Emerging Technologies for Future Sustainability: Proceedings of the 2nd International Conference on Biomass Utilization and Sustainable Energy; ... Sept., Malaysia (Green Energy and Technology) [1st ed. 2023] 9819916941, 9789819916948
This book presents high-quality peer-reviewed articles from the 2nd International Conference on Biomass Utilization and
140 39 17MB
English Pages 594 [564] Year 2023
Organization
Preface
Contents
Sustainable Biomass Resources for Decarbonising the Economy
Phytoremediation Potential of Salvinia Molesta to Reduce Ni and Cd from Simulated Wastewater
1 Introduction
2 Materials and Methods
2.1 Plant Sampling
2.2 Experimental Design
2.3 Water and Plant Sample Collection
2.4 Determination of Heavy Metals Concentration
2.5 Data Analysis
3 Results and Discussions
3.1 Removal Efficiency of Ni and Cd by S. Molesta
3.2 Distribution of Heavy Metals in S. Molesta
3.3 Bioconcentration Factor and Translocation Factor
3.4 Tolerance Index and Toxicity Symptoms of S. Molesta Exposed to Ni and Cd
4 Conclusions
References
Soil Risk Assessment on the Usage of Molasses-Based Distillery Effluent for Paddy Irrigation: Heavy Metals Content
1 Introduction
2 Materials and Methods
2.1 Sampling Site
2.2 Sampling and Preparation of Effluent Sample
2.3 Soil Sample Collection and Analysis
2.4 Heavy Metal Pollution Analysis
2.5 Data Analysis
3 Results and Discussion
3.1 Characteristics of the Effluent Final Discharge
3.2 Heavy Metals Concentration in Soil Samples
3.3 Geoaccumulation Index (Igeo) and Pollution Load Index (PLI)
4 Conclusion
References
Effects of Soil Conditioners on Rice Growth and Soil Properties Under Water Stress at Vegetative Stage
1 Introduction
2 Material and Methods
2.1 Preparation of Paddy Plant Samples
2.2 Soil Chemical Properties
2.3 Water Stress Behavior Treatment
2.4 Plant Growth
3 Results and Discussion
3.1 Soil Physicochemical Properties
3.2 Plant Growth
4 Conclusion
References
Soil Amelioration Effects on Morphology Traits of Upland Rice Root–Shoot and Soil Productivity Under Water Deficit
1 Introduction
2 Materials and Methods
3 Results and Discussion
3.1 Effects of Soil Ameliorant on the Soil Productivity Under Water Deficit
3.2 Effect of Soil Ameliorant on the Morphological Traits of Rice Root–Shoot Under Water Deficit
3.3 Correlation on the Effects of Different Soil Ameliorant on Soil Productivity and Rice Morphological Traits Under Water Deficit
4 Conclusion
References
Biomass Conversion Technologies for Bioenergy and Biofuels
Comparison of Corn and Tapioca Starch Binders on the Characteristic of Rice Straw Charcoal Briquettes
1 Introduction
2 Methodology
2.1 Charcoal Preparation
2.2 Charcoal Briquette Preparation
2.3 Charcoal Briquette Characterization
3 Results and Discussion
3.1 Thermal Degradation of Char
3.2 Fixed Carbon and Volatile Matter
3.3 Ash and Moisture Content
3.4 Ignition Time and Burning Rate
4 Conclusion
References
Pretreatment of Leucaena Leucocephala Using Deep Eutectic Solvent for Ethanol Production by Kluyveromyces Marxianus UniMAP 1–1
1 Introduction
2 Materials and Method
2.1 Sample Preparation
2.2 DES Preparation
2.3 Pretreatment and Enzymatic Hydrolysis
2.4 Fermentation and Sample Analysis
3 Results and Discussion
3.1 DES Pretreatment Versus Acid and Alkaline Pretreatment
3.2 Effect of DES on Subsequent Enzymatic Hydrolysis
3.3 Fermentation Profile from Differently Treated L. leucocephala
4 Conclusion
References
Deep Eutectic Solvent Pretreatment of Rubber Seed Shells for Cellulose and Hemicellulose Production
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Preparation of RSS and DES
2.3 Compositional Analysis
2.4 Physicochemical Properties of the Synthesized DESs
2.5 Pretreatment of RSS by DESs
2.6 Analytical Method by High-Performance Liquid Chromatography (HPLC)
2.7 Statistical Analysis
3 Results and Discussion
3.1 Compositional Analysis of RSS
3.2 Physicochemical Characterization of Synthesized DESs
3.3 Functional Group Analysis
3.4 RSS Pretreatment by Synthesized DESs
3.5 Analysis of Liquid Fraction After DES Pretreatment
4 Conclusion
References
Inhibition Study on the Growth of Clostridium Saccharoperbutylacetonicum N1-4 (ATCC 13564) for the Production of Biobutanol in ABE Fermentation
1 Introduction
2 Materials and Methods
2.1 Inoculum Preparation
2.2 Medium Preparation
2.3 ABE Batch Fermentation
2.4 Analytical Method
3 Results and Discussion
3.1 Effects of Sugar Degradation Product and Butanol Addition on the Growth of Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) in Biobutanol Production
3.2 Effects of Sugar Degradation Product and Butanol Addition in Biobutanol Production
4 Conclusion
References
Thermogravimetric Analysis on Empty Fruit Bunch, Rice Husk, and Rice Straw for Feedstock in Biomass Gasification
1 Introduction
2 Method
2.1 Determination of Biomass Properties
2.2 TG/DTA Analysis
3 Results and Discussion
3.1 Properties of Biomass
4 Conclusion
References
A Review on Enhancement of Oil Palm Solid Waste Through Torrefaction
1 Introduction
2 Production of Oil Palm Solid Waste
3 Properties of Oil Palm Solid Waste
3.1 Lignocellulosic Oil Palm
3.2 Proximate Analysis
3.3 Ultimate Analysis
4 Torrefaction Process
5 Conclusion
References
Energy Efficiency of Briquettes from Queen Pineapple (Ananas Comosus [Linn.] Merr.) Wastes Using Three Organic Binders
1 Introduction
2 Materials and Methods
2.1 Material
2.2 Experiment
3 Results and Discussion
4 Conclusion
References
Optimization of Biobutanol Production from Detoxified Palm Kernel Cake Hydrolysate by Clostridium Acetobutylicum YM1
1 Introduction
2 Materials and Methods
2.1 Microorganism and Medium Preparation
2.2 Chemicals Employed
2.3 Preparing Palm Kernel Cake Hydrolysate
2.4 Biobutanol Production
2.5 Central Composite Design and Statistical Analysis
2.6 Analytical Methods
3 Results and Discussion
3.1 Optimization of Biobutanol Fermentation Conditions
3.2 Influence of Initial pH, Incubation Temperature and Inoculum Size on the Production of Biobutanol
3.3 Adequacy Check and Confirmation of the Model
4 Conclusion
References
Mixed Matrix Membrane (MMMs) as Membrane Based Separation Technology: A Review
1 Introduction
2 Performance Enhancement of MMMs
2.1 Polymer Membrane
2.2 Types of Additives
3 Fabrication Technique for MMMs
3.1 Phase Inversion
3.2 Dip Coating
4 Characterization of MMMs
4.1 Physical Characterization
4.2 Chemical Characterization
5 Current Application of MMMs
6 Conclusion
References
Application of Machine Learning for Biogas Production from Lignocellulosic Biomass
1 Introduction
2 Materials and Methods
2.1 Data Collection and Preprocessing
2.2 Machine Learning Algorithms Selection and Tuning
3 Results and Discussion
3.1 Model Prediction Accuracy
3.2 Feature Evaluation
4 Conclusion
References
Biomass Conversion to Intermediates and Products
Utilization of Spent Coffee Ground as Adsorbent for Nitrate Removal
1 Introduction
2 Methodology
2.1 Materials and Chemicals
2.2 Modification of Spent Coffee Grounds as Adsorbent
2.3 Adsorption Experiment
2.4 Analysis of Nitrate Concentration
3 Results and Discussion
3.1 Adsorption Performance of Acid-Pretreated SCG
3.2 Effect of pH on Nitrate Adsorption Performance
3.3 Effect of Adsorbent Dosage on Nitrate Adsorption Performance
4 Conclusion
References
Nitrate Adsorption Using Spent Coffee Ground: Kinetics, Isotherm, and Thermodynamic Studies
1 Introduction
2 Methodology
2.1 Materials and Chemicals
2.2 Preparation of Adsorbent and Adsorbate
2.3 Batch Adsorption Experiment
2.4 Nitrate Concentration Analysis by Brucine Method
2.5 Adsorption Capacity and Removal Efficiency
2.6 Adsorption Kinetics
2.7 Adsorption Isotherms
2.8 Adsorption Thermodynamic
3 Results and Discussion
3.1 Effect of Contact Time
3.2 Effect of Initial Nitrate Concentration
3.3 Effect of Temperature
3.4 Adsorption Isotherm Analysis
3.5 Adsorption Kinetics Analysis
3.6 Thermodynamic Analysis
4 Conclusion
References
Tamarind Seed Modified by CuFe Layered for Caffeine Removal from Aqueous Solution
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Preparation of TSAC
2.3 Equilibrium Study
2.4 Isotherm Study
3 Results and Discussion
3.1 Characteristics of Samples
3.2 Adsorption Study—Effect of IR on Caffeine Uptakes and Removal
3.3 Adsorption Isotherm
4 Conclusion
References
Synthesis of Pineapple Peel Based Activated Carbon Via Microwave Irradiation Technique for Methylene Blue Dye Removal
1 Introduction
2 Materials and Methods
3 Materials and Preparation of PPAC
3.1 Batch Equilibrium Study
3.2 Isotherm Study
3.3 Kinetic Study
4 Results and Discussion
4.1 Characteristics of Samples
4.2 Batch Equilibrium Adsorption
4.3 Adsorption Isotherm
4.4 Adsorption Kinetic
5 Conclusion
References
Preparation of Edamame Bean Pod Based Activated Carbon for Methylene Blue Dye Adsorption
1 Introduction
2 Materials and Methods
2.1 Materials and Preparation of EBPAC
2.2 Batch Equilibrium Study
2.3 Isotherm Study
2.4 Kinetic Study
3 Results and Discussion
3.1 Batch Equilibrium Adsorption
3.2 Adsorption Isotherm
3.3 Adsorption Kinetic
4 Conclusion
References
Activated Carbon Adsorbent Using Desiccated Coconut Residue for Removing Methylene Blue Dye
1 Introduction
2 Materials and Methods
2.1 Materials and Preparation of DCRAC
2.2 Batch Equilibrium Study
2.3 Isotherm Study
2.4 Kinetic Study
3 Results and Discussion
3.1 Batch Equilibrium Adsorption
3.2 Adsorption Isotherm
3.3 Adsorption Kinetic
4 Conclusion
References
Synthesis of Microwave-Assisted Mango Peel Based Activated Carbon for Methylene Blue Dye Removal
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Preparation of MPAC
2.3 Batch Equilibrium Study
2.4 Isotherm Study
2.5 Kinetic Study
3 Results and Discussion
3.1 Batch Equilibrium Adsorption
3.2 Adsorption Isotherm
3.3 Adsorption Kinetic
4 Conclusion
References
Optimization Study of Reactive Orange Dye Removal by Casuarina Equisetifolia Using Response Surface Methodology (RSM)
1 Introduction
2 Materials and Methods
2.1 Preparation of Adsorbent
2.2 Batch Adsorption Studies
2.3 Experimental Design Using Response Surface Methodology (RSM)
2.4 Optimization Studies
3 Results and Discussion
3.1 Experimental Results
3.2 Statistical Analysis for the Response
3.3 Process Optimization
4 Conclusion
References
Utilization of Spent Mushroom Compost in Grey Oyster Mushroom Cultivation
1 Introduction
2 Materials and Methods
2.1 Preparation of Agar Media
2.2 Preparation of Mycelium and Spawn Culture
2.3 Preparation of Spent Mushroom Compost
2.4 Combination of Different Formulation Ratios of SMC Substrate Bags
2.5 Spawn Inoculation and Cultivation
2.6 Statistical Analysis
3 Results and Discussion
3.1 Growth Curve of Mycelium Colonization in Substrate Bags
3.2 Mycelium Growth Rate from Different Formulation Substrates
3.3 Fruit Body Weight from Different Formulation Substrates
4 Conclusion
References
Effect of Latex Coating on the Physical Properties of Calcium Alginate Beads
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Preparation of Solutions
2.3 Preparation of Calcium Alginate Beads
2.4 Coating of Calcium Alginate Beads
2.5 Determination of Bead Size and Bead Shape
2.6 Determination of Thickness of Latex Coating Layers
2.7 Statistical Analysis
3 Results and Discussion
3.1 Effect of Alginate Concentration on Diameter and Sphericity Factor of Beads.
3.2 Effect of Dripping Tip Diameter on Diameter and Sphericity Factor of Beads
3.3 Thickness of Latex Coating Layers
4 Conclusions
References
Screening and Optimization Biosynthesis of Iron Nanoparticle Using Watermelon Rind as Reducing and Stabilizing Agent
1 Introduction
2 Materials and Methods
2.1 Material and Sample Preparation
2.2 Extraction of WMR
2.3 Biosynthesis of FeNPs
2.4 Experimental Design and Statistical Analysis
2.5 Characterization of Synthesized FeNPs
3 Results and Discussion
3.1 ANOVA Analysis of Biosynthesis FeNPs
3.2 Optimization of Biosynthesis FeNPs
3.3 Characterization of Biosynthesized FeNPs
4 Conclusions
References
Impact of Power Supply on Electro-Precipitation of Nickel Hydroxide from Industrial Electronic Waste
1 Introduction
2 Methodology
2.1 Electroplating Wastewater
2.2 Electro-Precipitation Procedure
3 Results and Discussion
3.1 Electro-Precipitation Using AL-SS Electrodes
3.2 Recovery of Nickel Precipitate
4 Conclusion
References
Optimization of Nickel Electrowinning from Simulated Watts Bath of Electronics Industrial Waste
1 Introduction
2 Materials and Methods
2.1 Chemical and Material
2.2 Preparation of Electrolyte (Treated Solution)
2.3 Preparation of Electrode
2.4 Cyclic Voltammetry Analysis
2.5 Optimization Parameter of Electrowinning
2.6 Analytical Method
3 Results and Discussion
3.1 Determination of Element and Its Concentration in Watts Bath
3.2 Cyclic Voltammetry
3.3 Electrowinning Process
4 Conclusion
References
Bio-based Packaging Materials for Fruit and Vegetables-Current Applications and Future Trends: A Review
1 Introduction
2 Bio-based Polymer
2.1 Polymers Extracted from Biomass
2.2 Animal Derived Biopolymer
2.3 Synthetic Polymers from Biomass Monomers
3 Applications of Bio-based Fruit and Vegetable Packaging
3.1 Films and Coatings
4 Conclusions
References
Deep Eutectic Solvent-Assisted Synthesis of Nanocrystalline Cellulose Adsorbent for Silver Nitrate Removal
1 Introduction
2 Materials and Methods
2.1 Synthesis of Acidic Deep Eutectic Solvent
2.2 Extraction and Production of Nanocrystalline Cellulose Using Acidic DES
2.3 Fourier Transform Infrared Spectroscopy (FTIR)
2.4 Biosorption Isotherm and Kinetic Studies
3 Results and Discussion
3.1 Synthesis of Acidic Deep Eutectic Solvent (DES)
3.2 Production and Characterization of Nanocrystalline Cellulose Using Acidic DES
3.3 Evaluation of Silver Nitrate Biosoprtion Isotherm and Kinetics
4 Conclusion
References
Protein Extraction of Momordica Charantia Seed Assisted by Ultrasound Extraction
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Methods
3 Results and Discussion
3.1 Effect of Studied Parameter Against Protein Extraction
4 Conclusion
References
Intensification of Antioxidant-Rich Extract from Moringa Oleifera Leaves Using Different Solvents: Optimization and Characterization
1 Introduction
2 Materials and Chemicals
2.1 Raw Material Preparation
2.2 Extraction of Plant Using Soxhlet Extraction Method
2.3 Determination of Pythochemical Analysis
2.4 Statistical Analysis
2.5 Fourier Transform Infrared Spectroscopy (FTIR)
3 Results and Discussion
3.1 Optimization of the Solvent Extraction Method Using Central Composite Design
3.2 Total Phenolic Content (TPC)
3.3 Total Flavonoid Content (TFC)
3.4 Fourier Transform Infrared Spectroscopy (FTIR)
4 Conclusions
References
Effect of Glycerol as Plasticizing Agent on the Mechanical Properties of Polyvinyl Alcohol/Banana Peel Powder Blended Film
1 Introduction
2 Methodology
2.1 Materials
2.2 Synthesis of PVA/BPP Blended Film Using Solution Casting Method
3 Characterization of BPP and PVA/BPP Blended Film
3.1 Attenuated Total Reflectance Spectroscopy (ATR)
3.2 Thermogravimetric Analysis (TGA)
3.3 Determination of Tensile Properties of PVA/BPP Blended Film
3.4 Determination of Biodegradability of PVA/BPP Blended Film
4 Results and Discussion
4.1 Attenuated Total Reflectance Spectroscopy (ATR)
4.2 Thermogravimetric Analysis (TGA)
4.3 Tensile Properties
4.4 Biodegradability Test
5 Conclusion
References
Pharmacognostic Evaluation of Zingiber Officinale and Curcuma Longa from Perlis for Therapeutic Discovery
1 Introduction
2 Experimental
2.1 Preparation of Raw Material
2.2 Quality Control Analysis of C. Longa and Z. Officinale
2.3 Extraction Technique of C. Longa and Z. Officinale
2.4 Phytochemicals Screening of C. Longa and Z. Officinale
2.5 Antioxidant Assay of C. Longa and Z. Officinale Extract
3 Results
4 Discussion
5 Conclusion
References
Characterization of Valorized Pinewood Sawdust to Engineered Activated Biochar
1 Introduction
2 Methodology
2.1 Preparation of Honeycomb-Like Tubular Biochar
2.2 Characterization
3 Results and Discussion
3.1 SEM Analysis
3.2 FTIR Analysis
3.3 EDS Analysis
4 Conclusion and Recommendations
5 Footnote
5.1 Competing Interests
5.2 Funding
References
Profiling of Bioactive Compounds and Bioactivity of the Kenaf (Hibiscus Cannabinus L.) Leaf Extract
1 Introduction
2 Methodology
2.1 Sample Preparation
2.2 Moisture Content Analysis
2.3 Extraction Method
2.4 Total Phenolic Content (TPC)
2.5 Total Flavonoid Content (TFC)
2.6 Antioxidant Activity (2,2-Diphenyl-1-Picrylhydrazyl/DPPH Assay)
2.7 Antimicrobial Activity
2.8 Statistical Analysis
3 Results and Discussion
3.1 Moisture Content
3.2 Total Phenolic Compound (TPC) and Total Flavonoid Content (TFC)
3.3 Antioxidant Activity
3.4 Antimicrobial Activity
4 Conclusion
References
Preparation and Characterization of Cellulose Acetate from Rice Straw
1 Introduction
2 Experimental
2.1 Materials
2.2 Isolation of Cellulose
2.3 Acetylation of Cellulose
2.4 Characterization
3 Results and Discussion
3.1 Physical–Chemical Properties
3.2 Thermal Properties
4 Conclusion
References
Green Synthesis and Characterization of Graphene Quantum Dots from Key Lime Juice
1 Introduction
2 Experimental
2.1 Materials
2.2 Preparation of Graphene Quantum Dots (GQDs)
2.3 Characterization
3 Results and Discussion
4 Conclusion
References
Optimization of an Ultrasound-Assisted Extraction Method for Phenolic Content in Momordica Charantia Seeds and Its Antifungal Activity Against Pleurotus Ostreatus Green Mold Pathogen
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Ultrasound-Assisted Extraction Process
2.3 Experimental Design
2.4 Total Phenolic Content (TPC) Analysis
2.5 Antifungal Activity Assay
2.6 Statistical Analysis
3 Results and Discussion
3.1 Optimization of Ultrasound-Assisted Momordica Seed Extraction
3.2 Antifungal Activity Assay
3.3 Mycelial Growth Inhibition
4 Conclusion
References
Optimization of Rice Bran Protein Extraction Using Choline Chloride-Glycerol Deep Eutectic Solvent Using Response Surface Methodology (RSM)
1 Introduction
2 Methodology
2.1 Sample Preparation
2.2 Synthesis and Characterization of Deep Eutectic Solvents (DESs)
2.3 Extraction of Protein
2.4 Optimization of Protein Extraction
2.5 Characterization of DES and Protein
3 Results and Discussion
3.1 Characterization of DES
3.2 Response Surface Methodology
3.3 Effect of Parameter on Rice Bran Protein Yield
3.4 Optimization of Protein Yield
3.5 Validation of Data
3.6 Characterization of Protein
4 Conclusion
References
Bioeconomy Sustainability, Impacts and Policies
Traditional Paddy Farmers’ Perception of Bioeconomy Social Change on Adapting Internet of Things for Precision Farming
1 Introduction
2 Bioeconomy Social Change
3 Traditional Paddy Farmers’ Perception on Bioeconomy Social Change
3.1 Muda Agricultural Development Authority (MADA)
3.2 Internet of Things in Precision Agriculture—Drone Usage in Paddy Cultivation
4 Research Method and Findings
4.1 Data Collection and Data Analysis
4.2 Results and Discussion
5 Conclusion
References
Bioeconomy of Local Soybean Farming to Increasing Commodity Competitiveness
1 Introduction
2 Methodology
2.1 Research Action
2.2 Data Analysis
3 Result and Discussion
3.1 Profile of Soybean Farming in Indonesia
3.2 Competitiveness of Soybeans
3.3 Economic Valuation of Soybean Farming
3.4 Potential of Soybean Economic Benefits to Be Competitive
3.5 Policy Implications
4 Conclusion
References
Top Agricultural Commodities for Agropolitan Development in Nagan Raya District, Aceh, Indonesia
1 Introduction
2 Method of Research
2.1 Research Data
2.2 Analysis Method
3 Results and Discussion
3.1 Production of Agricultural Crops in Nagan Raya Regency (Descriptive Analysis)
3.2 LQ Analysis of Main Agricultural Commodities Nagan Raya Regency
3.3 Priority Analysis of Leading Commodity Development by Shift Share Analysis (SSA)
3.4 Priority for Leading Commodity Development
4 Conclusions
References
Spent Mushroom Medium Compost as a Soil Conditioner for the Initial Stage of Paddy Growth
1 Introduction
2 Material and Methodology
2.1 Preparation of Spent Mushroom Medium Compost
2.2 Preparation of Granular Urea
2.3 Preparation of Paddy Pot Plotting
3 Results and Discussions
3.1 Physico-Chemical Properties of SMM Compost and Granular Urea
3.2 Effect of SMM Compost and GU on Paddy Soil Characteristic
3.3 Effect of SMM Compost and GU on Paddy Growth
4 Conclusion
References
Bioenergy Integration
Microbial Fuel Cell: Simultaneous Bioremediation and Energy Recovery Technology
1 Introduction
2 Energy Production in Malaysia
3 Microbial Fuel Cell (MFCs)
3.1 Concept
3.2 Microbial Fuel Cell Design
3.3 Materials of Construction
3.4 Voltage Generation in MFCs
4 Substrates
4.1 Sludge
4.2 Chicken Manure
4.3 Fermentation Substrate for Electricity Generation by Microorganism
4.4 Acetate
4.5 Glucose
5 Lignocellulosic Biomass
5.1 Activated Sludge
5.2 Food Waste
6 Conclusion
References
Green Renewable Energy: Microbial Fuel Cell Technology
1 Background of Study
1.1 Non-Renewable Energy
1.2 Renewable Energy
1.3 Comparison Between Renewable Energy and Non-Renewable Energy
1.4 History of Microbial Fuel Cells (MFCs)
2 Microbial Fuel Cells (MFCs)
2.1 Concepts
2.2 Components for Construction of MFCs
2.3 Types of Microbial Fuel Cell (MFC) Design
2.4 Single Chamber MFCs
3 Double-Chamber MFCs
3.1 Stacked Microbial Fuel Cells (MFCs)
3.2 Comparison Between Microbial Fuel Cells (MFC) and Anaerobic Digester
4 Bacillus Subtilis as Electrogenic Bacteria in Microbial Fuel Cell
5 Substrates Used in MFCs
5.1 Agriculture Waste
5.2 Lignocellulose Biomass
5.3 Poultry Waste
6 Pre-treatment of Substrate to Improve the Performance of MFCs
6.1 Thermal Pre-treatment
6.2 Alkaline Pre-treatment
6.3 Sonication Pre-treatment
7 Conclusion
References
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Green Energy and Technology
Hafiza Shukor · Hairul Nazirah Abdul Halim · Hui Lin Ong · Boon-Beng Lee · Mohd Hanif Mohd Pisal Editors
Emerging Technologies for Future Sustainability Proceedings of the 2nd International Conference on Biomass Utilization and Sustainable Energy; ICoBiomasSE 2022; 20–21 Sept., Malaysia
Green Energy and Technology
Climate change, environmental impact and the limited natural resources urge scientific research and novel technical solutions. The monograph series Green Energy and Technology serves as a publishing platform for scientific and technological approaches to “green”—i.e. environmentally friendly and sustainable—technologies. While a focus lies on energy and power supply, it also covers “green” solutions in industrial engineering and engineering design. Green Energy and Technology addresses researchers, advanced students, technical consultants as well as decision makers in industries and politics. Hence, the level of presentation spans from instructional to highly technical. **Indexed in Scopus**. **Indexed in Ei Compendex**.
Hafiza Shukor · Hairul Nazirah Abdul Halim · Hui Lin Ong · Boon-Beng Lee · Mohd Hanif Mohd Pisal Editors
Emerging Technologies for Future Sustainability Proceedings of the 2nd International Conference on Biomass Utilization and Sustainable Energy; ICoBiomasSE 2022; 20–21 Sept., Malaysia
Editors Hafiza Shukor Faculty of Chemical Engineering and Technology Universiti Malaysia Perlis Arau, Perlis, Malaysia Hui Lin Ong Faculty of Chemical Engineering and Technology Universiti Malaysia Perlis Arau, Perlis, Malaysia
Hairul Nazirah Abdul Halim Universiti Malaysia Perlis Arau, Perlis, Malaysia Boon-Beng Lee Faculty of Chemical Engineering and Technology Universiti Malaysia Perlis Arau, Perlis, Malaysia
Mohd Hanif Mohd Pisal Faculty of Chemical Engineering and Technology Universiti Malaysia Perlis Arau, Perlis, Malaysia
ISSN 1865-3529 ISSN 1865-3537 (electronic) Green Energy and Technology ISBN 978-981-99-1694-8 ISBN 978-981-99-1695-5 (eBook) https://doi.org/10.1007/978-981-99-1695-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Organization
Organization Secretary Ku Syahidah Ku Ismail, Universiti Malaysia Perlis, Malaysia
Technical Chairs Hafiza Shukor, Universiti Malaysia Perlis, Malaysia Hairul Nazirah Abdul Halim, Universiti Malaysia Perlis, Malaysia Hui Lin Ong, Universiti Malaysia Perlis, Malaysia Mohd Hanif Mohd Pisal, Universiti Malaysia Perlis, Malaysia Boon-Beng Lee, Universiti Malaysia Perlis, Malaysia
Technical Reviewers Safa Senan Mahmod, Universiti Malaysia Perlis, Malaysia Khairul Farihan Kasim, Universiti Malaysia Perlis, Malaysia Eng Giap Goh, Universiti Malaysia Terengganu, Malaysia Faqih Ahmad Shuhaili, Universiti Malaysia Perlis, Malaysia Norshah Aizat Shuaib, Universiti Malaysia Perlis, Malaysia Alina Rahayu Mohamed, Universiti Malaysia Perlis, Malaysia Ismariza Ismail, Universiti Malaysia Perlis, Malaysia Kai Ling Yu, Universiti Tenaga Nasional, Malaysia Khairuddin Md Isa, Universiti Malaysia Perlis, Malaysia Nabilah Aminah Lutpi, Universiti Malaysia Perlis, Malaysia Noor Ainee Zainol, Universiti Malaysia Perlis, Malaysia Noor Hasyimah Rosman, Universiti Kebangsaan Malaysia, Malaysia
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Nor Hidawati Elias, Universiti Malaysia Perlis, Malaysia Nurul Ashraf Razali, Universiti Malaysia Terengganu, Malaysia Sharifah Hanis Yasmin Sayid Abdullah, Universiti Sultan Zainal Abidin, Malaysia Umi Fazara Md Ali, Universiti Malaysia Perlis, Malaysia Wan Hasnidah Wan Osman, Universiti Malaysia Kelantan, Malaysia Ahmad Ilyas Rushdan, Universiti Teknologi Malaysia, Malaysia Chuan Li Lee, Universiti Putra Malaysia, Malaysia Khalisanni Khalid, Malaysian Agricultural Research and Development Institute, Malaysia Mohd Asmadi Mohammed Yussuf, Universiti Teknologi Malaysia, Malaysia Noor Hasyierah Mohd Salleh, Universiti Malaysia Perlis, Malaysia Norazilah Abdul Halif, Universiti Malaysia Perlis, Malaysia Nurfatimah Mohd Thani, Universiti Kebangsaan Malaysia, Malaysia Razi Ahmad, Universiti Malaysia Perlis, Malaysia Reymark Maalihan, Batangas State University, Philippines Sharifah Shahnaz Syed Bakar, Universiti Malaysia Perlis, Malaysia Stephanie Yen San Chan, Curtin University Malaysia, Malaysia Zafifah Zamrud, Universiti Malaysia Perlis, Malaysia Noormaizatul Akmar Ishak, Universiti Malaysia Perlis, Malaysia Amira Mohd Nasib, Universiti Malaysia Perlis, Malaysia Azalina Mohamed Nasir, Universiti Malaysia Perlis, Malaysia Azrinawati Mohd Zin, Universiti Teknologi MARA, Malaysia Chin Leong Wooi, Universiti Malaysia Perlis, Malaysia Liza Bautista-Patacsil, University of the Philippines Los Baños, Philippines Mary Donnabelle Balela, University of the Philippines, Philippines Nik Muhammad Azhar Nik Daud, Universiti Malaysia Perlis, Malaysia Norliana Yusof, Universiti Sultan Zainal Abidin, Malaysia Nur Suhaili Mohd Yatim, Universiti Malaysia Perlis, Malaysia Nurhadijah Zainalabidin, Universiti Malaysia Perlis, Malaysia Nurliyana Ahmad Zawawi, Universiti Teknologi MARA, Malaysia Qian Yee Ang, Universiti Malaysia Perlis, Malaysia Rafizah Rahamathullah, Universiti Malaysia Perlis, Malaysia Rahimah Othman, Universiti Malaysia Perlis, Malaysia Rozaini Abdullah, Universiti Malaysia Perlis, Malaysia Shing Fhan Khor, Universiti Malaysia Perlis, Malaysia Siti Nur Aishah Mat Yusuf, Universiti Malaysia Perlis, Malaysia Tow Leong Tiang, Universiti Malaysia Perlis, Malaysia Yan Yan Farm, Universiti Malaysia Sabah, Malaysia Zunaida Zakaria, Universiti Malaysia Perlis, Malaysia
Preface
The 2nd International Conference on Biomass Utilization and Sustainable Energy in 2022 (ICoBiomasSE 2022) was held online due to COVID-19 restrictions, from 20 to 21 September 2022. By bringing up the theme “Emerging Technology for Future Sustainability”, the conference provided the setting for discussing recent developments in a wide variety of topics that include, but not limited to: • • • • • • • • •
Sustainable resources for decarbonizing the economy; Biomass technologies and conversion for bioenergy and biofuels; Biomass technologies and conversion to intermediate bioenergy carriers; Bioeconomy sustainability, impacts, and policies; Bioenergy integration; Biomass to biomaterials production; Bioseparation technologies in biomass conversion; Biomass for wastewater treatment; Artificial intelligence related to the topics above.
Notably, ICoBiomasSE 2022 is the second conference organized by the Centre of Excellence for Biomass Utilization (CoEBU), Universiti Malaysia Perlis (UniMAP) along with Taiwan–Malaysia Innovation Centre for Clean Water and Sustainable Energy (WISE), UniMAP, and UKM-YSD Chair for Sustainability, Universiti Kebangsaan Malaysia (UKM) as the co-hosts. The two-day conference had accepted a total of 45 papers through the peer-review process to make sure the interest, innovation, and application of the research are relevant to the theme brought by the conference. The editors would like to thank all participants for their contributions to ICoBiomasSE 2022. Also, the editors gratefully acknowledge the time and effort
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of the keynote speakers, reviewers, and committee members in making the conference a success. Special thanks from ICoBiomasSE 2022 committee members to Springer for the technical support. Arau, Malaysia
Hafiza Shukor Hairul Nazirah Abdul Halim Hui Lin Ong Boon-Beng Lee Mohd Hanif Mohd Pisal
Contents
Sustainable Biomass Resources for Decarbonising the Economy Phytoremediation Potential of Salvinia Molesta to Reduce Ni and Cd from Simulated Wastewater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nur Zaida Zahari, Malvin Julius, Fera Nony Cleophas, Farrah Anis Fadzliatul Adnan, Kamsia Budin, and Rohana Tair Soil Risk Assessment on the Usage of Molasses-Based Distillery Effluent for Paddy Irrigation: Heavy Metals Content . . . . . . . . . . . . . . . . . Nuratikah Ghazali, Ku Syahidah Ku Ismail, Roslaili Abd Aziz, Ahmad Radi Wan Yaakub, Md Nabil Ab Adzim Saifuddin, Nyvee Inthano, Ng Hock Hoo, and Ayob Katimon Effects of Soil Conditioners on Rice Growth and Soil Properties Under Water Stress at Vegetative Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mawaddah Saleh, Nurul Qistina Mohd Liza, Roslaili Abdul Aziz, Mohd Nazry Salleh, and Sahibin Abd Rahim Soil Amelioration Effects on Morphology Traits of Upland Rice Root–Shoot and Soil Productivity Under Water Deficit . . . . . . . . . . . . . . . . Mawaddah Saleh, Sangavi MohanRaj, Roslaili Abdul Aziz, Mohd Nazry Salleh, and Sahibin Abd Rahim
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Biomass Conversion Technologies for Bioenergy and Biofuels Comparison of Corn and Tapioca Starch Binders on the Characteristic of Rice Straw Charcoal Briquettes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Syed Nuzul Fadzli Syed Adam, Firuz Zainuddin, Noor Zulaika Salleh Morgan, and Hazmi Helmi Saroni
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Pretreatment of Leucaena Leucocephala Using Deep Eutectic Solvent for Ethanol Production by Kluyveromyces Marxianus UniMAP 1–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Zulhilmi Ishak, Khadijah Hanim Abdul Rahman, Ahmad Anas Nagoor Gunny, Habibollah Younesi, and Ku Syahidah Ku Ismail Deep Eutectic Solvent Pretreatment of Rubber Seed Shells for Cellulose and Hemicellulose Production . . . . . . . . . . . . . . . . . . . . . . . . . . Nur Zatul Iffah Zakaria, Norshakilla Afendi, Ahmad Anas Nagoor Gunny, Habibollah Younesi, and Ku Syahidah Ku Ismail Inhibition Study on the Growth of Clostridium Saccharoperbutylacetonicum N1-4 (ATCC 13564) for the Production of Biobutanol in ABE Fermentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muhd Arshad Amin, Hafiza Shukor, Noor Fazliani Shoparwe, Muaz Mohd Zaini Makhtar, Peyman Abdeshahian, and Sulaiman Olenrewaju Oladokun
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Thermogravimetric Analysis on Empty Fruit Bunch, Rice Husk, and Rice Straw for Feedstock in Biomass Gasification . . . . . . . . . . . . . . . . . 113 Nur Afiqa Syaheera Damahuri, Nurulnatisya Ahmad, Nor Fadzilah Othman, Ab Aziz Mohd Yusof, Kahar Osman, and Kamariah Md Isa A Review on Enhancement of Oil Palm Solid Waste Through Torrefaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Nur Rahimah Ibrahim, Razi Ahmad, and Mohd Azlan Mohd Ishak Energy Efficiency of Briquettes from Queen Pineapple (Ananas Comosus [Linn.] Merr.) Wastes Using Three Organic Binders . . . . . . . . . . 135 Michelle S. Carbonell, Al Rey C. Villagracia, Hui Lin Ong, and Ma. Kathrina M. Pobre Optimization of Biobutanol Production from Detoxified Palm Kernel Cake Hydrolysate by Clostridium Acetobutylicum YM1 . . . . . . . . . 147 Abdualati Ibrahim Al-Tabib, Rafidah Jalil, Hassimi Abu Hasan, and Mohd Sahaid Kalil Mixed Matrix Membrane (MMMs) as Membrane Based Separation Technology: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Kavita Pusphanathan, Hafiza Shukor, Noor Fazliani Shoparwe, Muaz Mohd Zaini Makhtar, Nor’ Izzah Zainuddin, and Nora Jullok Application of Machine Learning for Biogas Production from Lignocellulosic Biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Anuchit Sonwai and Patiroop Pholchan
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Biomass Conversion to Intermediates and Products Utilization of Spent Coffee Ground as Adsorbent for Nitrate Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Viga Rajiman and Hairul Nazirah Abdul Halim Nitrate Adsorption Using Spent Coffee Ground: Kinetics, Isotherm, and Thermodynamic Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Viga Rajiman, Hairul Nazirah Abdul Halim, and Lian See Tan Tamarind Seed Modified by CuFe Layered for Caffeine Removal from Aqueous Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Muhammad Azan Tamar Jaya, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad Synthesis of Pineapple Peel Based Activated Carbon Via Microwave Irradiation Technique for Methylene Blue Dye Removal . . . . 219 Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Iylia Idris, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad Preparation of Edamame Bean Pod Based Activated Carbon for Methylene Blue Dye Adsorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad Activated Carbon Adsorbent Using Desiccated Coconut Residue for Removing Methylene Blue Dye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad Synthesis of Microwave-Assisted Mango Peel Based Activated Carbon for Methylene Blue Dye Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad Optimization Study of Reactive Orange Dye Removal by Casuarina Equisetifolia Using Response Surface Methodology (RSM) . . . . . . . . . . . . 261 Muhammad Zal Ikram Muhamad Yusop, Mardawani Mohamad, Norzila Mohd, and Wan Hasnidah Wan Osman
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Utilization of Spent Mushroom Compost in Grey Oyster Mushroom Cultivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Zarina Zakaria, Thomas Teoh Chee Seng, Siti Nazrah Zailani, Khairul Akhbar Ahmad Zabidi, and Shahidol Kofli Salim Effect of Latex Coating on the Physical Properties of Calcium Alginate Beads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Yee-Ming Peh, Chee-Seng Lew, Boon-Beng Lee, Farizul Hafiz Kasim, Akmal Hadi Ma’Radzi, Md Nabil Ab Adzim Saifuddin, Ahmad Radi Wan Yaakub, and Mohd Asri Yusoff Screening and Optimization Biosynthesis of Iron Nanoparticle Using Watermelon Rind as Reducing and Stabilizing Agent . . . . . . . . . . . 289 Rozaini Abdullah, Nurul Fazliana Ahmad, Sharifah Zati Hanani Syed Zuber, and Noraini Razali Impact of Power Supply on Electro-Precipitation of Nickel Hydroxide from Industrial Electronic Waste . . . . . . . . . . . . . . . . . . . . . . . . . 303 Huzairy Hassan, Mismisuraya Meor Ahmad, Goh Xiu Hui, Muhammad Shazaril Amin Mohd Sabri, Maznah Ismail, and Umi Fazara Md Ali Optimization of Nickel Electrowinning from Simulated Watts Bath of Electronics Industrial Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Nurul Zufarhana Zulkurnai, Yap Mei Hua, Umi Fazara Md Ali, Mohd Irfan Hatim Mohamed Dzahir, Naimah Ibrahim, and Fathiah Mohamed Zuki Bio-based Packaging Materials for Fruit and Vegetables-Current Applications and Future Trends: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Noor Shazwani Razman, Farizul Hafiz Kasim, Ahmad Anas Nagoor Gunny, Subash C. B. Gopinath, and Mohd. Asmadi Mohammed Yussuf Deep Eutectic Solvent-Assisted Synthesis of Nanocrystalline Cellulose Adsorbent for Silver Nitrate Removal . . . . . . . . . . . . . . . . . . . . . . 339 Ahmad Anas Nagoor Gunny, Nur Humairah Aminuddin, Azalina Mohamed Nasir, Raja Hasnida Raja Hashim, Mohd Faizal Ab Jalil, Mohamad Azlan Ahamad Seeni Pakir, Mohamed Mydin M. Abdul Kader, and Ateeq Rahman Protein Extraction of Momordica Charantia Seed Assisted by Ultrasound Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Muhamad Ikmal Sirozi, Noor Hasyierah Mohd Salleh, Zarina Zakaria, Norhidayah Abd Aziz, Siti Aminah Mohd Hassan, and Mohd Amin Zainal Abidin
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Intensification of Antioxidant-Rich Extract from Moringa Oleifera Leaves Using Different Solvents: Optimization and Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Monisha Devi Elan Solan Marimuthu, Rahimah Othman, Siti Pauliena Mohd Bohari, and Wei Jinn Ooi Effect of Glycerol as Plasticizing Agent on the Mechanical Properties of Polyvinyl Alcohol/Banana Peel Powder Blended Film . . . . . 375 Yee Ling Tan, Yi Peng Teoh, Zhong Xian Ooi, Siew Hoong Shuit, Qi Hwa Ng, Peng Yong Hoo, Sim Siong Leong, and Chong Yu Low Pharmacognostic Evaluation of Zingiber Officinale and Curcuma Longa from Perlis for Therapeutic Discovery . . . . . . . . . . . . . . . . . . . . . . . . . 391 Anwardi Jamil, Lim Pei San, Nik Muhammad Azhar Nik Daud, Mohd Asraf Mohd Zainudin, Mohd Qalani Che Kasim, Muhammad Izzat Ridzuan, Nurul Husna Khairuddin, and Amirul Ridzuan Abu Bakar Characterization of Valorized Pinewood Sawdust to Engineered Activated Biochar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Fanthagiro Rossi Stuart Majing, Yen San Chan, Inn Shi Tan, Yie Hua Tan, and Mohd Dinie Muhaimin Samsudin Profiling of Bioactive Compounds and Bioactivity of the Kenaf (Hibiscus Cannabinus L.) Leaf Extract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 Siti Zulaikha Mohd Shukri, Nik Muhammad Azhar Nik Daud, Amirul Ridzuan Abu Bakar, Siti Suriani Arsad, and Mohd Asraf Mohd Zainudin Preparation and Characterization of Cellulose Acetate from Rice Straw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Nur Atirah Afifah Sezali, Hui Lin Ong, Mohd Hanif Mohd Pisal, Nora Jullok, Maria Carla Manzano, Al Rey Villagracia, and Ruey-an Doong Green Synthesis and Characterization of Graphene Quantum Dots from Key Lime Juice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Nur Atirah Afifah Sezali, Siew Suan Ng, Hui Lin Ong, Mohd Hanif Mohd Pisal, Al Rey Villagracia, and Ruey-An Doong Optimization of an Ultrasound-Assisted Extraction Method for Phenolic Content in Momordica Charantia Seeds and Its Antifungal Activity Against Pleurotus Ostreatus Green Mold Pathogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Norhidayah Abd Aziz, Noor Hasyierah Mohd Salleh, Nur Umi Masjida Ahmad Fauzi, Zarina Zakaria, Azlina Harun Kamaruddin, Subash Chandra Bose Gopinath, Amira Farzana Samat, and Muhamad Ikmal Sirozi
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Optimization of Rice Bran Protein Extraction Using Choline Chloride-Glycerol Deep Eutectic Solvent Using Response Surface Methodology (RSM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Kamalea Amaleena Farhana Kamal Ramlee, Intan Nurfazliyana Muhammad Nor, Mohd Sharizan Md Sarip, Mohd Asraf Mohd Zainudin, Amirul Ridzuan Abu Bakar, Mohd Azizi Nawawi, and Nik Muhammad Azhar Nik Daud Bioeconomy Sustainability, Impacts and Policies Traditional Paddy Farmers’ Perception of Bioeconomy Social Change on Adapting Internet of Things for Precision Farming . . . . . . . . . 477 Noormaizatul Akmar Ishak, Mohd Fisol Osman, Ummi Naiemah Saraih, Syed Zulkarnain Syed Idrus, Nurulisma Ismail, Evawaynie Valquis Md Isa, and Syed Putera Syed Jamaluddin Bioeconomy of Local Soybean Farming to Increasing Commodity Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 Fachrur Rozi, Purwantoro, and Yanti Rina Darsani Top Agricultural Commodities for Agropolitan Development in Nagan Raya District, Aceh, Indonesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Abdul Latif, Abubakar Karim, Sugianto Sugianto, Romano, M. Faisi Ikhwali, and Muhammad Rusdi Spent Mushroom Medium Compost as a Soil Conditioner for the Initial Stage of Paddy Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Siti Nazrah Zailani, Jia Jun Ong, Zarina Zakaria, and Khairul Akbar Ahmad Zabidi Bioenergy Integration Microbial Fuel Cell: Simultaneous Bioremediation and Energy Recovery Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Kavita Pusphanathan, Melven Tuesday, Mohamad Farhan Mohamad Sobri, Muaz Mohd Zaini Makhtar, Noor Fazliani Shoparwe, Hafiza Shukor, and Nor’ Izzah Zainuddin Green Renewable Energy: Microbial Fuel Cell Technology . . . . . . . . . . . . 561 Melven Tuesday, Kavita Pusphanathan, Mohamad Farhan Mohamad Sobri, Muaz Mohd Zaini Makhtar, Noor Fazliani Shoparwe, and Hafiza Shukor
Sustainable Biomass Resources for Decarbonising the Economy
Phytoremediation Potential of Salvinia Molesta to Reduce Ni and Cd from Simulated Wastewater Nur Zaida Zahari, Malvin Julius, Fera Nony Cleophas, Farrah Anis Fadzliatul Adnan, Kamsia Budin, and Rohana Tair
Abstract The objective of this study was to investigate the potential of S. molesta as phytoremediation agent to reduce heavy metals Ni and Cd from simulated wastewater. S. molesta was collected from Tuaran, Sabah, cleaned and acclimatised in Faculty of Science & Natural Resources (FSNR) lake water and mixed with different concentrations of Ni and Cd at 1 and 3 mg/L, respectively. The concentrations of Ni and Cd in simulated wastewater and tissue parts were analysed using Atomic Absorption Spectrometer (AAS). The effects of increasing concentrations of Ni and Cd on the removal efficiency, the distribution of heavy metal patterns in plant tissues, the bioconcentration factor (BCF), translocation factor (TF), relative treatment efficiency index (RTEI) and tolerance index (TI) were investigated. The results showed that S. molesta is efficient in removing Ni and Cd at 1 mg/L with a removal efficiency of 87.34 and 93.55%, respectively. The RTEI value is in the range of 0.77–0.81. It was also found that S. molesta managed to accumulate Cd in its roots up to 1400 mg/kg at 1 mg/L concentration. The high accumulation of heavy metals in roots and low accumulation in the shoot suggested that rhizofiltration is the main phytoremediation mechanism for Ni and Cd. This plant revealed to tolerate Ni and Cd at 1 mg/ L concentration with TI range of 0.99–1.02 and minimal physical changes. It was
N. Z. Zahari (B) · M. Julius · K. Budin · R. Tair Faculty of Science and Natural Resources, Universiti Malaysia Sabah, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia e-mail: [email protected] K. Budin e-mail: [email protected] R. Tair e-mail: [email protected] F. N. Cleophas · F. A. F. Adnan Small Islands Research Center, Universiti Malaysia Sabah, UMS Road, Kota Kinabalu, 88400 Sabah, Malaysia e-mail: [email protected] F. A. F. Adnan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_1
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suggested that the use of S. molesta can be considered as a suitable candidate for heavy metals pollution in water. Keywords Phytoremediation · Salvinia molesta · Heavy metals · Bioconcentration factors · Translocation factors · Relative efficiency treatment index · Tolerance index
1 Introduction Due to the rapid development, contamination of the aquatic environment has become a serious problem. Untreated wastewater from industries such as metal processing, mining and automotive contain heavy metals that can easily accumulate, attributable to the persistence characteristic of heavy metals towards degradation in the environment [1]. The discharge of heavy metals into water bodies can have a harmful impact towards the aquatic ecosystem and consequently, human health [2]. Nickel and cadmium are among the commonly found heavy metals in water due to their commercial uses. There have been numerous reports on the effects of nickel and cadmium exposure in aquatic organisms. For instance, a study by Nabinger et al. [3] found that exposure to nickel at 0.025 mg L−1 resulted in lower heart rate, delayed hatching, and morphological changes in zebrafish. Meanwhile, cadmium according to Hui Zhang et al. [4] can cause spinal deformities in embryo. Health effects are also not limited to humans, capable to be fatal if prolonged or excess exposure [5, 6]. Therefore, it is of utmost importance that an effective wastewater treatment system is in place before discharging it to the environment. Various types of conventional treatment exist but often have high energy requirement, carbon emission and high maintenance cost [7]. Phytoremediation, the application of plants to remove pollutants in water has the potential to be an alternative to the conventional treatment system with lower energy and maintenance cost. It consists of several different mechanisms, namely, phytoextraction, phytostabilisation, rhizofiltration and phytovolatilisation, with each having its own advantages [8]. Free-floating aquatic plants’ availability, high yield, and ease of harvesting make them the most suitable to be used in phytoremediation. Thus, different species of aquatic plants have been employed with varying degrees of success [2]. Aquatic plants such as Pistia stratiotes [9], Salvinia molesta, Lemna spp., Azolla pinnata, Landoltia punctata, Spirodela polyrhiza, Marsilea mutica, Eichhornia crassipes and Riccia fluitans are frequently used in phytoremediation [10]. S. molesta is a free-floating plant native to south-eastern Brazil that has shown great efficiency for treating water due to its rapid growth, tolerance, and high capacity for removing certain pollutants even against other macrophytes such as E. crassipes and P. stratiotes [11]. In Sabah, it is recognised as an Invasive Alien Species that has infested many natural lakes, forcing the local authorities to seek biological control to curb the growth [12]. The present study investigation demonstrates phytoremediation
Phytoremediation Potential of Salvinia Molesta to Reduce Ni and Cd …
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potential of Salvinia molesta exposed to increasing concentrations of Ni and Cd in simulated wastewater. The effect and accumulation of these metals on tissues part with reference to bioconcentration factor (BCF) and translocation factor (TF), relative treatment efficiency index (RTEI) and tolerance index (TI) were studied. Knowledge on utilising the invasiveness trait can be valuable in treating large water bodies and thus, this research can contribute to evaluating whether S. molesta is suitable for phytoremediation.
