2014) 2. Chernova, N.M.: Ochyshchennia pryrodnykh vod vid spoluk marhantsiu iz zastosuvanniam sorbenta-katalizatora. A.V. Dumansky Institute of Colloid and Water Chemistry, NAS of Ukraine, Kyiv (2014) 3. Hirol, M.M.: National Report on the Quality of Drinking Water and the State of Drinking Water Supply in Ukraine in 2003. Rivne (2005) 4. Stashuk, V.A.: Scientific Principles of Management of the Water Management and Reclamation Complex of Ukraine. Instytut hidrotekhniky i melioratsii UAAN, Kyiv (2009) 5. Skurativska, I., Skurativskyi, S., Popov, O., Viktoriia, D., Mykhliuk, E., Dement, M.: Complex oxygen regimes of water objects under the anthropogenic loading. In: Zaporozhets, A. (eds.) Systems, Decision and Control in Energy III. Studies in Systems, Decision and Control, vol. 399. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-87675-3_20 6. Nykoladze, H.Y.: Improving The Quality of Underground Waters. Moscow (1987) 7. Ellis, D., Bouchard, C., Lantagne, G.: Removal of iron and manganese from groundwater by oxidation and microfiltration. Desalination 130, 255–264 (2000). https://doi.org/10.1016/ S0011-9164(00)00090-4 8. Wilmarth, W.A.: Removal of iron, manganese and sulfides. Water Wastes Eng. 5(54), 131–141 (1988) 9. Conner, D.O.: Removal of iron and manganese. Water Sewage Works. 28, 68 (1989) 10. Kulakov, V.V., Soshnykov, E.V., Chaikovskyi, H.P.: Deferrization and Demanganation of Underground Waters. DVHUPS, Khabarovsk (1998) 11. Yudovych, Y.E., Ketrys, M.P.: Basic Regularities of Manganese Geochemistry. Komi Scientific Center of the Ural Branch of the Russian Academy of Sciences, Syktyvkar (2013)

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12. Tebo, B.M., Geszvain, K., Lee, S.-W.: The molecular geomicrobiology of bacterial Manganese(II) Oxidation. In: Barton, L., Mandl, M., Loy, A. (eds.) Geomicrobiology: Molecular and Environmental Perspective, pp. 285–308. Springer, Dordrecht, (2010). https://doi.org/ 10.1007/978-90-481-9204-5_13 13. Yudovych, Y.E.: Geochemistry of manganese. Komi Scientific Center of the Ural Branch of the Russian Academy of Sciences, Syktyvkar (2014) 14. Yudovich, Y.E.: Paradoxes of manganese geochemistry. Komi Scientific Center, Ural Branch of the Russian Academy of Sciences. Bull. Inst. Geol. 5(209), 19–24 (2012) 15. Hem, J.D.: Chemical equilibria and rates of manganese oxidation. United States Government Printing Office, Washington (1963). https://pubs.usgs.gov/wsp/1667a/report.pdf 16. Hem, J.D.: Reactions of metal ions at surfaces of hydrous iron oxide Geochim. Cosmochim. Acta 4(41), 527–538 (1977). https://doi.org/10.1016/0016-7037(77)90290-3 17. Listova, L.P.: Physico-chemical studies of the conditions for the formation of oxide and carbonate ores of manganese. AN SSSR, Moscow (1961) 18. Yudovich, Y.E.: Why do Fe-Mn nodules have cores? Komi Scientific Center, Ural Branch of the Russian Academy of Sciences. Bull. Inst. Geol. 8, 7–10 (2007) 19. GEM Software (GEMS) Home. Paul Scherrer Institute (2022). https://gems.web.psi.ch/ 20. Kulik, D.A., et al.: GEM-selektor geochemical modeling package: revised algorithm and GEMS3K numerical kernel for coupled simulation codes. Comput. Geosci. 17, 1–24 (2013). https://doi.org/10.1007/s10596-012-9310-6 21. Krainov, S.R., Ryzhenko, B.N., Shvets, V.M.: Geochemistry of underground waters. In: Theoretical, applied and ecological aspects. Nauka, Moscow (2004). https://www.geokniga.org/ books/22282 22. Borysov, M.V., et al: Methods of Geochemical Modeling and Forecasting in Hydrogeochemistry. Nauka, Moscow (1988). https://www.geokniga.org/books/6835 23. Filipchuk, V.L., Filipchuk, L.V.: Peculiarities of extraction of iron ions from waste water of industrial enterprises. Bull. Eng. Acad. Ukraine. 3–4, 263–266 (2010)