2 Materials and Methods 2.1 Plant Sampling S. molesta was collected in a pond at Jalan Topokon Wangkod, Tuaran, Sabah (6°11' 41.9" N, 116°19' 32.0" E). The collected plants were rinsed with tap water to remove debris and then acclimated and cultivated in a tank (38 × 25 × 11) cm with FSNR’s lake water. The plant was placed in outdoor condition under natural sunlight exposure period for 14 days, whereby new shoot growth was observed based on the previous study [9] (Fig. 1).
Fig. 1 Sampling area of S. molesta at Tuaran, Sabah
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2.2 Experimental Design Fresh weight 100 g of plants was used in different sets of experiments, which includes Ni and Cd metals at varying concentration levels, 1 and 3 mg/L which far exceeds any permissible standard in water. Observation on the plants’ physical appearance was done during the experiment period to assess its tolerance level and toxicity symptoms. The pH, conductivity, nutrients, and heavy metals content in the FSNR’s water were analysed before carrying out the experiment to determine the control perimeter. The experiment was performed by growing 100 g fresh weight of S. molesta in vertical reactor. The tank was filled with 20 L of FSNR lake water and heavy metals Ni, and Cd were added at different concentration levels as mentioned. The experiment was performed in duplicate to evaluate the ability of S. molesta in removing the heavy metals. Control experiments were carried out in which the first tank is filled with simulated wastewater and heavy metals only, and another tank is filled with S. molesta and FSNR’s lake water only (Fig. 2).
Experiment I
Experiment II
Control 1 and 3 mg/L of Ni
1 and 3 mg/L of Cd
Fig. 2 Illustration of experimental design on heavy metals removal by S. molesta
Phytoremediation Potential of Salvinia Molesta to Reduce Ni and Cd …
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2.3 Water and Plant Sample Collection Polypropylene bottles soaked with 0.5 N of HCl and rinsed with deionised water were used to collect and store the water sample for heavy metal analysis as recommended by APHA 3030B. Water samples were collected throughout the 14 days in duplicates to obtain average. For plant samples, different parts of the plant were collected for analysis for the duration before and after the 14 days experiment. The plant samples were cut into different parts, which are roots and shoots, then dried at 70 °C for 24 h. After dried, the plant was powdered using an agate mortar and 0.2 g of the powdered sample was used for wet digestion.
2.4 Determination of Heavy Metals Concentration The preparation of the simulated wastewater for heavy metal analysis was based on APHA 3030B standard method whereby, a 0.45 µm pore size membrane filter (Whatman 47 mm diameter) prewashed with 50 mL deionised water was used to filtrate the water samples. A water sample was collected and filtered through a membrane filter using a vacuum pump. Next, the filtrate was acidified to pH 2 using concentrated HNO3 and stored in an acid-rinsed polyethylene bottle at 4 °C prior to analysis. The heavy metal concentration was analysed using AAS [13]. The digestion of S. molesta followed the procedures instructed in USEPA method 3050B due to its usage validity on the selected heavy metals by previous studies [14]. Dried powdered sample of 0.2 g from each part of the plant was transferred into a conical flask that act as a digestion vessel. Then, 5 mL of concentrated HNO3 was added into the flask and refluxed for 30 min at 95 °C. To minimise contamination and high metal losses due to evaporation, the sample was covered with watch glass. The presence of dark vapours suggests that the sample has been oxidised. 5 mL of concentrated HNO3 was added while heating until the effervescence from the sample subside which signals the completion of digestion. Then, the plant sample is allowed to continue to evaporate until the volume of the sample is around 5 mL while covered with glass lid to prevent contamination and loss of heavy metals. The conical flask was washed with distilled water and filtered through a 0.45 µm pore size membrane filter (Whatman 47 mm diameter) into a 100 mL volumetric flask, followed by dilution using distilled water until the mark. The sample was transferred into a polyethylene bottle and stored at 4 °C for analysis using AAS.
2.5 Data Analysis The phytoremediation potential of S. molesta was evaluated using several parameters and as guided below. The evaluation includes observation of physical changes and
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analysis of both nickel and cadmium content in the medium and plant. The results were reported as a mean of duplicate since the experimental data were collected twice. Standard deviation was calculated using Microsoft Excel to determine the precision of the data collected by measuring the dispersion of the value from the mean. Pearson’s correlation coefficient was used to determine if there is any relationship between the initial concentration and the removal efficiency, BCF and TF values. Microsoft Excel was used to compute the r value with degree of freedom of 2. Removal Efficiency of Heavy Metals by S. molesta The efficiency of S. molesta in removing heavy metals from wastewater was evaluated in percentage using the formula below [15]. Removal efficiency (%) =
C0 - C1 × 100% C0
(1)
where C0 : Initial concentration of heavy metal in simulated wastewater. C1 : Final concentration of heavy metal in simulated wastewater. Relative Treatment Efficiency Index (RTEI) Relative treatment efficiency index is an index proposed by Marchand et al. [16] to assess the effect of the treatment on the removal of heavy metal to the control experiment as used by some phytoremediation studies [9]. RTEI value ranges from 1 to −1 by which a value near 1 indicates improvement in removal by the treatment, 0 indicates no effect on the treatment and the value approaching −1 shows inhibition on the removal of heavy metals. RTEI =
(T - C) (T + C)
(2)
where T: Removal efficiency of treatment experiment. C: Removal efficiency of control experiment. Distribution of Heavy Metals in Plant Parts S. molesta was collected on day 0 and day 14 of the experiment to analyse the concentration of heavy metals distributed in selected parts of the plant (shoot and roots) using the formula derived by Uwah et al. [17] Concentration (mg/kg) = where
Concentration(mg/L) × V M
(3)
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V: Final volume of solution after digestion, L. M: Weight of sample measured, kg. Tolerance Index (TI) During the experiment, any changes to the plant such as yellowing and wilting were monitored and photographed to measure its tolerance and toxicity symptoms to different heavy metals and concentrations. The observation was recorded and summarised in the table to show the effect of each heavy metal treatment throughout the experiment period. Tolerance index was calculated to assess the ability of the plant to grow in the presence of heavy metals to the control experiment which is the plant without the presence of heavy metal based on the formula derived by Wilkins [18] that has been used in multiple studies in assessing plant’s tolerance towards heavy metal toxicity [19]. Tolerance Index (%) =
Dry weight of treated plant (g) × 100% Dry weight of control plant (g)
(4)
Determination of the Phytoremediation Mechanism Using Indices (Translocation Factor and Bioconcentration Factor) The TF value, also known as shoot–root quotient, measures the ability of a plant, in this case, S. molesta to translocate the metal from roots through shoots as a ratio [20]. As for BCF value, it measures the ratio of the concentration of heavy metal in the plant to the heavy metal concentration in the medium, that is the simulated wastewater for this experiment [20]. Translocation Factor (TF) =
Cshoot Croot
(5)
where Cshoot : Concentration of heavy metal in shoot or aerial part of the plant, mg/kg. Croot : Concentration of heavy metal in root, mg/kg. Bioconcentration Factor (BCF) =
Cplant Cwater
where Cplant : Concentration of heavy metal in the plant, mg/kg. Cwater : Concentration of heavy metal in water, mg/kg.
(6)
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3 Results and Discussions 3.1 Removal Efficiency of Ni and Cd by S. Molesta The variation of heavy metal Ni and Cd concentration vs time in treatment reactor with S. molesta for concentration studied (1 and 3 mg/L) is shown in Fig. 3a–d. On the contrary, metal controls were recorded and plotted in the graph. The removal of Ni and Cd by S. molesta from simulated wastewater was studied over 14 days period. The results show that removal efficiency was recorded at 87.34 and 9.26% for Ni at 1 mg/L and 3 mg/L, while Cd removal efficiency was higher at 1 mg/L with 93.56% and 11.42% at 3 mg/L. On the contrary, heavy metals in control treatment decreased slightly over time due to the adsorption of the metals to the surface of the bioreactor which was similarly reported in another study by Ingole and Bhole [21]. Overall, the results showed that, with the increase in metal concentrations in the treatment, the removal efficiency was decreased due to the toxicity effect towards the plants. The removal efficiency for nickel is comparable to a study by Mohanty et al. [22], that achieved slightly lower 76% efficiency at 1.1 mg/L with only 4 g fresh weight compared to 100 g used in this study, while cadmium removal is comparable to study by Sreekumar and John [23] that removes 91.16% of cadmium, albeit at a lower initial concentration of 0.6 mg/L with 500 g fresh weight. Previous studies reported that Ni concentration above 1.5 mg/L has been found to affect biomass growth of S. molesta by as much as 50% through a study by Mohanty et al. [22], whilst Rolli [24] determined that 2 mg/L and above of Cd concentration inhibits the plant’s growth through reduction of carbohydrate content and increase in chlorophyll activity whereby, stimulation of chlorophyll synthesis is related to phytochelatins which itself is one of the major detoxification mechanism in plant since when cadmium enters the plant roots, it damages root system and morphology. The removal treatment efficiency index (RTEI) for the experiment is tabulated in Table 1. The index is used to quantify the actual plant effect on the heavy metal from the water by comparing the removal efficiency by the plant to the removal efficiency in plant-less control experiments. The index range from 1 to −1, where values close to 1 indicates a strong removal effect by plant, while nearing −1 shows inhibition of removal. A value of 0 means the plant has no effect. Based on Table 1, the RTEI value calculated ranges from 0.77 to 0.81. The highest RTEI is observed in Ni at 1 mg/L treatment, while Cd at 1 mg/L was the lowest among all. The discrepancy in control treatment may be due to external factors such as due to sedimentation, adsorption to clay particles and organic matter and co-precipitation with secondary minerals [25].
Phytoremediation Potential of Salvinia Molesta to Reduce Ni and Cd … 1.2 0.7 mg/L
Fig. 3 Residual metal concentrations (mg/L) against time (day) for removal of Ni and Cd by S. molesta at 1 mg/L (a–b) and 3 mg/L (c–d)
11
0.2 -0.3
0
1
2
3
4
5
T-Ni
6 7 Days
(a)
8
9 10 11 12 13 14
Control-Ni
mg/L
1.2 0.7 0.2 -0.3
0
1
2
3
4
5
T-Cd
6
7
Days
8
9 10 11 12 13 14
Control-Cd
mg/L
(b) 3.4 3.2 3 2.8 2.6 2.4 2.2 2 0
1
2
3
4
5
6 7 Days
T-Ni
8
9 10 11 12 13 14
Control-Ni
mg/L
(c) 3.4 3.2 3 2.8 2.6 2.4 2.2 2 0
1
2
3
4
5
6
7
8
9 10 11 12 13 14
Days T-Cd
Control-Cd
(d)
3.2 Distribution of Heavy Metals in S. Molesta Figure 4 shows the highest mean concentration in the root sample Cd at 1 mg/L (1473.55 mg/kg) followed by Ni (1270.03 mg/kg) at the same concentration. As for shoots, the highest mean concentration is Cd with 578.70 mg/kg at 3 mg/L
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Table 1 Removal Treatment Efficiency Index of each treatment studied Residual concentration (mg/ Removal efficiency (%) L
Treatment
Ni Cd
RTEI
Treatment
Control
Treatment
Control
1 mg/L
0.8655
0.0876
87.34
9.15
0.81
3 mg/L
0.2797
0.0323
9.26
1.02
0.80
1 mg/L
0.9255
0.1264
93.56
12.33
0.77
3 mg/L
0.3494
0.0442
11.42
1.43
0.78
mg/kg
concentration, while Ni recorded 367.12 mg/kg in 3 mg/L single treatment. The accumulation in roots for Ni (r = −0.7839) and Cd (r = −0.9636) shows a positive correlation, while, for shoot, both show positive correlation Ni (r = 0.6939) and Cd (r = 0.6747). The noticeable decrease in accumulation in the roots part in sample Ni at 3 mg/L concentration might be due to the plants were harvested when the roots already showed signs of decaying, causing the accumulated heavy metals in roots to leach. This result is in line with Mohanty et al. [22] whereby the accumulation of Ni in S. molesta was increasing until 1.3 mg/L concentration, as at 1.5 mg/L concentration whereby the biomass gain is reduced, the accumulated Ni is lower (Fig. 4). In other treatments, Cd had the highest mean accumulation in roots for both concentrations that exceeds 500 mg/kg, in accord with the removal efficiency performance. Oppositely, Cd accumulation in shoot is noticeably lower than Ni at 1 mg/ L. This suggests that the root is acting as a barrier against mobilisation of Ni and Cd to other parts of the plant, translocation is often restricted between root system and shoot system to protect the aerial parts. Such movement restriction can be caused by the formation of complexes between heavy metals and chelators, which are then immobilised in either extracellular or intracellular spaces of the root. Besides, the alteration of physicochemical characteristics of water body by aquatic macrophytes, including S. molesta can affect the bioavailability of the heavy metals for the uptake, 1600 1400 1200 1000 800 600 400 200 0
Root Shoot
1
3 Ni
1
3 Cd
concentrations (mg/L)
Fig. 4 Mean concentration of Ni and Cd in root and shoot of S. molesta
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thus increasing the accumulation on roots [26, 27]. It can be concluded that the concentration is mostly greater in root.
3.3 Bioconcentration Factor and Translocation Factor Bioconcentration factor was used to determine the plant’s ability to accumulate heavy metals. A BCF value over 1000 is required to be considered as a hyperaccumulator [28]. Based on the data shown in Fig. 5, the highest BCF in root is Cd (1438) followed by Ni (1327) in 1 mg/L. The results showed that S. molesta is a hyperaccumulator for Ni and Cd at a low concentration of 1 mg/L for root, suggesting S. molesta is a hyperaccumulator plant. As for BCF in shoot, the highest value was recorded in treatment Ni with 328 respectively. For translocation factor, the results showed that the value of TF ranges from 0.11 to 0.96. The highest was found in Ni at 3 mg/L, while the lowest is in Cd at 1 mg/L. Experiment conducted by Shingadgaon & Chavan [29], calculated a TF of 0.722 and 0.005 for Ni and Cd in S. molesta, respectively, while Wickramasinghe & Jayawardana [30], show a TF value of 0.3 for both. So, the TF at 1 mg/L treatments are in line with previous studies, while the high TF at 3 mg/L, are likely due to the accumulated metals leached which causes the shoots to have higher accumulation. Based on the distribution of heavy metal, BCF and TF values in 1 mg/L, it suggests that the phytoremediation mechanism of Ni and Cd by S. molesta is rhizofiltration. The high accumulation in roots and low accumulation in the shoot indicates that the metals are absorbed into the roots for it to be translocated to the upper parts instead of adsorbed [28]. This finding is similar to Rachmadiarti & Trimulyono [31] that concludes S. 1600
1.4
1400
1.2
1200
BCF
0.8
800
0.6
600
0.4
400
0.2
200 0
1
3
1
Ni
3
Cd concentration (mg/L)
BCF (root)
BCF (shoot)
TF
Fig. 5 Mean BCF value (root and shoot) and translocation factor for Ni and Cd
0.0
TF
1.0
1000
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molesta accumulates lead mainly in root and low accumulation in shoots. The BCF and TF values are shown in Fig. 5.
3.4 Tolerance Index and Toxicity Symptoms of S. Molesta Exposed to Ni and Cd The tolerance of the plant towards exposure to heavy metals treatment was assessed through observing physical changes in plants and determining the tolerance index. Tolerance Index as mentioned compares the dry weight of plant after exposure to heavy metals to plants that grew without heavy metal exposure. Value equal or more than 1 indicates the plant capacity to tolerate the heavy metal toxicity, while less than 1 indicates possible inhibition of growth by the exposure. As shown in Table 2, treatment exposed to 1 mg/L concentration of Ni and Cd showed the TI value was in the range of 0.99–1.02, indicating that the plant growth was not inhibited by the Ni and Cd exposure. As for 3 mg/L, the TI for both metals is 0.96. The plants cultured with 3 mg/L were harvested when residual concentration began to rise back instead of decreasing, an indication the plant has reached its limit tolerating the toxicity symptoms. In addition, signs of decaying roots and fragile leaves were also observed. Reduction in S. molesta root length and leaf size has been studied when exposed to 2 mg/L of Ni over 12 days compared to controls, indicating reduced new biomass growth due to toxic effects [23]. Examples of toxicity symptoms observed during the phytoremediation are shown in Fig. 6. Table 2 Toxicity symptoms and tolerance index of S. molesta observed within 14 days experiment
Experiment
Toxicity symptoms observed within 14 days experiment
Tolerance index
Day 7
Day 14
Ni 1 mg/L
No changes
No changes
1.02
Ni 3 mg/L
Root fragmented
−
0.96
Cd 1 mg/L
No changes
Leaves yellowing
0.99
Cd 3 mg/L
Shoots fragmented Roots fragmented
−
0.96
Phytoremediation Potential of Salvinia Molesta to Reduce Ni and Cd …
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Fig. 6 Toxicity symptoms of S. molesta exposed to a Cd at 1 mg/L; b Ni at 3 mg/L and c Cd at 3 mg/L
4 Conclusions The results show that, in low concentration of 1 mg/L, the plant removes up to 87% of Ni and Cd (93%) from the water body. Overall, the accumulation of heavy metal mainly occurs in roots as compared to shoots. Cd has the highest mean accumulation in roots (1473 mg/kg) as compared to shoot. Thus, the accumulation in the roots is in line with their respective removal efficiency. Consequently, the plant is a good accumulator for both metals and treatments at 1 mg/L with a value BCF of more than 1000 in roots. The TF value for all treatments except for Ni at 3 mg/L is below 1, in line with previous phytoremediation studies. Based on the heavy metal distributions, BCF and TF, the phytoremediation mechanism is suggested to be rhizofiltration. Moreover, the plant was able to tolerate when exposed to Ni and Cd at 1 mg/L concentration. However, S. molesta shows some toxicity symptoms with yellowing leaves and decaying roots and it is proved with the decreasing removal efficiency and lower TI value. Acknowledgements We would like to express our gratitude to the Faculty of Science & Natural Resources, Universiti Malaysia Sabah (UMS) for providing the necessary facilities to complete this study successfully.
References 1. Ahmed G, Takuwa D, Chibua IT, Bagai Z, Morekisi L, Shoniwa H, Sethebe B, Sichilongo K (2016) Comparison of new ultrasonic digestion approaches for plant matrices in the analysis of trace metals by inductively coupled plasma-optical emission spectroscopy analysis: contrast with USEPA method 3050B. Commun Soil Sci Plant Anal 47(4):512–520 2. Ansari AA, Naeem M, Gill SS, AlZuaibr FM (2020) Phytoremediation of contaminated waters: An eco-friendly technology based on aquatic macrophytes application. Egypt J Aquat Res 46(4):371–376
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3. Ekperusi AO, Sikoki FD, Nwachukwu EO (2019) Application of common duckweed (Lemna minor) in phytoremediation of chemicals in the environment: State and future perspective. Chemosphere 223:285–309 4. Genchi G, Carocci A, Lauria G, Sinicropi MS, Catalano A (2020) Nickel: Human health and environmental toxicology. Int J Environ Res Public Health, 17(3). 5. Ingole NW, Bhole AG (2002) Removal of heavy metals from aqueous solution by water hyacinth (Eichhorbia crassipes). J Water Supply: Res Technol-Aqua (2003), 52 (2): 119–118 6. KOPEL (2020) Environment monitoring report 2019. http://www.forest.sabah.gov.my/pin supu/PDF/Publication/13. KOPEL Conservations Project 2019 Report V5 (MPV July 01 2020).pdf 7. Marchand L, Mench M, Jacob DL, Otte ML (2010) Metal and metalloid removal in constructed wetlands, with emphasis on the importance of plants and standardized measurements: A review. Environ Pollut 158(12):3447–3461 8. Mishra VK, Tripathi BD (2008) Concurrent removal and accumulation of heavy metals by the three aquatic macrophytes. Biores Technol 99(15):7091–7097 9. Mohanty M, Pradhan C, Satapathy KB (2021) Phytoremediation of aqueous solutions contaminated with Nickel (Ni) by exploitation of azolla microphylla kaulf & salvinia molesta mitchell: A novel bioseparation process for waste water treatmen, pp. 289–298 10. Mustafa HM, Hayder G (2021) Recent studies on applications of aquatic weed plants in phytoremediation of wastewater: A review article. Ain Shams Eng J 12(1):355–365 11. Mustafa HM, Hayder G (2021b) Cultivation of S. molesta plants for phytoremediation of secondary treated domestic wastewater. Ain Shams Eng J 12(3):2585–2592 12. Nabinger DD, Altenhofen S, Bitencourt PER, Nery LR, Leite CE, Vianna MRMR, Bonan CD (2018) Nickel exposure alters behavioral parameters in larval and adult zebrafish. Sci Total Environ 624:1623–1633 13. Naghipour D, Ashrafi SD, Gholamzadeh M, Taghavi K, Naimi-Joubani M (2018) Phytoremediation of heavy metals (Ni, Cd, Pb) by Azolla filiculoides from aqueous solution: A dataset. Data Brief 21:1409–1414 14. Nirola R, Megharaj M, Palanisami T, Aryal R, Venkateswarlu K, Naidu R (2015) Evaluation of metal uptake factors of native trees colonizing an abandoned copper mine—a quest for phytostabilization. J Sustain Min 14(3):115–123 15. Rachmadiarti F, Trimulyono G (2018) The efficacy of Salvinia molesta Mitch. and Marsilea crenata Presl. as phytoremediators of lead pollution. J Appl Hortic (www.Horticultureresear ch.Net) J Appl Hortic 20(1):48–51. www.horticultureresearch.net 16. Rahimzadeh MR, Rahimzadeh MR, Kazemi S, Moghadamnia AA (2017) Cadmium toxicity and treatment: An update. Caspian J Intern Med 8(3):135 17. Rai PK (2021) Heavy metals and arsenic phytoremediation potential of invasive alien wetland plants Phragmites karka and Arundo donax: Water-Energy-Food (W-E-F) Nexus linked sustainability implications. Bioresource Technology Reports 18. Razak MR, Aris AZ, Zakaria NAC, Wee SY, Ismail NAH (2021) Accumulation and risk assessment of heavy metals employing species sensitivity distributions in Linggi River, Negeri Sembilan, Malaysia. Ecotoxicol Environ Saf, 211 19. Rolli N (2015) Toxic effect of cadmium on aquatic macrophyte salvinia molesta. Int J Curr Res 04:14343–14347 20. Salt DE, Smith RD, Raskin I (1998) Phytoremediation. Annu Rev Plant Biol 49(1):643–668 21. Schwantes D, Gonçalves AC, da Schiller A, Manfrin APJ, Campagnolo MA, Veiga TG (2019) Salvinia auriculata in post-treatment of dairy industry wastewater. Int J Phytorem 21(13):1368– 1374 22. Shingadgaon SS, Chavan BL (2019) Evaluation of Bioaccumulation Factor (BAF), Bioconcentration Factor (BCF), Translocation Factor (TF) and Metal Enrichment Factor (MEF) Abilities of aquatic macrophyte species exposed to metal contaminated wastewater. Int J Innov Res Sci 8(1) 23. Sreekumar, A., & John, J. 2018. Removal of Pollutants from Pestilent Water using Selected Hydrophytes. Trends in Biosciences, 11(7), 1618–1621. https://www.cabdirect.org/cabdirect/ abstract/20203075439.
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24. Uwah EI, Gimba MSB, Gwaski PA (2012) Determination of Zn, Mn, Fe and Cu in spinach and lettuce cultivated in Potiskum, Yobe State. Nigeria 1:69–74 25. Wickramasinghe S, Jayawardana CK (2018) Potential of aquatic macrophytes eichhornia crassipes, pistia stratiotes and salvinia molesta in phytoremediation of textile wastewater. J Water Secur 4:1–8 26. Wilkins DA (1978) The measurement of tolerance to edaphic factors by means of root growth. New Phytol 80(3):623–633 27. Yan A, Wang Y, Tan SN, Mohd Yusof ML, Ghosh S, Chen Z (2020) Phytoremediation: A promising approach for revegetation of heavy metal-polluted land. Front Plant Sci 11:359 28. Zaida N, He JL, Sahibin AR, Vun LW, Fera NC (2021) Relative Treatment Efficiency Index of Eichhornia crassipes in Removing Cd, Pb and Ni from Wastewater. IOP Conf Ser: Mater Sci Eng, 1144(1) 29. Zhang H, Cao H, Meng Y, Jin G, Zhu M (2012) The toxicity of cadmium (Cd 2+) towards embryos and pro-larva of soldatov’s catfish (Silurus soldatovi). Ecotoxicol Environ Saf 80:258– 265 30. Zhang W, Pan X, Zhao Q, Zhao T (2021) Plant growth, antioxidative enzyme, and cadmium tolerance responses to cadmium stress in Canna orchioides. Hortic Plant J 7(3):256–266 31. Zhou Y, Li S, Shi Y, Lv W, Shen T, Huang Q, Li Y, Wu Z (2013) Phytoremediation of chromium and lead using water lettuce pistia stratiotes L.). Appl Mech Mater 401–403:2071–2075
Soil Risk Assessment on the Usage of Molasses-Based Distillery Effluent for Paddy Irrigation: Heavy Metals Content Nuratikah Ghazali, Ku Syahidah Ku Ismail, Roslaili Abd Aziz, Ahmad Radi Wan Yaakub, Md Nabil Ab Adzim Saifuddin, Nyvee Inthano, Ng Hock Hoo, and Ayob Katimon
Abstract Heavy metal contamination in the soil is becoming a serious issue for food safety and human health. This study aims to quantify the concentration of cadmium (Cd), chromium (Cr), and lead (Pb) in paddy soil before and after irrigation with molasses-based distillery effluent in Perlis, Malaysia. Samples of effluent together with soil samples from two sampling plots were collected and analyzed using an inductively coupled plasma mass spectrometry (ICP-MS). It was found that the heavy metals in the effluent used for irrigation did not exceed the standard limit given by the Department of Environment (DOE) of Malaysia and the Food and Agriculture N. Ghazali · K. S. K. Ismail (B) · R. A. Aziz · A. R. W. Yaakub · M. N. A. A. Saifuddin · A. Katimon Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia e-mail: [email protected] R. A. Aziz e-mail: [email protected] A. R. W. Yaakub e-mail: [email protected] M. N. A. A. Saifuddin e-mail: [email protected] A. Katimon e-mail: [email protected] K. S. K. Ismail · M. N. A. A. Saifuddin Centre of Excellence for Biomass Utilization, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia N. Inthano · N. H. Hoo Fermpro Sdn Bhd, Lot 2, Kawasan Perindustrian Chuping, Bukit Keteri, 02450 Kangar, Perlis, Malaysia e-mail: [email protected] N. H. Hoo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_2
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Organization (FAO) Standards for irrigation. Cr concentration was 0.01 mg/L, while Cd and Pb were not detected. The concentrations of heavy metals for the selected elements in paddy soil were also below the critical soil concentration enforced by the New York State Department of Environmental Conservation for normal soil. The heavy metal concentration’s contamination level was assessed by using the geoaccumulation index (Igeo ) and the obtained data showed that all the selected elements can be classified as class 0 (uncontaminated). The results also showed that the Igeo before paddy planting was already high, and it might be due to anthropogenic activities. Pollution Load Index (PLI) values of all the soil samples were uncontaminated as the result showed PLI < 1. The study’s findings support the assertion that the soil samples were not significantly contaminated with the studied heavy metals before and after irrigation with molasses-based distillery effluent. Keywords Heavy metals · Geoaccumulation Index · Pollution Load Index · Paddy soil · Risk assessment
1 Introduction Molasses is one of the largest raw materials used for ethanol production. As the demand for ethanol increased, the amount of wastewater from the distilleries also increased. Several companies in Malaysia ferment the diluted sugarcane molasses and produce ethanol for industrial uses and beverages. The effluent contains a high source of macronutrients and other micronutrients and various researchers have been conducted on its suitability to use as organic fertilizer in agricultural practices [1]. The reuse of the effluent for agricultural irrigation purposes is also a potential solution to reduce the use of inorganic fertilizer as well can utilize the huge amount of effluent discharged. However, effluent irrigation needs to be further evaluated to ensure its application is not harmful to both the crops and the soil. Paddy (Oryza sativa L.) is a member of the grass family, which makes up the majority of the human diet in Asia [2] and it needs all the good nutrients for human consumption. Since rice is a staple food in Malaysia and many other countries, it is crucial to monitor and evaluate the heavy metal content in paddy soil in order to determine the likelihood that paddy soils will become contaminated [3]. Soil and water pollution caused by agricultural activity is one of the major issues that has sparked worldwide concern. Heavy metals such as chromium (Cr), cadmium (Cd), and lead (Pb) have a toxic effect on soil, plants, and animals and they are also known to be carcinogenic [4]. Agricultural land and water, soil, and crops, are frequently contaminated with heavy metals, which eventually make their way into the human food chain. They are toxic and may poison plants, which would stunt their growth [5]. Thus, the assessment of heavy metal contamination is an important aspect of the long-term effects to the soils and agricultural area. Multiple indices such as geoaccumulation index (Igeo ) and Pollution Load Index (PLI) were widely used for environmental risk assessment [6]. These indexes identify the pollution level of the
Soil Risk Assessment on the Usage of Molasses-Based Distillery …
21
soils and are very significant to give an understanding of pollution levels in the plots tested for the studied heavy metals. In this study, the effluent from a molasses-based distillery was irrigated as organic fertilizer in the selected paddy plots. Analysis was done on the effects of specific heavy metal concentrations that built up in the paddy soils and categorized using Igeo and PLI to identify the pollution level during land application.
2 Materials and Methods 2.1 Sampling Site The study area was conducted in molasses-based distillery wastewater treatment plant (open pond) in Fermpro Sdn Bhd, Chuping, Perlis, where the effluent was ensured to meet the requirements under the Environmental Quality (Industrial Effluent) Regulations 2009. The effluent was tested for irrigation in trial paddy plots located in Semadong, Perlis. The Control Plot was located at latitude 06°32' 54.2'' , longitude 100°14' 43.7'' , while Plot 1 was at latitude 06°32' 50.2'' , longitude 100°14' 44.7'' . Plot 1 is the treatment plot where irrigation using the effluent and the control plot was irrigated with the usual water sources from nearby drainage.
2.2 Sampling and Preparation of Effluent Sample About 50 ml of the effluent sample was collected in triplicates from the anaerobic pond of the ethanol distillery. The wastewater samples were then digested using 5 ml of nitric acid following the American Public Health Association (APHA) Method 3030E [7]. The samples were then slowly boiled on a hotplate until the volume was reduced to about 15 ml. This solution was cooled to room temperature and subsequently filtered using the Whatman No 1 filter paper. The filtrate was then diluted 50 times using ultrapure water before being analyzed using inductively coupled plasma mass spectroscopy (ICP-MS) NexION 300X (Perkin Elmer, United States).
2.3 Soil Sample Collection and Analysis The paddy soil samples were taken before effluent irrigation and after the harvest period. Nine top soil samples within 0–30 cm depth were taken by using a soil auger (Eijkelkamp, Netherlands) [8]. The soil samples were then dried in the oven (105 °C for 24 h), grounded, and sieved (63 µm), followed by acid-digestion according to APHA Method 3030H using a ratio of 3:1 for nitric acid (HNO3 ) to perchloric
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acid (HClO4 ). The clear digested liquid was filtered through a Whatman No 1 filter paper. The samples were then transferred into 50-ml centrifuge tubes and filled up to 50 ml with ultrapure water. The content of heavy metals was determined using the ICP-MS.
2.4 Heavy Metal Pollution Analysis The determined heavy metal content was compared to the standard limit recommended by Standard B in the Fifth Schedule of Environmental Quality (Industrial Effluents) Regulations 2009 provided by the Department of Environment (DOE) Malaysia, and also with the maximum concentration of trace elements in irrigation water by Food Agriculture Organization (FAO). For soil samples, the geoaccumulation index (Igeo ) introduced by Muller (1997) [6] and Pollution Load Index (PLI) were applied to measure the contamination of the sediment under assessment. Values of the Igeo and PLI are defined in Eqs. (1) and (2), respectively. ( Igeo = log2
Cn 1.5Bn
) (1)
where Cn and Bn represent the concentration of the metal n in the studied soil sample and the background value of the metal n, respectively [9]. Table 1 shows the Igeo value and its contamination level. PLI = (CF1 × CF2 × CF3 × · · · × CFn)1N
(2)
where CF is the contamination factor (Csample /Cbackground ) of the element and N is the number of metals. Table 1 Index of geoaccumulation (Igeo ) and its contamination level
Igeo Value
Class
Quality
≤0
0
Not polluted
0−1
1
Not polluted–Moderately polluted
1 to 2
2
Moderately polluted
2 to 3
3
Moderately polluted–polluted
3 to 4
4
Polluted
4 to 5
5
Polluted–Highly polluted
>5
6
Highly polluted
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2.5 Data Analysis The statistical analysis was performed using MS Excel and Statistical Package for Social Sciences (SPSS) for Windows (Version 21) which involved computing the mean and standard deviation (SD) for the different variables measured in the effluent and soil samples. Statistical significance was computed with a significance level of P ≤ 0.05. Coefficient correlation analysis was done to find out the heavy metal’s characteristics in both effluent and soil samples.
3 Results and Discussion 3.1 Characteristics of the Effluent Final Discharge Wastewater from a molasses-based ethanol distillery was tested in paddy irrigation due to its high nutrients and organic content [10]. Table 2 shows the mean concentration of heavy metals in the wastewater collected. In terms of nutrients, K has the highest concentration in the effluent which was 11,532.14 ± 128.71 mg/L, followed by P and Mg with 169.86 ± 49.65 mg/L and 209.30 ± 74.81 mg/L, respectively. These elements are essential supplements in helping plant growth and productivity [11]. All the micronutrients and heavy metals observed were below the limits as indicated by the Food and Agriculture Organization (FAO) for the reuse of wastewater in cultivation [12], Standard B in the Fifth Schedule, Environmental Quality (Industrial Effluent) Regulations 2009, and the Malaysia Ground Water Quality Standard for Agriculture 2019 by the Department of Environment Malaysia. However, the BOD, COD, and suspended solid was higher compared to limit in the Standard B which was due to the presence of higher biodegradable organic waste in the effluent [13].
3.2 Heavy Metals Concentration in Soil Samples Table 3 shows the concentrations of heavy metals in the soil samples before and after being irrigated with the wastewater. Pb showed the highest concentration in both plots before irrigation with mean concentrations of 30.36±0.83 mg/kg and 23.12±0.77 mg/kg for Control Plot and Plot 1, respectively, whereas Cr showed the highest concentration in soil samples for both plots after the harvest period with the mean concentrations in Control Plot and Plot 1 being 19.16±2.05 mg/kg and 45.49±1.89 mg/kg, respectively. All the heavy metals did not exceed the critical soil concentration limit enforced by the New York State Department of Environmental Conservation for normal soil [14]. Interestingly, the mean concentration for all elements was already high before paddy planting compared to after harvested period which might be caused by various reasons
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Table 2 Mean concentration (mg/L) of elements in the effluent Mean ± SD (n = 72)
Standard B, DOEa (2009) (ppm)
MGWQSb (2019) (ppm)
FAO Standards for irrigation (ppm
Magnesium, Mg
209.30 ± 74.81
n/a
n/a
n/a
Potassium, K
11,531.14 ± 128.71
n/a
n/a
n/a
Phosphorus, P
169.86 ± 49.65
n/a
n/a
n/a
Copper, Cu
0.01 ± 0.00
1.00
0.20
0.20
Ferum, Fe
4.37 ± 2.01
5.00
n/a
5.00
Manganese, Mn
0.12 ± 0.09
1.00
0.20
0.20
Cadmium, Cd
ND
0.02
0.01
0.01
Chromium, Cr
0.01 ± 0.01
0.05
0.10
0.10
Lead, Pb
ND
0.50
n/a
5.0
Temperature
26.0 °C
40.0 °C
n/a
n/a
pH
7.4
5.5–9.0
n/a
7.0–8.0
BOD
2,750
40
n/a
n/a
COD
13,750
400
n/a
n/a
Suspended solid
8,500
100
n/a
50–100
Parameters
Macronutrient
Micronutrient
Heavy metals
Physicochemical properties
a
Standard B in Fifth Schedule, Environmental Quality (Industrial Effluents) Regulations 2009 Malaysia Ground Water Quality Standard for Agricultural 2019 * n/a = not available * ND = not detected b
Table 3 Heavy metals content in soil samples before paddy planting and after harvest Elements
Cadmium, Cd
Mean concentration (mg/kg) ± SD Before paddy planting
After harvest
Control plot
Control plot
0.18 ± 0.01
Plot 1
0.22 ± 0.01 ND
Plot 1 ND
Critical soil concentration (mg/ kg) 3−8
Chromium, 23.38 ± 0.04 17.35 ± 0.02 19.16 ± 2.05 45.49 ± 1.89 75−100 Cr Lead, Pb a b
30.36 ± 0.83 23.12 ± 0.77 4.79 ± 1.38
n/a: not available ND: Not Detected
15.50 ± 3.64 100−400
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such as anthropogenic factors and nature of the soil [15]. The anthropogenic factors include chemical fertilizer, use of pesticides, and farming tractors from the previous paddy cultivation which causes heavy metals accumulation in the soils [16]. While the reduced heavy metals concentrations after irrigation might be due to the heavy metals being translocated into deeper layers of soil as studied by Satpathy et al. [3]. The estimated yield produced in the paddy plot irrigated with wastewater was 7.86 ± 0.05 t/ha compared to the Control plot, 1.41±0.05 t/ha. This result was in line with the study by Gorfie et al. [17] which produced 318.33 g/plant compared to 250 g/plant of fresh leaf lettuce after irrigated with 100% brewery wastewater and 100% control water, respectively. This might be caused by the effluent’s nutrients and organic matter content.
3.3 Geoaccumulation Index (Igeo ) and Pollution Load Index (PLI) The assessment of the soil samples contamination before and after soil irrigation was carried out by using the Geoaccumulation Index (Igeo ) and Pollution Load Index (PLI) for Cr, Cd, and Pb as shown in Table 4. For Igeo assessment, it shows that the entire element analyzed in the study plots were unpolluted and categorized as Class 0. The value was similar as detected by Affum et al., [18] in their research primarily focused on crops irrigated with untreated groundwater and municipal waste. Since the PLI values in all the sampling points were below 1, it indicated that the soil samples in both sampling plots were in unpolluted condition for the studied heavy metals. This condition is in agreement with the previous research by Yu et al. [19] which obtained PLI value lower than 1 for all metals including Cd, Cr, Ni, Cu, Pb, and Zn after vegetable irrigation with untreated contaminated water. Table 4 Geoaccumulation index (Igeo ) and Pollution Load Index (PLI) of the soil samples before irrigation and after Plot
Before irrigation
After harvest
Igeo value
PLI
Cr
Cd
Pb
Control plot
−2.36
−0.07
−0.36
Plot 1
−2.79
−0.12
−0.75
Igeo value
PLI
Cr
Cd
Pb
0.86
−2.65
0.00
−3.03
0.46
0.79
−1.40
0.00
−1.33
0.76
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4 Conclusion This study proved that the molasses-based distillery wastewater irrigation has improved paddy yield. However, the effect of the heavy metals content before and after wastewater irrigation needs to be analyzed. The determined heavy metal (Cr, Cd, Pb) concentration in the effluent discharged was found to be below the standard limit given by the FAO for the reuse of wastewater in cultivation, Standard B in the Fifth Schedule, Environmental Quality (Industrial Effluent) Regulations 2009 and the Malaysia Ground Water Quality Standard for Agricultural 2019 by the Department of Environment Malaysia. Cr, Cd, and Pb were detected in the soil samples before paddy planting, while only Cr and Pb were detected after harvest. Also, the average heavy metal content observed was below the permitted critical soil concentration limit enforced by the New York State Department of Environmental Conservation for normal soil. Igeo values for these three elements in soil samples tested revealed that the soil samples were considered as Class 0 which is an unpolluted level. Pollution Load Index (PLI) also shows the same pollution level as Igeo resulted in unpolluted for Cd, Cr, and Pb. In conclusion, the wastewater used for irrigation did not risk the soil to be polluted by the selected heavy metals. However, further study regarding the application of the effluent for the land application is needed to evaluate more on its potential use. Acknowledgements This work was financially supported by Fermpro Sdn Bhd which was awarded to KSKI via Technopreneur UniMAP Sdn Bhd under grant number TUSB/Projek/2020(01).
References 1. Abdullah MZ, Manap NRA, Saat A, Hamzah Z, Abas MT (2015) Evaluation of heavy metal contamination levels of Balok river sediments in Pahang, Malaysia based on geoaccumulation index and supported with enrichment factor. Malays J Anal Sci 19(4):707–714 2. Abraham Samuel F, Mohan V, Jeyanthi Rebecca L (2014) Physicochemical and heavy metal analysis of sugar mill effluent. J Chem Pharm Res 6(4):585–587 3. Affum AO, Osae SD, Kwaansa-Ansah EE, Miyittah MK (2020) Quality assessment and potential health risk of heavy metals in leafy and non-leafy vegetables irrigated with groundwater and municipal-waste-dominated stream in the Western Region. Ghana. Heliyon 6(12):e05829 4. Ahmad Zubir AA, Mohd Saad FN, Dahalan FA (2018) The study of heavy metals on sediment quality of Kuala Perlis coastal area. E3S Web Conf, 34, 1–8 5. Baird BR, Eaton DA, Rice WE (2017) Standard methods for the examination of water and wastewater, American public health association. In: American Public Health Association, American Water Works Association, Water Environment Federation 6. Gomaa NA-R, Nadia RAN, Yehia AH, Mahmoud AMA-D, Mohamed MN, Mohamed F (2016) Effect of different treatments on heavy metal concentration in sugar cane molasses. Int. J. Agric. Biosyst. Eng. 2016(1):43–48 7. Gorfie BN, Tuhar AW, Keraga AS, Woldeyohannes AB (2022) Effect of brewery wastewater irrigation on soil characteristics and lettuce (Lactuca sativa) crop in Ethiopia. Agric Water Manag, 269(April), 107633
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8. Inboonchuay T, Suddhiprakarn A, Kheoruenromne I (2015) Distribution and Concentration of major and trace elements in paddy soils and rice plant of Khorat Basin Northeast Thailand. J Agric Sci 48(3):147–156 9. Ismail B, Yap D, Adezrian J, Khairiah J, Ahmad-Mahir R (2009) The uptake of heavy metals by paddy plants (Oryza sativa) in Kota Marudu, Sabah, Malaysia. J Agric Environ Sci 6(1):16–19 10. Jamila Alfaraas AM, Khairiah J, Ismail BS, Noraini T (2016) Effects of heavy metal exposure on the morphological and microscopical characteristics of the paddy plant. J Environ Biol 37(5):955–963 11. Jia Z, Li S, Wang L (2018) Assessment of soil heavy metals for eco-environment and human health in a rapidly urbanization area of the upper Yangtze Basin. Sci Rep 8(1):1–14 12. Mahfooz Y, Yasar A, Guijian L, Islam QU, Akhtar ABT, Rasheed R, Irshad S, Naeem U (2020) Critical risk analysis of metals toxicity in wastewater irrigated soil and crops: a study of a semi-arid developing region. Sci Rep 10(1):1–10 13. Mng’ong’o M, Munishi LK, Ndakidemi PA, Blake W, Comber S, Hutchinson TH (2021) Accumulation and bioconcentration of heavy metals in two phases from agricultural soil to plants in Usangu agroecosystem-Tanzania. Heliyon, 7(7), e07514 14. Osman NA, Ujang FA, Roslan AM, Ibrahim MF, Hassan MA (2020) The effect of palm oil mill effluent final discharge on the characteristics of Pennisetum purpureum. Sci Rep 10(1):1–10 15. Rezaeian M, Tohidi Moghadam M, Kiaei MM, Mahmuod Zadeh H (2020) The effect of heavy metals on the nutritional value of Alfalfa: comparison of nutrients and heavy metals of Alfalfa (Medicago sativa) in industrial and non-industrial areas. Toxicological Research 36(2):183–193 16. Satpathy D, Reddy MV, Dhal SP (2014) Risk assessment of heavy metals contamination in paddy soil, plants, and grains (Oryza sativa L.) at the east coast of India. BioMed Research International, 2014 17. Senbayram M, Gransee A, Wahle V, Thiel H (2015) Role of magnesium fertilisers in agriculture: Plant-soil continuum. In: Crop and Pasture Science, vol 66, Issue 12. pp 1219–1229 18. Yu H, Chen F, Ma J, Khan ZI, Hussain MI, Javaid I, Ahmad K, Nazar S, Akhtar S, Ejaz A, Sohail M, Nadeem M, Hamid Y, ur Rahman MH (2022) Comparative evaluation of groundwater, wastewater and canal water for irrigation on toxic metal accumulation in soil and vegetable: Pollution load and health risk assessment. Agric Water Manag, 264(January), 107515 19. Zulkafflee NS, Mohd Redzuan NA, Nematbakhsh S, Selamat J, Ismail MR, Praveena SM, Yee Lee S, Abdull Razis AF (2022) Heavy metal contamination in oryza sativa l. at the eastern region of malaysia and its risk assessment. Int J Environ Res Public Health, 19(2)
Effects of Soil Conditioners on Rice Growth and Soil Properties Under Water Stress at Vegetative Stage Mawaddah Saleh, Nurul Qistina Mohd Liza, Roslaili Abdul Aziz, Mohd Nazry Salleh, and Sahibin Abd Rahim
Abstract The main objectives of this research were to determine the response of inorganic and organic soil conditioners to phenotypic traits of rice and soil physicochemical properties of soil under water deficit. The experiments were designed in a 2 × 2 factorial with duplicates for 20 weeks in the greenhouse. The treatments consisted of different types of soil ameliorants and a hybrid mixture of substrates which were natural zeolite, GFOC, hybrid (natural zeolite + GFOC), and control. All treatments received 60% water capacity, while control received 100% water after 30 days of sowing (DAS). The soil physicochemical properties were observed along with phenotypic traits of rice such as plant height. The study found that there were slight changes in pH value before and after those treatments, ranging between 4.7 and 5.4 with optimum pH of 4.9 in hybrid treatment. Hybrid treatment soil exhibits a significant increase in moisture content between 5.286 and 7.623%, while control treatment exhibits a decrease in moisture content from 6.835 to 4.934%. When compared to all treatments at 14 DAS (vegetative stage), plants treated with hybrid soil conditioners displayed the highest plant height of 18.3 cm, followed by GFOC (17.8 cm), natural zeolite (16.3 cm), and control (14.6 cm). However, at 28 DAS hybrid and GFOC treatments started to wither and completely died after 49 DAS compared to control and natural zeolite treatments. Nonetheless, there was a M. Saleh · N. Q. M. Liza · R. A. Aziz (B) · M. N. Salleh Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia e-mail: [email protected] M. N. Salleh e-mail: [email protected] R. A. Aziz Biomass Utilization Organization, Centre of Excellence (CoEBU), Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia M. N. Salleh Geopolymer and Green Technology, Centre of Excellence (CEGeoGTech), Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia S. A. Rahim Green Frontier Sdn Bhd, Industrial Area, Negeri Sembilan, 71800 NilaiNilai, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_3
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fluctuation pattern of plant height for control treatment compared to natural zeolite showing no changes after being put under water stress at 30 DAS. It was found that the soil conditioner helps the plants survive in unfavorable soil conditions with proper nurture to improve rice growth performance. Keywords Soil conditioners · Geochemistry · Zeolite · Water deficit
1 Introduction Rice is the largest crop that consumes water in the agricultural sector [1]. The growths of rice are divided into three stages; vegetative stages, reproductive stages, and ripening stages. Rice goes through their vegetative stage typically for 55–85 days. As soon as the rice seed germinates into a seedling, the vegetative stages begin. This stage stops just before panicle initiation when the seedling stops rising in height. Limited irrigation implies controlling the soil water deficit during specific phases of crop growth, a method that has grown increasingly essential in recent years in places with limited water supplies. Rice needs water for three reasons; evapotranspiration, infiltration and percolation, and basic water management practices such as land preparation and drainage before tiller stage. Water deficit in soil can significantly affect the acquisition of nutrients by paddy roots and their movements towards shoots which answered why rice growth does not often respond to nutrient input [2]. Water deficiency can fundamentally affect the growth and survival of plants and change plant characteristics [3]. However, at molecular, cellular, and physiological levels, rice’s response to water deficit stress varied between species and genotypes, duration, and intensity of drought [4]. It is known that rice responds differently to water deficit stress according to whether they are plant upland or lowland. As a result, rice has distinct genetic characteristics associated with tolerance to water deficits. Even though plants have developed various adaptation strategies or mechanisms to survive and grow in the presence of drought, a grand challenge in abiotic stress biology is how the early signals are transduced within the plant [5]. Soil conditioners are soil modifications that by increasing aeration, water holding capacity, and nutrients, enhance the soil structure [6]. They loosen compacted hard pan and clay soils and release nutrients that are locked up. Depending on what they are made of, soil conditioners may also boost or lower pH levels [7]. Use of soil conditioners is a new green eco-friendly technology for sustainable agriculture production and lessens the harmful effects on the environment. Even though plants have developed various adaptation strategies or mechanisms to survive and grow in the presence of drought, a grand challenge in abiotic stress biology is how the early signals are transduced within the plant [5]. Soil conditioner is an important factor to the soil as it can improve soil nutrients, increase the cation exchange capacity (CEC) of soils, and can also be used to improve water retention or water deficits in certain parts of the soil. Soil alteration greatly increases the photosynthetic efficacy of seedlings under the stress of drought [8].
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31
However, for the rice tree and their soil conditions, the optimal application doses and soil conditioner forms are uncertain and remain a challenge for future study. Research on paddy growth in early growing stages under water-limited conditions will be useful for optimizing the crop production systems which normally entirely depend on rainfall, of which the distribution is usually uneven. Therefore through this study, the effects of different soil conditioners to improve the water stress on early growth and yield of flooded rice will be investigated further.
2 Material and Methods 2.1 Preparation of Paddy Plant Samples Greenhouse Setup In an open-space area, pot experiments had been performed to research the effectiveness of silicon on the enhancement of paddy morpho-physiological traits and growth responses to watered stresses. It was predicted that the paddy plants were to experience an average day/night temperature of 32°c/24°c and natural daylight. The location of the setup was at Kampung Panggau, Perlis. Soil and Treatments Preparation The soil samples were taken at a depth of 0–30 cm from the soil surface located in Kampung Panggau, Kangar, Perlis (6.428068, 100.164809). The soil had been used as a method to reflect the form of rice fields. Prior to transplant, soil in pots needed to be flooded for 1 or 2 days to allow water to soak into the soil. Before planting, the land had been moistened to field potential. Prior to germination, seeds were soaked in distilled water for about 12 h. 3 days before germinating; urea was applied into soil or top dressing at first tillering which is when the 5th leaf appeared at a ratio of 4:3:2:1, together with the studied amendments which were the green frontier organic conditioner (GFOC), clinoptilolite zeolite (CZ), and hybrid mixture at a ratio of 1:1 between GFOC and clinoptilolite zeolite at their predetermined dosages. The clinoptilolite originated from Desa Pendamaran, Indonesia, while GFOC is supplied by Green Frontier Sdn, Bhd. company as the research alliance. The pots were arranged randomly in the opened space area. Watering had been administered as necessary to keep soil moist but not saturated and watered level in the pots was always below the upper edge. The GFOC, clinoptilolite zeolite, and hybrid mixture at a ratio of 1:1 between GFOC and clinoptilolite zeolite at their predetermined dosages had been applied uniformly and mixed thoroughly between 0 and 5 cm of soil depth in each pot before germination, to support the uptake of nutrition by the plants. To determine the dosage of the soil conditioner, the weight of the soil was taken. The calculation for the dosage as follows:
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M. Saleh et al.
Dosage o f soil conditioner = 0.5 × the actual weight o f soil
(1)
Local cultivars of paddy seed had been provided by local farmers for this study. Rice seeds had been carefully germinated in the prepared plastic pots. Each treatment pot had been thinned to only healthy seedlings per pot with enough spacing. Pesticides were not used throughout the experiment. Weeding and thinning to keep only vigorous seedlings in pots had been done manually, as necessary.
2.2 Soil Chemical Properties Soil pH Ten grams of the soil samples were placed into a 50-ml beaker, and then 25 ml of distilled water was added to the beakers. The mixture was stirred and left to rest for 30 min. The mixture was stirred again for another 30 s. pH meter was used to determine the pH level of the mixture [9]. Electrical Conductivity Ten grams of soil were inserted into the beaker and 20 ml of distilled water was also added to the same beaker. The mixture was stirred for 30 s and was left to rest for 30 min. Next, this mixture was filtered into a clean container and was stirred again. A conductivity meter was used to determine the EC of the soil samples [10]. Moisture Content and Organic Matter of Soil The soil samples were weighted for wet weight. All the samples were placed into the oven at 105 °C for 24 h. After the samples were taken out from the oven, all of them were weighed again for dry weight (W1) [11]. Moistur e Content(%) =
W et W eight − Dr y W eight × 100 W et W eight
(2)
The same soil samples were placed into the furnace for another 4 h at 400 °C. The sample weight (W2) had been recorded after it was taken out from the furnace and cooled to room temperature. Organic Matter (%) =
W1 − W2 × 100 W1
(3)
Effects of Soil Conditioners on Rice Growth and Soil Properties Under …
33
2.3 Water Stress Behavior Treatment Water stress treatments involved lowering all the soil moisture content to 60% from the actual content and keeping it at that new low level for an extended length of time of 2 weeks. In this study, two levels of moisture regimes were imposed: (i) the control pots which had been well-watered throughout the growing period as according to normal moisture contents at 100% field capacity, while (ii) all the amendmentsupplied pots only received about 60% of field capacity at 30 days of sowing (DAS) as water deficit treatment, where both treatments were duplicated. Starting at 30 DAS, the treatment pots other than control pots were subjected to vegetative drought stress by withholding water for the rice cultivars for 14 days. After 14 days, all pots were then well watered as needed. The growths of the paddy plants were studied at vegetative stage to see the effects of water deficit towards the paddy and soil. Water deficit stress was characterized as a condition in which the water potential and turgor of plants are insufficient to meet normal functions of the paddy and soil.
2.4 Plant Growth The plant growth had been measured as the relative increase in plant height over time. The plant height was measured using a measuring tape once a week by holding the tape close to the crop stem. The plant height was measured from the ground base level to the longest tip of fully expanded leaf at vegetative stage.
3 Results and Discussion 3.1 Soil Physicochemical Properties pH Value of Soil The highest pH recorded before treatment was from the Hybrid 1(2) treatment which was 4.883 ± 0.031 as shown in Fig. 1, proving that the soil is acidic and with a mean below 5, the soil was strongly acidic. High levels of acid in soil were an effect of hydrogen ions replacing basic elements carried by soil colloids, such as calcium, magnesium, sodium, and potassium [12]. The pH of the soil lowers as the amount of hydrogen ions in the soil grows, making it more acidic. Heavy metal concentrations in rice plants were substantially influenced by soil pH [13]. When comparing the mean values between before and after treatments, there were no significant changes indicated by the pH value. However, the availability of several nutrients and the activity of soil microbes are affected by changes in soil pH [14]. The pH of the soil has an impact on nutrient availability, elemental toxicity, and microbial
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M. Saleh et al.
Fig. 1 Soil pH before and after treatment
activity. Despite that, many rice species may thrive in soil with a pH of 4.5−8.5 [15]. Based on the results, most treatments have decreased the soil pH except for Hybrid 1(2) which has a slight increase in soil pH after the treatment ended due to a higher ratio of zeolite than Hybrid 2(1). Moisture Content In Control 1, there is a decrease in moisture content before and after treatment from 6.835 to 4.934% as shown in Fig. 2. Considering that soil with treatment of GFOC, Zeolite Composite and Hybrid had a higher amount of percentage after the treatment indicates there is an increase in moisture content more than 2%. Soil conditioner has the ability to activate nutrients in the matrix, improve soil physical and chemical properties, increase nutrient content, and promote plant nutrient absorption. It gives the nutrients needed to soil to build up dry matter, encourage vegetative growth, and plants to reproduce. Paddy plants demand a lot of water, while it is at an 80% growth spurt. However, once the paddy plant reaches an 81% growth rate, the amount of water applied to it until it is harvested is lowered [16]. Organic Matter of Soil As shown in Fig. 3, all of the treatments indicate a rate of decline. The amount of organic matter from before the treatment is in high percentage environment. One of the factors that may affect the decrease in the organic matter was the process of tillage. Tillage introduces oxygen into the soil and elevates its average temperature, accelerating the decomposition of organic materials. Despite the fact that soil erosion is a severe threat to soil fertility and food security, it may potentially result in increased carbon retention at the landscape scale [17]. Another reason that may affect the decline of organic matter in the soil was the soil hydrology. A much wetter soil had less oxygen that was available for the organic matter in soil to decay. Since the paddy were planted in a treatment pot, the absence of decomposing organisms or an accelerated rate of decay as a result of changes in
Effects of Soil Conditioners on Rice Growth and Soil Properties Under …
35
Fig. 2 Soil moisture content before and after treatment
Fig. 3 Soil organic matter before and after treatment
natural or manmade processes causes a decrease in organic matter in the soil [18]. On the other hand, the findings show a correlation between the soil pH and organic matter as an increase in soil acidity leads to the decrease in organic matter, where the soil cannot reach its natural buffering capacity. Electrical Conductivity (EC) The test was done to see the amount of saline in the soil. It was a crucial metric for determining the health of the soil. Salinity had an impact on crop yields, crop suitability, plant nutrient availability, and soil microbial activity, all of which have an impact on critical soil processes. A saline soil usually contains a lot of soluble salts (Fig. 4). The soil’s EC before treatment is in the average of < 0.15 mS/cm which determined that the soils were salt-free since it was below 0. The soil’s EC after treatment shows an average of < 3.5 mS/cm. Thus, even after soil conditioner was applied the salinity
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Fig. 4 Electrical Conductivity before and after treatment
of the soil maintaining the soils’ salt-free condition, remained quite low. Salinity has a wide range of effects on plants. Because salinity is a polygenic characteristic, the variance in response is therefore quite unpredictable. However, soils with high sodium salt concentrations have additional issues, including poor soil structure, poor penetration or drainage, and toxicity crops [19]. In saline-sodic soil with salinity levels less than 4 dS/m1 [20], paddy rice is a good crop for land cultivation [21]. Plant respiration and photosynthesis are also affected by salinity. Paddy is sensitive to salinity during the early vegetative phases [22]. The low level of soil’s salinity may be due to the process of submerging the soil for one week before the paddies were planted. Submerged soils improve rice crops by providing a more suitable environment for rice roots, in addition reducing salinity in the soil. Through mass flow and diffusion, the presence of free water in the soil relieves paddy development from water shortages and promotes the availability and accessibility of paddy nutrients [23]. Lower zeolite content in hybrid treatments may describe the significant increase of the EC (1.5–3.4 mS/cm) as the soil’s ability to retain water declined due to water stress factor; hence soils were losing the water retention capacity.
3.2 Plant Growth The majority of the seedlings had their first set of true leaves by the second day after they were planted, which was just two days after they were initially germinated as shown in Table 1. The seedling grew two more leaves after the first one. Initially, a new leaf emerges every 3–4 days. All seedlings grew 5–7 cm on the fifth day. After almost two weeks from the day of seedlings, the paddies grew to a length of about 15–20 cm. From seed to harvest, paddy cultivars take vastly different amounts of
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time. The planting date has a big impact on these times, and in the tropics, it was usual to see plants mature 5 months or more after seeding [24]. At week 2 which is 14 days after sowing, plants treated with hybrid soil conditioners display the highest plant height of 18.3 cm, followed by GFOC (17.8 cm), natural zeolite (16.3 cm), and control (14.6 cm) as shown in Fig. 5. In comparison to the treatment that did not include any soil amendments, the application of soil amendments resulted in significantly increased plant height [25]. According to [26], rice plant height at 15, 30, and 45 days after planting, which is during the early tillering stage, was significantly higher between 24.56 and 59.59 cm compared to the control treatment using organic amendments. Table 2 displays the plants in pots treated with the GFOC and hybrid treatments started to wither at week 4–week 7 (Day 28–49 after seedlings). Withering normally occurred as a result of poor management practice, such as excessively dry or too wet conditions during the nursery stage, as well as less nutrients provided [27]. The accumulation of water in the soil encourages the loss of nitrogen in the soil through Table 1 Seedling emergence phase of paddy plant Day 1 of germination
Day 2 of germination
Fig. 5 Average growth of paddy plant according to treatments
Day 5 of germination
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runoff, leaching, and denitrification, all of which are detrimental to the growth of plants, which may be attributed to smaller containers used for seedlings [28]. In comparison to all four types of treatments, the seedlings in control pots and natural zeolite however grew abundantly. This finding is supported by the fact that natural zeolite has a high water holding capacity which enables the plant to get enough nutrients even under water stress [29]. By Day 49 after seedlings, the withered paddy was almost completely dried. The healthier paddies were then continued kept to be nurtured properly. As shown in Table 3 below, however after week 9, the paddies’ growth were drastically declined and finally show no growth pattern. The paddies may also have been affected by the virus that causes ragged stunting in rice. Rice ragged stunt virus causes partially exerted panicles, empty grains, and plant density loss, which affects yield. Infected plants’ leaves have a ragged appearance, normally happens during early growth stages in paddy leaving the short, serrated edges of leaf blades outgrowths [30]. While some of them may survive, the paddy yields may still critically reduce as it also resulted in delayed flowering stage, partial panicle emergence, and produced infertile grains in the next growth stage, leading to uneconomical paddy yields. Even though the paddies were properly monitored under sufficient care, the remaining paddy leaves were started to turn brownish yellow at the margin of the leaves by Day 70 after seedlings. This may be due to a lack of potassium present in Table 2 Tillering phase of paddy Day 14 after seedling
Day 28 after seedling
Day 49 after seedling
Table 3 Vegetative lag phase of paddy Day 63 after seedling
Day 70 after seedling
Day 105 after seedling
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the soil. Potassium is required for a variety of plant processes, including cell wall strength in rice plants [31]. K-deficient tissue is frequently accompanied by dark brown necrotic patches, and in extreme cases, leaf tips might turn yellow–brown before drying out and becoming necrotic [32]. Figure 5 shows the average growth of the paddy plant after 20 weeks of planting. At week 2 which is 14 days after sowing, plants treated with hybrid soil conditioners display the highest plant height of 18.3 cm, followed by GFOC (17.8 cm), natural zeolite (16.3 cm), and control (14.6 cm). However, at week 4 both plants with treatments GFOC and hybrid show decreased trend compared to the natural zeolite and control. After week 5, there was no growth attained for plants treated with GFOC and hybrid treatments and they eventually died at week 7. Even though the plant appears to be healthy, root rot had occurred beneath the surface of the soil as a result of damp, poorly drained soil resulting in the plant to die. This could be explained by the deleterious effect in response to excessive soluble micronutrient such as Al in soil which resulted in slowing or stopping of root growth due to undesirable soil pH conditions [33]. Moreover, at week 7, plants without treatments showed positive growth in height about 3 cm, however, decreased after week 11. In comparison, plants with natural zeolite showed consistent height along 16 weeks indicating their higher resilience towards water deficit and stress environment. However, the plants cannot withstand the water stress and showing poor performances causing the plants to ultimately die at week 20. In relation to the soil properties indicated after the treatments, higher soil acidity has proven in decreases the availability of plant essential nutrients and in another way also enhance the toxicity level of certain elements, most likely aluminum, ferum, and manganese in soil medium. Plus, low soil pH value lessens the availability of macro and secondary nutrients as such calcium, magnesium, and sulfur and affects the microbial activity in soil in several negative ways, hence believed to contribute to the poor growth of rice plants in all treatments.
4 Conclusion Based on the results, treatments with soil conditioners did not increase the pH of the soil to the suitable pH. Instead, results revealed that there were minimal changes in pH value before and after treatment which remains unideal to the plant growth as the most suitable soil pH for paddy soil should be around 4.5–8.5 [15]. The soil conditioners used in this study also had minor effects on the moisture content and organic matter in the soil. A pattern of declining moisture content had been shown. However, the soil with hybrid soil conditioner had shown a significant increase in moisture content with a mean of 2%. Through evaporation and plant transpiration, moisture content is a major variable in governing the exchange of water and heat energy between the soil and the atmosphere. Organic matter in the soil acts as a repository for nutrients and water, reduces pressure and surface fissuring, and improves water infiltration. However, control treatment shows a decrease in moisture content from 6.835 to
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4.934%. When compared to all treatments at 14 DAS, plants treated with hybrid soil conditioners demonstrated the highest plant height of 18.3 cm and optimum value of pH soil which was 4.9. Nonetheless, there was a fluctuation pattern of plant height for control treatment compared to clinoptilolite zeolite which showed no changes after being put under water stress at 30 DAS. Thus, it can be concluded that the soil conditioner can help the plants survive and thrive in unfavorable soil conditions with proper nurture to improve rice growth performances. Acknowledgements The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS-RACER) under a grant number of RACER/1/2019/WAB01/ UNIMAP//1 from the Ministry of Education Malaysia.
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Soil Amelioration Effects on Morphology Traits of Upland Rice Root–Shoot and Soil Productivity Under Water Deficit Mawaddah Saleh, Sangavi MohanRaj, Roslaili Abdul Aziz, Mohd Nazry Salleh, and Sahibin Abd Rahim
Abstract The main objectives of this research were to determine the effects of ameliorant supplementation on rice root and shoot morphology under water deficit, by studying the impacts of soil conditioners on soil chemical properties. The experiments were arranged in split plots with duplicates for one season in the greenhouse. The treatments consisted of two different types of soil ameliorants and a hybrid mixture of substrates which were natural zeolite, GFOC, hybrid (natural zeolite + GFOC), and control treatments. All the treatments received 60% of water capacity; except for the non-treatment (control) which received 100% of water. The soil chemical properties were observed along with physiological traits of rice root and shoot, including survival rate (%), length (cm), and dry weight (g) of root and shoot of rice crop. It was found that soil chemical properties of the hybrid supplement were within the optimum range with pH 7.42, 4.23% of organic matter, and 121.3 mS/ m of electrical conductivity which indicated that plant rice with hybrid is the most effective treatment among the others with the survival rate of 95.3%, has the longest root–shoot length of 69.4 cm and the heaviest root–shoot weight of 64.4 g. The hybrid treatment also demonstrates a strong positive relationship between rice crop M. Saleh · S. MohanRaj · R. A. Aziz (B) · M. N. Salleh Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia e-mail: [email protected] M. N. Salleh e-mail: [email protected] R. A. Aziz Biomass Utilization Organization, Centre of Excellence (CoEBU), Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia M. N. Salleh Geopolymer and Green Technology, Centre of Excellence (CEGeoGTech), Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia S. A. Rahim Green Frontier Sdn Bhd, Industrial Area, Negeri Sembilan, 71800 NilaiNilai, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_4
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morphological features and soil productivity. Therefore, soil ameliorant is proven to boost soil productivity and plant growth by reducing the adverse effects of drought stress. Keywords Organic conditioner · Rice morphology · Geochemistry · Water stress
1 Introduction Plant roots are an essential component of the plant–soil ecology, and their functions to provide water and nutrients to the plant [1]. Investigating the morphological traits that contribute to the ameliorants is therefore essential for improving crop performance [2]. Root morphological and physiological properties have a significant impact on shoot growth and overall production. As a direct consequence of increased yields, the root system of the crop will exhibit improved root absorption capacity, as well as improved water and nutrient absorption. [3]. Furthermore, the root system influences above-ground growth and biomass yield, and root development and dispersion in the soil profile dictate crop plant nutrient uptake and water extraction capability [4]. Agriculture is now faced with the challenge of feeding a rapidly growing population while also dealing with increasing water scarcity. Rice, which feeds roughly half of the world’s population, can only be grown efficiently in irrigated fields. More than half of the world’s population gets their primary nutrition from rice. A variety of abiotic and biotic stresses impact negatively on its cultivation, with drought being one of the most damaging abiotic strains due to its severe impact on cultivation and productivity [5]. Water scarcity reduces crop growth and productivity, which can be attributed to slow physiological processes. The duration of the rice crop’s wet season and the plant’s water stress at different stages of development affect its drought susceptibility. Similarly, biophysical conditions such as temperature, climate, and landform can contribute to a water shortage. Environmental factors and inadequate irrigation facilities can result in a water shortage, affecting soil productivity and root systems [6]. However, the most important factor to consider as a foundation is soil productivity; the higher the soil productivity, the greater the extraction of nutrients and water uptake processes. The initial challenge facing rice productivity is the degradation of soil. Soil degradation distracts the function of the root–shoot system [7]. Conversion toward resources such as modification of agricultural land for urbanization, industrialization, and expansion of housing areas, contribute to decrease in the soil quality. These processes can be improved by generating suitable agricultural machinery or technologies depending on the type of soil and crop. It has been demonstrated that the addition of organic amendments to soils, such as compost or manure, can improve the function of the soil by increasing the capacity of the soil to hold water, the porosity of the soil, and the surface area of the soil [8]. Organic amendments have the potential to assist in the stabilization of the soil structure and the reduction of the soil bulk density (Db), thereby creating an environment
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that is conducive to the development of robust root systems in plants. Not only do organic amendments give plants that are actively growing the nutrients they need but also increase the concentrations of those nutrients that are available to plants in the soil [9]. Both the overall number of productive tillers and the leaf area of paddy were able to increase as a result of the application of ameliorant in paddy fields [10]. Natural clinoptilolite zeolite is a natural resource with a high cation exchange capacity for absorption and generates spaces for root system aeration and development [11]. This soil conditioner prevents soil compaction and increases infiltration. Meanwhile, the Green Frontier Organic Conditioner (GFOC) is a soil conditioner with a composition ratio of 5:5:7 (N:P:K) that improves fertilizer quality and productivity. The advantages of GFOC include reduced fertilizer and water charge utilization as a result of nutrient and water capture in the root system until the plant is ready to use them. In this study, the applicability of natural clinoptilolite zeolite and GFOC were first prepared to be used as organic ameliorant. Therefore, the effects of soil ameliorants on morphology traits of upland rice root–shoot and soil productivity under water deficit have been examined.
2 Materials and Methods This research was conducted from January to March 2021, at the greenhouse of the Faculty of Chemical Engineering & Technology (FKTK), UniMAP, Perlis. The research design was arranged in split plots with duplicates. The treatments consisted of two different types of soil ameliorants and a hybrid mixture of substrates which were inorganic clinoptilolite zeolite (CZ), green frontier organic conditioner (GFOC), hybrid (CZ + GFOC), and control treatments. The clinoptilolite originated from Desa Pendamaran, Indonesia, while GFOC is supplied by Green Frontier Sdn Bhd. company as this research alliance. An organic GFOC, inorganic ZC, and hybrid soil conditioner were applied uniformly 10 days after transplanting (DAT) for 2 weeks and mixed thoroughly between 0 and 5 cm of soil depth in each pot to support the uptake of nutrition by the plants. The soil samples were taken at a depth of 0–30 cm from the soil surface located in the Matin paddy field in Perlis. Stones, gravel, and plant debris were removed from the soil. The soil was then weighed about 1 kg before carefully filling it in each pot. The ameliorant was added according to the treatment except for the control treatment, where the dosage was fixed for 0.5 g of soil ameliorant for 1 kg of soil. The soil samples are then wetted to 60% of field capacity before planting at 31 days after seeding (DAS), which means the soil in pots will be flooded for one to two days to maintain the water level at 3 cm above the soil surface. The paddy variety used in this study was MR219, a local cultivar of paddy seed provided by the Malaysian Agricultural Research and Development Institute (MARDI). Seeds were first cleaned in distilled water to remove any organic contaminants and own in trays for 31 days. The rice seedlings were then transplanted after
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30 days into the experimenting pots with a standard diameter and depth for subirrigation that leads to efficient root development. Then, each treatment pot is planted with 4 seedlings per hill (2 hills in each pot) at a shallow depth (±3 cm) with enough spacing. The seedlings along with the soil intact with the roots removed from the tray carefully and planted in pots immediately to reduce the plant stress. Two levels of moisture regimes were imposed in this study; as for control pots, they were maintained well-watered throughout the growing period that is according to moisture contents at 100% field capacity; and the treatment pots under water deficit, which only received about 60% of field capacity at 74 days after transplanting (DAT) in ameliorant-supplied pots. Each treatment was double-replicated for accuracy. Four seedling plants per pot were maintained until harvest time. At 40 DAT, the treatment pots other than control pots were subjected to vegetative drought stress by withholding water for the cultivars for 14 days (40–54 DAT). At 55 DAT, all pots will be watered as needed. At the end of the vegetative stage, selected physiological traits of rice root will be evaluated along with the soil’s chemical properties as results of ameliorations. The data analysis of this research was evaluated by using analysis of variance, and the correlation between soil quality and physiological traits of the rice crops was analyzed by a correlation regression model.
3 Results and Discussion 3.1 Effects of Soil Ameliorant on the Soil Productivity Under Water Deficit The data on soil productivity is recorded on pH, electrical conductivity (uS/m), organic matter (%), and micronutrient (ug/L) chemical characteristics of the soil. The observation was made on the maximum amount of treatment dosages are CZ with 1.23 g/kg soil, GFOC with 1.99 g/kg soil, and hybrid with 1.25 g/kg soil. Results show that the range of soil pH values is from 6.5 to 7.45, indicated as slightly acidic to slightly alkaline. As the standard value of pH is around 6–7.5, hence the range of value tends to cooperate during the cultivation process, as shown in Table 1 and Fig. 1 below. A pH of 4.5–8.5 is identified as ideal for rice growth [12]. Moreover, it is reasonable to conclude that the use of compost as a soil amendment has the potential to raise the pH of the soil, thereby producing more favorable soil conditions for the cultivation of rice, which ultimately results in improved rice growth performance [13]. In addition to soil electrical conductivity, the amount of salts in soil is measured by the electrical conductivity (EC) of the soil (salinity of soil). It is a crucial metric for determining the health of the soil. The optimal electrical conductivity range for soil is 110–570 millisiemens per meter (mS/m) [14] showing the soil is nutrient-balanced or sufficient as shown in Fig. 2.
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Table 1 Effects of treatment supplement on the soil productivity under water deficit Field capacity (%)
Treatment
Soil chemical properties
Min
100
Control
pH
7
EC (uS/m)
107.3
OM (%)
4.69
pH
6.5
EC (uS/m)
117.2
OM (%)
4.31
pH
7.28
EC (uS/m)
114.7
OM (%)
4.62
pH
7.39
EC (uS/m)
117.2
OM (%)
3.85
60
Natural zeolite
GFOC
Hybrid
Max 7.02 110.1 5.08 6.7 122.0 5.73 7.38 118.3 5.89 7.45 119.2 3.97
Mean ± stdev 7.01 ± 0.01 108.7 ± 1.98 4.89 ± 0.28 6.58 ± 0.09 119.48 ± 2.08 5.17 ± 0.67 7.32 ± 0.04 116.80 ± 1.51 5.48 ± 0.58 7.42 ± 0.03 122.0 ± 7.51 4.23 ± 0.40
Fig. 1 Effects of soil ameliorant on the soil pH and organic matter (%) during the growth of rice crop
Based on Fig. 2, the effect of controlled and treated soil on electrical conductivity. Controlled soil has an average value of 108.70 mS/m on a scale of 100 DAT, which is 1.2% lower than the normal range. The greatest mean value for hybrid supplement is 122 mS/m, which is within the optimum range. The control value for 10 DAT is 107.6 mS/m, but the value for the hybrid is 121.3 mS/m, indicating that the hybrid sample had balanced electrical conductivity since the beginning study. This number indicates that the plant morphological features are positive as soil ameliorant helps in improved soil salinity [15].
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Fig. 2 Effects of soil ameliorant on the soil electrical conductivity during the growth of rice crop
Moreover, the available micronutrients that were studied and observed include boron (B), zinc (Zn), copper (Cu), and nickel (Ni), as presented in Fig. 3. Based on the graph, the control soil contains 86.12 ug/L of copper, whereas 181.2 ug/L in GFOC soil. Clay soils normally retain copper in their accessible forms. Copper however can be leached from sandy soils with little organic matter. Micronutrients’ presence has a direct association with paddy yield, growth, and development.
Fig. 3 The concentration of boron (B), zinc (Zn), copper (Cu), and nickel (Ni) on the different soil ameliorant
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3.2 Effect of Soil Ameliorant on the Morphological Traits of Rice Root–Shoot Under Water Deficit The effects of clinoptilolite zeolite, green frontier organic conditioner, and hybrid soil ameliorants or treatments on morphological features of rice root shoots were explored in this study, and a correlation was made between treatment and soil productivity. Table 2 presents the data gathered from the plant morphological traits observation. In comparison to other ameliorants and controls, the hybrid ameliorant has the highest percentage of survivors in Table 2. Table 2 Effects of soil ameliorant on the morphological traits of rice root shoot under water deficit Mean ± stdev
Field capacity (%)
Treatment
Morphological traits
Min
Max
100
Control
Tiller number
3
4
Root–shoot length (cm)
55.2
57.9
56.6 ± 1.91
Root–shoot weight (g)
52.5
58.7
55.6 ± 4.38
60
Natural zeolite
GFOC
Hybrid
4 ± 0.71
2.1 ± 0.07
Root thickness (cm) 2.0
2.1
Survival rate (%)
6
6
Tiller number
3
4
Root–shoot length (cm)
56.7
62.6
59.5 ± 2.68
Root–shoot weight (g)
47.6
56.5
50.9 ± 3.98
75 ± 0 4 ± 0.5
2.5 ± 0.28
Root thickness (cm) 2.2
2.8
Survival rate (%)
6
8
Tiller number
3
4
Root–shoot length (cm)
53.5
58.4
56.1 ± 2.03
Root–shoot weight (g)
51.2
53.4
52.2 ± 0.91
Root thickness (cm) 1.9
2.5
2.2 ± 0.25
Survival rate (%)
6
7
Tiller number
3
6
Root–shoot length (cm)
61.2
77.1
69.4 ± 6.71
Root–shoot weight (g)
62.7
65.2
64.4 ± 1.18
Root thickness (cm) 2.4
2.8
2.6 ± 0.18
Survival rate (%)
8
7
87.5 ± 0.82 4 ± 0.5
78.1 ± 0.5 6 ± 1.41
95.3 ± 0.48
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Fig. 4 Effects of soil ameliorant on the root–shoot length (cm) and root–shoot weight (g) of rice crop
Our findings showed the healthiest plant had a survival rate of 95.3% which is associated with the hybrid supplement, whereas the control produced the least healthy plant at a much lesser survival rate of 75%. The control sample has the lowest survival rate due to a lack of micronutrients; even at full field water capacity; it lacks sufficient soil productivity to support the plant. Furthermore, results on the effects of soil ameliorants on the root–shoot length (cm) and root–shoot weight (g) of rice crops were shown in Fig. 4. Again, hybrid treatment revealed the optimum results with the longest root–shoot length of 69.4 cm, and the heaviest root–shoot weight of 64.4 g. The plants in hybrid also developed the most effective panicles that are the panicles bearing the fertile rice, these later resulted in the highest root–shoot weight compared to other treatments with regulated soil. Due to the multiple empty panicles, the minimal root–shoot weight under natural zeolite is 50.9 g. From the soil productivity itself, finishing the soil with a hybrid supplement improves plant maturity and growth. Low availability of water and nutrients commonly leads to a greater root:shoot ratio as evidenced in control and much higher in hybrid traits as the addition of soil conditioners. Large root systems are known to reduce lodging in cereals by reinforcing the plant structures [16] and vigorous root systems developed healthier plants. Zeolite particles adhered to the root surface enhancing the organic matter solubilization and nutrient capture by improving the soil exploration through increased root:shoot length and storage of plant resources in thicker roots [17]. Hence, supplementing the soil with both ZC and GFOC clearly improves plant maturity and growth and finally enhances the crop’s yield significantly. Other than that, the effects of a soil ameliorant on the number of tillers, survival rate, and root thickness (cm) of a rice crop are shown in Fig. 5. On a scale of 1–10, the root thickness ranges from 0.5 to 0.7 cm (at 100 DAT). The minimal root thickness is 2.1 cm under control and 2.2 cm under GFOC after the treatment supplement,
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Fig. 5 Effects of soil ameliorant on the tiller number and root thickness (cm) of rice crop
showing mild variations. This is because the control soil has enough water, whereas GFOC has enough amelioration under 60% of water capacity. The maximum root thickness on hybrids is 2.6 cm. The number of tillers in plants is then calculated per seed, with the highest mean value of tiller being 6 on the hybrid treatment and the lowest being 4 on the control and other treatments. This shows that soil ameliorants also improved most of the morphological features of the rice crop [18].
3.3 Correlation on the Effects of Different Soil Ameliorant on Soil Productivity and Rice Morphological Traits Under Water Deficit The aim of this research is to establish a correlation between the effects of the ameliorant on root and soil variations. For clinoptiloite zeolite supplement, Fig. 6 represents the relationship between root–shoot length and weight and soil pH. The trend line on the graph indicates a positive link. The linear relationship between morphological characteristics and pH value is described by the sample correlation coefficient. The r value for root–shoot length is 0.39, indicating a moderately positive relationship between pH and root–shoot length. The value of r = 0.17 indicated a very weak positive relationship between pH and root–shoot weight. Additionally, Fig. 7 shows the correlation between the root–shoot length and weight against the soil pH value for a hybrid supplement. Based on the graph the trend line shows a positive relationship. The sample correlation coefficient was calculated to describe the linear relationship between morphological traits and pH value. The r
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Fig. 6 Correlation between rice morphological traits and soil productivity under clinoptilolite zeolite supplement
value for root–shoot length is 0.37, moderate positive relationship between pH values, and for root–shoot weight is 0.60 indicating a strong positive relationship between pH values. The application of soil amendments improved the soil’s chemical properties, including an increase in soil pH, which resulted in less restriction on root elongation, volume, and surface area when the soil was under water stress [19]. In addition, Fig. 8 revealed a correlation between root–shoot length and weight and soil electrical conductivity under a hybrid supplement. The trend line is generally favorable, the r = 0.74 indicates a strong positive linear relationship between morphological features (length) and electrical conductivity, while the r = 0.97 indicates a strong positive linear relationship between rice root–shoot weight and electrical conductivity. The greater electrical conductivity, which is still within acceptable limits, helps to keep soil pH in balance. However, salt-stressed soil has a high concentration of soluble salts that can hinder plant growth [20]. Rice growth rate was slowed, metabolic changes were accelerated, and the plant’s ability to absorb water and nutrients was impaired. [21]. Therefore, proper soil productivity allows the plant to grow in a healthy and nutrient-rich environment.
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Fig. 7 Correlation between rice morphological traits and soil productivity (pH) under hybrid supplement
Fig. 8 Correlation between rice morphological traits and soil productivity (EC) under hybrid supplement
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4 Conclusion Based on the results, the sample with the ameliorant supplement showed balanced soil productivity even though the treatment soil has only 60% field water capacity and undergoes water stress conditions for 2 weeks. In addition, among the various ameliorants, soil with a hybrid supplement has the highest mean value in soil productivity and morphological traits of rice root shoot, with a maximum weight of 64.4 g, indicating that the panicles are bearing fertile grains and a healthy root system to supply the plant with nutrients and water. Furthermore, hybrids treatment also produced the highest survival rate at 95%. The significant survival rate of hybrid treatment is supported by the substantial improvements in the root traits and root–shoot morphological ratios, hence proving that improving tolerance of abiotic stress such as water and nutrient uptake should be mainly targeted in belowground crop systems, relatively. Root–shoot biomass, length, and thickness density are the prime considerations for plants’ resilience to abiotic factors by enhancing the belowground resources capture through the exploitation of a more sustainable approach, i.e., soil conditioners application [22]. Therefore, both soil ameliorants are proven to boost soil productivity and improve plant growth in areas with limited water supply, which helps in breeding for drought tolerance in rice. The overall results demonstrate a strong positive relationship between rice crop morphological features and soil productivity. It can be concluded that amending soil with organic fertilizers in combination with activated zeolite clinoptilolite can be a beneficial approach for decreasing chemical fertilizer application rates and improving the sustainability of agricultural systems. Acknowledgements The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS-RACER) under a grant number of RACER/1/2019/WAB01/ UNIMAP//1 from the Ministry of Education Malaysia.
References 1. Meng F, Xiang D, Zhu J, Li Y, Mao C (2019) Molecular mechanisms of root development in rice. Rice 12(1):1–10 2. Lynch JP, Chimungu JG, Brown KM (2014) Root anatomical phenes associated with water acquisition from drying soil: targets for crop improvement. J Exp Bot 65(21):6155–6166 3. Chai Q, Gan Y, Zhao C, Xu HL, Waskom RM, Niu Y, Siddique KH (2016) Regulated deficit irrigation for crop production under drought stress. A review. Agron Sustain Dev 36(1):1–21 4. Griffiths M, York LM (2020) Targeting root ion uptake kinetics to increase plant productivity and nutrient use efficiency. Plant Physiol 182(4):1854–1868 5. Tang KHD (2019) Climate change and paddy yield in Malaysia: A short communication. Glob J Civ Environ Eng 1:14–19 6. Comas LH, Becker SR, Cruz VMV, Byrne PF, Dierig DA (2013) Root traits contributing to plant productivity under drought. Front Plant Sci 4:442 7. Kumar R, Kumar R, Prakash O (2019) Chapter-5 the Impact of Chemical Fertilizers on Our Environment and Ecosystem. Chief Ed 35:69
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8. Loper S, Shober AL, Wiese C, Denny GC, Stanley CD, Gilman EF (2010) Organic soil amendment and tillage affect soil quality and plant performance in simulated residential landscapes. HortScience 45(10):1522–1528 9. Kartika K, Lakitan B, Wijaya A, Kadir S, Widuri LI, Siaga E, Meihana M (2018) Effects of particle size and application rate of rice-husk biochar on chemical properties of tropical wetland soil, rice growth and yield. Aust J Crop Sci 12(5):817–826 10. Saputra RA, Sari NN (2021) Ameliorant engineering to elevate soil pH, growth, and productivity of paddy on peat and tidal land. In: IOP Conference series: earth and environmental science, vol 648, no 1, p 012183. IOP Publishing 11. Fabricio TR, Oscarlina LW, Eduardo BM, Eliana FD, Zoraidy ML, Joo MN (2018) Physical, chemical, and microbiological evaluation of a compost conditioned with zeolites. African J Agric Res 13(14):664–672 12. Özkan B, Dengiz O, Demira˘g Turan ˙I (2019) Site suitability assessment and mapping for rice cultivation using multi-criteria decision analysis based on fuzzy-AHP and TOPSIS approaches under semihumid ecological condition in delta plain. Paddy Water Environ 17(4):665–676 13. Abdul Halim NSA, Abdullah R, Karsani SA, Osman N, Panhwar QA, Ishak CF (2018) Influence of soil amendments on the growth and yield of rice in acidic soil. Agronomy 8(9):165 14. Bratoev K, Beloev H, Mitkov A, Mitev G (2020) On the possibility of conducting fast and reliable soil tests. Mech Agric Conserv Resour 66(2):71–76 15. Cataldo E, Salvi L, Paoli F, Fucile M, Masciandaro G, Manzi D, Mattii GB (2021) Application of zeolites in agriculture and other potential uses: A review. Agronomy 11(8): 1547 16. Zhang W, Yao X, Duan X, Liu Q, Tang Y, Li J, Li G, Ding Y, Liu Z (2022) Foliar application uniconazole enhanced lodging resistance of hybrid indica rice by altering basal stem quality under poor light stress. Agron J 114(1):524–544 17. Kong D, Wang J, Valverde-Barrantes OJ, Kardol P (2021) A framework to assess the carbon supply–consumption balance in plant roots. New Phytol 229(2):659–664 18. Sun Y, He Z, Wu Q, Zheng J, Li Y, Wang Y, Chen T, Chi D (2020) Zeolite amendment enhances rice production, nitrogen accumulation and translocation in wetting and drying irrigation paddy field. Agric Water Manag 235:106126 19. Kartika K, Sakagami JI, Lakitan B, Yabuta S, Akagi I, Widuri LI, Nurrahma AHI (2021) Rice husk biochar effects on improving soil properties and root development in rice (Oryza glaberrima Steud.) exposed to drought stress during early reproductive stage. AIMS Agric. Food 6(2):737–751 20. Hussain S, Zhang JH, Zhong C, Zhu LF, Cao XC, Yu SM, Jin QY (2017) Effects of salt stress on rice growth, development characteristics, and the regulating ways: A review. J Integr Agric 16(11):2357–2374 21. Riaz M, Arif MS, Ashraf MA, Mahmood R, Yasmeen T, Shakoor MB, Fahad S (2019) A comprehensive review on rice responses and tolerance to salt stress. Adv Rice Abiotic Stress Tolerence: 133–158 22. Carvalho P, Foulkes MJ (2018) Roots and uptake of water and nutrients. Encyclopedia of sustainability science and technology, pp 1–24
Biomass Conversion Technologies for Bioenergy and Biofuels
Comparison of Corn and Tapioca Starch Binders on the Characteristic of Rice Straw Charcoal Briquettes Syed Nuzul Fadzli Syed Adam, Firuz Zainuddin, Noor Zulaika Salleh Morgan, and Hazmi Helmi Saroni
Abstract Agricultural waste was abundant and commonly burnt on the landfilled due to no significant uses. Rice straw was rarely used in proper scientific work for the production of charcoal briquettes. Rice straw can be converted into an alternative charcoal briquette which is used for generating heat energy. However, the suitable type and concentration of binder used for the briquette production were still unclear. The aim of this study is to make the properties comparison between corn and tapioca starch as binder used in rice straw charcoal briquettes. Chopped rice straw was combusted in oven at 260 °C for 4 h in order to produce char powder. Each kind of starch and char powder was thoroughly mixed together and then compacted into charcoal briquettes by using a carbon steel die. Charcoal briquette samples were then analysed for volatile matter, fixed carbon, moisture content, ash content and burning rate. It was found that corn and tapioca starch binders with different binder concentrations affect slightly different characters and properties of charcoal briquettes product. Keywords Charcoal · Briquettes · Rice straw · Binders · Biowaste
S. N. F. S. Adam (B) Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, 02600 Pauh, Perlis, Malaysia e-mail: [email protected] F. Zainuddin · N. Z. S. Morgan Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] H. H. Saroni Fairmont Industries Sdn Bhd, No 2 Jalan Wawasan 3/Ku7, Sungai Kapar Indah, 42200 Klang Selangor, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_5
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1 Introduction In many countries, a huge amount of biowaste such as straws, shells, stalks, husks, wood and others are produced every year and this residue is disposed of by burning in the landfills. This is due to the enormous amounts of residues produced were not established for other economical and productive uses. However, burning and firing of biowaste is discouraged because of economic, environment and health safety reasons. Nowadays, biowaste is recognized as an economic resource value which has not yet been treasured. Nevertheless, agricultural waste can be used as an alternative energy source. These residues could be processed into briquettes for heat production as an alternative fuel [1]. By using agricultural waste as energy source, it supports climate change mitigation by reducing acid rain, soil erosion, pollution of water and waste disposal pressure [2]. Rice straws are composites of natural plant, consisting of cellulose as main fibre, hemicellulose as interconnected branch and lignin as binder. Cellulose and hemicelluloses are organic fibre, while lignin is the cell wall [3]. Rice straw is a rice by-product that is produced and available when harvesting paddy. Rice straw collection is laborious but the collection logistics shall be improved through baling equipment, but for most farmers, the high cost makes it not economical [4]. Hence, technologies applied for rice straw collection and application must be efficient enough to compensate for the high cost involved. Rice straw is a potential biomaterial that potentially is converted into charcoal briquette products [5]. Making charcoal briquettes from rice plant would have a good potential to create a new form of sustainable energy as these biowastes could be processed into briquettes for heat production as an alternative fuel. High-quality charcoal briquettes showed by fast firing time and lower on smokes, high maximum temperature and low burning rate. Yet, there were no details scientific work has been established to utilize rice straw for charcoal briquette purpose. Binders and additives are very important for improving properties, providing mechanical strength and increasing bulk and energy densities of the charcoal briquettes. A binder may be in the form of liquid or solid which forms a matrix to create strong adhesive and bonding between particles. There is a need to evaluate the addition of binders to address the various challenges associated with the production of charcoal briquettes. In general, the addition of binder influences the properties of charcoal briquettes, especially the combustion [5]. Studies by Sen et al. [6] found that briquettes show significant improvement in binders-related physical and mechanical properties such as bulk density and compressive strength [6]. According to Sadiq and Nasiru [7], fabricating briquettes with organic binders such as molasses, starch and gum arabic has particularly improve the caloric value of charcoal briquettes [7]. Meanwhile, starch decreases or retards briquettes’ combustion. Borowski et al. [8] showed that binders affect the briquettes charcoal fire where it is more appropriate to use briquettes with native wheat starch as binder [8]. Miao et al. [9] found that starch addition in charcoal was needed to obtain sufficient compressive strengths, but significantly decreased the green densities of the briquettes [9]. Arewa et al. [10] found that charcoal briquettes combustion (made from rice husk) was enhanced
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when cassava peels and also cassava starch were utilized as binders. However, the properties of the rice husk charcoal briquettes were found to give better performance with the use of cassava peels as the binder agent [10]. Up to this point, there were no literature studies reported on the potential of corn starch as binder for the production of biomass waste-based charcoal briquettes. For this reason, the aim of this study is to evaluate the characteristic of charcoal briquettes made with corn starch as binder and a comparison was also made with the more common binder, tapioca starch, in preparations of the briquettes. The selection of corn starch for comparison is due to the huge availability and lower cost, as well as the availability of tapioca starch. The final properties and characteristics of charcoal briquettes made from rice straw and binder addition were discussed in this study.
2 Methodology The main raw materials used in this study were rice straw plant waste, corn starch and tapioca starch powder. Rice straw (paddy type MR297) which was obtained from Muda Agricultural Development Authority (MADA), Alor Setar, Malaysia, were used for char powder preparation. Meanwhile, organic starch powder produced from tapioca and corn was each used as the binding component with the addition of plain water. Corn and tapioca starch powder were obtained from local supply under the ‘Bunga Raya’ brand. Rice straw was sorted manually to remove any obvious impurities or contaminant material before it was cut to smaller size and dried in a forced induction universal oven for a day at 80 °C to remove moisture.
2.1 Charcoal Preparation At first, dried rice straws were heated at 260 °C temperature for 4 hours in a convection oven to form char (this temperature is chosen based on the thermogravimetry result discussed in 3.1). Then, the rice straw char were grounded to powder and sieved to 60 mesh. The corn and tapioca starch paste was prepared by particularly mixing and stirring of both starch powder and water and stirring at a constant temperature until coagulates. The binders were added into char powder at three different weight percentages; 4.0, 8.0 and 12.0wt.% and thoroughly mixed until uniform mixes were obtained.
2.2 Charcoal Briquette Preparation Each char specimens were labelled according to binder–char composition and binder type. Charcoal with corn starch binder was generally labelled as CS and detail with
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binder concentration used; CS4 (4% of corn starch), CS8 (8% of corn starch) and CS12 (12% of corn starch). Meanwhile, charcoal briquettes with tapioca starch binder were generally labelled as TS with TS4 for 4% of tapioca starch, TS8 for 8% of tapioca starch and TS12 for 12% of tapioca starch. These char/binder mixtures were compacted into briquettes using a 12 mm diameter steel mould by hydraulic press machine. Finally, these charcoal briquettes were again dried in oven for 12 h. More detail for char powder and charcoal briquette preparation can be referred from previous work [10].
2.3 Charcoal Briquette Characterization At first, the char powder was analyzed with Thermal Gravimetry Analysis (TGA) to determine its thermal degradation property. Then, the charcoal briquettes were subjected to other tests and characterizations including volatile matter, fixed carbon, ash content, moisture content, ignition time and burning rate. The results obtained from these tests were presented and discussed. Thermal Gravimetric Analysis (TGA) The char was initially analysed with Thermal Gravimetric Analysis (TGA) to determine the optimal combustion temperature for the rice straw waste to convert into char. About 3.0 mg of char powder was used during this test. The analysis was carried out in the range of 30–400 °C with the presence of inert argon gas to prevent oxidation reaction. The rate of heating was set at 20 °C per minute meanwhile the rate of gas flow was set at 80 ml per minute. Volatile Matter The percentage of volatile matter (PVM) was determined by using 3.0 g of crushed charcoal briquettes. The crushed briquettes were dried in oven before it was heated at 550 °C for 10 min in a muffle furnace. The weight of both sample conditions was taken. The PVM was calculated using Eq. (1) and the average calculated value of six samples was determined. PVM =
A−B × 100% A
(1)
where A is the weight of the oven-dried sample (g) and B is the weight of furnaceheated sample Fixed Carbon The percentage of fixed carbon (PFC) was determined by the difference in percentage of the total raw material in the other quantities, such as moisture, volatile matter and ash content. The PFC was calculated using Eq. (2) and the average calculated value of six samples was determined.
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P FC = 100 % − MC (%) − AC (%) − V M (%)
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(2)
where MC is the moisture content (%), VM is the volatile matter (%) and AC is the ash content (%). Moisture Content Percentage of moisture content (PMC) was determined by estimating the proportion of water mass in charcoal briquette samples. It was calculated using Eq. (3) and the average calculated value of six samples was determined. MC = (X 1 − X 2)/ X 2 × 100%
(3)
where X1 is the weight of standard sample (g) and X2 is the weight of dried sample (g). Ash Content Percentage of ash content (PAC) is the mass of remaining char left after charcoal briquette was burnt in a controlled environment. It was determined by combusting dried charcoal briquettes in normal air furnace at 550 °C for 4 h and the weight of its residue was recorded. The PAC was calculated using Eq. (4) and the average calculated value of six samples was determined. P AC = C/A × 100%
(4)
where C is the mass of ash obtained (g) and A is the mass of the oven-dried briquettes (g). Burning Rate Burning rate of charcoal briquettes is defined as the rate at which a specific mass of briquettes was burnt in air. Ignition time is the time taken for charcoal briquettes to ignite and it is a necessary factor to calculate the charcoal burning rate. In order to calculate the ignition time, a charcoal briquette sample was placed on a grid wire with stands. A Bunsen burner was used and it was put directly underneath the grid wire and the sample. Before starting, the flame of the burner was adjusted to blue color. The Bunsen burner was lighted on to ignite the sample and it was left in. Until the sample burns steadily, the burner was cut off and time taken was started. Time taken was stopped when the sample has stopped burning in total. The burning rate, Bs, was measured by using Eq. (5) and the average calculated value of six samples was determined. Bs = W/T where W is the initial weight of briquettes (g) and T is the Ignition time (min).
(5)
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Fig. 1 The thermal degradation of rice straw chars with different combustion temperature
3 Results and Discussion 3.1 Thermal Degradation of Char Thermogravimetric (TGA) results for chars prepared at different heating temperatures are shown in Fig. 1. The mass degradation that occurred in temperature ranges between 100 and 250 °C was related to water removal and moisture burnoff. Meanwhile, the decomposition of organic content such as lignin and hemicellulose parts occurred at a higher temperature range of 260–400 °C. [3]. The char combusted at 220 °C showed the greatest mass loss (%) compared to the char combusted at 260 °C which showed the lowest. This indicates that the higher the combustion temperature to produce char, the higher percentage of lignin, hemicellulose and other organic components contained in the rice straws were degraded. It is evident that raw chopped rice straw combusted in oven at 260 °C and heating for 4 h was more applicable and suitable to be used as char. Hence, the converting process of raw rice straw waste into char was carried out at 260 °C for 4 h in this study.
3.2 Fixed Carbon and Volatile Matter Figure 2 shows the bar graph for both the average percentage of volatile matter and fixed carbon of charcoal briquette samples made with the addition of tapioca starch and corn starch binder at different percentages. This result indicates that volatile
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matter obtained for tapioca starch charcoal briquettes ranged between 56.73 and 59.78%. This result shows some inconsistency where the data of volatile matter obtained for TS4 is at 59.64%, then slightly decreased at 56.73% before increased to 59.78% for TS12. This result can be expected as we deal with natural organic waste. The resulting trend is similar for corn starch charcoal briquettes as they have a volatile matter of 54.87, 54.12 and 60.79%, respectively. In general, corn starch charcoal briquettes have a higher percentage of volatile matter compared to tapioca starch charcoal briquettes. The volatile matter obtained for rice straw charcoal briquette in this study is lower than rice straw and sugarcane leaves briquettes with molasses binder found in other studies [11]. According to Akowuah et al. [12], agricultural residues generally contain high volatile matter of around 70%–80% [12]. Tamilvanan [13] in his studies used bituminous coal contains only about 35% of volatile matter. Due to fractional heat contribution of the volatile matter is higher in biowaste or agricultural residue, this type of waste is more reactive fuel when compared to coal which gives higher burning rate during volatilization phase. Tamilvanan in his study also stated that higher volatile matter percentage contributes to higher or faster ignition rate of charcoal briquettes [13]. Due to higher volatile matter of these briquettes compared to conventional coals, it is expected that the burning characteristic of these briquettes will be enhanced. Fixed carbon means the remaining portion of charcoal briquette sample after measuring moisture, volatile matter and ash content. Fixed carbon is the percentage of carbon of charcoal briquettes when overall volatile matter content was removed from
Fig. 2 Percentage of volatile matter and fixed carbon of charcoal briquettes
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the briquettes. Fixed carbon test provides an indication of the remaining carbon fraction after the volatilization phase. The fixed carbon reacts with oxygen to release heat [13]. From the result, it shows that fixed carbon for TS charcoal briquettes ranges from 3.01 to 9.49%. An increase in binder content has significantly increased charcoal’s fixed carbon percentage. The increment that occurred in fixed carbon percentage is most likely due to the concentration of organic binder used in the briquettes. The fixed carbon for CS charcoal briquettes ranged from 4.89 to 8.82%. However, the highest fixed carbon percentage was obtained for C8 at 8.82%; meanwhile, the lowest fixed carbon was obtained for C4 at 4.89%. Fixed carbon increased with increasing binder concentration as also can be observed in the previous work of Wang et al. [14]. In general, tapioca starch charcoal briquettes have higher percentage of fixed carbon compared to corn starch charcoal briquettes. High percentage of fixed carbon will enhance the heat value of charcoal briquette. Thus, it indicates that high fixed carbon remains in charcoal briquettes are always preferable and desirable. However, fixed carbon percent obtained in the study is found lower than fixed carbon of coal found in the previous study [13]. This result showed that TS charcoal briquettes exhibit higher content of volatile matter however at the same time have slightly lower fixed carbon value compared to CS briquettes.
3.3 Ash and Moisture Content The result of ash and moisture content of charcoal briquettes with different percentages of binder (corn starch and tapioca starch) is shown in Fig. 3. Ash content is the residual component of agricultural char powder, where the higher content of ash, the lower the energy contained in the char powder. In general, ash content for corn starch briquettes was found higher compared to tapioca starch briquettes. However, the values decreased from 22.56 to 15.34% with an increase in binder concentration. The trend is also similar with tapioca starch briquettes where the increase in binder concentration affects lower ash content. This indicates that binder helps in reducing ash content during burning. Meanwhile, moisture content was higher in tapioca starch briquettes and also showed decreased trend in value by increased binder concentration. The value of moisture content obtained for both rice straw charcoal briquette with corn starch and tapioca starch were found higher compared to rice husk briquettes with cassava starch and rice husk briquettes with cassava peel binder [10]. Moisture content is an important parameter for evaluating physical condition changes of charcoal briquettes during storage. Furthermore, higher moisture content reduces the binding strength of the charcoal briquettes and also reduces their bulk density. Low heat output and low combustion temperature were normally the characteristics of briquettes with higher moisture content. This result showed that CS briquettes have higher ash content, however, at the same time exhibit lower moisture content when comparing the result with TS briquettes.
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Fig. 3 Percentage of ash content and moisture content of charcoal briquettes
3.4 Ignition Time and Burning Rate Figure 4 shows the results of ignition time and burning rate for charcoal briquettes with different binders. In general, ignition time is the period of time taken (in minutes) for the charcoal briquettes to ignite. Initially, longer ignition time provides better charcoal briquette characteristics. Ignition time was found slightly longer in CS briquettes compared to TS briquettes. Additionally, the values increased from 15.62 to 18.19 min with an increase in binder concentration. Typically, the higher the ignition time, the higher the volatile matter of the briquettes. This is because longer time was needed to burn off the volatile component before the combustion starts [15]. The burning rate is the mass of briquettes which decomposes per minutes during direct ignition. Burning rate was found slightly higher for TS briquettes and the value was slightly decreased from 0.09168 to 0.08191 g/min with an increase in binder concentration. This can be explained by the fact that the difference in binder concentration shall contribute to different supplies of oxygen gas, thus influencing the combustion process. According to Sunardi et al. (2019), the bulk density of charcoal briquettes always affects the burning rate of those briquettes. Briquettes with higher density will have difficulty to burn; mean while, briquettes with less density will result in shatters of briquettes during the combustion process [16]. This result showed that CS briquettes took a longer time in ignition and thus affects in lower rate of burning compared to TS briquettes, and also increase in binder concentration will increase the ignition time.
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Fig. 4 Ignition time and burning rate of charcoal briquettes
4 Conclusion The preparation of charcoal briquettes made from local agricultural paddy straw waste and the properties comparison for different types and concentrations of binder used during the briquette preparation has been successfully conducted. In this study, different types and different concentrations of binder used have slightly affected different properties of the rice straw charcoal briquettes. Corn starch as a binder was found to be a better option in producing rice straw charcoal briquettes as they showed characteristics such as lower volatile matter, lower moisture content, higher fixed carbon value, higher ignition time and lower burning rate. Acknowledgements The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) given by the Ministry of Higher Education (MOHE) under a grant number of FRGS/1/2018/TK10/UNIMAP/02/19 and Universiti Malaysia Perlis (UniMAP).
References 1. Teixeira SR, Pena AFV, Miguel AG (2010) Briquetting of charcoal from sugar-cane bagasse fly ash (scbfa) as an alternative fuel. Waste Manage 30(5):804–807 2. Jekayinfa SO, Omisakin OS (2005) The energy potentials of some agricultural wastes as local fuel materials in Nigeria. Agric Eng Int 7:1–10
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3. Sain M, Panthapulakkal S (2006) Bioprocess preparation of wheat straw fibers and their characterization. Ind Crops Prod 23(1):1–8 4. Gadde B, Menke C, Wassmann R (2009) Rice straw as a renewable energy source in India, Thailand, and the Philippines: Overall potential and limitations for energy contribution and greenhouse gas mitigation. Biomass Bioenerg 33(11):1532–1546 5. Binod P, Sindhu R, Singhania RR, Vikram S, Devi L, Nagalakshmi S, Kurien N, Sukumaran RK, Pandey A (2010) Bioethanol production from rice straw: An overview. Biorsource Technology. 101(13):4767–4774 6. Sen R, Wiwatpanyaporn S, Annachhatre AP (2016) Influence of binders on Physical properties of fuel briquettes produced from cassava rhizome waste. Int J Environ Waste Manage 17(2):158–175 7. Sadiq U, Nasiru R (2013) Investigation on the effects of addition of binder and article size on the high calorific value of solid biofuel briquettes. J Natl Sci Research 3(12):30–35 8. Borowski G, St˛epniewski W, Wójcik-oliveira K (2017) Effect of starch binder on charcoal briquette properties. Int Agrophysics 31(4):571–574 9. Miao Z, Zhang P, Li M, Wan Y, Meng X (2019) Briquette preparation with biomass binder. Energy Sources, Part A: Recover, Util Environ Eff:1–11 (2019). 10. Arewa ME, Daniel IC, Kuye A (2016) Characterisation and comparison of rice husk briquettes with cassava peels and cassava starch as binders. Biofuels 7(6):671–675 11. Jittabut P (2015) Physical and thermal properties of briquette fuels from rice straw and sugarcane leaves by mixing molasses. Energy Procedia 79(20). Elsevier B.V 12. Akowuah JO, Kemausuor F, Mitchual SJ (2012) Physico-chemical characteristics and market potential of sawdust charcoal briquette. Int J Energy Environ Eng 3(1):1–6 13. Tamilvanan A (2013) Preparation of biomass briquettes using various agro- residues and waste papers. J Biofuels 4(2):47 14. Wang C, Wang F, Yang Q, Liang R (2009) Thermogravimetric studies of the behavior of wheat straw with added coal during combustion. Biomass Bioenerg 33(1):50–56 15. Onukak IE, Mohammed-Dabo IA, Ameh AO, Okoduwa SIR, Fasanya O (2017) Production and characterization of biomass briquettes from tannery solid waste. Recycling, 2(4) 16. Sunardi D, Mandra MAS (2019) Characteristics of charcoal briquettes from agricultural waste with compaction pressure and particle size variation as alternative fuel Int Energy Journal 19(3):139–147
Pretreatment of Leucaena Leucocephala Using Deep Eutectic Solvent for Ethanol Production by Kluyveromyces Marxianus UniMAP 1–1 Mohammad Zulhilmi Ishak, Khadijah Hanim Abdul Rahman, Ahmad Anas Nagoor Gunny, Habibollah Younesi, and Ku Syahidah Ku Ismail
Abstract Conventional pretreatment methods such as alkaline and acid pretreatment which were used in biorefineries to dissolve lignin and hemicellulose faces many drawbacks. These pretreatment methods were considered as toxic not only to the environment, but also to the biomass as further treatment using these solvents will lead to the production of hydroxymethyl furfural (HMF) and furfural, which can inhibit the production of ethanol. Recently, deep eutectic solvents (DES) have grown in popularity as an alternative solvent to substitute conventional pretreatment solvents. DES have a great number of advantages such as biodegradability, non-toxic, low volatility and low cost. Furthermore, DES also is a powerful solvent to dissolve lignin, thus this makes DES a superior solvent to be used in biorefineries compared to alkaline and acid pretreatment. In this study, Leucaena leucocephala seeds and pods were treated with choline chloride – glycerol (ChCl – Gly) based DES at 1:2 molar ratio, and the performance subjected to sugar released and ethanol production M. Z. Ishak · K. H. A. Rahman · A. A. N. Gunny · K. S. K. Ismail (B) Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia e-mail: [email protected] M. Z. Ishak e-mail: [email protected] K. H. A. Rahman e-mail: [email protected] A. A. N. Gunny e-mail: [email protected] K. H. A. Rahman · A. A. N. Gunny · K. S. K. Ismail Centre of Excellence for Biomass Utilization, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia H. Younesi Department of Environmental Science, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_6
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were analyzed using high performance liquid chromatography (HPLC). The values were then compared with the conventional pretreatment methods as the controls. The results suggested that DES pretreatment released slightly higher total sugar, 29.28 g/L compared to alkaline and acidic pretreatment at 28.77 and 24.94 g/L, respectively. On the other hand, the yield of ethanol after fermentation in DES treatment were among the highest, which was 0.287 g ethanol/g glucose with 56.27% conversion compared to theoretical yield. The purpose of this report is to offer further information in the interest of making DES as a good replacement for the conventional pretreatment method. Keywords Lignin · Leucaena leucocephala · Deep eutectic solvents · Kluyveromyces marxianus · Ethanol
1 Introduction Demand and interest for alternative fuel is growing in many countries nowadays as the fossil fuels supply decrease by time. Biofuel, especially bioethanol is blooming as one of the alternatives due to its sustainability. It uses biomass as their raw material, thus the utilization of plant-based product will be maximized. The second-generation bioethanol seems to be promising as it uses non-edible biomass such as straw and grass [1] that will not compete with food sources. In plant-based materials such as rice straw, three major components in the plant structure were lignin, hemicellulose and cellulose. Lignin, a fibrous material provides a rigid and hard structure to the plant [2], while hemicellulose and cellulose both contain polysaccharide that made up the plant cell wall. Generally, the straw contains 30−50% of cellulose, 10−20% hemicellulose and 10−30% lignin [2]. In biorefineries, lignin needs to be hydrolyzed to open up the cellulose and hemicellulose structure before being converted into fermentable sugars. A well-known conventional pretreatment used in biorefineries includes the use of acid and alkaline to solubilize the lignin. These methods were promising to be used in pretreatment as it uses a simple device and ease of operation [3]. However, the major drawbacks for both pretreatments are not being environmentally friendly and non-biodegradable [3]. Hence, a new class of versatile liquid known as deep eutectic solvents (DES) has evolved as an alternative to these conventional pretreatments. DES is considered as a new class of green solvents that emerged from ionic liquids (ILs) because they shared many properties and characteristics such as low toxicity, low volatility, low cost and biodegradable [4–7], making it a good solvent to be used. In biorefineries, DES acts as a solvent that can hydrolyze lignin and enhance the saccharification process [6]. Despite the fact that it has been identified as a potentially useful solvent for the pretreatment of biomass, the application towards biomass is still in its infancy [7]. In this work, DES pretreatment for the selected biomass, Leucaena leucocephala was studied and compared with the conventional pretreatment in terms of sugar released and ethanol production.
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2 Materials and Method 2.1 Sample Preparation Leucaena leucocephala seeds and pods were gathered from areas in Perlis, Malaysia and then dried in an oven (Binder, USA) at 60 °C for approximately 48 h. This was done to reduce the amount of water content in the plants and to eliminate any microorganisms that may have been present in the plants. The drying process continued until the samples reached a consistent weight at which point it was terminated. After the plants had been dried, they were placed in a blender (Electrolux, Malaysia) and grounded into powder. The processed samples were then stored in a vacuum desiccator with silica gel until further use.
2.2 DES Preparation Choline chloride (99%) (Sigma Aldrich, USA) was vacuum-dried for approximately 6 h at 80 °C before use. Glycerol which act as the hydrogen bond donor and choline chloride as the hydrogen bond acceptor were mixed according to 1:2 M (molar ratio). The mixture was then heated to 100 °C while being agitated until a homogeneous colourless solution was produced [8]. The created DES were stored in a vacuum desiccator with silica gel until it was ready to be used. Sulphuric acid and sodium hydroxide were prepared in 2% (w/v) for both acid and alkaline pretreatment. Preparation of both solutions were conducted in the fume hood, and were then kept in tight bottles until further use.
2.3 Pretreatment and Enzymatic Hydrolysis Approximately 5 g of the dried L. leucocephala was subjected to pretreatment with 100 mL of each solvent for 90 min at 80 °C [9]. After that, the supernatant were collected in 2 mL tubes and kept in the refrigerator until further analysis. The remaining solution were further hydrolyzed using 30 FPU cellulase (Cellulase from Aspergillus sp., Sigma). Citrate buffer (pH 4.8) was added to the samples and swirled for 24 h, at 150 rpm and 50 °C [9]. The sample solution was then centrifuged to separate the solids and supernatant. Afterwards, the supernatant was stored in the refrigerator for further investigation.
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2.4 Fermentation and Sample Analysis About 15 mL of the remaining solution was further used to conduct a simple fermentation setup. Kluyveromyces marxianus UniMAP 1–1 was inoculated into the fermentation media which contains YPD medium with yeast extract, 10 g/L; peptone, 20 g/ L; and dextrose 10 g/L. It was then transferred into fermentation bottle with 50 g/ L inoculum loading, pH 4.8, 50 °C for 72 h. The samples were then collected in 2 mL tubes. Concentrations of sugar released and ethanol produced from each treatment were analyzed by using high performance liquid chromatography (HPLC) (Shimadzu RID-10A, Japan) fitted with refractive index detector and Aminex HPX87H (300 mm × 7.8 mm) column. The column temperature was kept at 45 °C with 0.005 M H2 SO4 as the mobile phase at a flowrate of 0.6 mL/min [10]. The injection volume was 10 µL.
3 Results and Discussion 3.1 DES Pretreatment Versus Acid and Alkaline Pretreatment Performance for each of the pretreatment methods were determined based on the glucose and xylose released. Figure 1 shows the concentration of glucose and xylose released subjected to each pretreatment method. Glucose released from DES pretreatment was the highest among others with 3.77 g/L followed by 1.89 g/L and 1.42 g/ L in alkaline and acid pretreatment, respectively. Xylose however, was released the highest in alkaline pretreatment, 9.45 g/L, followed by DES and acid pretreatment at 8.82 and 7.99 g/L, respectively. This shows that DES has the potential to effectively solubilize and remove lignin from the biomass compared to acid and alkaline pretreatment. Effective removal of lignin from biomass will open the cellulose and hemicellulose structure, making it ready for the next enzymatic treatment. This was agreed by many researchers who proposed that DES does not only solubilize lignin, but it also helps to enhance the saccharification process [6, 7, 11, 12]. Previous research reported that DES with acidifiying polyols, for example acidic glycerol-based DES are more effective and is the fastest way to remove lignin from switchgrass, with only six minutes to get 82% lignin removal at 120 °C [6]. This proved that DES is a powerful solvent to remove lignin even under mild conditions.
3.2 Effect of DES on Subsequent Enzymatic Hydrolysis Performance of each solvent during pretreatment was further analyzed by conducting enzymatic hydrolysis to convert cellulose and hemicellulose into fermentable sugars.
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Glucose, Xylose (g/L)
12 10 8 6 4 2 0 DES (1:2 M ratio of ChCl:Glycerol)
Acid (H₂SO₄ 2% w/v) Glucose
Alkaline (NaOH 2% w/v)
Xylose
Fig. 1 Glucose and xylose concentrations after DES, acid and alkaline pretreatment
Figure 2 shows the glucose and xylose produced after each treatment. The overall trend of glucose released in each treatment was increased compared to previous pretreatment on lignin removal. This is due to the addition of cellulase enzyme attacking the cellulose, which was then converted to fermentable sugar. Only slight differences were reported between the solvents, with alkaline treatment producing the highest glucose at 28.60 g/L, followed by DES and acid at 27.45 g/L and 24.22 g/L, respectively. However, for xylose released, the concentrations produced were very low, because cellulase only attacks cellulose, producing only glucose. However, xylose concentration from DES pretreatment was the highest among others with 1.83 g/L, followed by alkaline and acid at 0.17 and 0.72 g/L, respectively. In terms of total reducing sugar, which combined both glucose and xylose, DES pretreatment produced the highest total sugar produced, with 29.28 g/L followed by alkaline and acid treatment at 28.77 and 24.94 g/L, respectively. Alkaline pretreatment gave higher pH, around pH 8–10 compared to other solvents. This high pH was then decreased to pH 4.8 during enzymatic hydrolysis to synchronize the optimum pH of cellulase enzyme. Large amount of water or solution would be used to wash the remaining alkaline [13] solution, thus increasing the cost of production. This is one of the major drawbacks for alkaline treatment [13]. Unlike DES, their pH value can be monitored by mixing a correct amount of hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA).
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Glucose, Xylose (g/L)
35 30 25 20 15 10 5 0 DES (ChCl:Glycerol)
Acid (H₂SO₄) Glucose
Alkaline (NaOH)
Xylose
Fig. 2 Glucose and xylose concentrations after enzymatic hydrolysis for DES, acid and alkaline pretreatment
3.3 Fermentation Profile from Differently Treated L. leucocephala Fermentable sugar produced from L. leucocephala can be converted into ethanol by a thermotolerant yeast such as K. marxianus [14]. Figure 3 shows the fermentation profile using the reducing sugars produced from acid, alkaline and DES pretreatments. The sugar concentrations for all treatment were fully consumed within 72 h. This shows that the sugar produced from the real biomass hydrolyzate was safely consumed by the yeast, resulting in increased ethanol concentration. As a comparison, sugars from acid treated L. leucocephala were consumed slower compared to ones treated by alkaline and DES. This might be due to the production of inhibitors at low pH for a prolonged time [13], resulting in slowing yeast metabolism, thus slowing the yeast activity rate. This is one of the major drawbacks using acid pretreatment [13]. Unlike DES, the DES solution can be recycled after each treatment, reportedly up to 10 times treatment without decreasing the pretreatment efficiency [15]. Hence, there were no inhibitors produced and very cost effective. Table 1 shows the final ethanol concentration produced from the fermentation process, including the yield. Alkaline pretreatment shows similar yield with DES pretreatment at 0.29 g/g, followed by acid with 0.26 g/g. Considering the results from the calculated yield, the concentration and productivities did not show any significant difference. This is due to the value of conversion that depends on the fermentable sugar released during pretreatment and enzymatic hydrolysis. Theoretically, the higher the fermentable sugar released, the higher the concentration of ethanol. Thus, alkaline treatment produced more ethanol because it has higher sugar released compared to the others. However, due to the high production cost within the alkaline treatment process [13], it is not the best treatment to be chosen. Unlike DES, the production cost for DES is slightly lower than the others [11, 13, 15].
Pretreatment of Leucaena Leucocephala Using Deep Eutectic Solvent … 7 6 5 4 3 2 1 0
30 25 20 15 10 5 0 0
3
6
9
Ethanol concentration (g/L)
Sugar concentration (g/L)
Fig. 3 Fermentation profile by K. marxianus UniMAP 1–1 using the reducing sugars produced from a acid pretreatment, b alkaline pretreatment and c DES pretreatment
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12 24 48 72
Time (h) Total reducing sugar
Ethanol
40
10
30
8 6
20
4
10
2
0
0 0
3
6
9
Ethanol concentration (g/L)
Sugar concentration (g/L)
(a)
12 24 48 72
Time (h) Total reducing sugar
Ethanol
10
35 30 25 20 15 10 5 0
8 6 4 2 0 0
3
6
9
12
24
48
72
Time (h) Total reducing sugar
Ethanol concenttration (g/L)
Sugar concentration (g/L)
(b)
Ethanol
(c)
Moreover, the ease of preparation of DES [7, 11] makes it more convenient to be used in biorefineries. Table 1 Final ethanol concentration and yield after fermentation from different pretreatments using Kluyveromyces marxianus UniMAP 1–1
Treatment
Ethanol concentration (g/L)
Y p/s (g/g)
Acid
6.32
0.26
Alkaline
8.30
0.29
DES
7.89
0.29
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4 Conclusion DES pretreatment resulted in the highest glucose concentration with 3.77 g/L, which showed that the lignin were solubilized and affects the cellulose and hemicellulose structure, making them ready for the enzymatic hydrolysis. During enzymatic hydrolysis, pretreatment using DES produced the highest glucose, 27.45 g/L, almost at par with alkaline pretreatment. Conversion of fermentable sugar to ethanol from DES yielded 0.29 g ethanol/ g glucose with 56.27% conversion from theoretical yield. Solubilization of lignin is a very important step to consider before enzymatic hydrolysis, and DES has shown high potential in lignin removal. Hence, more studies about DES would be beneficial to a successful biofuel conversion. Acknowledgements This work was supported by the Fundamental Research Grant Scheme awarded by the Ministry of Higher Education, Malaysia (FRGS/1/2018/STG05/UNIMAP/02/4). The authors also would like to thank the Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis for providing the analytical instruments throughout the study.
References 1. Alalwan HA, Alminshid AH, Aljaafari HAS (2019) Promising evolution of biofuel generations. Subj Rev Renew Energy Focus 28:127–139 2. Chen C, Deng X, Kong W, Qaseem MF, Zhao S, Li Y, Wu AM (2021) Rice straws with different cell wall components differ on abilities of saccharification. Front Bioeng Biotechnol 8 3. Zadeh ZE, Abdulkhani A, Aboelazayem O, Saha B (2020) Recent insights into lignocellulosic biomass. Processes 8(1):31 4. Gurkan BE, Maginn EJ, Pentzer EB (2020) Deep eutectic solvents: A new class of versatile liquids. J Phys Chem B 124(50):11313–11315 5. Płotka-Wasylka J, de la Guardia M, Andruch V, Vilková M (2020) Deep eutectic solvents versus ionic liquids: Similarities and differences. Microchem J 159 6. Chen Z, Ragauskas A, Wan C (2020) Lignin extraction and upgrading using deep eutectic solvents. Ind Crops Prod 147:112241 7. Wang W, Lee DJ (2021) Lignocellulosic biomass pretreatment by deep eutectic solvents on lignin extraction and saccharification enhancement: A review. Biores Technol 339:125587 8. Farooq MQ, Abbasi NM, Anderson JL (2020) Deep eutectic solvents in separations: Methods of preparation, polarity, and applications in extractions and capillary electrochromatography. J Chromatogr A 1633:461613 9. Kapsokalyvas D, Wilbers A, Boogers IALA, Appeldoorn MM, Kabel MA, Loos J, Van Zandvoort MAMJ (2018) Biomass pretreatment and enzymatic hydrolysis. Dynamics analysis based on particle size imaging. Microsc Microanal 24:517–525 10. Fonseca-Santos B, Gremião MPD, Chorilli M (2017) A simple reversed phase highperformance liquid chromatography (HPLC) method for determination of in situ gelling curcumin-loaded liquid crystals in in vitro performance tests. Arab J Chem 10(7):1029–1037 11. Yang L, Zheng T, Huang C, Yao J (2022) Using deep eutectic solvent pretreatment for enhanced enzymatic saccharification and lignin utilization of masson pine. Renewable Energy 195:681– 687 12. Okuofu SI, Gerrano AS, Singh S, Pillai S (2020) Deep eutectic solvent pretreatment of Bambara groundnut haulm for enhanced saccharification and bioethanol production. Biomass Convers Biorefinery 12(8):3525–3533
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13. Rezania S, Din MFM, Mohamad SE, Sohaili J, Taib SM, Yusof MBM, Kamyab H, Darajeh N, Ahsan A (2017) Review on pretreatment methods and ethanol production from cellulosic water hyacinth. BioResources 12(1):2108–2124 14. Pilap W, Thanonkeo S, Klanrit P, Thanonkeo P (2018) The potential of the newly isolated thermotolerant Kluyveromyces marxianus for high-temperature ethanol production using sweet sorghum juice. 3 Biotech 8(2):1–10 15. Yan G, Zhou Y, Zhao L, Wang W, Yang Y, Zhao X, Chen Y, Yao X (2022) Recycling of deep eutectic solvent for sustainable and efficient pretreatment of corncob. Ind Crops Prod 183:115005
Deep Eutectic Solvent Pretreatment of Rubber Seed Shells for Cellulose and Hemicellulose Production Nur Zatul Iffah Zakaria, Norshakilla Afendi, Ahmad Anas Nagoor Gunny, Habibollah Younesi, and Ku Syahidah Ku Ismail
Abstract Ethanol is a clean biofuel that can be produced from biomass, namely, rubber seed shells. Rubber seed shells (RSS) tend to be less worthy and are rapidly becoming an agricultural waste. The environment is getting incredibly degraded as the industrial world constantly expands and has become more technologically developed in industrial operations. Generally, the most common chemical method used in pretreatment is acid and alkaline based. However, this method is unsuitable since it causes a lot of problems such as inhibitors generation and high energy consumption during the pretreatment process. Therefore, in this study, deep eutectic solvent (DESs) was used as the green solvent to pretreat the RSS for more environmentally friendly production of cellulose and hemicellulose. Seven combinations of hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) were used to synthesize DESs based on their molar ratio and physiochemical properties (pH, viscosity, density, hydrogen bond) were examined. Later, the best synthesized DES to pretreat RSS based on the cellulose and hemicellulose content was evaluated. The chemical composition (cellulose, hemicellulose and lignin) for untreated and pretreated RSS were determined by using the ASTM and TAPPI methods. The experimental results showed that the higher cellulose production were obtained by using acidic based DESs; ChCl:Oxalic acid, ChCl:Lactic acid and ChCl:Formic acid (24.43, 20.42, and N. Z. I. Zakaria · N. Afendi · A. A. N. Gunny · K. S. K. Ismail (B) Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia e-mail: [email protected] N. Z. I. Zakaria e-mail: [email protected] A. A. N. Gunny e-mail: [email protected] A. A. N. Gunny · K. S. K. Ismail Centre of Excellence for Biomass Utilization, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia H. Younesi Department of Environmental Science, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_7
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20.31%, respectively). Meanwhile, higher hemicellulose was obtained when using ChCl:Ethylene glycol and ChCl:Urea, with 46.86% and 41.14%, respectively. Hence, the study showed that acidic and slightly acidic based DESs were able to produce high cellulose and hemicellulose from RSS. Keywords Deep eutectic solvent · Pretreatment · Rubber seed shells · Cellulose · Hemicellulose
1 Introduction In recent years, the oil and gas industry has been facing increasing demand for energy consumption from petroleum due to the change in lifestyle, population and economic growth. According to the Organization of Petroleum Exporting Countries (OPEC) latest monthly oil market report, global oil demand is expected to approach pre-pandemic levels of 100.8 million barrels per day (bpd) in 2022 [1]. However, the world’s oil supply will be drastically reduced by 2080 [2]. Therefore, to overcome this problem, the consumption of energy from biomass sources has been developed. Sustainable energy systems based on renewable biomass feedstocks are presently being developed on a worldwide scale [3]. Biomass as a renewable energy source is one of the most important sources of energy extracted from organic materials and natural resources [4]. Biomass is usually the waste produced from energy crops, agricultural and forest residues. Biomass sources from agricultural wastes can be degraded by the saccharification method, resulting in high fermentable sugar content. The fermentable sugar later can be utilized as feedstocks for biofuel production. In addition, Malaysia is a country rich in natural resources, since its environment and weather are conducive to the cultivation of a wide range of plants. As a result, biomass production in Malaysia, particularly in Peninsular Malaysia, has expanded dramatically in recent years [5] and it is expected to generate 110 million tonnes of biomass waste by 2020 [6]. Malaysia generated a significant amount of lignocellulosic agriculture waste as one of the world’s most important agricultural countries, with exports including palm oil, cocoa, and rubber [7]. The rubber tree is one of the largest crops in Malaysia and generates a large amount of waste such as rubber seeds when it falls from the tree and is left to degrade [8]. According to [9], in Malaysia, the rubber plantation area was 1.07 million hectares and is expected to produce 0.535 million metric of rubber seed shells tonnes per year. This readily available waste has the potential to be used in petroleum-based fuel replacement. Presently, many researchers are focusing on utilizing rubber seeds, specifically their kernel as feedstock for biodiesel and also to provide flexibility in several polymer-based composites due to their high oil content [10, 11]. However, its shells mostly are left on the plantation area. In general, the rubber seed shells (RSS) consist of lignin, cellulose and hemicellulose content making them suitable for the production of cellulose and hemicellulose for biofuel production, such as ethanol [11].
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Meanwhile, pretreatment is the critical step for bioethanol production which indirectly will affect the liberation of hexoses and pentoses in lignocellulosic biomass for fermentation to bioethanol and contribute a large portion to the capital cost. Therefore, the best pretreatment must assure a high sugar yield, minimum inhibitors generation, low cost, the separation of lignin and fortuitously hemicellulose which can be converted into other beneficial products, a low impact of extraction, washing, neutralization stages, and low cost of energy, reagents, catalysts and water [12]. All pretreatment techniques have their advantages and limitations. For instance, alkaline and acid pretreatment offer high efficiency towards delignification and/or hemicellulose removal and a simple operating process, but the generation of inhibitors during the process has affected the yield of bioethanol and also the high energy consumption [13]. Physicochemical pretreatment can pretreat lignocellulosic with high sugar yield and no or minimum inhibitor generation, but it requires high temperature and pressures which contribute to high cost. As biological pretreatment can be done in mild operating conditions with no or minimum inhibitors generation, however, it requires a long cultivation time and loss of carbohydrates [14]. Deep eutectic solvent (DES) is seemed to be one of the best candidates able to offer green and eco-friendly process pretreatment with minimum inhibitor formation, and adequate lignin removal while preserving a high amount of cellulose and hemicellulose since its physicochemical properties can be tailored based on the mixture of HBA and HBD [15]. Therefore, this study aims to synthesize several DES types (acidic and basic based) to pretreat RSS to remove lignin and produce cellulose and hemicellulose.
2 Materials and Methods 2.1 Materials The RSS was collected from the nearest rubber plantation (Perlis, Malaysia). Sodium hydroxide, sodium chlorite, sulfuric acid, glacial acetic acid, ethanol, chlorine chloride, glycerol, formic acid, urea, lactic acid and oxalic acid were purchased from Merck and used without purification.
2.2 Preparation of RSS and DES The RSS was washed thoroughly with distilled water to remove all the impurities and dried in the oven at 105 °C for 24 h. After that, RSS was ground using a heavyduty grinder (Glen Mills, UK) to obtain particle size of 1 mm, and kept in an air tight container prior to use. All DESs were prepared by mixing ammonium salt and hydrogen bond donor in different molar ratios as shown in Table 1. Choline chloride
84 Table 1 List of DES used in this study
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HBA
HBD
DES
Molar ratio (HBA: HBD)
Choline chloride
Lactic acid
ChCl:LA
1:2
Choline chloride
Oxalic acid
ChCl:OA
1:1
Choline chloride
Formic acid
ChCl:FA
1:2
Choline chloride
Ethylene glycol
ChCl:EG
1:2
Choline chloride
Urea
ChCl:U
1:2
Choline chloride
Glycerol
ChCl:Gly
1:2
Potassium carbonate
Glycerol
PC:Gly
1:7
was dried under vacuum at 80 °C for 6 h before use. Glycerol and ethylene glycol were used as received, and other DESs will be dried in the oven at 50 °C prior to use. The mixture was stirred and heated at 80 °C for 1 h or until a homogeneous and transparent liquid was obtained [16, 17].
2.3 Compositional Analysis Compositional analysis was conducted to examine the percentage of cellulose, hemicellulose and lignin in untreated and pretreated RSS. Holocellulose determination was done by following the method by ASTM 1104–56 with some modifications [18]. Two gram of the extractive-free sample was added into 250 ml Erlenmeyer flask with 150 ml of distilled water and the mixture was shaken slowly until homogeneous. Then, the mixture was heated in an oil bath at 70–80 °C with a 25 ml Erlenmeyer flask inverted in the neck of the reaction flask. Later, 0.5 ml of glacial acetic acid and 1.0 g of sodium chlorite was added into the flask. The mixture was incubated for 60 min. After 60 min, 0.5 mL of glacial acetic acid and 1.0 g of sodium chlorite was added again into the flask and this step was repeated for every 1 h within 3 h, with occasional swirling. Then, the flask was cooled in a cold-water bath and the mixture was filtered by a fritted glass crucible. The solid residue was washed with sufficient distilled water until free of acid and dried in an oven at 105 °C. After 24 h, the solid residue (holocellulose) was cooled in a desiccator for an hour before weighing. The holocellulose (%) will be calculated by using the following equation [18]:
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H olocellulose(%) =
W eight o f holocellulose(g) × 100 W eight o f f iber s(g)
85
(1)
The α-cellulose content was determined by using the ASTM D1103-60 method with some modifications [18]. First, 500 mg of holocellulose sample was placed into a 100 ml Erlenmeyer flask and 5 ml of 17.5% (w/v) NaOH, 20 °C was added into the flask. The glass rod was used to break the lumps of holocellulose. After 5 min, 2.5 ml of 17.5% (w/v) NaOH was added into the flask with thorough stirring and this step was repeated for another 2 times at an interval of 5 min. The suspension was incubated at 20 °C for 30 min. Then, 16.75 ml of cold distilled water was added, stirred and left for another 45 min at 20 °C. Later, the suspension was filtered by a fritted glass crucible and washed twice with 6.25 ml of cold 8.3% (w/v) NaOH. The residue was washed again with 6.25 ml of glacial acetic acid and several times with distilled water. The α-cellulose then was dried in an oven at 105 °C. After 24 h, the α-cellulose was cooled in a desiccator for an hour before weighing. The α-cellulose (%) was calculated using the following equation and the hemicellulose content was calculated as the remaining wt % of the α-cellulose. α − cellulose(%)in holocellulose =
W eight o f α − cellulose(g) × 100 (2) W eight o f holocellulose(g)
H emicellulose(%) = H olocellulose(%) − α − cellulose(%)
(3)
The procedure in determining insoluble lignin content was performed by following the method by TAPPI (T 222 om-02) [19]. First, 1.5 ml of 72% (w/w) sulfuric acid was added into 0.1 g of extractive-free sample in a 100 ml beaker while stirring with a glass rod to break the lumps of the sample. The beaker was placed into a water bath at 20 °C for 2 h with frequent stirring. Then, 56 ml of distilled water was added into the beaker and heated at 100 °C for 4 h. The suspension was filtered through a fritted glass crucible and washed with hot water to remove excessive acid on the sample. The lignin (solid residue) was dried in an oven at 105 °C until a constant weight was obtained. Lastly, the lignin was cooled in a desiccator for an hour before weighing. The insoluble-lignin content will be calculated as follows: I nsoluble lignin(%) =
solid r esidue(g) × 100 weight o f sample
(4)
2.4 Physicochemical Properties of the Synthesized DESs All DESs were poured into universal bottles and dried under vacuum overnight to remove any excess moisture prior to pH, viscosity, density and hydrogen bond formation measuring. The pH of the DES was assessed directly by inserting the
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electrode of the pH meter (OHAUS STARTER 300) into the DES. The pH buffer solutions (pH 4 and 7) were used to calibrate the measurements of the electrode. Viscosity was measured by a Brookfield DV2T Viscometer and the formation of hydrogen bond between HBA and HBD in the synthesized DES was performed by using Fourier transform infrared spectroscopy (FTIR/ Perkin Elmer) on the DES samples and their constituents to identify the functional groups involved in their structure. For density, a simple calculation was carried out where the DES was poured into a 1 mL of volumetric flask until the calibrated mark. The measurement was performed by measuring the weight of the DES in a volumetric flask at 25 °C. The density was calculated as follows: Density, ρ =
weight of the DES(g) volume of the DES(mL)
(5)
2.5 Pretreatment of RSS by DESs Briefly, 0.3 g of RSS samples was mixed with 6 g of DESs. The mixture was stirred at 150 rpm in an oil bath (100 °C) for 6 h. Then, the mixture was diluted with 6 g of hot water to prevent a large amount of extracted hemicellulose from precipitating. Next, the mixture was centrifuged at 9000 rpm at 20 °C for about 10 min. The supernatant (liquid fraction) was collected for HPLC analysis and the residues (solid fraction) was washed with distilled water by using a crucible glass filter until the pH of the filtrate is neutral, and dried in an oven at 50 °C until constant weight. The dried solid fraction later was used for compositional analysis.
2.6 Analytical Method by High-Performance Liquid Chromatography (HPLC) HPLC was used to analyze monomeric sugars in the liquid fraction sample after pretreatment. About 1–1.5 ml of samples were filtered by using a 0.02 mm syringe filter into HPLC vials before been analyzed by HPLC. The HPLC column that was used is HPX- 87H (Bio-rad Lab, USA) with a size of 300 × 7.8 mm with 5 mM H2 SO4 as the mobile phase at a flow rate of 0.6 ml/min and detected by Refractive Index Detector at 45 °C of the oven temperature. The standard curve for each monomeric sugar was constructed by using their standard.
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2.7 Statistical Analysis The data were calculated using Microsoft Excel and expressed as the mean ± standard deviation (SD). All the tested samples in this study were statistically analyzed using Analysis of Variance (ANOVA) which was used to determine whether there were any statistically significant differences between the means of two or more independent groups using Tukey’s method (95% confidence level, p < 0.05). The P-values of less than 0.05 were considered statistically significant. The P-value was performed using the Statistical Package for the Social Sciences (SPSS) software.
3 Results and Discussion 3.1 Compositional Analysis of RSS Compositional analysis of RSS was performed to determine the percentage of lignin, cellulose and hemicellulose contained in this agricultural waste. Significant values for these compounds are crucial in determining the potential of this agriculture waste for bioethanol feedstock. From the analysis as shown in Table 2, the highest compounds in RSS were hemicellulose (41.68%), followed by lignin (30.91%) and cellulose (18.32%). The values for hemicellulose and cellulose obtained from this study are slightly different from other previous studies. According to [20], they have reported on obtaining 66.8% of hemicellulose and 25.8% of cellulose in RSS. Different values for cellulose and hemicellulose among previous studies with this study may be correlated with different maturity, soil nutrients, climate factors, location, tree age and size [21, 22]. However, a similar pattern can be seen in this and previous studies which consist of more hemicellulose than cellulose. Meanwhile, 33.54% of insoluble lignin reported by [23] in RSS is comparatively similar to this study. The least content was found to be extractive with 9.08%. The extractive was a non-structural component such as fats and oil in the sample. Table 2 Composition of RSS
Composition
Percentage (%)
Cellulose
18.32 ± 0.36
Hemicellulose
41.68 ± 4.75
Lignin
30.91 ± 2.03
Extractive
9.08 ± 2.35
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3.2 Physicochemical Characterization of Synthesized DESs Since there were no extensive reports on the physicochemical properties of some of the synthesized DESs, hence this study has characterized the pH, viscosity, density and hydrogen bond formation of the DESs. Viscosity plays an important role of a solvent because it affects mass movement and ionic conductivity, and consequently the functionality of specific applications. Due to the obvious continuous network of hydrogen bonding between each constituent, DESs have high viscosities, which reduces the mobility of free species in DES mixtures. It is well known that as temperature rises, viscosity reduces. This is due to a significant decrease in hydrogen bonding interactions in DESs at higher temperatures. As reported in Table 3 the most viscous solvent was PC:Gly (6667 cP), followed by ChCl:U (666.7 cP) while ChCl:EG showed the lowest viscosity when compared with other synthesized DESs. Authors in [24] stated that by constantly adding glycerol to the DESs would result in a composite viscosity that is closer to pure glycerol, whereas adding more salt to the mixture significantly increases the viscosity. The density of the deep eutectic solvent is temperature dependent, decreasing linearly with increasing the temperature [25]. Authors in [25] expressed those densities of deep eutectic solvents that are higher than water. The density of DES was affected by the type of quaternary ammonium salt and a hydrogen bond donor, as well as the molar ratio. By referring to Table 3, the lowest density was found to be ChCl:EG (1.0532 g/cm3 ), followed by ChCl:FA (1.1124 g/cm3 ), and the highest density was ChCl:OA, 1.2270 g/cm3 . All the density values were in the range of 1.0532–1.2270 g/cm3 . The densities of DESs were determined by the packing or the molecular structure of DESs. When the HBA and HBD were mixed, the average hole radius decreased, hence increasing the DES’s density. This result was quite similar to the study by [26] which reported that the density of the DES and its temperature variation are strongly influenced by the molecular properties of the HBD and the molar ratio at which the DES was synthesized. According to [27], raising the temperature or water content in DESs results in decreased densities. Based on Table 3, the most acidic solvent was oxalic acid and followed by formic acid and lactic acid (0.00,0.63, and 0.69, respectively). This result was similar to Table 3 Viscosity, density and pH of synthesized DESs at ambient temperature DES
Viscosity (cP)
Density (g/cm3 )
pH
pH condition
ChCl:LA
133.30
1.1495
0.69
Acidic
ChCl:OA
166.70
1.2270
0.00
Acidic
ChCl:FA
66.67
1.1124
0.63
Acidic
ChCl:EG
33.37
1.0532
4.13
Slightly acidic
666.70
1.1821
10.24
266.70
1.1395
4.01
6667.00
1.1792
12.66
ChCl:U ChCl:Gly PC:Gly
Basic Slightly acidic Basic
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Table 4 Summarization on the major FTIR peaks in HBA and the peak shift in HBD after DES synthesis HBA/ HBD
Assignments
Wavenumber (cm−1 )
DES wavenumber (cm−1 )
ChCl
O–H stretching
3257
NA
C–N vibration
1081
O–H stretching
3391
3308
Carbonilic group
1715
1725
O–H stretching
3428
3326
Carbonilic group
1699
1724
O–H stretching
3392
3312
Carbonilic group
1699
1709
EG
O–H stretching
3290
3295
U
O–H stretching
3330
3314
N–H stretching
3428
Symmetric NH2 deformation vibration
1673
1680
Carbonyl stretching
1587
1603
O–H stretching
3275
3296
C–O bond
1030
1035
O–H stretching
3275
3264
C–H stretching
2932–2879
2872
Carbonate ions
1450–876
NA
LA OA FA
Gly (for ChCl) Gly (for PC) PC * NA:
not applicable
[28] where their pH for DESs based on oxalic acid was −0.033. This study has proved that the acid in the DESs structure has an effect on pH behaviors, and it was discovered that the HBD has a significant effect on the resulting pH. The nature of the HBD determines the acidity of the resulting mixture. In addition, the DESs based on ethylene glycol and glycerol showed that the pH values were 4.13 and 4.01, respectively. The presence of alcohol in DESs structures is most likely responsible for these values. Because ethylene glycol and glycerol contain acidic hydrogens in their structures, their pH is less than 7. In contrast, urea and potassium carbonate salt act dominantly over choline chloride and glycerol which contribute to the basic nature of the DES solvents.
3.3 Functional Group Analysis FTIR analysis of seven synthesized DES was performed on the DES and their constituents to identify the functional groups involved in their structure, and verify the formation of hydrogen bonds as shown in Fig. 1. Figure 1a–c shows the spectra of the
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HBA (ChCl), HBD (carboxylic acid), and synthesized DES for the ChCl:LA molar ratio of 1:2, ChCl:OA molar ratio of 1:1 and ChCl:FA molar ratio of 1:2. The shift of the O–H stretching in the lactic acid (3391 cm−1 ), oxalic acid (3428 cm−1 ) and formic acid (3392 cm−1 ) to the DES ChCl:LA (3308 cm−1 ), DES ChCl:OA (3326 cm−1 ) and DES ChCl:FA (3312 cm−1 ) can be seen. In addition, the shifts in the carbonilic group from 1715 cm−1 (LA) to 1725 cm−1 (DES ChCl:LA), 1699 cm−1 (OA) to 1724 cm−1 (DES ChCl:OA) and 1699 cm−1 (FA) to 1709 cm−1 (DES ChCl:FA) were present. These shifts are supposed to be present due to the hydrogen interactions between the functional groups. For the synthesized ChCl:EG molar ratio of 1:2 in Fig. 1d, the presence of an O–H vibration peak is seen at 3257 cm−1 for choline chloride, and 3290 cm−1 for ethylene glycol. The peak at 1081 cm−1 in the FTIR spectrum of choline chloride is indicative of the C-N vibration. While the O–H vibration in the spectrum of ethylene glycol is at 3290 cm−1 , the OH vibration of the DES ChCl:EG is shifted to 3295 cm−1 . This change of O–H vibration shows the presence of hydrogen bonds between HBD and choline chloride when DES ChCl:EG is obtained. For ChCl:U molar ratio of 1:2 in Fig. 1e, N–H and O–H stretching modes can be seen at 3428 cm−1 and 3330 cm−1 for urea and 3257 cm−1 for ChCl. In addition, urea has two bands around 1700−1600 cm−1 region of the spectrum, observed at 1673 cm−1 and 1587 cm−1 for symmetric and asymmetric NH2 deformation vibrations and a carbonyl stretching vibration, respectively. Upon forming DES of ChCl:U, these two bands appear to be near 1680 cm−1 and 1603 cm−1 , whereas N–H and O–H stretching modes have been shifted to a very broad peak at 3314 cm−1 suggesting that there is an equivalently broad distribution of hydrogen bonds with different hydrogen bond strengths (Perkins, 2013). Synthesis of ChCl:Gly for molar ratio of 1:2 as in Fig. 1f shows that the O–H stretching mode can be seen at 3257 cm−1 for ChCl and 3275 cm−1 for glycerol. In DES of ChCl:Gly, a strong broad peak appears at 3296 cm−1 which indicates intermolecular hydrogen bonds between ChCl and glycerol. The transmittance peaks at 1477 cm−1 and 955 cm−1 indicate the stretching vibration of CH2 and C–C bonds, respectively in the DES spectrum. Meanwhile, a strong transmittance peak of the CO bond appears at 1030 cm−1 for glycerol and shifts to 1035 cm−1 upon forming the DES ChCl:Gly. This shift is supposed to be present due to the hydrogen interactions between the functional groups. Finally, for the synthesized PC:Gly molar ratio of 1:7 in Fig. 1g, the peaks for carbonate ions were observed from 1450 cm−1 to 876 cm−1 in PC. While, O– H stretching mode and C-H stretch were observed at 3275 cm−1 and 2932 cm−1 to 2879 cm−1 in glycerol, respectively. In PC: Gly, a shift of the O–H stretching mode and C-H stretch peak appears at 3264 cm−1 and 2872 cm−1 , which indicates intermolecular hydrogen bonds between PC and glycerol.
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Fig. 1 FTIR spectra of synthesized DES, a ChCl:LA, LA and ChCl; b ChCl:OA, OA and ChCl; c ChCl:FA, FA and ChCl; d ChCl:EG, EG and ChCl; e ChCl:Urea, Urea and ChCl; f ChCl:Gly, Gly and ChCl; and g PC:Gly, Gly and PC
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3.4 RSS Pretreatment by Synthesized DESs A comparison of untreated and pretreated biomass samples was used to measure the performance of lignocellulosic biomass pretreatment. Compositional analysis of RSS after pretreatment process was carried out to examine the major compounds in RSS. The compositions of the RSS solid fraction are illustrated in Fig. 2. Based on Fig. 2, all synthesized DESs showed the positive performance to remove lignin by having a lower lignin percentage in solid fraction except for ChCl:FA pretreatment. One possible reason for high lignin content in ChCl:FA may be due to the formation of the xylan derived pseudo-lignin. Authors in [29] have reported that harsher conditions may contribute to the formation of pseudo lignin which ultimately caused a reduction in lignin loss. By successfully degrading the lignin structure in the RSS, the hemicellulose structure is now already exposed. It can be seen in Fig. 2 that ChCl:EG and CHCl:U (46.86 and 41.14%, respectively) are able to obtain higher hemicellulose content in pretreated RSS compared to other synthesized DES. PC:Gly pretreatment showed the lowest hemicellulose content (28.29%) when compared to untreated biomass which may be caused by further breakdown of hemicellulose into its monomeric compounds due to extreme pH conditions in this DES [29]. The same phenomenon may also occur for some portion of hemicellulose after being treated by acidic DES (ChCl:LA, ChCl:OA, ChCl:FA). For the cellulose content in pretreated RSS, acidic DES also gave the highest values together with ChCl:U (Fig. 2). The high cellulose content in acidic DES and ChCl:U may be due to more hemicellulose structure being hydrolyzed, and indirectly exposing more cellulose structures. The low cellulose content in ChCl:EG and ChCl:Gly (11.15 and 16.55%, respectively) was due to the high hemicellulose structure that creates a barrier to expose the cellulose structure. Whereas for PC:Gly, 50
Percentage (%)
40 30 20 10 0 Untreated ChCl:LA ChCl:OA ChCl:FA ChCl:EG ChCl:U ChCl:Gly PC:Gly DES type Lignin
Hemicellulose
Cellulose
Fig. 2 The composition of lignin, hemicellulose and cellulose in untreated RSS and after pretreatment of RSS
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Concentration (g/L)
0.8 0.6 0.4 0.2 0 ChCl-LA
ChCl-OA
ChCl-FA
ChCl-EG
ChCl-U
ChCl-Gly
PC-Gly
DES type Glucose Xylose
Fig. 3 The concentration of glucose and xylose in liquid fraction after DES pretreatment of RSS
it is assumed the lowest cellulose content (10.64%) is caused by further breakdown of cellulose to its monomeric compounds.
3.5 Analysis of Liquid Fraction After DES Pretreatment After pretreatment of rubber seed shell using DESs, the liquid mixture was collected and analyzed to determine the glucose and xylose present in the liquid fraction as presented in Fig. 3. After DES pretreatment there are some pretreated DES that showed low hemicellulose and cellulose content although with significant lignin removal. Thus, analysis of liquid fraction will indicate whether the hemicellulose and cellulose has been breakdown into their monomeric sugar (xylose and glucose) or further degradation of their monomeric sugar into furfural and 5-hydroxymethylfurfural (5-HMF). For acidic DES, it can be postulated that some of the hemicellulose and cellulose in the RSS has been degraded into xylose and glucose as they exist in the liquid fraction (Fig. 3). Whereas for other DES, no monomeric sugar was detected for these DES in liquid fraction. Therefore, it is assumed that the low hemicellulose and cellulose in PC:Gly may be due to further degradation of xylose and glucose into furfural and 5-HMF [29].
4 Conclusion Seven types of DES have successfully been synthesized and their physicochemical properties including viscosity, density, pH and hydrogen bond formation were measured and examined. As a result, ChCl:EG, exhibited the lowest viscosity and density, while ChCl:OA exhibited the lowest pH. FTIR spectra for all synthesized DESs displayed the formation of hydrogen bond between the HBA and HBD. In pretreating the RSS, the potential of each synthesized DESs was compared with
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untreated RSS. The highest lignin removal has been obtained from pretreatment by basic DES (PC:Gly). The highest cellulose and hemicellulose content in RSS were obtained after being treated by acidic DES (ChCl:OA) and slightly acidic DES (ChCl:EG), respectively. Hence, it is suggested that the best DES type to pretreat RSS to obtain high cellulose and moderate hemicellulose is ChCl:OA. However, further study on optimization for pretreatment condition for this DES type is required in achieving the optimum cellulose and hemicellulose content. Acknowledgements The authors would like to thank the Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis (UniMAP) for their support in this research.
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Inhibition Study on the Growth of Clostridium Saccharoperbutylacetonicum N1-4 (ATCC 13564) for the Production of Biobutanol in ABE Fermentation Muhd Arshad Amin, Hafiza Shukor, Noor Fazliani Shoparwe, Muaz Mohd Zaini Makhtar, Peyman Abdeshahian, and Sulaiman Olenrewaju Oladokun
Abstract In this present study, the inhibition effect of different concentrations of sugar degradation products in upstream processing (Hydroxymethylfurfural (HMF) and Furfural) and butanol as product inhibition in downstream processing on the growth of Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) for the production of biobutanol in ABE Fermentation has been investigated. It was found that the presence of HMF and Furfural is non-toxic to cell growth and biobutanol production at concentrations below 3 g/L in the fermentation medium. The specific growth rate for both HMF and furfural was 0.067 h−1 and 0.066 h−1 respectively M. A. Amin Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] H. Shukor (B) Centre of Excellence for Biomass Utilization, Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] N. F. Shoparwe Gold, Rare Earth & Material Technopreneurship Centre (GREAT), Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, 17600 GoldJeli, Kelantan, Malaysia e-mail: [email protected] M. M. Z. Makhtar (B) Bioprocess Technology Division, School of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia e-mail: [email protected] P. Abdeshahian Department of Biotechnology, Engineering School of Lorena, University of São Pailo, 12602-810 São Pailo, Brazil S. O. Oladokun Seanexus and Alfred Wagener Institute for Polar and Marine Research, 27570 Bremerhaven, Germany © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_8
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which is very close to the control medium without any inhibitor addition (0.068 h−1 ). Surprisingly, the addition of 1 g/L HMF has improved the yield of biobutanol from 0.020 g/g (control) to 0.034 g/g and the addition of 1 g/L Furfural has improved the yield of biobutanol to 0.042 g/g. Butanol inhibition study on the growth of C. saccharoperbutylacetonicum N1-4 (ATCC 13564) shows the decrease of specific growth rate from 0.071 to 0.065 h−1 when 5 g/L butanol was added. 15 g/L of butanol addition has caused a significant drop in the specific growth rate to 0.011 h−1 with an inhibitory effect of 85.7%. This result reveals that sugar degradation product has an inhibitory effect on the growth of microorganisms and biobutanol production at a certain concentration, and this ABE fermentation suffers from product inhibition. Therefore, the development of a robust strain is necessary to make this biobutanol industrially competitive even in the presence of the inhibitory compound. Keywords Biobutanol · Clostridium · ABE fermentation · Inhibitors
1 Introduction Biobutanol is a potential replacement biofuel for fossil-based liquid fuels as they become depleted, and it can be used as a transportation fuel that can be blended with gasoline or diesel in any proportion [1]. Researchers are currently exploiting several types of lignocellulosic biomass for the production of biobutanol, which not only mitigates environmental issues but also promotes the development of a circular economy [2]. Hydrolysis is one of the primary methods used to convert lignocellulosic biomass into biobutanol, and there are few suitable biomass sources that can be utilized such as sugar beet, sugarcane, corn, and wheat [3, 4]. However, hydrolysis of lignocellulose materials usually produces several types of degradation products that can be classified into three groups, namely weak acids, furan derivatives (sugar degradation), and phenolic elements (from lignin degradation) that have an inhibitory effect on Clostridium [1]. The concentration of these degradation products commonly depends on the conditions of treatment performed (acid concentration used for the hydrolysis process, time, and temperature of the hydrolysis reaction) and the type of substrate used. Furthermore, the effect of these degradation products during the fermentation process varies for different microorganisms according to their respective tolerance levels. For example, for C. beijerinckii BA 101, the presence of one of the degradation products of either HMF or furfural (3 g/L) was seen to stimulate ABE production. However, a combination of the two products will have a toxic effect on the growth of this bacteria [2]. The toxicity of these degrading substances inhibits and disrupts the pathways of most enzymes in the glycolysis process for most microorganisms, particularly in protein or RNA synthesis, and is also due to its hydrophobic nature [3, 4, 5]. Moreover, the presence of this substance in the fermentation medium usually makes the lag phase of a microorganism longer than usual [6]. Apart from that, many previous studies were performed to reduce or eliminate these degradation products that interfere with the production and productivity of a fermentation process
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by converting these degradation products to less toxic substances such as furfural biotransformation to furfural alcohol by yeast [7], absorbing using adsorbents such as resin polymers [5] and developed new strains that have a high tolerance to these degradation products [7, 8, 9]. The most cost-effective method that is also effective in overcoming the toxic effects of these degradation products is the hydrolysate dilution method using water [10, 11, 12]. “Chaotropic” is a term used to describe the toxic effects (butanol) on bacterial cells, especially Clostridium. These toxic effects cause solvent production and productivity to be low, specifically in cluster culture systems due to high cell and solvent accumulation in the fermenter and, in turn, cause cell membranes’ function and fluidity properties on cell membranes to be disrupted [13]. C. acetobutylicum and C. beijerinckii are among the Clostridium affected by butanol toxicity [14]. Butanol at certain concentrations has also been seen to disrupt cross-membrane electrical slopes and pH, lower ATP concentrations, and stop glucose uptake in ABE fermentation [15, 16]. Butanol toxicity can be seen at butanol concentrations as low as 7 up to 13 g/L butanol in the media and will cause a 50% inhibitory effect on clostridium cells [13, 17]. The resulting butanol will enter the cytoplasm of the membrane and alter the cell membrane’s structure, preventing the cell from functioning normally until the cell becomes damaged. These damaged cells are unable to regulate internal pH, making them permeable to adenosine triphosphate (ADP) and other ions that inhibit substrate uptake, resulting in cell lysis (cell wall rupture) or dormant cells that sporulate before butanol production ends [1]. These two types of degradation products namely HMF and furfural are better known as fermentation inhibitors and have toxic effects on the fermentation process. This depicts a major problem in producing biofuels and biochemicals from hydrolysates, especially ABE fermentation [18]. Not only that, a study by Qureshi et al., using C. beijerinckii P260 in ABE fermentation, stated that at one level of concentration, it was found that these two degradation products can help increase the productivity of ABE production. However, no studies have been performed on the effect of this degradation product on the growth of C. saccharoperbutylacetonicum N1-4. In this present study, we intend to identify the effect of different concentrations of sugar degradation products of HMF and furfural on the growth of C. saccharoperbutylacetonicum N1-4 and in the production of biobutanol. At the same time, a product inhibition study in biobutanol production also has been investigated.
2 Materials and Methods 2.1 Inoculum Preparation A stock culture of Clostridium saccharoperbutylacetonicum N1-4 was acquired from the Biotechnology Lab of the Chemical and Process Engineering Department at Universiti Kebangsaan Malaysia. 1 mL of stock culture was placed into 9 mL of
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TYA medium in a test tube, followed by a heat shock in boiling water and a cold shock in ice water to activate the microbe prior to incubation for 1–2 days at 30 °C in anaerobic conditions [19].
2.2 Medium Preparation The TYA medium was used in this study with an addition of 20 g/L glucose as the sole carbon source. The TYA medium had the following chemical composition (g/ L): tryptone, 6; yeast extract, 2; ammonium acetate, 3; KH2 PO4 , 0.5; MgSO4 .7H2O, 0.3; and FeSO4 .7H2 O, 0.01.
2.3 ABE Batch Fermentation The biobutanol fermentation was done in Schott Duran bottles with a working capacity of 150 mL and a volume of 250 mL. The fermentation medium was autoclaved for 15 min at 121 °C. The medium was made anaerobic by sparging it with oxygen-free nitrogen gas for 10 min. Fresh C. saccharoperbutylacetonicum N1-4 inoculum that had been cultured in TYA medium for 18 h was transferred aseptically to the medium. ABE fermentations were run at various inhibitory conditions. 3 sets of experiments were conducted separately for sugar degradation product inhibition in which the first and second sets were to study the effects of different concentrations (0.01, 0.5, 1, 3, and 10 g/L) for furfural and HMF separately in the fermentation medium. At the same time, the third set is a combination of furfural and HMF in the low and high concentration range (0.5 g/L Furfural + 0.5 HMF and 0.01 g/L Furfural + 0.01 g/L HMF) in the fermentation medium. The effect of butanol concentration on the growth of C. saccharoperbutylacetonicum N1-4 (ATCC 13564) was carried out for 72 h under anaerobic conditions in TYA media containing 20 g/L glucose at 30 °C, initial medium pH 6.5, and fresh inoculum concentration of 10%(v/v). For this experiment, several butanol concentrations, namely from 0 (control), 5, 10, 13, 14, 15, 16, 17, 17.5, 20, 25, and 30 g/L of butanol, were used and introduced into the fermentation medium to test the tolerance level of C. saccharoperbutylacetonicum N1-4 (ATCC 13564). All these fermentation processes were performed under the same anaerobic environmental conditions at 30 °C, initial pH 6.5, inoculum size 10% (v/v), and using TYA medium with a glucose carbon source of 20 g/L. Medium without adding degradation products was used as a control for this experiment. Growth of C. saccharoperbutylacetonicum N1-4 (ATCC 13564) was observed by taking optical density (OD) readings to see cell growth at specific time intervals, and samples were taken for analysis of biobutanol production and sugar consumption. Besides, in order to measure cell growth rate, the equation of growth kinetics has been applied which is as shown in Eq. (1):
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µ=
2.303 (lg OD2 − lg OD1 ) (t2 − t1 )
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(1)
where µ is the specific growth rate, OD1 is the minimum optical density during the exponential phase, OD2 is the maximum optical density during the exponential phase, T1 is the time during minimum optical density, and T2 is the time during maximum optical density.
2.4 Analytical Method Sugar concentrations were determined using high-performance liquid chromatography (HPLC) with model type of 12,000 Series, Agilent Technologies, Palo Alto, CA, USA, with a SUPELCOGEL C-611 HPLC column (300 7.8 mm ID) and a refractive index detector (RID) at 60 °C and a flow rate of 0.5 mL/min with 10– 4 M sodium hydroxide as the mobile phase. Using a gas chromatograph (7890A GC-System; Agilent Technologies, Palo Alto, CA, USA) with a flame ionization detector, and a 30-capillary column (Equity 1; 30 m0.32 mm1.0 m film thickness; Supelco, Bellefonte, PA, USA), the concentrations of ABE were determined. The oven temperature was designed to climb by 8 °C every minute from 40 °C to 130 °C. The temperatures of the injector and detector were adjusted at 250 °C and 280 °C, respectively. The flow rate of the carrier gas, helium, was fixed at 1.5 mL/min. Furfural and HMF were also analyzed using HPLC but with different columns namely 120A column with specifications of 25 cm × 4.6 mm; Jones Chromatography, Tempe AZ, USA [19]. On the other hand, detection was performed using a UV detector with a wavelength of 220 nm. Figure 1 shows the overall research methodology flowchart of this study.
3 Results and Discussion 3.1 Effects of Sugar Degradation Product and Butanol Addition on the Growth of Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) in Biobutanol Production One of the major drawbacks of treating lignocellulose biomass for fermented sugar producers is the formation of several degradation products. During the hydrolysis treatment process using chemicals such as acids under extreme conditions, there will be further degradation of sugars resulting in some types of degradation products such as furfural and 5-Hydroxymethyl furfural (HMF) in particular which have certain levels of toxicity that will limit and interfere with the efficiency of bio-conversion
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Fig. 1 The overall research methodology flowchart of this study
(fermentation) processes [20]. For example, pentose sugars can be further degraded to furfural during hydrolysis, whereas hexose sugars can be further degraded to HMF. The effect of different HMF concentrations on the specific growth rate of C. saccharoperbutylacetonicum N1-4 during ABE fermentation is shown in Fig. 2. The addition of 0.5 g/L HMF and 0.01 g/L HMF increased the specific growth rate of cells by 8.2% and 5.7%, respectively. Furthermore, this can be observed when comparing the experimental-specific growth rate with the addition of HMF with the control (medium without any addition of HMF) with a value of 0.068 h−1 . The increase in HMF concentration from 1 g/L to 10 g/L exerted an inhibitory impact on clostridium growth when the cell-specific growth rate decreased to 0.066 h−1 , 0.067 h−1 , and 0.038 h−1 , respectively. This condition explains that the cell growth of C. saccharoperbutylacetonicum N1-4 is disrupted, and the cells fail to divide rapidly when the presence of HMF at concentrations exceeding 3 g/L is present in the medium. Delgenes et al., in their study on the effect of degradation products on ethanol fermentation, showed that P. stipitis growth decreased by 43%, 70%, and 100% when HMF with concentrations of 0.5, 0.75, and 1.5 g/L respectively existed in the fermentation medium [21]. Moreover, the presence of this substance also causes the lag phase for cell growth to be longer [22]. In short, the cell’s specific growth rate was reduced even with the addition of a small amount of furfural with the lowest concentration of 0.01 g/L. The results show that furfural supplementation, even at low concentrations (as low as 0.01 g/L), can have a toxic effect on cell growth, especially for C. saccharoperbutylacetonicum N1-4, and provides information that these cells are not resistant to the presence of this type of inhibitors. Not only that, the effect of furfural in reducing specific growth rates has been shown in previous studies for inhibiting glycolytic enzymes in cells
Specific growth rate, μ (per hour)
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HMF concentration (g/L) Fig. 2 The effect of different concentrations of HMF on the specific growth rate of C. saccharoperbutylacetonicum N1-4 during ABE fermentation
Specific growth rate, μ (per hour)
[23]. Apart from that, in comparison to the effect of furfural on the specific growth rate of C. saccharoperbutylacetonicum N1-4, the result differs for Pichia stipites in ethanol fermentation, whereby furfural at a concentration of 0.5 g/L has no effect on cell growth and only has an inhibiting effect on total cell growth when the furfural concentration approaches 2 g/L [22]. A similar study using P. stipitis by Delgenes et al. revealed that furfural at concentrations of 0.5, 1.0, and 2 g/L inhibited the growth of P. stipitis by 25%, 47%, and 99%, respectively [21]. Nigam found that at a concentration of 1.5 g/L, furfural interfered with the growth of P. stipitis in ethanol fermentation, where there was a reduction of 90.4% in yield and 85.1% in ethanol productivity [24, 25]. Figure 3 shows the effect of different concentrations of furfural on the specific growth rate of C. saccharoperbutylacetonicum N1-4 during ABE fermentation. In ABE fermentation, there is also an inhibitory effect on the final product of fermentation, which is butanol, on the cells. Although butanol itself is a major product in ABE fermentation, its presence at a high concentration will have an inhibitory and toxic effect on cells. The threshold concentration of butanol carrying this inhibitory and toxic effect is 12–15 g/L [17]. Furthermore, high butanol concentrations in the fermentation medium will adversely affect the composition of phospholipids and fatty acids in cell membranes [26]. Thus, the purpose of this study was to determine 0.08 0.06 0.04 0.02 0
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Furfural concentration (g/L) Fig. 3 The effect of different concentrations of furfural on the specific growth rate of C. saccharoperbutylacetonicum N1-4 during ABE fermentation
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104 0.08 0.06 0.04 0.02 0 0
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Concentration of butanol added (g/L) Fig. 4 The effect of butanol addition on the specific growth rate of C. saccharoperbutylacetonicum N1-4
the level of tolerance of C. saccharoperbutylacetonicum N1-4 (ATCC 13564) to the concentration of butanol in the ABE fermentation medium. As shown in Fig. 4, the specific growth rate of C. saccharoperbutylacetonicum N14 (ATCC 13564) decreases as the butanol concentration in the fermentation medium increases, starting at 5 g/L and is found to decrease significantly at 15 g/L with an inhibitory effect of 85.7% compared to the control medium. Besides, no precise growth rate value could be established when the concentration of additional butanol was raised from 16 to 30 g/L. The findings of this study are consistent with those of Al-Shorghani et al., who demonstrated that the same strain had a butanol inhibitory effect at a critical concentration of 15 g/L butanol and Soni et al. in their study using C. saccharoperbutylacetonicum (ATCC 27,022) showed that 13 g/L of butanol will inhibit cell growth [19, 27]. It has also been reported that 14 g/L of butanol will cause total inhibition of cell growth for C. acetobutylicum [28, 29]. The term “chaotropic” refers to the action of butanol on bacterial cells that occurs when the function and fluidity of cell membranes are disrupted [15]. According to Sullivan et al., a physiological reaction to butanol involves changing the saturation to an unsaturation ratio of fatty acids in the cell membrane [30]. Bowles and Ellefson and Terracciano and Kashket previously observed that the presence of butanol could disrupt the transmembrane electrical gradients and pH, reduce ATP concentrations, and cause C. acetobutylicum to halt consuming sugar [15, 16]. At butanol concentrations of 14–15 g/L, growth and maintenance of pH gradients were likewise shown to be completely blocked [15, 31]. It turns out that the presence of butanol in ABE fermentation by C. saccharoperbutylacetonicum N1-4 (ATCC 13564) does not support cell growth and thus on solvent production [32], and butanol concentrations exceeding 15 g/L inhibit cell growth overall. However, the degree of toxicity of this butanol to microorganisms depends on many factors, such as the type of microbe used, the microbe’s physiological state, and the pH of the medium [33].
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3.2 Effects of Sugar Degradation Product and Butanol Addition in Biobutanol Production The kinetic parameters for 72 h of ABE fermentation with C. saccharoperbutylacetonicum N1-4 in an HMF-added medium are shown in Table 1. Adding 0.01, 0.5, and 1 g/L HMF to the ABE fermentation medium positively assisted C. saccharoperbutylacetonicum N1-4 in increasing biobutanol production. This statement is substantiated because when HMF is added at this concentration, biobutanol production increases to 0.443 g/L, 0.518 g/L, and 0.624 g/L, respectively, compared to control (0.405 g/L). Furthermore, the highest quantity of ABE (2.739 g/L) produced when 1 g/L of HMF is added to the medium exhibits the same pattern. These findings suggest that HMF at certain concentrations has a tolerance ability that can benefit biobutanol production and the amount of ABE formed. This is consistent with the research undertaken by Qureshi et al., who supplemented C. beijerinckii P260 with less than 3 g/L HMF and found that ABE productivity increased by 203–293% [34]. Besides, when HMF at concentrations ranging from 0.5 g/L to 3 g/L was added to the fermentation medium, cell concentration increased from 2.272 g/L to 2,299 g/L, 2,285 g/L, and 2,281 g/L, respectively. On the other hand, cell concentrations were low when HMF concentrations in the medium exceeded 3 g/L. Furthermore, findings on cell growth, ABE production, and residual glucose revealed that at concentrations below 3 g/L in the fermentation medium, HMF was non-toxic to cell growth, biobutanol, and ABE production. However, at concentrations of more than 3 g/L, HMF appears to negatively impact growth and product generation cells. Not only that, HMF at a concentration of 1 g/L was seen to have a sufficient inhibitory effect on growth and fermentation by S. cerevisiae [35], and for E. coli, HMF and furfural concentrations exceeding 0.9 g/L show a toxic effect in fermentation that produces ethanol [36, 37]. Table 2 shows the kinetic parameters for 72 h of ABE fermentation using C. saccharoperbutylacetonicum N1-4 in a furfural-added medium at different concentrations from small to large scale. The results indicated that the presence of furfural at a given concentration aided the formation of biobutanol to a greater yield than the control medium (0.405 g/L). A maximum of 0.99 g/L of biobutanol was produced upon the presence of furfural at a concentration of 3 g/L in the fermentation medium. The addition of more than 3 g/L furfural had the opposite effect, reducing the amount of biobutanol that could be generated to 0.214 g/L. In the presence of 3 g/L furfural, a similar trend was shown to apply to total ABE production, with a maximum of 3.368 g/L ABE, 0.049 g/g biobutanol yield, and 0.168 g/g ABE yield being formed. The presence of furfural at this concentration (3 g/L) was shown to be detrimental to cell development. This can be seen at the ostensibly low cell concentration (Table 2) (2.21 g/L) when furfural at a concentration of 3 g/L or higher was added to the fermentation medium, as opposed to the control cell concentration (2.273 g/L). Next, the addition of more than one inhibitor at a time in the ABE fermentation medium by C. saccharoperbutylacetonicum N1-4 was investigated to describe the present condition of mediums containing more than one type of inhibitor present after
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Table 1 The kinetic parameters for 72 h of ABE fermentation with C. saccharoperbutylacetonicum N1-4 in HMF-added medium Kinetic parameters
3 g/L HMF
1 g/L HMF
0.5 g/L HMF
0.01 g/L HMF
Control
Acetone (g/L) 0.006
0.014
0.003
0.003
0.003
0.005
Biobutanol (g/ 0.307 L)
0.235
0.624
0.518
0.443
0.405
Ethanol (g/L)
1.223
1.017
2.112
2.099
2.028
1.861
Total ABE (g/ 1.537 L)
1.265
2.739
2.620
2.474
2.271
Total Acids (g/L)
0.529
1.421
1.881
1.363
1.235
Initial glucose 20 (g/L)
20
20
20
20
20
Residual glucose (g/L)
5.724
0.000
1.771
1.818
1.560
0.000
Cell concentration (g/L)
1.754
2.281
2.285
2.299
2.254
2.273
ABE Yield (g/ 0.108 g)
0.063
0.150
0.144
0.134
0.114
Biobutanol Yield (g/g)
0.012
0.034
0.028
0.024
0.020
*
10 g/L HMF
0.600
0.022
HMF = 5-Hydroxymethyl furfural
Table 2 The kinetic parameters for 72 h of ABE fermentation with C. saccharoperbutylacetonicum N1-4 in furfural-added medium Kinetic parameters
10 g/L F
3 g/L F
1 g/L F
0.5 g/L F
0.01 g/L F
Control
Acetone (g/L)
0.045
0.103
0.003
0.061
0.106
0.005
Biobutanol (g/L)
0.214
0.990
0.774
0.380
0.765
0.405
Ethanol (g/L)
0.749
2.276
1.572
1.691
2.119
1.861
Total ABE (g/L)
1.008
3.368
2.349
2.132
2.989
2.271
Total acids (g/L)
0.527
1.254
1.404
1.118
2.244
1.235
Initial glucose (g/L)
20
20
20
20
20
20
Residual glucose (g/L)
11.887
0.000
1.645
3.040
1.642
0.000
Cell concentration (g/L)
1.639
2.219
2.360
2.309
2.371
2.273
ABE yield (g/g)
0.124
0.168
0.128
0.126
0.163
0.114
Biobutanol yield (g/g)
0.026
0.049
0.042
0.022
0.042
0.020
*
F = furfural
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chemical treatment of lignocellulose material. Finally, a comparison of cell growth and ABE solvent product production for a combination of two inhibitors was tested at concentrations of 0.01 and 0.5 g/L at the same ratio for HMF and furfural. The results of this comparison are presented in Table 3. The results of studies on cell growth, ABE production, and the amount of residual glucose showed that the combination of the two types of inhibitors, namely HMF and furfural in the concentration range of 0.01 and 0.5 g/L, did not have a toxic effect on cell growth, even for biobutanol production and ABE. However, in ABE fermentation, solvent production does not depend entirely on cell growth because this ABE solvent product is a secondary metabolite product. Therefore, a maximum of 0.72 g/L of biobutanol with 3.21 g/ L of total ABE could be produced when a combination of furfural and HMF at a concentration of 0.01 g/L compared to control experiments which could only produce 0.41 g/L of biobutanol and 2.27 g/L of total ABE. Furthermore, 100% sugar was used for the production of this solvent by C. saccharoperbutylacetonicum N1-4, and this indicates that the presence of these inhibitors at concentrations of either 0.01 or 0.5 g/L does not have a toxic effect on cell growth and does not interfere with internal cells using the existing sugars in the medium to undergo ABE fermentation. Table 3 shows the comparison of kinetic parameters for 72 h of ABE fermentation using C. saccharoperbutylacetonicum N1-4 in a medium supplemented with a combination of furfural and HMF. This is reflected in the fact that cell concentration increased from 2.273 g/L (control) to 2.358 g/L when a combination of inhibitors at 0.5 g/L was added to the medium and 2.322 g/L when a combination of inhibitors at 0.01 g/L was added to the medium. There appeared to be no significant change in cell concentration with the presence of both types of inhibitors at different concentrations (0.01 and 0.5 g/L), Table 3 Comparison of kinetic parameters for 72 h of ABE fermentation using C. saccharoperbutylacetonicum N1-4 in a medium supplemented with a combination of furfural and HMF
Kinetic parameters
0.01F + 0.01HMF
0.5F + 0.5HMF
Control
Acetone (g/L)
0.004
0.004
0.005
Biobutanol (g/L)
0.720
0.690
0.405
Ethanol (g/L)
2.490
2.131
1.861
Total ABE (g/L)
3.214
2.824
2.271
Total acids (g/L)
2.175
2.006
1.235
Initial glucose (g/ L)
20
20
20
Residual glucose (g/L)
0.000
0.000
0.000
Cell concentration 2.322 (g/L)
2.358
2.273
ABE yield (g/g)
0.161
0.141
0.114
Biobutanol yield (g/g)
0.036
0.034
0.020
*
HMF = 5-Hydroxymethyl furfural; F = furfural
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and it was still deemed this low. This study revealed that the presence of these two types of inhibitors at low concentrations below 0.5 g/L had no significant inhibitory effect on growth and product yield. Not only that, this is in contrast to the previous findings which reported that HMF and furfural produced by acid hydrolysis of lignocellulose base material hinder cell growth and fermentation of xylose by yeast [36]. However, PKC components are high in mannan, and the mannan hydrolysis process must produce more mannose sugar, which is a type of hexose sugar [38]. If this type of sugar undergoes further degradation, HMF type inhibitors are produced and must be discarded or reduced before hydrolysate is used in ABE fermentation. In general, bacteria are poisoned by butanol and this action on the bacterial cell is referred to as chaotropic. This effect is associated with membrane disruption and cell fluidity [39]. Not only that, 13–15 g/L butanol was known to completely inhibit Clostridial cell growth and pH gradient maintenance. This inhibitory mechanism explains why solvent-producing Clostridium cannot produce solvents at concentrations beyond 15 g/L butanol [39, 40]. Table 4 shows the kinetic parameters for 72 h of ABE fermentation using C. saccharoperbutylacetonicum N1-4 in different butanol concentration additions from 5 to 30 g/L. According to Table 4, the glucose concentration remained at the end of the incubation time increasing from 0.076 to 16.997 g/ L when initial butanol 5–30 g/L is present in the growth media. The concentration of acetone and butanol in the Acetone-Butanol-Ethanol (ABE) production was low relative to the concentration of butanol produced. The ratio concentration for ABE synthesis in this study using a specific strain of Clostridium, C. saccharoperbutylacetonicum N1-4, is consistent with the commonly recommended ratio of 3:6:1 for general Clostridium. Apart from that, ABE and biobutanol yield produced beyond the concentration of 10 g/L butanol was low and not significant, and this is mainly due to the butanol toxicity at high concentrations that results in product inhibition [39, 41]. Therefore, all of the inhibitors studied in this section, like the effect of degradation products such as HMF and furfural, and the effect of butanol concentration on the growth of C. saccharoperbutylacetonicum N1-4 (ATCC 13564) in ABE fermentation, have a significant effect on fermentation and will interfere with cell metabolism. In addition, it is very important to know things related to these inhibitors, such as concentration limits and microbial tolerance levels, and find ways to overcome them.
4 Conclusion Degradation products such as HMF and furfural are produced during the hydrolysis of lignocellulose materials and depict an inhibitory effect on Clostridium species. Therefore, this necessitates the need of investigating the inhibition effect to evaluate the influence of these degradation products on overall biobutanol production. The initial focus of the investigation was on the influence of various types of inhibitors on the growth of Clostridium saccharoperbutylacetonicum N1-4 during ABE fermentation. The results indicate that additions of 0.5 g/L HMF and 0.01 g/L HMF stimulate
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Table 4 Comparison of kinetic parameters for 72 h of ABE fermentation using C. saccharoperbutylacetonicum N1-4 in a medium with different butanol concentration additions Kinetic parameters
5 g/L B 10 g/L B 15 g/L B 20 g/L B 25 g/L B 30 g/L B Control
Acetone (g/L)
0.852
1.048
0.345
0.028
0.025
0.033
0.667
Biobutanol (g/L)
1.24
1.754
NA
NA
NA
NA
4.689
Ethanol (g/L)
0.389
0.440
0.233
0.062
0.069
0.079
0.300
Total ABE (g/L)
2.481
3.242
0.578
0.09
0.094
0.112
5.656
Initial glucose (g/L)
20
20
20
20
20
20
20
Residual glucose (g/ L)
0.076
0.526
10.440
16.731
14.998
16.997
NA
Cell concentration (g/ 1.795 L)
1.811
1.479
0.4422
0.4316
0.5011
1.868
ABE Yield (g/g)
0.125
0.166
0.006
0.002
0,002
0.004
0.283
Biobutanol Yield (g/ g)
0.06
0.09
NA
NA
NA
NA
0.234
*
B = Butanol
the growth of clostridium, increasing the specific growth rate of cells by 8.2% and 5.7%, respectively, however the cells are depleted when HMF concentrations in excess of 3 g/L are present in the medium. Furfural, on the other hand, inhibits the growth of Clostridium saccharoperbutylacetonicum N1-4 even at concentrations as low as 0.01. Adding 0.01, 0.5, and 1 g/L HMF to the medium aided C. saccharoperbutylacetonicum N1-4 in enhancing biobutanol production during ABE fermentation. Furthermore, when 1 g/L of HMF is added to the medium, the highest amount of ABE formed which is 2.739 g/L. These results imply that HMF at specific concentrations can enhance biobutanol production and ABE formation. Furthermore, as for the effect of butanol concentration during fermentation, it is known that the threshold concentration for this inhibitory and toxic effect is between 12 and 15 g/L. The results indicate that the specific growth rate of C. saccharoperbutylacetonicum N1-4 decreases as the butanol concentration in the fermentation medium increases, beginning at 5 g/L and decreasing significantly at 15 g/L with an inhibitory effect of 85.7%. However, no precise growth rate value could be determined when the concentration of additional butanol was increased from 16 to 30 g/ L. This signifies that additional research is necessary to further raise the tolerance level without affecting the proliferation of clostridium species significantly. All of the inhibitors studied in this section, including the effect of degradation products such as HMF and furfural and the effect of butanol concentration, on the growth of C. saccharoperbutylacetonicum N1-4 in ABE fermentation, have a significant effect on fermentation and will inhibit cell metabolism. Therefore, it is crucial to understand these inhibitors’ characteristics, such as their concentration limits and microbial tolerance levels, and most importantly to identify ways to overcome them. As indicated in the preceding section, biobutanol toxicity, which causes inhibition and disrupts product recovery, is the most significant drawback that many researchers
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are currently attempting to address. Numerous efforts have been made to overcome this problem, with a focus on strain enhancement strategies such as gene modifications, mutagenesis, and adaptive laboratory evolution (ALE). In comparison to all of these strategies, ALE is the most practical and cost-effective technique since it does not require a high level of technical competence or a thorough understanding of the strain down to the level of its metabolic genes which signifies the advantage of ALE over traditional genetic engineering. Several prior accounts of the successful use of ALE for the enhancement of microbes had been published. To date, however, ALE-based research on strain enhancement for Clostridium species in biobutanol synthesis is quite restricted. Therefore, future research may employ ALE as a potential strain improvement strategy to increase biobutanol tolerance for high biobutanol output. Acknowledgements The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under grant number FRGS/1/2021/TK0/UNIMAP/02/31 from the Ministry of Higher Education Malaysia.
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Thermogravimetric Analysis on Empty Fruit Bunch, Rice Husk, and Rice Straw for Feedstock in Biomass Gasification Nur Afiqa Syaheera Damahuri, Nurulnatisya Ahmad, Nor Fadzilah Othman, Ab Aziz Mohd Yusof, Kahar Osman, and Kamariah Md Isa
Abstract Thermogravimetry is a proven method used to describe the characteristics of biomass quickly and efficiently. In this study, three types of biomasses widely available in Malaysia which are rice husks (RH), rice straw (RS), and empty fruit bunch (EFB) from palm oil trees were evaluated using Thermogravimetric Analysis (TGA). The aim is to investigate their applicability as potential feedstock in the gasification process. The biomass was heated from 30 to 700 °C with a heating rate of 10 °C/min. From proximate analysis, the detection of C, O, and H suggested that EFB, RH, and RS can potentially be used as carbon precursors during the energy conversion process. TG and DTA plots show there are three different stages of weight loss during the pyrolysis of biomass tested: evaporation, pyrolytic decomposition, and passive pyrolysis. The EFB shows a higher potential for gasification compared to RH and RS as it contains a higher value of carbon and a higher heating value (HHV). Besides, EFB has a decrease in hemicellulose content and higher cellulose compared to RH and RS. The obtained results will be used as input parameters for designing and simulating biomass thermochemical conversion in the gasification process. N. A. S. Damahuri College of Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia N. Ahmad · A. A. M. Yusof · K. M. Isa (B) College of Engineering, Universiti Teknologi MARA Johor Branch, Pasir Gudang Campus, Johor, Malaysia e-mail: [email protected] N. Ahmad e-mail: [email protected] A. A. M. Yusof e-mail: [email protected] N. F. Othman TNB Research Sdn. Bhd, Kajang, Selangor, Malaysia e-mail: [email protected] K. Osman Faculty of Mechanical Engineering/UTM Cardiovascular Engineering Center, UTM, Universiti Teknologi Malaysia, Johor, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_9
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Keywords Thermogravimetric analysis · Derivative thermogravimetry · Rice husks · Rice straw · Empty fruit bunch
1 Introduction Climate change has had a significant impact on energy legislation. This pattern can be seen in the previous decade, boosting the usage of renewable energies while focusing on reducing the use of nonrenewable energy [1]. The situation encourages and enhances more studies on new energy sources. Thus, conventional energy supplies could be replaced. One of the alternatives is biomass. The method was focused to substitute coal and its critical role as a renewable resource [2, 3]. While biomass has been utilized around the world for thousands of years, lately many sectors and services devoted their operations to cultivation, storage, or handling. The demand indicates an increasing pattern that represents its market growth while it is associated with safety requirements [2]. However, the combustibility of biomass remains largely unknown and continues to occur [4]. Many experiments should then be carried out to correctly characterize the flammable properties of a biomass sample making it a difficult goal. Several studies have created techniques that enable the estimate of variables derived coming from different experiments [5, 6] to simplify the procedure. In recent years, thermogravimetric analysis (TGA) has proven to be extremely useful [2, 7–9] because of the unique information it provides, the minimal quantity of material needed for the test, as well as the test’s verifiability (human error is very low) [2, 7, 8]. The constraints that significantly affect minimum ignition energy have been extensively studied, not just for solids but also for gas combinations with the results revealing that constraints such as oxygen concentration, turbulence, and particle size, among others, have a significant impact on minimum ignition energy [10– 13]. Compared to coal or gas/air mixes, the number of studies focusing on biomass minimum ignition energy is less; however, the number of studies focusing on biomass minimum ignition energy is growing as the usage of biomass increases [14]. It is also recognized that the smallest amount of ignition energy may give information on the characteristics of the ignition process [2, 15]. On the opposite side, it is essential to have an accurate understanding of the composition of the biomass since this may have an impact on the ignition characteristics. Biomass is comprised of a diverse variety of components, the majority of which are lignocellulosic in nature [16]. Thus, they are comprised of three major components: cellulose, hemicellulose, and lignin. The composition of almost every type of lignocellulosic biomass has indeed been extensively researched; however, because the data in the literature varies from one study to another, different percentages of each component may be obtained for the same material [2, 17–19]. Typically, the biomolecule’s structure is determined using an Accelerated Solvent Extractor [20], although it is possible to obtain an estimate of the composition using TGA, a technique that has been used by several researchers [2, 8, 21, 22].
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Considering this context, the current research aims to establish the parameters acquired from TGA to create a preliminary estimate. TGA analyses with tiny samples and lower heating value rates can be carried out in a pure kinematic regime, i.e. without the constraints of heat and mass transmission [23]. The biomasses used in this study were all lignocellulosic, which allowed for the investigation of a compositional relationship between them. Everything that was learned from the experiments was thoroughly examined, and a thermal parameter was established because of this comprehensive examination. The goal of this research is to investigate biomass decomposition properties for the early stage of material categorization and to estimate its performance during the gasification process.
2 Method 2.1 Determination of Biomass Properties In this study, the biomass samples (EFB, RH, and RS) were sun-dried for several days to maintain the moisture content below 10% wt. The moisture content of the feedstock was measured until it is below 10% wt then only the sample is ready to be used. The feedstock was then pulverized and sieved using the shaker after being crushed and squeezed to a size less than 250 m by the crusher. The ultimate or elemental assessment was conducted by a CHNS-O Analyzer (FLASH2000 CHNS/O). Dulong’s equation was used to calculate the basic particle’s higher energy content (HHV), which was defined as Higher Heating value (HHV) (MJ/kg) = 0.338 C +1.428(H − O/8) + 0.095S
(1)
where C = carbon, H = hydrogen, O = oxygen, and S = sulfur were the weight percentages of primary contents in each material.
2.2 TG/DTA Analysis One of the commonly used methods to examine the pyrolytic characteristics of biomass feedstock and understand its degradation behavior during thermochemical conversion processes is Thermogravimetric and Differential Thermal Analysis (TG-DTA). It is a low-cost approach that enables quantitative or qualitative research with a small number of samples. TGA is a tool that is both rapid and dependable. A commercial TGA was utilized to thermally degrade milligram samples under monitored heating and environmental settings in an atmosphere of air or inert nitrogen to determine thermal stability and weight reduction. TGA is a useful tool for detecting
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the incorporation of nanoparticles and active chemicals into biopolymer films or membranes by monitoring the rise or fall in thermal degradation regions, as well as the shortest or deferred thermal degradation. By differentiating the TGA curve, a TG generates a TGA curve and a differential thermogravimetric (DTG) curve, which may be used to quantify the maximum weight loss of biomass. TGA can be done in a variety of environments, including inert, oxidative, and even vacuum. The TGA is depicted schematically in Fig. 1. Based on this, this study tested heat degradation using a TG/DTA analyzer. The feedstock was counted in a tiny alumina crucible (4–6 mg) and dried in an oven at an ambient temperature of 700 °C at six different heating rates of 10 °C/min. The testing was performed in an inert environment with nitrogen provided at a rate of 100 ml/min. Fig. 1 Schematic diagram of TGA [24]
Thermogravimetric Analysis on Empty Fruit Bunch, Rice Husk … Table 1 Properties of biomass
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Property (wt%)
EFB raw
Rice husk
Rice straw
Carbon, C
41.69
35.32
35.46
6.76
5.84
5.93
Hydrogen, H Nitrogen, N
5.10
1.50
2.31
Oxygen, O
46.45
57.34
56.31
HHV (MJ/kg)
15.46
10.04
10.39
3 Results and Discussion 3.1 Properties of Biomass The result of the ultimate analysis is shown in Table 1 for the percentages of carbon, hydrogen, nitrogen, and oxygen. All three biomass feedstocks have a negligible amount of sulfur due to their low sulfur concentration [25]. Based on a study by Kuihua et al. [32], the value of sulfur wt% is less than 0.6 for all biomass samples. HHV is one of the most important quality criteria since it reflects the thermochemical properties of biomass fuels. The heating value (HV) can be expressed in two ways: lower heating value (LHV) and greater heating value (HV) (HHV). The overall quantity of energy in the fuel, including the energy included in the water vapor in the exhaust gases, has a higher heating value. The energy contained in the water vapor is not included in the lower heating value. Although some manufacturers may use the LHV instead, which can cause confusion, the greater heating value is usually the best number to use for biomass fuel combustors [26]. Using Eq. (1), the calculated Higher Heating Value (HHV) of the three-biomass tested showed EFB having the highest HHV at 15.46 MJ/kg while rice husks and rice straw shows a similar value of HHV at 10.39 MJ/kg. This may be attributed to the high percentage of carbon in EFB compared to Rice husk and Rice straw. Table 1 shows C, O, and H are detected in the tested samples. C, O, and H values were high and suggested EFB, RH, and RS potentially be used as carbon precursors during the energy conversion process. Besides, a low value of N and negligible value of S suggest the emissions of toxic gases (particularly NOx and SO2 ) during the gasification process are very low [27]. The degradation of biomass samples was studied in the temperature range of 30– 700 °C with a 10 °C/min heating rate. Figure 2 shows the weight loss curves of EFB, RH, and RS components. From Fig. 2, the devolatilization behavior of all the biomass is very similar and shows three distinct stages. The first stage ranges from 100 °C to approximately 250 °C. The evaporation process takes place during this stage. This stage represents a mass loss of approximately 8%. The difference in the initial stage is not evident between the three samples tested which indicates that EFB, RH, and RS have similar content of moisture and volatile matter [28]. Thermal cracking of biomass components occurs at 310–340 °C. This stage has the most massive mass degradation with around 40%wt on average. This is commonly known as the second
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Fig. 2 Comparison of TG curve of EFB, RH, and RS
stage of pyrolytic decomposition and corresponds to the thermal degradation of hemicellulose and cellulose [29]. This also corresponds to the lignin slowly beginning to decompose. This stage decomposes about 4% of the total volatiles. The DTA curves for three types of biomass samples are shown in Fig. 3. In Fig. 3, a noticeable difference between the three types of biomass samples can be seen. RH and RS showed an extra curve at stage 2 while EFB shows a smoother curve compared to RS and RH. It is assumed that the extra curve in RH and RS was due to the hemicellulose decomposition and lack of thermal stability [30]. The EFB has a smooth line curve compared to RH and RS, which suggests that EFB has a decrease in hemicellulose content and higher cellulose decomposition [30]. No significant changes were observed in the DTA curves between the three samples tested for temperatures below 200 °C. This indicates that the differences in biomass composition are small at this temperature. From the results obtained, the most intensive process that occurred for EFB, RH, and RS took place in temperatures from 310 to 340 °C. It is expected that during those temperatures, high conversion of solid biomass into gas will occur. The high conversion of biomass into gas will provide the necessary heat for that endothermic process [31]. It is expected that the waste heat that is applied during the initial pyrolysis process will contribute to reducing the amount of heat generated in the combustion process and subsequently reduce the required air for combustion. This will then contribute to the increase of gasification efficiency using biomass and increase the gas quality.
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Fig. 3 Comparison of DTG curve of EFB, RH, and RS
4 Conclusion This study investigated the thermal decomposition characteristics of three biomass samples which are EFB, RS, and RH using the ultimate analysis and thermogravimetric analysis method. The result shows the detection of C, O, and H. It is suggested that EFB, RH, and RS potentially be used as carbon precursors during the energy conversion process. The thermal degradation processes of all biomass samples can be characterized by a three-stage reaction: evaporation, pyrolytic decomposition, and passive pyrolysis. The EFB has a decrease in hemicellulose content and higher cellulose compared to RH and RS. The obtained results will be used as input parameters for designing and simulating biomass thermochemical conversion in the gasification process. Acknowledgements The authors thank the Ministry of Education Malaysia (FRGS/1/2019/TK07/ UITM/03/4) and University Teknologi MARA, Malaysia, for supporting this work. This work was also supported by TNB Research Malaysia.
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A Review on Enhancement of Oil Palm Solid Waste Through Torrefaction Nur Rahimah Ibrahim, Razi Ahmad, and Mohd Azlan Mohd Ishak
Abstract Biomass is one of the renewable energy sources and is easily obtained in Malaysia. Due to the substantial amount of biomass waste generated by agricultural activities, Malaysia actually has great potential for biomass power generation. The oil palm industry is the largest contributor to biomass waste in Malaysia, particularly oil palm solid waste. The raw oil palm solid waste produced low-quality products during thermochemical conversion. Thus, the torrefaction process is one of the approaches to improve the characteristics of raw oil palm solid waste. Therefore, the objectives of this study are to review the production and characteristics of solid biofuel from oil palm solid waste via the torrefaction process. Torrefaction is a thermal conversion method of biomass in the low-temperature range of 200–300 °C. Different reaction conditions such as temperature and reaction time lead to several characteristics of biofuel products. The solid fuel of pretreated oil palm solid waste has enhanced overall quality and its characteristics after torrefaction. Keywords Oil palm solid waste · Torrefaction · Biofuel
N. R. Ibrahim · R. Ahmad (B) Faculty of Civil Engineering & Technology, Universiti Malaysia Perlis, Perlis, Malaysia e-mail: [email protected] N. R. Ibrahim e-mail: [email protected] R. Ahmad Water Research and Environmental Sustainability Growth (WAREG), Center of Excellence (COE), Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia M. A. M. Ishak Fossil and Biomass Energy Research Group, Perlis Branch, Universiti Teknologi MARA, Arau Campus, Arau, Perlis, Malaysia e-mail: [email protected] Faculty of Applied Science, Perlis Branch, University Teknologi MARA, Arau Campus, Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_10
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1 Introduction Given the recent rise in global energy use, renewable energies have gained significance. Affordable, sustainable, and renewable fuel is being sought after by renewable energy researchers [1]. One of the renewable energy sources, biomass, is readily available in Malaysia. Malaysia has a potential for biomass power generation because of the huge quantity of biomass waste produced by agricultural activities. In Malaysia, the oil palm sector is the major producer of biomass waste, notably oil palm solid waste (OPSW). It consists of oil palm fronds (OPF), oil palm trunk (OPT), palm kernel shell (PKS), empty fruit bunch (EFB), and mesocarp fibre (MF) [2]. The natural breakdown of OPSW causes serious environmental risks such air pollution, water pollution, and disease breakout, thus open burning is frequently favoured as a popular disposal technique due to the high creation rate of OPSW [3]. The possibility of using the OPSW as solid biofuel is apparent. The general drawbacks of raw biomass, which is intended for use as fuel, include poor grindability, low calorific value, low energy density, non-homogeneity, high moisture content, significant inorganic substances, and low combustion efficiency which decrease its viability as a solid biofuel feedstock [4]. Torrefaction in an anoxic environment at temperatures between 200 and 300 °C is one method for enhancing the properties of raw OPSW. Torrefaction improves biomass’s fuel qualities, such as carbon content and calorific value, as well as its hydrophobicity and grindability [5]. Moreover, by having low moisture content, it increases storability while reducing biodegradation. The hydroxyl group in raw biomasses is typically destroyed during torrefaction, changing their hydrophobic character from hygroscopic to hydrophilic. An in-depth study has been done on torrefaction pretreatment to enhance the physical and chemical properties of lignocellulosic biomass as a viable alternative to coal. Although torrefied products have many benefits, some cutting-edge production methods are still required if commercialisation is to become firmly established within the global energy production system. Therefore, the goals of this work are to review the production and characteristics of solid biofuel from OPSW via the torrefaction process.
2 Production of Oil Palm Solid Waste Oil palm industry by-products created during replanting, pruning, and milling processes are referred to as oil palm biomass [6]. Up to 311 million tonnes of oil palm biomass, including MS, EFB, PKS, OPT, and OPF, are produced annually in Malaysia, the second-largest producer of palm oil in the world [7]. The fresh fruit bunch (FFB) contains approximately 21% crude palm oil (CPO), 13.5–15% of MF, 22–23% of EFB, 6–7% of palm kernel, and 5.5–7% of PKS [8]. Pruning, harvesting, replanting, and milling operations produce these biomasses. While the bulk of OPF
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and OPT comes from plantations, the majority of EFB, PKS, and MF are produced during the milling process. Some of the biomass that is recycled and used in the palm oil mill process to produce steam and power in particular are EFB, MS, and PKS. Despite the fact that there are up to 3.6 and 21.7 million tonnes of OPT and OPF available, respectively, to be further processed into value-added products, only 50% of OPT and OPF are left in the plantation ground as mulch for the soils [9]. Based on the components, the industry of palm oil produces EFB, which is the most prevalent and easily accessible biomass, throughout the year. Fruits from the bunches are separated in a revolving drum used to extract palm oil during the threshing process to produce EFB [10]. EFB is either delivered back to the plants or used as fuel for the creation of bioenergy after the loose fruits are transported to a digester. After oil extraction, MF is the second-largest source of solid oil palm wastes derived from oil palm fruits, and it is often left in palm oil mills [11]. In the palm oil industry, MF, which remained as waste during the extraction process, is collected and burned to create steam to power generators. The silo’s nut/fibre separator produces MF, which is then followed by PKS, which is produced when the nuts are broken to release the palm kernel. PKS are the shell pieces that are still present after the nut has been taken out and crushed in the palm oil mill. Kernel shells may be easily transported in bulk from the production line to the intended use because they are a fibrous substance. Along with dust-like fractions and minute fibres, large and small shell fractions are mixed. Currently, oil palm fronds from plantations are regarded as waste since their biomass is not fully utilised. The majority of the year, when the palms are pruned for FFB harvesting, OPF is accessible. According to estimates, Malaysia produces 24.4 million mt dry matter of these felled and pruned oil palm fronds annually. Within a decade, this amount nearly doubled to over 40 million mt in 2004 [12]. OPT dry matter up to 74.5 t/ha was chipped and left in the field as mulch throughout the replanting season. This technique is primarily intended to preserve soil, prevent erosion, and ultimately benefit from nutrient recycling. Because of the decline in fruit output and oil productivity, every 25 years oil palm plants are planted again. The estimated yearly availability of dry OPF and OPT is 4 and 17 million t, respectively, based on a projected 5% oil palm planted area that needs to be replanted [8]. At the end of the dry season in the past, the trunks of felled oil palm trees were piled up and burned. The trunks are now broken into bits, scattered on the plantation between the replanting rows, and allowed to decay for nutrient recycling because burning them creates air pollution [13]. It takes several years for the trunk’s full breakdown [14]. Therefore, exporting solid fuels from nations that grow oil palm has significant potential, and torrefaction has been proposed as a possible alternative for residual biomass pretreatment in order to maximise the value chain.
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3 Properties of Oil Palm Solid Waste 3.1 Lignocellulosic Oil Palm Lignocellulosic materials hold great potential as biomass feedstock for producing biofuel as well as heat and power. A lignocellulosic biomass, oil palm biomass is mostly made up of cellulose, hemicellulose, and lignin. The main elements of oil palm wastes are lignin, cellulose, and hemicellulose. The main component of oil palm biomass is cellulose (C6 H10 O5 )n [15]. Cellulose is a polymer made up of beta-linked glucose monosaccharide molecules that form a linear polysaccharide. Hemicellulose is a saccharide polymer with five to six carbons made composed of arabinose, glucose, mannose, galactose, and xylose. Hemicellulose decomposes at 200–300 °C under typical torrefaction conditions, followed by cellulose, which decomposes at higher temperatures of 300–400 °C. Lignin is a complex polymer with an aromatic backbone that is built on phenyl propane. In lignocellulosic biomass, lignin is the most thermochemically stable polymer, decomposing at roughly 600 °C [8]. According to Chang et al. [3], high hemicellulose content oil palm solid waste displays a significant loss in mass and energy as well as poor CV. On the other hand, high-lignin oil palm solid waste is more suited for use as torrefaction feedstock since it loses less weight and retains more energy. Oil palm solid waste with a high hemicellulose concentration has a propensity to absorb moisture, while oil palm solid waste with a high lignin content has a reduced propensity to do so [3]. The remaining contents are made up of ash and extractives like minerals, starch, protein, sugar, resins, lipids, and tanning agents. It is possible to convert this substantial cellulose and hemicellulose concentration into simple sugars, which can then be used to make biochemicals or fuels. Table 1 shows that PKS is the most widely used fuel for thermal conversion because it contains the highest percentage of lignin [16]. Table 1 Chemical composition (dry basis) of oil palm solid wastes from mills [11] Type of biomass
Chemical component (% Dry wt.) Cellulose
Hemicellulose
Lignin
Extractives
Ash
Empty fruit bunches
38.3
35.3
22.1
2.7
1.6
Mesocarp fibre
33.9
26.1
27.7
6.9
3.5
Palm kernel shell
20.8
22.7
50.7
4.8
1.0
Oil palm frond
30.4
40.4
21.7
1.7
5.8
Oil palm trunk
34.5
31.8
25.7
3.7
4.3
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3.2 Proximate Analysis The proximate analysis takes into account the determination of the contents of carbon, ash, volatile matter, and moisture. The amount of moisture in biomass is a key factor in determining its bulk density and energy content. The bulk density increases with increasing moisture content, while the net calorific value (CV) decreases [17]. The amount of energy released after combustion decreases with increasing moisture content (lower CV) [18]. According to Table 2, OPT has the highest moisture level of oil palm waste at 76%, followed by OPF and EFB, which have respective moisture contents of 70 and 67%. According to Loh [18], the appearance of mould and other signs of enhanced biological activity during storage may result from having a high moisture content. By contrast, PKS and MF have less moisture at 12% and 37% respectively compared to others. The most important consideration while developing an oil palm-based power generation facility can be the moisture content [18]. Having the least amount of moisture in the biomass is important for the efficiency of thermochemical processes because high moisture content will make drying more expensive. For example, based on a study from Rago et al. [19], after undergoing the torrefaction procedure, the moisture content of mango branches was depleted. Therefore based on the kind of reactor used, a moisture level of about 20% is preferred for the torrefaction process because some systems can tolerate a feedstock moisture content of up to 35% [8]. Ash is the by-product of combustion that is still non-combustible. Typically, when torrefaction temperature rises, ash content rises as well, causing mass loss and the accumulation of large concentrations of metallic elements [8]. The high ash content of biomass is related to the high concentration of metallic metals therein, and thermal processing of the biomass has an equal impact on their concentration. The calorific value of OPSW can also decrease by high ash content. The fact that a solid fuel does not produce an excessive amount of ash during combustion is an excellent quality material. In contrast, the presence of ash can serve as a predictor of a high or low torrefied yield. According to Table 2, as compared to other oil palm waste, MF and EFB have greater ash contents. Table 2 High heating value and proximate analysis of oil palm biomass waste [8] Sample
Calorific value (MJ kg−1 )
Moisture content (wt.%)
Ash content (wt.%)
Volatile matter (wt.%)
Empty fruit bunches
18.88
67.00
4.60
87.04
Mesocarp fibre 19.06
37.09
6.10
84.91
Palm kernel shell
20.09
12.00
3.00
83.45
Oil palm frond 15.72
70.60
3.37
85.10
Oil palm trunk 17.47
75.60
3.35
86.70
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The volatile matter is one of the crucial factors that affects how solid fuel behaves. The high volatile matter content may help the combustion process burn more effectively. Biomass that has a high volatile matter content is also likely to ignite easily and proceed to become gasified or oxidised. Unwanted by-products of torrefaction include high quantities of tar and smoke created when biomass has a high volatile matter concentration, which indicates excessive volatilisation during torrefaction. As shown in Table 2, PKS has less volatile matter (83.45%) than other oil palm solid wastes. Regarding the CV or heat released during combustion, the fuel composition is crucial. When biomass is burned in the air, its CV expresses the amount of energy or heat that is released. As a result, CV reflects the most energy that might possibly be extracted from a biomass source. Table 2 contains the CV data for solid wastes from oil palm that have not undergone any sort of treatment. Oil palm residues have a calorific content of 18–21 MJ/kg on a dry basis. Due to their low moisture content and high calorific value, PKS and MF are widely utilised as fuel in the boilers that generate electricity in oil palm mills. PKS, MF, and EFB, which are mill residues, have a higher calorific value than OPF and OPT, which are plantation remains [15]. After torrefaction, the CV of the torrefied product often increases in comparison to the raw biomass [8].
3.3 Ultimate Analysis The ultimate analysis indicates the presence of carbon (C), hydrogen (H), oxygen (O), sulphur (S), and nitrogen (N) in oil palm solid wastes, whereas the minor elements are primarily inorganic-based. According to Table 3, the main constituents in all oil palm solid wastes are C, H, and O. When a fuel is burned, carbon plays a major role in the release of heat. Although oxygen in biomass aids in the burning of fuel, it lowers the CV of the biomass. It is undesirable for oil palm solid wastes to have a Table 3 Ultimate analysis of palm oil waste [15] Sample
Nitrogen (wt. %)
Carbon (wt. %)
Hydrogen (wt. %)
Oxygen (wt. %)
Sulphur (wt. %)
Empty fruit bunches
0.249
48.715
7.858
48.179
–
Mesocarp fibre
0.391
46.396
9.283
50.212
–
Palm kernel shell
0.043
57.909
12.600
49.99
–
Oil palm frond
12.402
48.431
10.476
46.75
–
Oil palm trunk
0.169
51.408
11.816
51.16
–
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high O concentration since it leads to a poor CV. In the combustion process, H also contributes significantly to the production of heat. However, biomass with high H content typically has low C content [8]. The quantity of harmful pollutants, such as NOx , depends on the nitrogen concentration. Oil palm waste is reported to be rich in O and low in N and S, with the exception of OPF, which has a higher N concentration. Because of their low S and N concentrations, most oil palm solid wastes are cleaner and more environmentally friendly fuels than coal. Often, moisture content and light volatiles with greater concentrations of H and O are removed from biomass as a result of torrefaction while retaining relatively higher concentrations of C [8]. The largest N content can be detected in OPF, which probably explains why the OPF is mulched in the majority of palm farms to improve soil fertility. The PKS and OPT have the highest carbon contents. In terms of its chemical composition, biomass fuel typically has less fixed carbon, less nitrogen, and sulphate [15].
4 Torrefaction Process Torrefaction is one method for enhancing the properties of raw OPSW. According to Tolero et al. [20], at temperatures between 200 and 300 °C, the thermochemical process of torrefaction is used to enhance the fuel quality of carbonaceous feedstock, such as biomass. Olugbade and Ojo [17] reported that by removing oxygen, lowering moisture content, and altering chemical composition in an inert environment, torrefaction is a moderate pyrolysis treatment method that improves the chemical and physical properties of untreated biomass. Additionally, as torrefaction lowers the moisture content of the biomass and raises its fixed carbon content, the raw biomass’s heating values would also increase [3]. Volatiles are emitted during torrefaction, and the torrefied biomass is left as a solid by-product. Temperature is the key factor affecting product distribution, followed by residence time, and type of biomass. The solid that has been torrefied has a higher net CV and better grindability [20]. In lignocellulosic biomass, cellulose, hemicellulose, and lignin make up the three main constituents. Major hemicellulose is broken down during torrefaction, however depending on the heating rate, reaction temperature, and residence duration, lignin and cellulose are broken down to a lower extent. Torrefaction also increases the energy yield of torrefied biomass because the carbon content increases. Additionally, it decreases the hemicellulose and moisture levels in biomass, extending its shelf life by preventing biodegradation while being stored. Due to these enhanced properties, the torrefied biomass has a substantially higher value as a fuel than the raw biomass in terms of carbon content and heating value [21]. Table 4 shows several studies on torrefaction have been conducted using various types of biomasses, different temperatures, and residence times.
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Table 4 Torrefaction of biomass at various parameters based on previous studies Biomass
Type of reactor
Parameter
Oil palm frond
Horizontal tubular furnace
Temperature at 200 to – The mild [22] 300 °C torrefaction temperature Time at 30 min (200–225 °C) showed insignificant improvements to calorific value and slight reductions in mass and energy yields continued to decrease – Moisture content and volatile matter decrease while fixed carbon increases with the increasing torrefaction temperature
Findings
Ref
Mesocarp fibre (MF) and palm kernel shell (PKS)
Cylinder torrefaction reactor
At various temperatures of 250–300 °C, residence time of 40 min and nitrogen flow rate of 1 l/min
– Gross calorific [23] value, fixed carbon content, and ash content all rise as torrefaction temperature rises, whereas volatile matter falls for both samples – The torrefied mesocarp fibre briquettes’ energy density is not considerably affected by the torrefaction temperature – The volume yield and mass yield of mesocarp fibre briquettes both decrease as torrefaction temperature rises – Similarly, after being torrefied at 250 °C, palm kernel shell briquettes had a very poor mass yield (continued)
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Table 4 (continued) Biomass
Type of reactor
Parameter
Findings
Ref
Empty Fruit Bunch Horizontal (EFB) and reactor Polyethylene (PE)
At constant – The CV of the pieces [24] temperature of 250 °C with a weight ratio and a flow rate of of 90:10 (EFB:PE) 1 l/min of nitrogen is higher than those of the pieces with a weight ratio of 95:5 – The mass yield of a weight ratio of 90:10 is greater than that of a combination with a weight ratio of 95:5 – Volatile matter of the 90:10 combinations is higher, but the fixed carbon is lower than 95:5
Corncobs
Torrefaction temperature (200, 240, and 280 °C) and the residence time (30, 60, and 90 min)
Fixed-bed torrefaction reactor
– Mass loss rises as [25] torrefaction temperature and residence duration rise – By raising the torrefaction temperature and the residence period, the biomass grinding throughput was increased – At a torrefaction temperature of 280 °C and a residence time of 90 min, the calorific value was at its maximum. All treatments had an energy yield that varied from 92.8 to 99.2% (continued)
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Table 4 (continued) Biomass
Type of reactor
Parameter
Waste newspaper (Np), and mango branches (MBr)
Bench-scale horizontal steel tubular reactor
At constant – When MBr was [19] temperature of 300 °C torrified at 300 °C, for 30 min of more mass was lost residence time than Np – Compared to their untreated counterparts, torrefied MBr and Np showed larger carbon contents and reduced oxygen and hydrogen contents – The dehydration, decarbonylation, and decarboxylation that took place during the thermal degradation were primarily responsible for the decrease in the amount of elemental hydrogen and oxygen in torrefied wastes
Findings
Ref
5 Conclusion The torrefaction method is an effective way to enhance the quality of oil palm solid wastes as a fuel with minimal energy loss, increase CV, greater moisture removal, improve grindability, and increase carbon content. The ideal temperature is 200– 300 °C, and the ideal retention time is 30–60 min. The large quantity of solid oil palm wastes that can be torrefied can satisfy the substantial energy requirements of palm oil mills and other industries. Additionally, the torrefaction process for processing oil palm solid wastes is simple, effective, and easy to use. It also takes little storage space and costs less to transport. These would be able to reduce the country’s dependency on fossil fuels and, over time, address the nation’s increasing energy needs. Acknowledgements The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under the grant number FRGS/1/2021/STG04/UNIMAP/02/1 from the Ministry of Higher Education Malaysia. The authors would like to express their gratitude to the Environmental Engineering Laboratory, Faculty of Civil Engineering & Technology UniMAP, for providing the facilities for this research.
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Energy Efficiency of Briquettes from Queen Pineapple (Ananas Comosus [Linn.] Merr.) Wastes Using Three Organic Binders Michelle S. Carbonell, Al Rey C. Villagracia, Hui Lin Ong, and Ma. Kathrina M. Pobre
Abstract Pineapple (Ananas comosus [Linn.] Merr.) farms generate a high volume of wastes composed of residual stalks, leaves, roots, and crowns including bruised butterballs which is equivalent to 70–80% of its production. Converting these wastes into biochar briquettes for bioenergy and biofuel application is needed to avoid water and soil contamination. In this work, we investigated the energy efficiency of Queen pineapple (QP) briquettes mixed with different starch binder’s raw material, namely the sweet potato (Ipomoea batatas), cassava (Manihot esculenta), and nami (Dioscorea hispida). The pineapple wastes were dried and carbonized using a drum-type carbonizer, while the sun-dried starch was extracted from the grated raw binder materials. The dried pineapple wastes mixed with the gelatinized starch were M. S. Carbonell (B) · A. R. C. Villagracia Department of Physics, College of Science, De La Salle University, 2401 Taft Ave, 0922 Manila, Philippines e-mail: [email protected] A. R. C. Villagracia e-mail: [email protected] M. S. Carbonell · Ma. K. M. Pobre Faculty of College of Arts and Sciences, Camarines Norte State College, Camarines Norte, 4600 Daet, Philippines e-mail: [email protected] A. R. C. Villagracia Advanced Nanomaterials Investigation and Molecular Simulations (ANIMoS) Research Unit, CENSER, De La Salle University, 2401 Taft Ave, 0922 Manila, Philippines H. L. Ong Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Perlis, Jejawi, Malaysia Centre of Excellence for Biomass Utilization and Taiwan-Malaysia Innovation Centre for Clean Water and Sustainable Energy (WISE Centre), Universiti Malaysia Perlis (UniMAP), 02600 Perlis, Jejawi, Malaysia H. L. Ong e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_11
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molded using a ten-port manual briquetting machine to produce the briquettes. Each set of briquettes was used to boil 500 ml of water, and the following quantities were measured: Water boiling time, length of briquette consumption, and density. Afterwards, the burning efficiency and heat transfer rate per unit mass of briquettes were computed. The results revealed that QP briquettes with Dioscorea hispida binder have the highest energy efficiency based on the mass burning rate and heat transfer rate of 3.71 g min−1 of 40.4 Jg−1 min−1 followed by 3.45 g min−1 and 26.36 4 Jg−1 min−1 for Ipomoea batatas binder, and, lastly, 3.30 g min−1 and 25.68 Jg−1 min−1 for Manihot esculenta binder, respectively. Dioscorea hispida is found to be the best starch binder source among the three crops for producing briquettes from QP wastes. Keywords Biofuel · Green energy · Energy efficiency · Binder
1 Introduction The Food and Agriculture Organization of the United Nations (FAO) in its “Major Tropical Fruits Preliminary Market Results 2019” reported that the Philippines is the second largest supplier of pineapples to world markets. It was shown in the data from July to September 2020 crops production survey of the Philippine Statistics Authority that pineapple production was estimated to be 651.13 thousand metric tons which was −8.6% lower than the recorded production in the same period of 2019 which is 712.29 thousand metric tons of production volume. The total land area of production for pineapple is 63, 903.53 hectares in the year 2019 and has increased to 64,026.34 hectares in the year 2020 [1]. The Food and Agriculture Organization of the United Nations’ (FAOSTAT, 2020) [2] recorded crop statistics for one hundred and seventy-three (173) products including the data which are expressed in terms of area harvested, production quantity, and yield. It covers the production of all primary crops for all countries and regions in the world. In terms of pineapple production quantity in the South East Asian region, the Philippines ranked first, followed by Indonesia, then Thailand out of the nine countries which are producing the said crop. The area harvested is most expansive in Thailand, followed by the Philippines. In terms of yield per hectare, Indonesia has the most yield, followed by the Philippines [2]. Base to the Philippine Statistics Authority (2020), the Philippines’ total volume of production was reported to be equivalent to 2.70 million metric tons with an annual average rate of production growth of 0.9%. Bicol Region is one of the top pineapple producers in the country [3]. Camarines Norte in the said region takes pride in being the fourth largest pineapple-producing province in the country. ‘Queen’ pineapple is a popular crop grown under coconut trees on small-scale farms with Type II climate as in the case of the province of Camarines Norte. The outstanding sweetness and crispiness of this pineapple variety are incomparable with other types of pineapple. ‘Queen’ is the sweetest type of pineapple (Ananas comosus) belonging to Bromeliaceae, or the ‘air’ plant family. It is the native variety of pineapple in the
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province and is locally termed as Formosa. Its golden and crispy yellow flesh contains nourishing substances like carbohydrates, Vitamin C, Vitamin A, phosphorous, and other nutrients [4, 5]. The Province of Camarines Norte has a total production area of 1, 950 hectares, and there are 2, 265 farmers engaged in cultivating Queen pineapple that is distributed to the twelve municipalities of the province. The majority of the farmers owned lands with an average farm size of 1.33 hectares. Among the twelve municipalities, Basud, Labo, San Lorenzo Ruiz, and San Vicente are the major producing municipalities of Queen pineapple in the province [4, 6]. In the study conducted by Mabeza, A.M. and Pili, A.S. [7], data showed that the edible part of the ‘Queen’ pineapple (Formosa) is about 21.6–30.4% of the whole fruit depending on its size. Almost 70–80% goes to waste which includes those with bruises and smaller sizes than butterball. The amount of wastes produced by consuming the fruit alone such as waste peels, eye trimmings, and masses of its core and including those with bruises and smaller sizes than butterball is about 71,900–83,884 mt/year. Similarly, the production of processed ‘Queen’ pineapple juice generates 40% waste per 400 kg of fruits per production which is done on an average of 12 days per month (Labo Progressive Multi-Purpose Cooperative). Grown largely, from harvesting the primary crop which is the good size marketable ‘Queen’ pineapple fruit, these high volumes of residual stalks, leaves, roots, and crowns were left behind, thus creating a problem as rotten farm wastes or waste agricultural biomass emit methane and leachate, and if the farmers chose to resort to open burning these wastes in order to clear their lands, it leads to generating CO2 and other local pollutants. Hence, improper management of residue farm waste or waste agricultural biomass is contributing to climate change, water and soil contamination, and local air pollution [8]. The International Energy Agency reported a comparison of the world’s total electricity generation during the span of time from 1973 to the year 2019. It was revealed that there has been a tremendous increase in the said generation from 6,131 TWh to 26,936 TWh reflecting the world’s ever-increasing energy demand. It was also shown that there has been a significant increase in electricity generation from natural gas and non-hydro renewables and wastes from 12.1% to 23.6% and 0.6% to 10.8% respectively, and a significant decrease in nonrenewable energy sources such as oil and coal [9]. Thirty percent (30%) of the expected world’s energy demand by 2050 will be provided by bioenergy. The development and utilization of this renewable energy was made possible by recent energy independence and climate change policies. Emerging technologies on these alternative energy generations from biomass which is lignocellulosic materials are now being commercialized. It has been utilized for cooking, heating, and lighting since the dawn of humans. The stored energy from the biomass produced annually by terrestrial plants is 3–4 times greater than the current global energy demand, thereby allowing for the continuous increase in the global development and utilization of bioenergy and biofuels particularly in the biopower sector [10]. Biochar production from biomass, various types of feedstocks, or agricultural wastes has been the focus of study in recent years. Advanced technologies and
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applications including different biochars are products derived from biomass energy [11]. The traditional approach (conventional pyrolysis) and modern approach (flash, vacuum, and microwave pyrolysis) are the thermochemical techniques used to produce biochar [12]. Some of the feedstocks used as raw materials for these thermochemical processes are rice and coffee husks [13], mesocarp fiber [14], Gayo Arabica coffee-pulp [15], food waste [16], cashew nutshell waste and soybean empty pods [17], and litchi peels [18]. The biochar is then densified through various briquetting techniques using organic binders such as starch to produce a low-cost briquette which makes it possible for commercialization [19]. The farm wastes and the particular type of binder have certain effects and interactions when combined on the physical properties and the heating value of the pyrolyzed briquettes [20]. Using binders can improve the quality of the briquettes, particularly their density which influences their durability, impact, and water resistance, and their compressive strength to withstand stresses from handling, transportation, and storage [21, 22]. It was reported that at the highest level (25%) of starch as binder, the briquettes produced have a better performance in properties indicating its qualities such as density, agglomeration, compaction, and combustion [23]. Cassava (Manihot esculenta) starch and cassava peel were used as binders for rice husk briquettes which resulted in an improved burning rate and water boiling test/ burning efficiency result [24]. It was found that the starch from potato tubers including sweet potatoes (Ipomoea batatas) produced stronger tablets and an effective binder in its formulation [25]. Since the produced tablets have higher tensile and mechanical strengths, it has the potential as a binder for more dense and compact biochar briquettes. The Intoxicating yam ‘nami’ (Dioscorea hispida) tuber has been found to contain 11.46% starch which can be used for various applications [26]. The high volume of agricultural wastes, availability, and abundance of cassava peels, sweet potato peels, and unexplored application of intoxicating yam (nami) in bioenergy generation and the quest for alternative fuel source had propelled the conduct of this research. In the quest for an alternative source of fuel through briquetting technology, the properties of binders used for densification are not investigated extensively. This paper investigated the effect of using three different starch binders on the energy efficiency of briquettes from Queen Pineapple char.
2 Materials and Methods 2.1 Material The raw material used in this study was Queen Pineapple (QP) biomass collected from DA-RFO V farmer-cooperators, Labo Progressive Multi-Purpose Cooperative farms, Daet public market, and Caayunan Multi-Purpose Cooperative in Camarines Norte, Philippines. The QP biomass was sun-dried and then carbonized to produce
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Fig. 1 Raw material and their extracts for use as binders: a sweet potato, b cassava, and c nami
the queen pineapple biochars. Three root crops, sweet potato (peels), cassava (peels), and nami, were used as binders in the present work. Raw Biomasses and Charcoals. The agricultural wastes from different parts of the Queen Pineapple mother plant were used for this work as shown in Fig. 1. The raw biomasses were carbonized using a drum-type carbonizer. Binders. The binders used in this work are natural organic binders which do not compete with food security for humans. They were prepared using the three root crops, sweet potato (peels), cassava (peels), and nami. For the sweet potato and cassava, only the peels were used to extract the starch while for the nami, its flesh was used. Each root crop was processed individually. First, it was weighed, then grated and the starch was extracted manually using cheesecloth. The volume of the filtrate was then measured using the graduated cylinder. This was allowed to settle for 24 h and the water was removed through decantation. The volume of the pure wet starch was measured before it was sun-dried to produce the dried starch.
2.2 Experiment Charcoal Briquetting Process. In producing the charcoal briquettes, the starch was mixed with 300 ml of boiling water until it gelatinized for about 3 min. It was then mixed with 300 g of carbonized raw material for each treatment. The mixture was then molded using the ten-port manual briquetting machine to produce ten (10) charcoal briquettes. The briquettes were then sun-dried for several days until the desired moisture content was reached. Energy Efficiency Parameters. Evaluation of three samples of each briquette material using different binders was conducted based on the following energy parameters such as burning efficiency, length of consumption, moisture content, and density. Burning efficiency (Heat Transfer). Efficiency in the use of charcoal normally means transferring the maximum amount of the heat content of the charcoal to the object to be heated. The burning efficiency was determined by getting the length of time it takes to boil 0.5 L of water using the QP feedstock coal from the different treatments.
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Fig. 2 Procedure in Briquetting QP char
Length of consumption. It was determined by taking the time it takes for the QP feedstock coal to be completely burned into ashes. Density. It was calculated by dividing the mass by the volume of the QP feedstock coal. The mass of the charcoal was measured using the analytical balance while the volume was measured using the water displacement method. Figure 1 shows the three root crops or tubers with starch content used as the binder for the QP briquette. The (a) sweet potato and the (c) cassava peels were grated, while (b) nami flesh was also grated and the one used as a source of starch. Manual extraction, filtration, and decantation were conducted to produce dried starch. It was also reported that other binder materials aside from starch can be used for the briquetting or pelleting process such as biosolids and microalgae [27], and sawdust [28]. The procedure for the QP Char Briquetting process shown in Fig. 2 involves gelatinizing the dried starch, then mixing it with the charred QP at a certain proportion, and molding it manually using a ten-port briquetting machine to produce ten compact and dense QP briquettes.
3 Results and Discussion As shown in Table 1, the energy efficiency parameters of raw biomass QP charcoals with different binders replicated three times were calculated in this work. A higher duration of boiling period was recorded for QP briquettes with cassava binder. QP
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briquettes with sweet potato binder briquettes presented a high duration of boiling period and QP briquettes with nami binder showed a relatively low duration of boiling period. The best briquette regarding the duration of the boiling period or burning efficiency in the study was exhibited by QP briquettes with nami as the binder. The samples which presented a high duration of the boiling period showed also a high length of consumption. QP briquettes with nami binder had a lower length of consumption. The highest burning rate was recorded for QP briquettes with nami binder compared to that with sweet potato and cassava binder. Data in Table 1 shows the mean energy efficiency parameters of the various biochar briquettes with different types of binders. It reveals that cassava starch is commonly used as a binder for biochar briquettes. The result of the QP briquettes for each parameter is comparable to that of the result in the study conducted by Ana, G.R. and Fabunmi, V.T. [29]. It is worthy to note that the amount of QP biochar briquettes and the amount of water used for the water boiling test or burning efficiency determination in the present paper is half of the amount used in the related study. Nevertheless, the result is comparable, and QP biochar with nami starch performed best in terms of burning efficiency and burning rate among the starches used as a binder in this study. Cassava starch for QP briquettes has the best length of consumption which is higher than that of the result for corncob, and sawdust briquettes. With a sufficient amount of 250–300 g queen pineapple briquettes, it is possible to boil 1 L of water within 25 min which is similar to the results in the study conducted where after the water boiling test, the result has shown that 200 g of each bio-composite briquettes was enough to boil 1 L of water within the same period of time [30]. Results in the study conducted by Kpelou, P., et al. [31], however, show a burning efficiency and burning rate that is higher in performance as compared with the result of the present study and the study conducted by Ana, G.R. and Fabunni, V.T. (2016) [29] due to the amount of water used to completely evaporate in the study which is 0.2 L of water only. Nevertheless, corncobs using bombax binder have the best performance in terms of the energy efficiency parameters in their study. Figure 3 shows the mean and standard deviation of the QP briquettes’ performance in the energy efficiency parameters such as burning rate, heat transfer rate, and density. The results show that QP briquettes with nami binder have a low standard deviation in terms of the burning rate which means that the data are clustered around the mean for the burning rate parameter. However, a high standard deviation of the same QP briquettes in the density parameter indicates that the data are more spread out in the said parameter. Moreover, the standard deviation is close to zero in the density of QP briquettes with cassava binder which indicates that data points are close to the mean density, while the standard deviation in the heat transfer parameter is higher which indicates that the heat transfer is above the mean. T-test was further used to compare the mean difference of the energy efficiency parameters among the three binders. Results show that in terms of burning rate and density, cassava and nami have no significant difference between each other, but in terms of specific heat and power, the figure shows that there is a significant difference between the two. Sweet potato and nami, however, are significantly different in terms of specific heat
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Table 1 Mean Energy Efficiency Parameters of the Biochar Briquettes and Binders Biochar briquette
Binder
Corncob
Corncob
Coconut shell
Rice husk
Sawdust
Queen pineapple
Reference
Burning efficiency (min)
Length of consumption (min)
Burning rate (g/min)
Bombax costatum’s calyx
3.4–4.8
–
5.93–7.71
Cissus Repens’s bark
3.8
–
7.29
5.4–7.1
–
4.98–5.37
Cissus Repens’s bark
10.3–14
–
2.90–2.93
Bombax costatum’s calyx
5.5–12.3
–
7.55
Cissus Repens’s bark
5.5–11.8
–
3.85–6.33
Cassava starch
11 ± 1.4
53.5 ± 3.54
9.37 ± 0.62
Paper
9 ± 1.4
70 ± 4.24
7.16 ± 0.43
Cassava starch
12 ± 1.4
135 ± 1.4
3.7 ± 0.4
Paper
11 ± 1.4
130 ± 7.07
3.85 ± 0.21
Cassava starch
62.5 ± 3.54
143 ± 2.83
3.5 ± 0.07
Paper
52 ± 5.67
107 ± 2.8
4.67 ± 0.12
Cassava starch
12.5 ± 2.12
60 ± 4.24
8.35 ± 0.59
Paper
7.75 ± 2.48
58 ± 2.8
8.6 ± 0.42
Cassava peel starch
14
90
3.3 ± 0.34
Sweet potato peel starch
18
85.33
3.45 ± 0.20
70
3.71 ± 0.14
Tender coconut Bombax husks costatum’s calyx
Palm kernel shells
Energy efficiency parameter
Nami starch 13
[31]
[29]
This paper
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Fig. 3 Mean and standard deviation of energy efficiency of QP briquettes. Note Means with the same letter in a graph are not significantly different from each other
and power. Cassava and sweet potato are significantly different in terms of burning rate and density parameters.
4 Conclusion This study investigated the energy efficiency of QP briquette using three organic binders (sweet potato peel, cassava peel, and nami). The results show that nami binder showed the best energy efficiency. The highest length of consumption was recorded for QP briquettes with cassava binder. The type of binder concentration affects the energy efficiency parameters. The current results showed the potential to use Nami binders in QP biomass charcoal briquettes, however, further works and
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investigations need to be performed in order to determine the optimal binder rate for each biomass charcoal. In addition, preliminary studies should be carried out in order to characterize the binder, and to make a proximate and ultimate analysis of the QP briquettes as well as their thermomechanical properties. Acknowledgements The authors would like to thank the Department of Science and Technology, the Philippines, for the financial assistance provided for the researchers under the Department of Science and Technology—Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (DOST-PCAARRD) GIA Fund, Department of Science and Technology—Science Education Institute Accelerated Science and Technology Human Resource Development Program—National Science Consortium (DOST-SEI ASTHRDP-NSC) Scholarship Grant, the Department of Physics, De La Salle University and Universiti Malaysia Perlis (UniMAP) for the technical assistance in completing this work, and Camarines Norte State College administration for their support.
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Optimization of Biobutanol Production from Detoxified Palm Kernel Cake Hydrolysate by Clostridium Acetobutylicum YM1 Abdualati Ibrahim Al-Tabib, Rafidah Jalil, Hassimi Abu Hasan, and Mohd Sahaid Kalil Abstract Oil palm is the most consumed vegetable oil in the world. However, a large amount of palm kernel cake (PKC) is left behind as residue while undergoing the process of extracting oil from palm kernel. PKC is a substrate which can be used for producing biobutanol through acetone–butanol–ethanol (ABE) fermentation by using little known aerotolerant solventogenic strain, Clostridium acetobutylicum YM1. Current study utilized response surface methodology (RSM) based on a central composite design (CCD) to examine the effect of inoculums’ size (5– 15%), incubation temperature (30–37) and initial pH (5 to 7) as operation variables in biobutanol production from detoxified palm kernel cake hydrolysate (DSAPKC) by Clostridium acetobutylicum YM1. RSM was utilized to optimize these conditions so that the production of biobutanol can be maximized. ABE fermentation conditions were fitted in a quadratic model acquired from RSM to forecast the production of biobutanol as a function of investigated conditions. The optimum conditions for the production of biobutanol were inoculum size of 15%, incubation temperature of 37 °C and pH 6.3. Fermenting detoxified SAPKC using Clostridium acetobutylicum YM1 under these optimal conditions produced 4.73 g/L of biobutanol which was 10% more than the predicted value. Keywords Palm kernel cake · Clostridium acetobutylicum YM1 · Acetone–butanol–ethanol · Fermentation · Response surface methodology
A. I. Al-Tabib (B) College of Applied Science Technology, (CAST) Al-Wata, Tripoli 218, Libya e-mail: [email protected] R. Jalil Forest Research Institute Malaysia (FRIM), 52109 Kepong, Selangor, Malaysia e-mail: [email protected] H. A. Hasan · M. S. Kalil Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_12
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1 Introduction Lately, the scientific community focuses on the usage of renewable resources in Acetone–butanol–ethanol (ABE) to produce biofuels. Biobutanol is a four-carbon alcohol (C4 H10 O) obtained through the fermentation of ABE using strains of clostridium. Biobutanol is an important biofuel because it has more advantages compared with ethanol. Biobutanol can be used to replace gasoline without requiring any modification to be done to the automobile engines. Furthermore, biobutanol has lower vapor pressure, higher caloric value, lower freezing point, less corrosiveness and a better miscibility with gasoline [1–3]. Lignocellulose is a renewable source of carbon that consists of lignin, cellulose and hemicellulose [4]. Lignocellulosic biomass can be a source of fermentable sugars. One of the greatest challenges faced during the process of ABE fermentation is the cost of feedstock [5, 6]. Many recent studies have examined the usage of low cost and non-edible feedstock for instance de-oiled rice bran, rice bran, corn stover, sweet sorghum bagasse and wheat straw [2, 7–9]. In tropical countries for example Malaysia and Indonesia, the Palm Kernel Cake (PKC) is one of the main by-products of the palm oil industry. Malaysia Palm Oil Board (MPOB) has stated that the PKC production in Malaysia has increased to 3,148,899 tons in 2016 [10]. The main elements of lignocellulose in PKC are hemicellulose (61.5%) and cellulose (11.6%) [11]. Generally, by-products from oil palm are utilized in the production of animal feed but this is not the most economical way of utilizing the wastes [12]. Various types of solvent-producing clostridia strains are utilized in biobutanol production through the fermentation of ABE for instance Clostridium pasteurianum DSM 525 [13], Clostridium acetobutylicum ATCC824 [14], Clostridium beijerinckii BA101 [15], Clostridium saccharoperbutylacetonicum N1-4 [16]. The C. acetobutylicum YM1 which is recently discovered in Malaysian agricultural soil is a hyper-biobutanol producing strain [17, 18]. RSM is a popular statistical tool utilized during the chemical and biological optimization process. Nevertheless, previous studies have stated that the optimum value of process parameters is system-specific [19, 20]. RSM utilizes mathematical and statistical tools to build models that can optimize and examine the effect of selected variables on targeted response. There are many RSM statistical methods for instance the one-factor design, the central composite design, the D-Optimal and the Box– Behnken design. The most commonly utilized RSM tool is the central composite design (CCD) whereby each numeric variable is assigned 5 levels (−α, −1, 0, +1 and +α) [21; 22]. In the current study, the influence of key fermentation parameters (inoculums size, incubation temperature and initial pH) on biobutanol production from DSAPKC hydrolysate by C. acetobutylicum YM1 are examined using RSM with CCD so that the production of biobutanol can be optimized. This enables the PKC to be used as a possible feedstock for ABE fermentation using C. acetobutylicum YM1 strain. Furthermore, fermentation of biobutanol at optimized conditions is carried out in flask
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scale. Lastly, production of biobutanol is confirmed after taking into consideration all optimized fermentation conditions.
2 Materials and Methods 2.1 Microorganism and Medium Preparation C. acetobutylicum YM1 stock culture was acquired from the biotechnology laboratory, Research Centre for Sustainable Process Technology (CESPRO), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43,600, Bangi, Selangor, Malaysia. A tryptone-yeast extract-acetate (TYA) medium was utilized to prepare the inoculum of C. acetobutylicum YM1. Microbial inoculum was made by adding 1.0 mL of the spore suspension of C. acetobutylicum YM1 into 9.0 mL of the TYA medium that was heated in boiling water for 1.0 min. Next, the culture was chilled in ice water, and nurtured at 30 °C for 1 or 2 days under anaerobic environment. After that, this culture was sub-cultured in a TYA medium and nurtured for 18–20 h before it can be utilized as an inoculum source. The TYA composition contained glucose (30.0 g/L), ammonium acetate (3.0 g/L), yeast extract (2.0 g/L), tryptone (6.0 g/L), FeSO4 .7H2 O (0.01 g/L), KH2 PO4 (0.5 g/L) and MgSO4 .7H2 O (0.3 g/L) [23].
2.2 Chemicals Employed The chemicals and solutions listed below were used in this study (Table 1). The abbreviated names of suppliers are included, unless otherwise stated; all chemicals used were of analytical grade. Table 1 List of chemicals used in this study
Materials
Manufacturers
Ammonium acetate
Merck, Germany
D-glucose
R & M, UK
Ferrous sulfate heptahydrate
R & M, UK
Sulfuric acid
J.T Baker, USA
Dipotassium phosphate
R & M, UK
Monopotassium phosphate
R & M, UK
Magnesium sulfate heptahydrate
R & M, UK
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2.3 Preparing Palm Kernel Cake Hydrolysate Palm kernel cake (PKC) was acquired from Malaysia Palm Oil Board (MPOB). PKC was treated with diluted sulfuric acid [2% (v/v)]. After that, the generated hydrolysate was detoxified using activated charcoal to decrease or remove fermentation inhibitors [24, 25]. The detoxified hydrolysate was utilized as the fermentation medium for biobutanol production by C. acetobutylicum YM1 in batch culture.
2.4 Biobutanol Production All experiments were done using 100.0 ml serum bottles with 50.0 ml working volume. At the beginning, the pH of DSAPKC hydrolysate was regulated to the required value using 10.0 M of NaOH or 1.0 M HCl solution. The DSAPKC hydrolysate was flushed with oxygen-free nitrogen to create an anaerobic environment. After that, the sterilization process was carried out by autoclaving at 121 °C for 15 min before inoculation. DSAPKC hydrolysate was inoculated with required amount of actively growing C. acetobutylicum YM1 cells (bacterial cells grown in TYA medium for 18 to 20 h at 30 °C under anaerobic environment). The serum bottle cultures were incubated at the required temperature in ovens for 72 h.
2.5 Central Composite Design and Statistical Analysis The main issues which affect the ABE fermentation process were examined so that a higher level of biobutanol production could be obtained from ABE fermentation of DSAPKC hydrolysate. CCD was utilized to identify the optimal combination of initial pH (X 1 ), incubation temperature (X 2 ) and inoculums’ size (X 3 ) for maximum production of biobutanol. Each parameter was given coded values as [−1.682, −1, 0, 1, 1.682] (Table 2): initial pH (4.32, 5, 6, 7, and 7.86), temperature (27.61, 30, 33.5, 37, and 39.39) and inoculum size (1.59, 5, 10, 15, and 18.41). Table 2 Levels of process variables for central composite design (CCD) Variable
Symbol
Actual level
Coded value
pH
X1
4.32
5
6
7
7.68
−1.682
−1
0
1
1.682
Temperature (°C)
X2
27.61
30
33.5
37
39.39
−1.682
−1
0
1
1.682
Inoculum size (%)
X3
1.59
5
10
15
18.41
−1.682
−1
0
1
1.682
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A total of 20 experiments including six center points were produced by utilizing Design-Expert version 6.0.10 (DOE, Stat Ease, United States) and done randomly as shown in Table 3. Linear regression analysis had been utilized to fit the experimental data with a second-order model as obtained from Eq. (1): Y = β0 +
3 i=1
βi X i +
3
βii X i2 +
3 3 i
i=1
βi j X i X j
(1)
j
whereby Y is the measured reaction (biobutanol concentration), X i and X j are the independent variables, β 0 represented the intercept, and β i , β ii and β ij are linear coefficient, quadratic coefficients and interaction coefficient, respectively. The fitted model was assessed by utilizing one-way analysis of variance ANOVA (analysis of variance), residual plots and lack-of-fit test. Batch fermentation was done under Table 3 Biobutanol production for each design pattern of the central composite design experiment (CCD) Run
X1
X2
X3
Biobutanol (g/L)
1
6.00
33.50
10.00
3.04
2
7.00
30.00
5.00
0.24
3
6.00
33.50
10.00
2.68
4
6.00
33.50
18.41
4.77
5
6.00
39.39
10.00
2.5
6
7.68
33.50
10.00
0.21
7
5.00
37.00
5.00
1.59
8
4.32
33.50
10.00
1.05
9
5.00
30.00
5.00
3.79
10
7.00
37.00
5.00
0.98
11
6.00
27.61
10.00
2.12
12
6.00
33.50
1.59
2.67
13
6.00
33.50
10.00
2.32
14
6.00
33.50
10.00
2.41
15
6.00
33.50
10.00
2.80
16
7.00
30.00
15.00
3.08
17
6.00
33.50
10.00
2.87
18
5.00
30.00
15.00
3.00
19
7.00
37.00
15.00
3.72
20
5.00
37.00
15.00
3.7
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optimum settings to verify the optimum settings forecasted by RSM. Next, a comparison was made between the results obtained from this experiment and the value of the reaction was forecasted using the regression model.
2.6 Analytical Methods The culture specimens were centrifuged at 10,000 rpm for 5.0 min in order to isolate the sediments. The clear supernatant obtained was stored at −18 °C. The ABE solvents and organic acids namely acetic acid and butyric acid concentrations were calculated by a gas chromatography system (7890A GC-System; Agilent Technologies, Alto, California, USA) which was fitted with a flame ionization detector and a 30-m capillary column (Equity1; 30 m × 0.32 mm × 1.0 μm film thickness; Supelco Co., Bellefonte, Pennsylvania, United States). Temperature of the injector was fixed at 250 °C and the temperature of the detector was 280 °C. Flow rate of helium which was utilized as the carrier gas was set at 1.5 mL/min.
3 Results and Discussion 3.1 Optimization of Biobutanol Fermentation Conditions In the current research, Design-Expert version 6.0.10 (DOE, Stat Ease, United States) was utilized to examine the influence of initial pH (X 1 ), incubation temperature (X 2 ) and inoculums’ size (%) (X 3 ) on production of biobutanol by C. acetobutylicum YM1 from DSAPKC hydrolysates. Table 2 showed the CCD of the three independent variables and the production of biobutanol obtained from 20 runs. The highest production of biobutanol was obtained in run 4 (4.77 g/L). To estimate the test error, six center points were included in the experimental design (same inoculum size, initial pH and incubation temperature). The regression analysis of the data obtained from 20 runs resulted in the following quadratic model Eq. (2): Biobutanol concentration = +8.94749 + 3.00188*initial pH − 0.44818 × X 2 − 1.50266 × inoculum size − 0.65944 × initial pH2 − 5.34478E − 003 × incubation temperature2 + 0.0117321 × inoculum size2 + 0.10286 × initial pH × incubation temperature + 0.10650× initial pH × inoculum size + 0.020000 × incubation temperature × inoculum size
(2)
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3.2 Influence of Initial pH, Incubation Temperature and Inoculum Size on the Production of Biobutanol The results obtained for the statistical analysis by ANOVA for the fitted model, independent variables and interaction of variables were illustrated in Table 4. The low probability value Prob > F (0.0003) indicated that the model fitted very well and was very suitable for production of biobutanol from DSAPKC hydrolysate. ANOVA of the model also illustrated the significant influence of the quadratic influence of inoculum size and initial pH on the production of biobutanol (p < 0.05). On the other hand, incubation temperature had insignificant influence on the production of biobutanol (p > 0.05). The F value of model was 11.79 which showed that the chosen model was highly significant. The model fit was tested using coefficient of determination R2. The result showed that 91.38% of the unevenness in the response can be clarified using the model. The model could not explain only 8.62% of the total unevenness. X 1 2 , X 2 2 and X 3 2 the quadratic terms; X 1 X 2 , X 1 X 3 and X 2 X 3 the interaction terms. Figures 1, 2 and 3 illustrated the 3D response surface graphs of the interactive influence of the three factors on biobutanol production. Figure 1 represented the simultaneous influence of incubation temperature and initial pH at a constant inoculum size. The influence of initial pH was more significant than incubation temperature on biobutanol concentration. pH is the main factor that influences the production of biobutanol during ABE fermentation [19, 26]. The 3D plot showed that there was no significant interaction between incubation temperature and initial pH. When the Table 4 Analysis of variance of experimental results for biobutanol production by C. acetobutylicum YM1 from DSAPKC hydrolysate Source
Sum of squares
Model
DF
Mean square
F Value
Prob > F
24.38
9
2.71
11.79
0.0003
Initial pH
X1
2.19
1
2.19
9.54
0.0115
Temperature (°C)
X2
0.020
1
0.020
0.086
0.7755
Inoculum size (%)
X3
7.97
1
7.79
34.67
0.0002
X 12
6.27
1
6.27
27.27
0.0004
X 22
0.062
1
0.062
0.27
0.6154
X 32
2.70
1
2.70
11.76
0.0064
X1 X2
1.04
1
1.04
4.51
0.0596
X1 X3
2.27
1
2.27
9.87
0.0105
X2 X3
0.98
1
0.98
4.26
0.0658
Residual
2.30
10
0.23
Lack of fit
1.92
5
0.38
5.01
0.0507
Pure error
0.38
5
0.076
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Fig. 1 Surface plot illustrates the influence effects of incubation temperature and initial pH on biobutanol production by C. acetobutylicum YM1
initial pH was increased to the optimum level (6.3) at a constant inoculum size and incubation temperature production of biobutanol was enhanced. A research done by Wang and Blaschek [27] revealed that an increase in the pH value of the juice of maize stalk caused biobutanol production to increase whereby the maximum concentration of biobutanol was obtained at pH value of 6.7. Furthermore, the optimal initial pH for the highest biobutanol concentration by C. beijerinckii ATCC 10,132 was 6.5 [28]. Figure 2 showed the combined influence of initial pH and inoculum size on biobutanol production at a constant incubation temperature. An increase in the initial pH led to a rise in the production of biobutanol as a higher size of inoculum was utilized. However, increasing the initial pH level above the optimum level would result in a reduction in the production of biobutanol. This phenomenon occurred because pH was the key element in determining the acidogenic and solventogenic phase [14, 29]. A rise in inoculum size would raise the concentration of biobutanol to a maximum level. The 3D plot clearly illustrated that the interaction between the initial pH and the inoculum size had a significant influence (P < 0.05) on the production of biobutanol. Figure 3 showed the influence of inoculum size and incubation temperature on the production of biobutanol at a constant initial pH. Different incubation temperatures
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Fig. 2 Surface plot illustrates the influence of inoculum size and initial pH on the production of biobutanol by C. acetobutylicum YM1
with low inoculum size (5%) had a low influence on the production of biobutanol. However, at a higher inoculum size (15%) the production of biobutanol increased as the incubation temperature was raised from 30 to 37 °C. When the inoculum size was increased, the number of cells in the fermentation medium was raised [30]. A higher concentration of cells would cause a reduced lag phase with highly active cells. The influence of incubation temperature on ABE fermentation was determined by the substrate and the kind of microorganism utilized in the ABE process [31, 32]. Clostridia could breed and produce solvents effectively if the incubation temperature ranged between 20 and 45 °C [33].
3.3 Adequacy Check and Confirmation of the Model The model obtained can be utilized to forecast the response value (biobutanol concentration) that can be acquired from certain value of variables. It needs to be tested to confirm that it gives a satisfactory estimate to the real system or else it might offer misleading or poor results [34]. Figure 4 illustrates the obtained concentration of biobutanol versus the concentration predicted by the model. It illustrates that the
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Fig. 3 Surface plot illustrates the influence of inoculum size and incubation temperature on the production of biobutanol by C. acetobutylicum YM1
acquired and forecasted values are in good agreement as all the points were very close to the diagonal line. The residuals from the least square fit play a major role in the assessment of the suitability of the model. Figure 5 illustrates the residuals and normal probability. All the results acquired are close to the continuous line which indicated that there is no significant violation of model normality. Based on the model obtained, numerical optimization is utilized to regulate the optimal combination of process parameters for maximum biobutanol production. The optimal conditions are found to be an inoculum size of 15%, initial pH value of 6.37 and incubation temperature of 37 °C. In order to validate the applicability of the obtained model, and a set of fermentation experiment is carried out by inoculating C. acetobutylicum YM1 in DSAPKC hydrolysate at optimal conditions. The actual value of the final biobutanol production acquired from the experiment was 4.73 g/L which was 10% greater than the predicted value (4.30 g/L) using RSM. This signified the strength of the obtained model in this research and the potential value of PKC hydrolysate in the production of biobutanol using C. acetobutylicum YM1.
Optimization of Biobutanol Production from Detoxified Palm Kernel … Fig. 4 Diagnostic plots of the normal probability of studentized residuals for the production of biobutanol
Fig. 5 Diagnostic plots of the actual and predicted values
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4 Conclusion Optimization for the production of biobutanol from fermentation of detoxified palm kernel cake hydrolysate (DSAPKC) by Clostridium acetobutylicum YM1 was effectively done using RSM with variations of incubation temperature, inoculums size and initial pH. The RSM revealed that the optimum level of incubation temperature was 37 °C, inoculums’ size was 15% and initial pH was 6.37. The ANOVA results revealed good agreement between the predicted values and the experiment results. This research showed that RSM is a suitable tool for optimizing the production of biobutanol. Acknowledgements The authors wish to express their gratitude to the Universiti Kebangsaan Malaysia for financing this research work.
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Mixed Matrix Membrane (MMMs) as Membrane Based Separation Technology: A Review Kavita Pusphanathan, Hafiza Shukor, Noor Fazliani Shoparwe, Muaz Mohd Zaini Makhtar, Nor’ Izzah Zainuddin, and Nora Jullok
Abstract Mixed matrix membrane (MMMs) is an innovative membrane basedseparation technology that plays an essential role in liquid and gas separation and purification recently. This review emphasizes mainly on the current MMMs technology. The discussion begins with a background of the MMMs technologies, followed by a comparison between the MMMs technology porous and non-porous membranes. Following that, state-of-the-art MMMs are featured, which contain a variety of polymers and non-polymers, as well as inorganic fillers and materials. The binary filler approach is also explained, which combines two filler materials to achieve synergistic improvements in MMMs. The development of new robust, high-performance materials is one type of revolutionary membrane preparation approach for harsh and inconsiderate environments. In comparison to pristine polymeric membranes, blended mixed matrix membranes with polymer, solvent, and additives are believed for efficient performance. In addition, fabrication strategies for MMMs preparation are addressed. The fabrication technique can be used to improve membrane performance in a number of ways, including resilience to extremes in K. Pusphanathan Faculty of Chemical Engineering and Technology, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia H. Shukor (B) · N. Jullok Centre of Excellence for Biomass Utilization, Faculty of Chemical Engineering & Technology, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] N. F. Shoparwe Rare Earth & Material Technopreneurship Centre (GREAT), Faculty of Bioengineering and Technology, University Malaysia Kelantan, Jeli Campus, 17600 GoldJeli, Kelantan, Malaysia e-mail: [email protected] M. M. Z. Makhtar (B) Bioprocess Technology Division, School of Industrial Technology, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang, Malaysia e-mail: [email protected] N. I. Zainuddin Indah Water Konsortium, Lorong Perda Utama 13, Bukit Mertajam 14300, Pulau Pinang, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_13
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process conditions and higher mixture resolution when separating gases and liquids. After that, membrane characterization is performed to analyze the membrane’s structural and morphological properties. Based on that, critical evaluation of the performances of the MMMs based on the characterization of the membrane is evaluated in context. Finally, the opportunities, as well as future prospects for the integration of MMMs units for process intensification in various sectors, are also significant of the review. Keywords Mixed matrix membrane (MMMs) · Membrane technology · Polymer · Membrane fabrication
1 Introduction Generally, a membrane is an interface that separates the two phases and restricts the transport of various chemical species through it. There is a wide range of membrane compositions possible, including those that are homogeneous, heterogeneous, symmetric, asymmetric, charged, and neutral. There are a number of applications for nanoparticles (NPs) in the fabrication of the polymer and NPs blended membranes, an example of NPs is titanium dioxide (TiO2 ). Due to various interactions between the nanoparticle surface, polymer chains, and solvents during membrane development, it is important to design membranes with the appropriate structure [1]. Hydrophilicity and pore size are key qualities of a membrane that can be improved by modifying the membrane. Controlling membrane fouling is also aided by the hydrophilic nature of nanoparticles and the presence of functional groups on their surface. There is a wide range of polymers that can be used for this purpose [2]. Some examples are polyvinylidene fluoride (PVDF), polyamide (PA), polysulfone (PSf), and polyethersulfone (PES). It is considered that the performance of blended mixed matrix membranes, which consist of polymer, solvent, and additives, is superior to that of neat polymeric membranes. Improved membrane performance can be achieved during fabrication not only by making membranes more resistant to process extremes but also by increasing the efficiency with which mixtures are resolved during liquid and gas separation [3]. The incorporation of perfect MMMs with inorganic fillers is shown in Fig. 1.
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Fig. 1 Schematic diagram of ideal mixed matrix membrane
2 Performance Enhancement of MMMs 2.1 Polymer Membrane Organic membranes are usually made up of various polymers, among which the typical ones are cellulose acetate (CA), polyamide (PA), polysulfone (PSf), polyethersulfone (PES), polyvinylidene fluoride (PVDF) and polypropylene (PP). Polymeric membranes are relatively cheap, easy to manufacture, available in a wide range of pore sizes, and have been widely used in various industries. Nevertheless, most of the polymeric membranes have limitations on one or more operating conditions either pH, temperature, pressure, and many more which hinder their wider applications. As intended, PVDF (polyvinylidene fluoride), PES (polyethersulfone), and polysulfone (PSf) is one of the most frequently cited polymers because of its widespread use in pressure driven separation applications; however, it is important to bear in mind that the solvents used to prepare the membranes, like NMP (N-methylpyrrolidone), are harsh polar aprotic solvents (dimethyl-formamide). Polyethersulfone (PES) has become a pivotal polymer-based membrane due its ease in forming a dense top layer, good heat resistance, environmental endurance, affordability, and ease of availability. It is one of the frequently used polymer membranes to create a mixed matrix membrane. Carboxylation also occurs with PES, exchanging the hydrogen atom of the aromatic ring for one of the carboxyl group’s own. That’s why it became more hydrophilic and antifouling [4]. PES is now widely used in medical applications and sewage treatment membranes, thanks to its exceptional mechanical and thermal qualities. Two heavy metals that contaminate groundwater are cadmium (Cd2+ ) and lead (Pb2+ ) ions, both of which were instantly removed from binary and ternary aqueous solutions at concentrations of 10 ppm and 50 ppm, respectively, by using the PES membrane. Cd2+ ion rejection was highest (61.3%) and second highest (55.4%) when starting with a ratio of 50 ppm Pb2+ ions to 10 ppm Cd2+ ions and 10 ppm Pb2+ ions to 50 ppm Cd2+ ions, respectively [5]. The rejection and penetration flux of the PES membranes for the ternary aqueous solutions rose as the starting concentration of the heavy metal was reduced. When the metals’ starting concentration was 10 ppm, the maximum permeation flux measured using PES was 21.6 L/m2 h, and the greatest metal rejection measured
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using PES was 48.3% for Cd2+ ions and 40% for Pb2+ ions. PES has been frequently utilized as a promising material in therapeutic diagnostics aside from this one, such as hemodialysis. Another typical membrane used to develop mixed matrix membranes is polysulfone (PSf) membrane. It is an incredibly stable polymer that can withstand extreme temperatures. Even at high temperatures and under light stress, PSf maintains its strength against detergents and hydrocarbon oils [6]. The physicochemical qualities of this membrane include thermostability, chemical stability to a wide range of chemicals (including different bases, acids, and chlorine), enough mechanical strength, and tolerable processability. PSf membranes are used and adapted for applications such as water and sewage treatment, membrane distillation, air pollution control, gas separation, isolators for lithium-ion batteries, and nanocomposite support. PSf membrane is employed in the nanofiltration (NF) process to isolate acetic acid from glucose.
2.2 Types of Additives 2.2.1
Inorganic Fillers
Inorganic additives, also referred to as fillers, can be used to modify membranes. Examples of additives include polymers and inorganic nanoparticles. Due to their competition in the permeability and selectivity aspects, polymeric and inorganic fillers in membranes have faced difficulties [9]. The efficiency of polymeric and inorganic membranes in the separation processes can be increased by combining nanoparticle fillers within the polymer matrix. The hydrophilicity of the membrane can be increased by combining biocompatible hydrophilic polymers with poreforming substances such as polyethylene glycol (PEG) and polyvinylpyrrolidone (PVP) [10]. By successfully separating the desired solute, a proper distribution of additives in an appropriate ratio will have a positive influence on the membrane’s performance. In reality, one of the most crucial techniques for producing the necessary membranes throughout the production of membranes has been the incorporation of organic or inorganic components. Addition of hydrophilic chemicals to the dope solution constitutes blend alteration during membrane preparation. Since the additive can be both enhanced on the surface of the membrane and embedded in the bulk of the membrane, it is able to exert control over the corresponding properties of both regions. Production of industrial PSf ultrafiltration (UF) membranes uses hydrophilic polymers as additives, such as polyvinylpyrrolidone (PVP) or polyethylene glycol (PEG).
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Binary Fillers
Synergistic interactions between binary fillers, such as inorganic and organic fillers, boost membrane performance. Hybrid membrane (HM), which consists of inorganic fillers dispersed in a polymer matrix, is highly regarded by scientists due to its exceptional physical and thermal qualities [10]. However, consistent inorganic filler dispersion and distribution is challenging to produce because similar inorganic filler particles often clump and have poor dispersion, which impacts the membrane’s properties [11]. It is also often mentioned that the resulting hybrid membranes increased either selectivity or gas and liquid permeability but not both [11]. High gas and liquid permeability and selectivity are desirable properties for a membrane, which is what prompted the development of binary fillers HM. To improve the overall membrane gas and liquid separation performance and homogeneously distribute nanoparticles in HM, the binary fillers dispersion method was recently developed. In earlier research, silicalite-1 (S1C) and metal–organic framework (MOF) were combined with PSf to study the synergistic effect of binary fillers for gas separation [12]. For instance, two partly organic MOFs and ZIF-8 were also included since they guarantee the polymer matrix’s chemical affinity. An 88% increase in gas permeability was attained as a result of the disruption of the inorganic filler increasing the polymer matrix-free volume. Additionally, compared to HM, the combination of inorganic fillers exhibits optimal performance with a 50% improvement in selectivity. This is because as different types of particles were incorporated into the matrix, particle dispersion was improved. Recent research have found that using carbon nanotubes (CNT) and graphene oxide (GO) to create HM for CO2 /CH4 separation, which combines the binary fillers technology from wastewater treatment, considerably improves membrane performance. In addition, a number of earlier research have demonstrated the use of binary fillers to test their effectiveness on the separation performance, as shown in Table 1.
3 Fabrication Technique for MMMs 3.1 Phase Inversion Phase inversion method is one of the well-known methods to fabricate the membrane which was intiated by Loeb and Sourirajan in 1960 [6]. Many studies have been conducted since then, and we can now assert that we have a firm grasp on the kinetics and thermodynamics of solutions, which is essential for comprehending the phase inversion process. For certain uses, it is now feasible to fine-tune not only the pore size of a membrane, but also its general shape, pore connectivity, and the presence of macrovoids. A polymer additive, NPs, and polymer membrane also referred to as a dope solution are dissolved in an organic solvent as the first step in the phase inversion strategy from a thermodynamic perspective to create a stable homogeneous
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Table 1 Utilisation of various binary fillers to test its efficiency on the separation performance based on previous studies Binary fillers
Application
Separation performance
References
PEG/TiO2 Elimination of lead ions
On the coating layer’s lateral surface as well as its top layer, metal ions would steadily build up. The cellulose acetate (CA) mixed matrix membranes’ ion rejection efficiency would be improved by low TiO2 concentration and PEG with the highest molecular weight
[13]
PEG/ Carbon Carbon dioxide nanotubes removal (CNT)
Indicatively, the MMMs containing 5 wt% [14] PEG-g-CNTs showed an increase in CO2 permeability of 52.4% at 1.5 bar, as well as improvements of 81% and 74% in CO2 /N2 and CO2 /CH4 perm-selectivities, and up to 43.4% increases in tensile modulus and a 12.5% rise in tensile strength
Polyvinyl alcohol (PVA)/ TiO2
Bovine Serum Albumin removal
In contrast to pure TiO2 , the modified membrane surface containing TiO2 showed good dispersion properties and interfacial adhesion with the polymer matrix membrane. The proportion of PWF is 94%
[15]
PEG 200/ Graphene
Lactic acid removal
Lactic acid removal is about 83% for the modified membrane which is nearly similar as pure membrane because PEG 200 binding is not so efficient with graphene
[16]
PVP/ Chitosan
Uremic toxin removal, Bovine Serum Albumin removal
The immobilization of chitosan nanoparticles [17] improved the removal of uremic toxins by the PSf membrane. PSf membrane combined with CNP also showed the highest UF coefficient (116 ml/ m2 .h.mmHg) and clearances of urea (85%), creatinine (67%), and lysozyme (49%) among all membrane combinations
Carbon coated alumina Nickel doped titanium dioxide (CCA/ Ni–TiO2 )
Discharge of The incorporation of 0.25 wt% CCA/Ni–TiO2 into PSf [18] Cr3+ heavy showed the highest PWF. The permeate flux obtained was approximately 231.50 L/m2 .h metal rejection capacity from dye wastewater
solution [20]. The created dope solution is then cast onto a glass plate or a nonwoven support as a thin film using a casting knife; a slot die may also be used in place of a casting knife. In order to lower its Gibbs free energy, the cast film is then placed in a thermodynamically unstable environment, where the phases spontaneously separate and freeze [21]. The difference between phase separation and solidification must be clearly understood.
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Nonsolvent-induced phase separation (NIPS), thermally-induced phase separation (TIPS), vapor-induced phase separation (VIPS), and evaporation-induced phase separation (EIPS) are the most frequent forms of phase separation techniques. Each strategy has its own name and concept, yet they all share the same fundamental ideas. Usually, solidification occurs after phase separation [12]. NIPS is the process of exposing the solution to a non-solvent, which is typically distilled water. This technique is widely employed in the production of porous polymeric membranes. As the solvent in the film is replaced by the non-solvent, phase inversion occurs. This procedure yields an asymmetric polymeric membrane with a dense selective layer and a porous supporting sublayer [22]. For TIPS to work, the solution must be cooled to the point where its solubility is lost. Making a dope solution of solvents and polymers at a temperature close to the melting point of the polymer, casting it into a film, and then cooling it to a lower temperature are the steps involved in the phase-inversion process known as TIPS [23]. Phase separation, brought on by the temperature shift, leads to the growth of a solid film. When the solution is put in a humid environment, the vapor from the evaporating solvent is absorbed by the film, resulting in VIPS/EIPS. The NIPS approach is the most popular method, followed by the TIPS strategy due to the latter’s flexibility. As can be seen in Fig. 2, there are many different ways to accomplish the desired phase inversion [12]. Fig. 2 Phase inversions methods
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3.2 Dip Coating Spinning polymer membranes into hollow fiber and flat sheet MMMs, which are excellent for large-scale industrial applications due to their expansive area, is an essential part of the fabrication of polymer membranes for gas separations. Spin coating is convenient for research and rapid prototyping since it is quick to set up and yields consistent coatings of varying thicknesses with little waste [24]. The membrane is coated with a polymer or organic material using a “dip coating” process. To fast attach to the support layer, the typical coating polymer should have remarkable properties including hydrophobicity and a negative charge. Polymers in this class can be made via sulfonation, and they include materials like sulfonated poly(ether ether ketone) and sulfonated poly(ether sulfone) (SPEEK). The coating layer may enhance the performance of the support layer by, for example, making it stronger and improving its separation properties. Selecting a coating polymer requires thinking about some fundamental features [25]. The strength and stability of the polymer, its capacity to form layers, its ease of solubility in solvents, its price, and its crosslinking capability are all important considerations. Submerging a dry membrane in a coating solution, waiting for the coating ingredient to react with the substrate, and then drying the resulting membrane are the three basic processes in the dip coating process.
4 Characterization of MMMs 4.1 Physical Characterization 4.1.1
Surface Analysis Method
The surface and volumetric structures of membranes will be characterized using SEM. It generates images by scanning the surface of a membrane sample with a focused electron beam. SEM can be used to examine the structure of a membrane, including fouling mechanisms in relation to porosity and pore size distribution in terms of pore obstruction [26]. Gold, platinum, palladium, chromium, and iridium will be used to coat the membrane sample before it is scanned in the SEM [27]. The thickness of a coating layer might range from 2 to 5 nm, depending on the metal used [28]. The membrane needs to be cut into smaller pieces and treated with liquid nitrogen for 1 min before it can be cracked. The sample will be attached vertically using double-sided carbon adhesion foil [29]. Before the SEM test, the membrane’s surface and cross-sectional area will be coated with a thin layer of platinum under vacuum using sputter coating [30].
Mixed Matrix Membrane (MMMs) as Membrane Based Separation … Table 2 Porosity testing on the PVDF MMMs prepared with different solvent compositions [32]
4.1.2
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Membranes
Solvent composition
Surface porosity (%)
M1
DMSO
1.42
M2
DMAc
0.17
M3
DMF
0.2
M4
TEP
0.09
Porosity
The porosity of a membrane can be determined using its dry weight membrane. Distilled water was used to immerse the membrane [31]. The weight of the wet membrane was then measured after the excess wet membrane was removed with filter paper. The wet membrane was dried in a 25 °C oven for 10 h. Equation (1) was used to calculate the measured weight of the dry membrane. ε(%)
WW − Wd × 100% d d Ww − W +W Dw Dp
(1)
whereby ε is the membrane porosity, W w is the wet membrane weight (g), W d is the dry membrane weight (g), Dw is the pure water density (1.0 g/cm3 ) and Dp is the polymer density (1.37 g/cm3 ). Previous studies on the porosity of polyvinylidene fluoride (PVDF) MMMs using different solvent mixes are summarized in Table 2. Solvent mixtures including triethyl phosphate (TEP), dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF), and dimethyl acridine (DMAC) were investigated [32]. The surface porosities of several membranes are listed below. M1 > M3 > M2 > M4. The M4 membranes, which were produced in either pure DMSO or a combination of DMSO and other solvents, and showed the highest levels of surface porosity of any membranes tested. Furthermore, M1 was synthesized with a surface porosity of 1.42% when using only DMSO as the solvent, which was much higher than the surface porosities of M4, M2, and M3 synthesized using the other three reagents. These findings indicate that the generated membranes tended to generate a porous skin layer when DMSO was used as a pure solvent or as a com-ponent of the solvent. It was also discovered that the mixture of solvents, with the exception of DMF/TEP, encouraged the growth of a porous skin layer in comparison to the pure solvent.
4.1.3
Atomic Force Microscopy (AFM)
AFM can be used to discover about the membrane’s surface roughness, hole arrangement, and hole size. For AFM, a probe is scanned in a direction perpendicular to the mean plane, where mechanical interactions between the probe and the sample are exploited (xy). The end of the cantilever is where the probe’s tip is affixed for
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use. An optical system detects the cantilever’s vertical deflection at contact with the surface [33]. To adjust for the imperfection of the selected scan size, the mean plan of the surface can be subtracted. Hydrophilic probes induce a greater shift than hydrophobic probes when interacting with hydrophilic membrane surfaces. The membrane’s susceptibility to fouling can be estimated from the AFM analysis by calculating the surface roughness [34]. Scanning at a rate of 0.25 Hz across a scanning area of 10 m × 10 m, tapping mode AFM will be used [35].
4.1.4
Contact Angle (CA) Evaluation
A contact angle goniometer will be used to measure the hydrophilicity of the membrane surface at room temperature [36]. The contact angle will be measured as soon as the deionized water reaches the membrane. Two glass slides will be used to mount the flat sheet membrane sample with the upper surface facing upward. Using a micro syringe powered by a motor, we will place a water droplet of about 0.2 L on top of the dry membrane layer while it is at room temperature. The droplet’s contact angle with the membrane is then measured at a long working distance. The contact angle of the final result will be calculated using the “DROP” image software suite. Each sample will have 10 readings taken, and the average will be used to calculate the mean values in order to account for potential experimental error. In accordance with previous research [37], the hydrophilicity of the GO/PI MMMs was evaluated by calculating the water contact angle (CA) at the membrane surface. As shown in Fig. 3, the water CA of a virgin PI membrane is around 92°. As the GO is incorporated into the PI matrix, the water CA of the resulting GO/PI MMMs decreases due to the presence of hydrophilic groups. By increasing the GO loading in the PI matrix to 1 wt%, the water CA of the GO/PI MMMs decreases to 59°, indicating a more hydrophilic membrane surface. These occurrences result from the phase immersion process, which causes GO nanosheets to move from the membrane’s underside to its overside. However, after the GO concentration rises above 2 wt%, the water CA of the GO/PI MMMs climbs to 75°. Casting solutions containing up to 2 wt% GO are likely too viscous to allow for solvent/non-solvent exchange during phase inversion. The contact angle rises because GO nanosheets take longer to reach the membrane surface.
4.2 Chemical Characterization 4.2.1
Fourier Transform Infrared Spectroscopy (FTIR)
The FTIR technique was used to determine the functional group on the membrane’s surface. The surface chemistry of produced membranes can be determined using FTIR spectroscopy [38] by monitoring the shifts in chemical interactions between molecules. Commonly, FTIR spectrometers record spectral information from 4000
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100 90
[VALUE]°
Contact angle (°)
80
84 °
70
[VALUE] °
60 [VALUE] °
50 40 30 20 10 0 PI
0.5 wt% GO/PI
1 wt% GO/PI
2 wt% GO/PI
Membranes Fig.3 Water contact angle (CA) of PI membrane and GO/PI MMMs prepared with different GO loading [37]
to 425 cm−1 , covering a large spectrum. Advantageously, unlike a dispersive spectrometer, which can only measure the intensity across a small range of wavelengths, this one can do so simultaneously. Using a technique called ATR (Attenuated Total Reflectance), scientists were able to analyze a sample’s composition and structure with light. For FTIR Spectroscopy, ATR is a popular sampling method. The FTIR was coupled to a 45° incidence diamond crystal and had an OMNI-sample attenuated total reflection (ATR) smart accessory. In typical cases, 32 scans were taken at a resolution of 4 cm−1 for each spectrum. According to the data in Table 3, the manufactured membranes’ FTIR spectra exhibit bands characteristic of PDVF membranes between 700 and 1400 cm−1 , as well as bands suggestive of PVA and carboxylation treatment at higher wave numbers. The C-H asymmetric stretching vibration of the methylene group in PVP is responsible for the bands over 2850–2950 cm−1 [39]. The stretching vibration of carbonyl (CQO) groups in the glutaraldehyde crosslinking solution and the PVP is associated with peaks in the 1650 to 1750 cm−1 range [40]. The asymmetric vibration of hydroxyl (–OH) groups introduced by the PVA coating accounts for the large peak in the membrane at about 3300–3500 cm−1 [41].
4.2.2
X-ray Diffraction (XRD)
X-ray diffraction analysis of membranes demonstrates the presence of polymer and additives in the membrane’s polymer structure. The influence of nano-particles on the crystalline or amorphous membranes, as well as the XRD peaks for those locations, was investigated. Comparison of the diffraction beam of a given component
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Table 3 Formulations of PVDF MMMs [39] Types of chemical Absorption (cm−1 ) compound
Compound class
PVDF
Vibration of methylene group range about Small peak 700–1400 cm−1 corresponding to α and β crystal phase around 750 α crystal-phase Peak around 850 till 1400 is the β crystal phase
PVA
2850–2950 cm−1
Vibration of hydroxyl group and alkane group
PVP
1650–1750 cm−1
Carbonyl groups
to a standard reference database [42] provides a visual representation of the purity and crystal properties of the components. The Cu–K radiation over a Bruker X-ray diffractometer will be used for the analysis. Data was collected using Cu–K X-rays with a wavelength of 1.5406 nm and a scanning rate of 100–600 (2-angle) scans per second at a resolution of 0.1972°. The included software was used to conduct the analysis (EVA and Expert). Inter-planar spacing values (Eq. (2)) were determined using Bragg’s law, and the crystallite size (Eq. (3)) was determined using the Debye–Scherrer equations for the polymer and the additives. Bragg’s Law: λ = 2dhkl sin∅
(2)
whereby λ is the wavelength of X-ray source (0.15406 nm), dhkl is the interplanar spacing, sin∅ is the diffraction angle in degree (°). Debye–Scherrer Equation: D=
Kλ βcos∅
(3)
whereby D is the particle diameter in nm, λ is the wavelength of the X-ray source (0.15406 nm), K is constant equals to 0.9, β is the full width half maximum (FWHM) of the X-ray diffraction peak, ∅ is the diffraction angle in degree (°). Table 4 depicts the previous research about XRD analysis on pure PSf MMMs, PSf is incorporated TiO2 , PSf is incorporated MWCT as well as PSf is incorporated TiO2 and MWCT. The presence of TiO2 and MWCNT in the polymeric network of membranes is confirmed by XRD scans [43]. PSf showed a broad peak (2θ in the range of 12–20°), which corresponds to the PSf’s amorphous structure. PSf/TiO2 nanocomposite showed sharp peaks at 2θ = 26° and 2θ = 50°, which are attributed to the high crystallinity of TiO2 nanoparticles.
Mixed Matrix Membrane (MMMs) as Membrane Based Separation … Table 4 Peak position 2θ and intensity of PSf MMMs, PSf/TiO2 , PSf/MWCT, PSf/ TiO2 /MWCT
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Chemical compound
Peak Position, 2θ
Intensity
PSf
19.1
9000
PSf/TiO2
48.5
3550
PSf/ MWCNT
28.9
2745
PSF/TiO2 /MWCNT
20.1
8500
5 Current Application of MMMs As mixed matrix membranes (MMMs) have proven to be so versatile, scientists are continuing to investigate how to best fabricate them. MMMs technology is widely recognized as one of the most effective methods for separating liquids and gases. Membrane separation is a process that uses membrane technology to transport substances between two fractions. Membrane process applications are depicted in the Fig. 4. In addition to their usage in medical devices, MMMs have lately been employed in filtering wastewater of organic contaminants.
Fig. 4 Current applications of MMMs
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Membrane-based water treatment technologies can provide technologically highquality treated water as well as economic benefits such as an easy installation and low cost per unit of production when compared to other desalination plants, making them an excellent alternative for dealing with wastewater containing both traditional and emerging contaminants. Heavy metals in aqueous solutions are often removed using membrane-based techniques due to their relative simplicity, maximum removal efficiencies, and, most crucially, low energy requirements [44]. For example, reverse osmosis (RO) mem-branes are well-known for their exceptional heavy metal rejection (95%). This is because the selective layer is so densely cross-linked that its pores are so small between 0.25 and 0.8 nm in diameter [45], greatly limiting the passage of soluble heavy metals. However, due to RO’s high energy consumption, employing thick RO mem-branes for heavy metal removal is not a smart idea. The most costeffective strategy for getting rid of metals is to use ultrafiltration (UF) membranes, which are porous. Heavy metals are efficiently rejected at rejection rates >90% by UF membranes, although these membranes have a significantly greater pore size and limited size exclusion capacity [46]. Previous studies of functionalized porous membranes for enhanced heavy metal rejection sought to strike a balance between energy consumption and removal efficiency that would be both commercially practicable and effective. The goal is to increase the separation of soluble heavy metal ions by including adsorbent filler materials like zeolite and PSf for nickel separation into the membrane matrix. Furthermore, it has been claimed that the incorporation of inorganic minerals into the membrane polymer matrix frequently improves the matrix’s physical, morphological, and chemical reactivity, which could be advantageous for the long-term process of wastewater treatment. Absorbent hemodialysis membranes, which are based on the idea of mixed matrix membranes (MMMs), are a different approach to blood purification membranes that can increase the biocompatibility of hemoperfusion sorbents (activated carbon) [47]. The flexibility of the membrane concept allows for particle-loaded membranes to be made from nearly any polymeric material and particle. Combining hemodialysis membranes with tiny, functionalized particles (sorbents) is a novel method for making adsorptive membranes [48]. Adsorption onto biocompatible porous supports via a combination of diffusion and convection could be employed for endovascular blood purification. The hemodialysis and adsorption processes can be combined, thanks to the membrane’s properties. Smaller particles trapped in porous matrix supports may have more surface area and travel less distance to active sites when compared to larger particles. With this method, it is possible to stop the release of microscopic microparticles, particle coalition, and maybe sorbent fragmentation [49]. Doublelayer MMMs adsorbers for blood toxin elimination were developed by inserting activated carbon within cellulose acetate macroporous membranes. Additionally, MMMs can be used to create the other applications described in Table 5.
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Table 5 Alternate uses for MMMs’ technology Application
Description
Membrane processes
References
Food
For the clarification of wine, beer, fruit juices and syrups
Microfiltration/ Ultrafiltration
[32]
Organic acid recovery
Acetic acid (AA), a possible inhibitory molecule produced as a byproduct during acid hydrolysis and from fermentation of biofuel
Ultrafiltration
[19]
Beverage recovery
Removal of yeast from alcoholic beverages
Microfiltration
[50]
Agriculture
Deal with freshwater reclamation from Reverse osmosis, wastewater streams. A promising prospect Membrane for agricultural water production, and the bioreactors possible recovery of nutrients from saline waters and wastewaters
[51]
Air protection
Carbon dioxide separations
[52]
Ultrafiltration
6 Conclusion The present study attempted to briefly summarize the most common utilization of MMMs novel technology approach towards for liquid and gas separation. The essential part is the enhancement of MMMs performance by adding inorganic filler into the membrane matrix. Furthermore, the studies compile recently used techniques to fabricate the mixed matrix membrane via phase inversion method and dip coating. In order to analyze the elemental composition as well as surface morphology on the membrane, scanning electron microscopy (SEM) analysis can be utilized. Membrane porosity analysis is a standard technique for determining the typical or average size of the pores on a membrane. Due to the knowledge acquired from prior and current research for understanding the benefits of utilization of MMMs technology, it was applied in wastewater purification and hemodialysis. However, further studies are required for large scale implementation of the techniques in the industrial gas and liquid separation processes. In addition, future works can be carried by investigating the performance of different solvents and secondly, testing of permeation properties (permeability and selectivity) of produced membranes including a pre-saturation process. Acknowledgements The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under a grant number of FRGS/1/2018/TK10/UNIMAP/02/2 and FRGS/1/2021/TK0/UNIMAP/02/31 from the Ministry of Higher Education Malaysia.
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Application of Machine Learning for Biogas Production from Lignocellulosic Biomass Anuchit Sonwai and Patiroop Pholchan
Abstract Biogas production from lignocellulosic biomass is a complex anaerobic digestion of organic matters that needs to be properly controlled and monitored to improve process efficiency and stability. Machine learning (ML) was utilized to predict the specific methane yield (SMY) and identify features important to biogas production from lignocellulosic biomass. In this work, three ML algorithms, i.e. random forest (RF), support vector regression (SVR), and kernel ridge regression (KRR) were applied to predict SMYs, using 17 input feature datasets from biomass properties and biogas system parameters. Results showed that KRR was the most suitable algorithm for SMY prediction and identification of variables influencing biogas production from lignocellulosic biomass, with the highest prediction accuracy; R2 and RMSE of 0.86 and 0.07, respectively. Biomass compositions (cellulose, hemicellulose and lignin) were found to have a significant impact on SMY prediction, in which lignin was identified as the key component having the greatest influence on biogas production from lignocellulosic biomass. This research revealed that the suitable ML algorithm had the potential to be applied to optimize or predict the uncertain parameters of biogas production from the lignocellulosic feedstock. Keywords Machine learning · Biogas production · Specific methane yield · Lignocellulosic biomass
1 Introduction Renewable energy is the never-exhausted energy that can be regenerated from natural resources such as solar, wind, water, geothermal and biomass. Currently, many countries are trying to find new alternative energy sources to replace fossil fuels so that A. Sonwai · P. Pholchan (B) Department of Environmental Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand e-mail: [email protected] A. Sonwai Graduate School, Chiang Mai University, Chiang Mai 50200, Thailand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_14
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the environment is not affected and greenhouse gas emissions that cause global warming can be reduced. In addition, renewable energy helps to reduce the import of fuel and can be used as the policy to encourage the community to participate in energy production. Thailand is an agricultural country, where biomass is a natural energy storage that can be used to generate energy. These organic matters are obtained from residues from agriculture or industrial wastes, such as rice husks, rice straw, bagasse, wood chips, cassava residue, corn cobs, wastewater, and animal manure. Organic matter conversions by anaerobic digestion produce biogas having methane (CH4 ) and carbon dioxide (CO2 ) as the main constituents. Nevertheless, biogas production from lignocellulosic biomass is a complex anaerobic digestion of organic matters and requires a wide range of system operating conditions depending on the system being used. In addition, an anaerobic digestion process relies on the activity of various microorganisms to synergistically degrade organic matter [1]. This process often poses a problem in controlling the bioreactor to maximize methane production without system failure. The risk of system failure is often caused by multiple additions or changes of the feedstock as well as changes in temperature in the system which directly affect the microorganisms [2]. For biogas production from biomass, the composition of each biomass is an important factor in the decomposition process. The decomposition of organic biomass under anaerobic conditions is the hydrolysis step which limits the reaction due to the complex structure of biomass [3]. Microorganisms normally decompose parts of cellulose to produce mainly biogas while lignin hinders its degradation [4]. Therefore, biogas production process utilizing biomass as the feedstock needs to be properly controlled and monitored to improve process efficiency and stability. Determination of variables or parameters that are significantly important to biogas production from lignocellulosic biomass should reveal variables that directly affect the system. This allows key factors to be identified in the system, resulting in better control and monitoring of system performance. Machine Learning (ML) has emerged as an interesting method for data analysis and model development. It can provide insights into the relationship between data (input) and result (output) to create models for result predictions. The application of ML can help to find or design experiments for the most effective results, including optimization of process parameters for efficient bioconversion technologies [5]. In addition, results can be predicted in advance from the available data which can reduce the scope, time and cost of experimentation, including finding optimum available resources for the system enabling them to be sustainably utilized [6]. ML has been applied to anaerobic digestion process optimization, prediction of uncertain parameters and detection of disturbances in the system [7]. Several algorithms are used for the in-depth assessment of anaerobic digestion processes, such as random forest (RF), support vector regression (SVR), and kernel ridge regression (KRR). However, it is not widespread to use ML to determine the potential and suitable conditions for biogas production from lignocellulosic biomass. This research aimed to develop ML models to predict the specific methane yield (SMY), including to profoundly explore the relationships and features important in the biogas production from lignocellulosic biomass.
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2 Materials and Methods 2.1 Data Collection and Preprocessing The experimental data of biogas production used for generating and testing prediction models were collected from previous publications (210 datasets). This dataset was used to determine relationships among SMY, biomass characteristics, and factors affecting the biogas system. In this work, 17 input features were selected to construct the datasets (Table 1). The output or target variable in this study was SMY. The preliminary work was necessarily undertaken to avoid discrepant values or outliers that would decrease the preciseness of the future models and remove data for which all parameters were not clearly defined. Data cleaning was performed by removing duplicates or incomplete data, while outliers were checked after a correlation graph was obtained. In addition, the standardization of datasets was conducted for ML algorithms. In this study, Yeo-Johnson’s non-linear transformation was used to standardize our datasets as shown in Eq. (1): Table 1 Set of parameters (input features) and target value (output) Parameters (input) Ratio of
substratea
(%)
Name
Value
Mean
SD
Sub
5.0–100.0
70.2
34.3
Size substrate (mm)
Size
0.2–60.0
14.3
12.4
Volume of reactor (L)
Vol
1.0–472.0
20.3
71.7
Organic loading rates (kg
VS/m3
OLR
0.5–7.0
2.5
1.3
Hydraulic retention time (d)
d)
HRT
2.0–221.0
33.2
25.7
Temperature (°C)
Temp
20.0–55.0
37.7
6.4
Volatile solid (%TS)
VS
53.5–97.6
Volatile fatty acid (mg/L)
VFA
37.9–14,836.4
pH
pH
Carbon content (%w/w) Nitrogen content (%w/w)
79.5
12.6
2,018.4
2,734.7
3.8–8.2
7.3
0.6
C
3.3–81.5
44.4
12.1
N
0.5–10.2
1.7
1.0
Carbon to nitrogen ratio
CN
3.6–74.8
32.9
17.3
Cellulose (%TS)
Cel
9.0–45.5
31.6
10.8
Hemicellulose (%TS)
Hemi
9.8–32.6
22.4
5.4
Lignin (%TS)
Lig
2.4–42.0
9.1
7.8
VS removal (%)
VS re
18.1–82.2
50.0
13.5
Methane (%)
CH4
21.7–69.0
52.8
5.4
SMY
7.7–486.0
230.9
87.6
Target Value (output) SMY (L CH4 /kg VS)
Note a Ratio of substrate is the proportion (by weight of VS) of a biomass used in co-digestion with other feedstocks
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xi
=
(x˜i + 1)λ − 1 /λ, i f λ /= 0 and x˜i ≥ 0 ln(x˜i + 1), i f λ = 0 and x˜i ≥ 0
⎧ ⎪ ⎪ ⎪ ⎨
− ⎪ (−x˜ i + 1)2−λ − 1 /(2 − λ), i f λ /= 2 and x˜i < 0 ⎪ ⎪ ⎩ −ln(−x˜ i + 1), i f λ = 2 and x˜i < 0
(1)
Where x i is a transformed feature data from a raw feature x i , and λ is the transformation parameter, which is determined by maximum possibility estimation. The subscript i runs from 1 to N which is the number of data in each feature. All input features were normalized with z-score standardization according to Eq. (2):
xi = x i − x /σ
(2)
where xi is the normalized value of each input feature, x i is the original value of input feature, while x and σ are the mean value and standard deviation of each input feature, respectively.
2.2 Machine Learning Algorithms Selection and Tuning The feasibility of predicting biogas production from prerational parameters using several ML algorithms and model was examined. The ML was built using Python’s scikit-learn library. Three ML algorithms, i.e. RF, SVR and KRR were evaluated to obtain the best algorithm for the selected feature space since every dataset had its own unique data structure [8]. The whole datasets were randomly split as training and test groups with an 80:20 ratio. To attain the best hyperparameters, a randomized search with five-fold cross validation was used to optimize hyperparameters for all the models (Table 2), and then the model was retrained using the best parameters and tested with remaining 20% of data. Performances of predicted models were assessed based on the coefficient of determination (R2 ) and root mean squared error (RMSE) for the test data. R2 showed the relationship’s strength between the dependent and independent variables, while RMSE was used to measure errors of predicted values compared to their real values [9]. Theoretically, the higher the R2 and the lower RMSE represented the better model accuracy. The calculation formulas of R2 and RMSE are shown in Eqs. (3) and (4): R =1− 2
n
ex p Yi
i=1
RMSE =
/
− Yi
pr ed
n
2 ex p 2 ex p Yi − Y avg /
(3)
i=1
2 1 n ex p pr ed Yi − Yi i=1 N
(4)
Application of Machine Learning for Biogas Production … Table 2 Optimized hyperparameters of the four algorithms to predict SMY
ML model
Hyperparameters
Optimized value
RF
‘n_estimators’
564
‘max_depth’
16
‘max_leaf_nodes’
124
‘min_samples_leaf’
1
‘min_samples_split’
3
‘C’
1000
‘gamma’
0.0519
‘kernel’
‘rbf’
‘tol’
0.0180
‘gamma’
0.0173
‘kernel’
‘rbf’
‘alpha’
0.0492
SVR
KRR
ex p
183
pr ed
where Yi and Yi are the experimental and predicted values, respectively, while ex p Y avg is the average of all experimental values. In this study, impacts of all input features on SMY predictions were assessed. Three subsets were examined in this work; Case 1: all full feature datasets, Case 2: without Sub, Size and Vol, and Case 3: without Cel, Hemi and Lig. These three subsets were optimized for their own hyperparameters by the randomized search with five-fold cross validation. To examine and interpret the relationship characteristics of the two most important features, partial dependence plots (PDPs) were used. The relationship characteristics of the input feature and the target were represented by 1-dimensional PDPs, while the 2-dimensional PDPs showed the interaction of two input features to target.
3 Results and Discussion 3.1 Model Prediction Accuracy Table 3 shows the accuracy of predictions from various ML models in terms of R2 and RMSE values for all the three cases. Based on the test results (Table 3), the test using a total of 17 input features (Case 1) was able to provide the most accurate prediction by KRR (R2 = 0.86 and RMSE = 0.07) while SVR and RF had R2 values of 0.85 and 0.84, respectively. These results showed that every input feature had a good relationship with the target value (SMY) and having multiple input features could provide better prediction accuracy. When the dataset was examined without Sub, Size and Vol (Case 2), the R2 was reduced to 0.77–0.82, indicating that the ratio of the raw materials (biomass), the size of biomass and the volume of the reactor
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Table 3 Prediction accuracy in terms of R2 and RMSE of RF, SVR and KRR algorithms ML Model RF
SVR
KRR
Case
Train
Test
R2
RMSE
R2
RMSE
Case 1
0.9675
0.0360
0.8395
0.0809
Case 2
0.9671
0.0366
0.8191
0.0857
Case 3
0.9649
0.0388
0.6631
0.1154
Case 1
0.8982
0.0670
0.8555
0.0844
Case 2
0.8661
0.0713
0.8124
0.0914
Case 3
0.8353
0.0741
0.6597
0.1167
Case 1
0.9217
0.0513
0.8609
0.0741
Case 2
0.8639
0.0673
0.7703
0.0946
Case 3
0.8300
0.0752
0.6614
0.1154
had an impact on SMY prediction. When considered without biomass compositions (Case 3), the R2 value was greatly reduced to approximately 0.66 and RMSE of approximately 0.11 in all three models, indicating that the biomass component had a high interaction with the SMY. From the KRR model, Fig. 1 illustrates the scatter of the predicted values compared to the training and testing values for SMY. The black trend line represents the positions where the predicted values are exactly equal to the test values. Obviously, the predictive accuracy of the training cases was higher than that of the test cases for all three inputs. It was found that the training of Case 1 had a higher R2 value than the other cases (R2 = 0.92), while Case 2 and 3 had values of 0.86 and 0.83, respectively. For the RMSE value, it was found that all three cases were between 0.05 and 0.08. For the testing, Case 1 gave the most accurate results with R2 = 0.86 and RMSE = 0.07, suggesting that the 17 input features interacted well among them for predicting target values (SMY). To determine the correlation of the input dataset, the test was performed with the dataset without Sub, Size and Vol (Case 2). Values of R2 and RMSE were found to be reduced to 0.77 and 0.09, implying that Sub, Size and Vol affected the predicted SMY values. In addition, when using the dataset without cellulose, hemicellulose and lignin (Case 3), R2 was substantially reduced (R2 = 0.66 and RMSE = 0.11). This clearly indicated that the biomass composition greatly affected the SMY prediction. This finding was consistent with Herrmann et al. [4] who reported from the laboratory experiment that the lignin content of biomass was the most significant variable determining SMYs. However, determination of important features by ML is considered to be superior to the laboratory experiment method as it could confirm variables affecting predictions by the datasets.
Application of Machine Learning for Biogas Production … Fig. 1 Distribution of the prediction data against the test dataset of SMY by KRR a Case 1 b Case 2 and c Case 3
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3.2 Feature Evaluation To determine the correlation or factor affecting the SMY, all input features were prioritized by ML as shown in Fig. 2. The identified important features were largely based on the biomass composition, particularly the lignin and cellulose. These results were consistent with the R2 value (Table 3), where biomass composition was found to be an important variable in predicting SMY outcomes. In addition, lignin has been reported to be a key parameter affecting biogas production from lignocellulosic biomass in which higher lignin content leads to lower SMY obtained [4]. The third and fourth important features were the size of the biomass and the ratio of feedstock (biomass). According to previous studies, different biomass ratios had been used for both mono-digestion and co-digestion. Fermentation using higher biomass ratios tended to yield higher SMY values [10]. As the rate-limiting step for biogas production from biomass was the hydrolysis, reduction of the size of biomass resulted in a duration for hydrolysis being shortened [11]. In addition, pH and temperature were the fifth and sixth important parameters with similar scores. Both parameters have been found to be important for evaluating system performance. Grass digestion at thermophilic conditions could be conducted at relatively higher organic loading rates and provide SMYs superior to that obtained under mesophilic conditions [12]. Effects of pH on anaerobic digestion is also well-documented and the reaction outside optimum pH of 6.80–7.20 [13] could lessen the process efficiency.
Fig. 2 Feature importance of SMY
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Fig. 3 One-dimensional PDPs of a lignin and b cellulose and c Two-dimensional PDPs between lignin and cellulose
Correlation of the two most important features on one and two-dimensional PDPs is shown in Fig. 3. The highest SMY value was obtained when the optimal lignin content was 5.4%. Increase of lignin content resulted in the slope of the curve being greatly reduced, indicating that high lignin could adversely affect SMY values (Fig. 3a). On the other hand, higher cellulose content was shown to improve SMY values in which the optimum was in the range of 37–46% (Fig. 3b). From the interaction of lignin and cellulose on target values presented in Fig. 3c, it was found that the biomass composition suitable for biogas production should be low in lignin with sufficient cellulose. This was in agreement with higher SMY values obtained when the biomass was pretreated to remove lignin contents [14, 15].
4 Conclusion ML was used to predict the SMY and identify important features for biogas production from lignocellulosic biomass. The KRR model was found to be the suitable ML algorithm for predicting SMY with R2 and RMSE of 0.86 and 0.07, respectively. Lignin was identified as the feature importance having the greatest influence on biogas production from lignocellulosic biomass. Low lignin and high cellulose contents were found to provide higher SMY values. Results of this study could be further used to find the suitable raw materials or ratio of raw materials for efficient biogas production.
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Acknowledgements The authors would like to thank the Faculty of Engineering and Graduate School of Chiang Mai University for Teaching Assistant and Research Assistant Scholarships to one of the authors (Anuchit Sonwai).
References 1. Khanal SK (2008) Anaerobic biotechnology for bioenergy production: principles and applications, 1st edn. John Wiley & Sons, Hoboken, New Jersey 2. Tian G, Yang B, Dong M, Zhu R, Yin F, Zhao X, Wang Y, Xiao W, Wang Q, Zhang W, Cui X (2018) The effect of temperature on the microbial communities of peak biogas production in batch biogas reactors. Renew Energy 123:15–25 3. Adney WS, Rivard CJ, Shiang M, Himmel ME (1991) Anaerobic digestion of lignocellulosic biomass and wastes. Appl Biochem Biotechnol 30(2):165–183 4. Herrmann C, Idler C, Heiermann M (2016) Biogas crops grown in energy crop rotations: Linking chemical composition and methane production characteristics. Biores Technol 206:23– 35 5. Wang L, Long F, Liao W, Liu H (2020) Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms. Biores Technol 298:122495 6. Meena M, Shubham S, Paritosh K, Pareek N, Vivekanand V (2021) Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling. Biores Technol 340:125642 7. Andrade Cruz I, Chuenchart W, Long F, Surendra KC, Renata Santos Andrade L, Bilal M, Liu H, Tavares Figueiredo R, Khanal SK, Fernando Romanholo Ferreira L (2022) Application of machine learning in anaerobic digestion: perspectives and challenges. Bioresource Technology 345:126433 8. Onsree T, Tippayawong N (2021) Machine learning application to predict yields of solid products from biomass torrefaction. Renew Energy 167:425–432 9. Phromphithak S, Onsree T, Tippayawong N (2021) Machine learning prediction of celluloserich materials from biomass pretreatment with ionic liquid solvents. Biores Technol 323:124642 10. Wall DM, Allen E, Straccialini B, O’Kiely P, Murphy JD (2014) Optimisation of digester performance with increasing organic loading rate for mono- and co-digestion of grass silage and dairy slurry. Biores Technol 173:422–428 11. Kaur M (2022) Effect of particle size on enhancement of biogas production from crop residue. Mater Today: Proc 57:1950–1954 12. Voelklein MA, Rusmanis D, Murphy JD (2016) Increased loading rates and specific methane yields facilitated by digesting grass silage at thermophilic rather than mesophilic temperatures. Biores Technol 216:486–493 13. Neshat SA, Mohammadi M, Najafpour GD, Lahijani P (2017) Anaerobic co-digestion of animal manures and lignocellulosic residues as a potent approach for sustainable biogas production. Renew Sustain Energy Rev 79:308–322 14. Abraham A, Mathew AK, Park H, Choi O, Sindhu R, Parameswaran B, Pandey A, Park JH, Sang B-I (2020) Pretreatment strategies for enhanced biogas production from lignocellulosic biomass. Biores Technol 301:122725 15. Kasinath A, Fudala-Ksiazek S, Szopinska M, Bylinski H, Artichowicz W, RemiszewskaSkwarek A, Luczkiewicz A (2021) Biomass in biogas production: Pretreatment and codigestion. Renew Sustain Energy Rev 150:111509
Biomass Conversion to Intermediates and Products
Utilization of Spent Coffee Ground as Adsorbent for Nitrate Removal Viga Rajiman and Hairul Nazirah Abdul Halim
Abstract The annual rise in global coffee consumption has resulted in large amounts of discarded spent coffee ground following the brewing process. Spent coffee ground is a biomass waste that can be utilized in various applications. The present study aimed to assess the possibility of using spent coffee grounds as an alternative adsorbent to remove nitrate from aqueous solutions. Batch adsorption experiments were performed at 298 K and the spent coffee ground underwent a chemical pre-treatment using hydrochloric acid (HCl) at different concentrations. The results showed that the most suitable pre-treatment concentration was 0.4 M of HCl. Experiments were also conducted to study the effects of the solution pH (pH 3–9) and adsorbent dosage (0.2–1.0 g) in terms of nitrate removal efficiency. The experimental data showed that the highest nitrate removal efficiency occurred at pH 4 with 58 ± 0.69% removal efficiency. The adsorption performance was improved from 34% to 68 ± 0.58% as the adsorbent increased from 0.2 g to 1.0 g. These results showed that spent coffee ground could potentially be utilized as the adsorbent for removing nitrate from aqueous solutions. Keywords Water treatment · Nitrate · Adsorption · Biomass · Spent coffee ground
1 Introduction Spent coffee ground (SCG) is the solid remnant of the coffee making process. The coffee industry generates approximately 6 million tons of coffee waste yearly [1]. SCG is rich in organic compounds, such as fatty acids, lignin, cellulose, hemicellulose, and other polysaccharides that can be utilized and sold for other purposes [2]. V. Rajiman · H. N. A. Halim (B) Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia e-mail: [email protected] H. N. A. Halim Centre of Excellence for Biomass Utilization, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_15
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However, disposing of SCGs in landfills might lead to serious pollution problems [3], because the nitrous compounds in SCGs could leach into water sources, leading to marine eutrophication [4]. Consequently, algae could generate in abundance in water bodies. Additionally, the decomposition of SCGs in landfills could also contribute to global warming, as this waste emits methane, which is a greenhouse gas, to the atmosphere. Recently, the use of environmentally friendly and cost-effective materials that can be implemented as alternative adsorbents has been debated in water purification applications. The high availability of coffee waste throughout the seasons in every country has attracted the attention of researchers. To date, different forms of coffee waste have been utilized as adsorbents, which showed remarkable adsorptive efficiencies for adsorbing organic and inorganic contaminants in water. The SCG contains both micropores and mesopores, giving it a large surface area for increased adsorption capacity, thus, adding to the point of using SCGs as a potential adsorbent [5]. SCG modification to enhance its adsorption capacity has also been widely discussed. Dai et al. [6] reported the adsorption of nitrobenzene in water using sodium hydroxidemodified SCGs as the adsorbent. The NaOH-modified SCGs showed higher removal efficiencies compared to unmodified SCGs. Mohammed et al. [7] similarly observed that SCGs activated by nitric acid successfully adsorbed 93% of Fe ions in 0.5 mg/ L of Fe solution. Moreover, SCG modification by combining potassium hydroxide and urea as activating agents produced numerous excellent properties for the carbon material, and produced excellent methylene blue adsorption capacity [8]. The literature review showed that SCG is a sustainable adsorbent, because it can be used in environmentally and economically friendly approaches, as well as being a part of waste reduction efforts, which are mostly focused on heavy metal and dye removal in water. However, the adsorption of nitrate onto SCGs is barely discussed. Water pollution caused by high concentrations of nutrients, such as nitrate, is a serious environmental concern, because it can lead to eutrophication. Nitrate concentration that exceeds the safe limit in drinking water is a serious hazard that can have a huge impact on living organisms. Based on the WHO guideline, the maximum nitrate concentration in water is 50 mg/L for daily activities and the requirement for wastewater before it can be discharged into the environment [9]. Therefore, this study aimed to improve the properties of SCGs for nitrate adsorption by conducting acid pre-treatment. The adsorption performance of treated SCGs was also investigated based on the effects of process parameters, including pH value and adsorbent dosage. The collected experimental data were analysed in terms of removal efficiency percentage.
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2 Methodology 2.1 Materials and Chemicals SCGs were collected from a fast-food restaurant outlet located in Kangar, Perlis. Hydrochloric acid (HCl, 36%), concentrated sulphuric acid (H2 SO4 , 96%), sodium hydroxide (NaOH), potassium nitrate (KNO3 ), brucine dihydrate (C23 H27 N2 O4 ), and sulfanilic acid (C6 H7 NO3 S) were purchased from Fisher Scientific at analytical grade quality.
2.2 Modification of Spent Coffee Grounds as Adsorbent SCGs were sieved to obtain 250–350 µm particles, rinsed with distilled water to remove foreign elements, and then, placed in an oven (FD 115, Binder) at 383 K. The dried SCGs were pre-treated by soaking them in 0.1, 0.2, 0.3, and 0.4 M of HCl for 24 h to activate the adsorption sites. The activated SCGs were filtered from the acid solution and rinsed with distilled water to remove the remaining HCl. After the pre-treatment steps, the treated SCGs were decolorised by being rinsed in boiling water for several times. The decolorised SCGs were then filtered and dried in an oven (FD 115, Binder) at 383 K.
2.3 Adsorption Experiment The stock solution for synthetic nitrate-contaminated water was prepared by diluting 0.1 g of KNO3 into 1,000 ml of distilled water, which was then stirred at 120 rpm for 2 h at 303 K. Nitrate removal was studied at different pH (3, 4, 5, 6, 7, 8, and 9) of the stock solution. The pH of 100 mg/L of KNO3 stock solution was altered by adding 1 M HCl and 1 M NaOH. The SCGs dosage in the stock solution with ratio 1:10 (g/L) was prepared for the adsorption experiments. The pH value that showed the highest nitrate removal efficiency was used in the next parametric study. The effect of adsorbent dosage was studied using 0.2, 0.4, 0.6, 0.8, and 1.0 g of SCGs that were added to 100 mg/L of stock solution and altered to pH 4. The samples were placed in an incubator shaker (Certomat BS-1, Sartorius Biotech) to be continuously shaken for 24 h at 150 rpm and 298 K. Each experiment was conducted in triplicate.
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2.4 Analysis of Nitrate Concentration The final nitrate concentration in the samples was analysed using the Brucine method [10]. Samples were diluted to approximately 10 mL with distilled water and mixed with 10 ml of H2 SO4 . As a precautious step, due to the exothermic reaction during the mixing process, the test tube was not covered with a rubber stopper and the solution was cooled down under running tap water. Next, 0.5 mL of brucine-sulfanilic acid solution was added into the nitrate solution and the mixture was mixed well before it was heated in the water bath at 373 K for 25 min. The final solution was cooled down under flowing water and diluted to a total volume of 25 mL with distilled water. The absorbance value of the solution was measured using a UV–Vis spectrophotometer (Genesys 20, ILAC MRA) at 410 nm. Nitrate concentration was obtained from the standard calibration curve. The percentage of nitrate removal efficiency (R) was calculated using Eq. (1): R=
C O − Cr × 100 CO
(1)
where C o and C r are the initial and residual nitrate concentrations (mg/L), respectively.
3 Results and Discussion 3.1 Adsorption Performance of Acid-Pretreated SCG SCGs underwent acid pre-treatment using 0.1, 0.2, 0.3, and 0.4 M of HCl. The absorption performance was analysed using 60 mL of 100 mg/L nitrate concentration and 0.6 g of adsorbent dosage at 298 K of operating temperature. Based on Fig. 1, the removal percentage linearly increases with increasing HCl concentration from 0.1 to 0.4 M. Untreated SCGs showed the lowest removal percentage of nitrate at 42 ± 0.69% compared to acid pre-treated SCGs. Chemical pre-treatment was applied in this study to modify the raw material by breaking its rigid structure, and thus, increasing its pore size and surface area. Consequently, the highest nitrate removal performance was observed with SCGs that underwent pre-treatment using 0.4 M of HCl, with 64 ± 0.51% of removal efficiency. This result proved that pretreatment with 0.4 M of HCl is the most efficient condition to improve the adsorption capacity of the adsorbent. Depending on the condition of the pre-treatment, a higher acid concentration can increase the biodegradability of the complex structure of this biomass, resulting in increased surface area and pore sizes [11, 12]. Therefore, pre-treatment was the most significant step to enhance the removal efficiency of the adsorbent. Thus, SCGs treated with 0.4 M of HCl was used in subsequent experiments to ensure the most effective condition for nitrate removal.
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Fig. 1 Adsorption performance of nitrate onto SCGs at different acid pre-treatment conditions (bars represent standard deviation of the mean)
3.2 Effect of pH on Nitrate Adsorption Performance The influence of pH was studied in the range of 3 to 9, at 298 K of operating temperature. Based on the results presented in Fig. 2, the highest removal percentage of nitrate (58 ± 0.69%) is at pH 4. This study found that nitrate removal efficiency was increased from 51 ± 0.76% at pH 3 to 58 ± 0.69% at pH 4. This result was in line with the result reported by Viglašová et al. [13] on nitrate removal using bamboo-based biochar. However, the opposite result was obtained when pH 9 was used, with decreasing removal efficiency from 50 ± 0.51% at pH 5 to 37 ± 0.61%. This observation can be explained by the negative charges in the solution (OH− ), which would increase at higher pH values, resulting in the inhibition of the active sites by nitrate ions (NO3 − ). Moreover, the low nitrate ion adsorption at pH 3 was due to positively charged ions (H+ ) interacting with nitrate ions (NO3 − ), which decreased the efficiency of the adsorbent [14]. Additionally, pH was the most affecting variable in nitrate adsorption, because it can influence the surface charge of the adsorbent, the degree of ionization of the pollutants, and the extent of functionality dissociation over the active sites of the adsorbent and the structure of the pollutant. Therefore, pH 4 was chosen to further study the effect of other parameters to ensure the most efficient nitrate removal performance by SCGs.
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Fig. 2 Adsorption performance of nitrate onto SCGs at different pH values (bars represent standard deviation of the mean)
3.3 Effect of Adsorbent Dosage on Nitrate Adsorption Performance The effect of adsorbent dosage was conducted at 0.2, 0.4, 0.6, 0.8, and 1.0 g of SCGs. The adsorption experiment was fixed at 100 mg/L of nitrate concentration and 298 K of operating temperature. Figure 3 shows the analysis results of the removal of nitrate using SCGs. Nitrate removal efficiency was increased from 33 ± 0.54 to 68 ± 0.58% when the adsorbent dosage was increased from 0.2 to 1.0 g. It can be seen that the increasing trend was significant from 0.2 to 0.8 g of adsorbent dosage. The increment observed can be attributed to the increased number of active sites available at higher adsorption dosage, resulting in the increase of absorption capacity [15]. However, by increasing the adsorbent dosage from 0.8 to 1.0 g, the amount of nitrate uptake began to slightly decrease. This observation may also be attributed to the overlapped, or aggregated adsorption sites, which consequently, decreased the surface area of the adsorbent [16]. This factor positively corelated to any available exchangeable adsorption sites on the adsorbent surface area to interact with the adsorbate.
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Fig. 3 Adsorption performance of nitrate onto SCGs at different adsorbent dosages (bars represent standard deviation of the mean)
4 Conclusion The present study has highlighted that SCGs could potentially be utilized as an adsorbent in nitrate removal. The adsorption performances of nitrate onto SCGs were investigated based on the effects of pre-treatment conditions, namely, pH and adsorbent dosage. Hence, the findings from the experiments are concluded as follows: 1. The adsorption performance of SCGs was significantly enhanced when HCl concentration was increased during pre-treatment. The highest nitrate removal percentage was obtained when SCGs pre-treated using 0.4 M of HCl were used, with 64 ± 0.51% of removal efficiency. 2. The most favourable solution pH for nitrate adsorption was 4, with 58 ± 0.69% of removal efficiency. 3. The removal efficiency was increased at higher adsorbent dosage, with the highest removal percentage of 68 ± 0.58% obtained with 1.0 g of SCGs. Acknowledgements The authors would like to acknowledge Universiti Malaysia Perlis for the financial support and usage of facilities while conducting this study.
References 1. Janissen B, Huynh T (2018) Chemical composition and value-adding applications of coffee industry by-products: a review. Resour Conserv Recycl 128:110–117 2. Campos-Vega R, Loarca-Piña G, Vergara-Castañeda HA, Dave Oomah B (2015) Spent coffee grounds: A review on current research and future prospects. Trends Food Sci Technol 45:24–36
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3. Fernandes AS, Mello FVC, Thode Filho S, Carpes RM, Honório JG, Marques MRC, Felzenszwalb I, Ferraz ERA (2017) Impacts of discarded coffee waste on human and environmental health. Ecotoxicol Environ Safety 141:30–36 4. Schmidt Rivera XC, Gallego-Schmid A, Najdanovic-Visak V, Azapagic A (2020) Life cycle environmental sustainability of valorisation routes for spent coffee grounds: From waste to resources. Resour Conserv Recycl 157:104751 5. Aouled Mhemed H, Marin Gallego M, Largeau JF, Kordoghli S, Zagrouba F, Tazerout M (2020) Gas adsorptive desulfurization of thiophene by spent coffee grounds-derived carbon optimized by response surface methodology: Isotherms and kinetics evaluation. J Environ Chem Eng 8:104036 6. Dai Y, Zhang D, Zhang K (2016) Nitrobenzene-adsorption capacity of NaOH-modified spent coffee ground from aqueous solution. J Taiwan Inst Chem Eng 68:232–238 7. Mohamed KN, Yee LL (2019) Removal of Fe Ion from polluted water by reusing spent coffee grounds. Pertanika J Sci Technol 27:1077–1090 8. Sukhbaatar B, Yoo B, Lim JH (2021) Metal-free high-adsorption-capacity adsorbent derived from spent coffee grounds for methylene blue. RSC Adv 11:5118–5127 9. World Health Organization: Nitrate and nitrite in drinking-water. Background Document for Development of WHO Guidelines for Drinking-Water Quality. https://www.who.int, last accessed 10 August 22 10. Bain G, Allen MW, Keppy NK (2009) Analysis of nitrate nitrogen (NO3− ) in water by the EPA approved brucine method. Thermo Fish Sci 51862:1–2 11. McNutt J, He Q (2019) Spent coffee grounds: A review on current utilization. J Ind Eng Chem 71:78–88 12. Mora Alvarez NM, Pastrana JM, Lagos Y, Lozada JJ (2018) Evaluation of mercury (Hg2+) adsorption capacity using exhausted coffee waste. Sustain Chem Pharm 10:60–70 13. Viglašová E, Galamboš M, Danková Z, Krivosudský L, Lengauer CL, Hood-Nowotny R, Soja G, Rompel A, Matík M, Brianˇcin J (2018) Production, characterization and adsorption studies of bamboo-based biochar/montmorillonite composite for nitrate removal. Waste Manage 79:385– 394 14. Pei LY, Suhaidi AN, Zulkifli SM, Hassim SH, Kanakaraju D, Chin LY (2017) Modified Spent Tea Leaves as Bioadsorbent for Methyl Orange Dye Removal. Pertanika J Sci Technol 25:73–84 15. Mondal NK, Ghosh P, Sen K, Mondal A, Debnath P (2019) Efficacy of onion peel towards removal of nitrate from aqueous solution and field samples. Environ. Nanotechnol Monitor Manage 11:100222 16. Naga Babu A, Reddy DS, Kumar GS, Ravindhranath K, Krishna Mohan GV (2018) Removal of lead and fluoride from contaminated water using exhausted coffee grounds based bio-sorbent. J Environ Manage 218:602–612
Nitrate Adsorption Using Spent Coffee Ground: Kinetics, Isotherm, and Thermodynamic Studies Viga Rajiman, Hairul Nazirah Abdul Halim, and Lian See Tan
Abstract Excess amount of nitrate in water bodies can have harmful effects on humans and aquatic life. In this current study, the effectiveness of spent coffee grounds as adsorbents in nitrate adsorption from an aqueous solution was investigated. Spent coffee ground (SCG) was activated using hydrochloric acid (HCl) and used in the batch adsorption experiment. The removal performance was evaluated at different contact times ranging between 1 and 6 h. Removal efficiency was increased with the longest contact time of 6 h. Meanwhile, when nitrate concentration was increased from 100 to 500 mg/L, the removal efficiency was also increased from 39 to 78%. A temperature range of 298–328 K was applied in this study and the optimum operating temperature for nitrate adsorption was found to be at 308 K. Based on the experimental data, the Freundlich model showed R2 at 0.9802, which was the highest for the adsorption of nitrate using SCGs. In addition, the pseudo-first order kinetics model fitted the nitrate adsorption trend the best (R2 = 0.9652). The thermodynamic parameters obtained from this study described nitrate adsorption using SCG as endothermic in nature that required an external energy source for the interaction. This study has proven that spent coffee grounds activated by HCl have the potential of being an adsorbent for nitrate removal. Keywords Adsorption · Nitrate removal · Spent coffee ground · Water pollution
V. Rajiman · H. N. A. Halim (B) Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia e-mail: [email protected] H. N. A. Halim Centre of Excellence for Biomass Utilization, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia L. S. Tan Department of Chemical and Environmental Engineering (CHEE), International Institute of Technology (MJIIT), Universiti Teknologi Malaysia (UTM), Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_16
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1 Introduction Excessive nitrate content in water could lead to various diseases for humans, such as infant cyanosis syndrome due to methemoglobinemia, central nervous system birth defect, stomach cancer, and other diseases [1]. High nitrate concentration in water bodies can also endanger aquatic life. Eutrophication could occur when the high concentration of nitrate stimulates the overgrowth of aquatic plants and algae, which could prevent atmospheric oxygen and sunlight from penetrating into the water body. Moreover, the decomposition of plants on the surface of water bodies by microorganisms might lead to a phenomenon called anoxia, where the microorganisms would consume most of the dissolved oxygen [2]. The lack of dissolved oxygen in the water can negatively impact aerobic organisms. Nowadays, excessive discharge of nitrate has become one of the most critical environmental issues because water is a basic need for all living organisms. Thus, nitrate concentration must be monitored before untreated wastewater is discharged into water bodies. Based on the World Health Organization guideline, the max limit of nitrate concentration in water is 50 mg/L [3]. Nonetheless, nitrate can be removed from water and among the numerous purification technologies available, adsorption is considered the most economical approach. The valorisation of agricultural wastes, industrial byproducts, and biomass materials as adsorbents is also getting more attention from researchers, because these materials are considerably low cost, non-toxic, and biocompatible with most adsorbates. Biomass is one of the preferred alternative adsorbents due to its physico-chemical characteristics and availability in large quantities. Spent coffee grounds (SCGs) are waste from the brewing process. It is noted that more than 6 million tons of SCGs are generated annually as waste from coffee industries [4]. Due to the large generation of coffee waste, utilization of SCGs is an attractive option to be considered as a potential adsorbent. The effectiveness of coffee waste as adsorbent for water purification applications, such as from heavy metals [5] and dye removal [6, 7] has been proven. This data shows that SCGs have the potential of being used as an alternative adsorbent in removing contaminations. However, the use of SCGs in the contaminant removal application possess the advantages of being low cost, abundant, and ecofriendly adsorbent. Also, a limited number of studies are available on the adsorption of nitrate using SCGs. In this study, the performance of nitrate adsorption onto SCG as the adsorbent was investigated for batch operation. The adsorption process was studied on the effects of different operating parameters: contact time, initial nitrate concentration, and operating temperature, which were evaluated based on removal efficiency. The adsorption mechanism was described based on the analyses of kinetics, isotherm, and thermodynamic parameters.
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2 Methodology 2.1 Materials and Chemicals SCGs were collected from a fast-food restaurant located in Kangar, Perlis, Malaysia. Sodium hydroxide (NaOH), potassium nitrate (KNO3 ), brucine dihydrate (C23 H26 N2 O4 ), sulfanilic acid (C6 H7 NO3 S), concentrated sulfuric acid (H2 SO4 ), and hydrochloric acid (HCl) were obtained from Fisher Scientific, Malaysia. All chemicals utilized in experiments were of analytical grade quality.
2.2 Preparation of Adsorbent and Adsorbate The collected SCGs were ground and sieved to 250–350 µm in size. Distilled water was used to remove foreign components from the SCGs before they were dried in an oven (FD 115, Binder) at 383 K. Then, the dried SCGs were soaked in HCl for 24 h to activate their sorption sites. The activated SCGs were filtered out and then, distilled water was used to rinse the remaining HCl. Finally, the activated SCGs were decolourized by being continuously rinsed with boiling water. The decolourized SCGs were then filtered and dried in the oven at 383 K. The stock solution of synthetic nitrate for the adsorption experiment was prepared by adding KNO3 into distilled water. The solution was stirred for 2 h at 303 K and 120 rpm, while 1 M HCl and 1 M NaOH were used to adjust the pH solution at pH 4.
2.3 Batch Adsorption Experiment The effects of contact time (1–6 h), operating temperature (298–328 K), and nitrate concentration (100–500 mg/L) were examined for batch operation. The amount of SCGs added to the nitrate stock solution was fixed at a ratio of 1:10 for SCG (g) to the stock solution (L). Samples were continuously shaken in an incubator shaker (Certomat BS-1, Sartorius Biotech) under 150 rpm and 298 K of operating conditions. Once the absorption process achieved equilibrium condition, the SCGs were filtered from the solution using Whatman No.1 filter paper. The final nitrate concentration after the adsorption process was tested using the Brucine method. Each experiment was conducted in triplicate.
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2.4 Nitrate Concentration Analysis by Brucine Method The remaining nitrate concentration in the samples was tested using the Brucine method. Approximately 2 mL of a sample was measured and distilled water was added to dilute it to 10 mL. Then, H2 SO4 solution was added at a ratio of 1:4 to distilled water. This step was an exothermic reaction, thus, the opening of the test tube was not closed by a rubber stopper and the mixture was cooled down under running tap water. Next, 0.5 mL of the Brucine-sulfanilic acid solution was added to the mixture, before it was heated in a water bath at 373 K for 25 min. The final mixture was allowed to cool down to room temperature and then diluted using distilled water to 25 mL. The absorbance value of the final mixture was determined using a UV-Vis spectrophotometer (Genesys 20, ILAC MRA) at 410 nm. Nitrate concentration was obtained from the standard calibration curve.
2.5 Adsorption Capacity and Removal Efficiency The maximum nitrate adsorption capacity, q (mg/g), of the absorbent and the removal efficiency, R (%), of nitrate onto SCG were calculated using Eqs. (1) and (2), as follows: (Co − Cr )V m
(1)
C O − Cr × 100 CO
(2)
q= R=
where C 0 and C r represent the initial nitrate concentration and the residual concentration (mg/L), respectively. V represents the initial sample volume (L) and m represents the adsorbent mass (mg).
2.6 Adsorption Kinetics A kinetics study was conducted to determine the parameters and the adsorption rate of nitrate onto the SCGs. There are two adsorption kinetics models frequently used to interpret data obtained from experiments, namely, pseudo-first order and pseudosecond order kinetics models. The linear forms of these models are shown by Eqs. (3) and (4): ln(qe − qt ) = ln(qe ) −
k1 t 2.303
(3)
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(4)
where qe and qt represent the adsorbate concentration at equilibrium (mg/g) and at time t (mg/g), respectively. Meanwhile, k 1 and k 2 represent the first-order (1/min) and second-order adsorption constant (g/mg min), respectively.
2.7 Adsorption Isotherms Adsorption isotherms were able to describe the interaction between the equilibrium adsorption capacity of nitrate adsorbed onto SCG and the equilibrium concentration of nitrate in liquid at a constant temperature [8]. The most common equations used to discuss adsorption isotherms are the Langmuir and Freundlich models. The Langmuir model assumes that a monolayer adsorption onto a homogeneous surface with a specific amount of adsorption sites occurs during the adsorption process. Meanwhile, the Freundlich model applies to a multilayer adsorption and assumes adsorption that occurs on a heterogeneous surface. Equations (5) and (6) express the Langmuir and Freundlich models, respectively: Ce 1 Ce = + qe bq0 q0 logqe = logK f +
1 logCe n
(5) (6)
where, q0 and C e denote the maximum monolayer coverage capacity (mg/g) and the equilibrium concentration (mg/L) of nitrate on the sample, respectively. The terms b and K f represent the Langmuir (L/mg) and Freundlich isotherm constants (mg/g), respectively, while, n is the intensity of adsorption.
2.8 Adsorption Thermodynamic A thermodynamic study on the experimental data can explain the behaviour of the adsorption, such as the energy change of the adsorbent following the adsorption process and the mechanism involved. The enthalpy change (ΔH ° , J/mol), entropy change (ΔS ° , J/mol K), and free energy change (ΔG° , J/mol) can be generated from the experimental data to further describe the adsorption mechanism. Thus, the values of ΔH ° , ΔG° , and ΔS ° in this study were obtained using the following Eqs. (7), (8), and (9): KC =
qe Ce
(7)
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ΔG ◦ = −RTlnK C lnK C = −
(8)
ΔH ◦ ΔS ◦ + RT R
(9)
where T and R represent the temperature (K) and universal gas constant (8.134 J/ mol K), respectively. Meanwhile, K c represents the adsorption equilibrium constant (L/g).
3 Results and Discussion 3.1 Effect of Contact Time The effect of contact time on nitrate removal performance was investigated by conducting the adsorption experiment at different time intervals of 1–6 h. Figure 1 shows that the increasing contact time leads to an increase of nitrate removal efficiency. When the contact time was increased from 1 to 4 h, nitrate removal gradually increased from 4.7 to 21%, and the highest nitrate removal efficiency of 44% was achieved after 6 h. By increasing the contact time, nitrate was exposed longer to the active sites of the adsorbents, which enhanced the sorption activities [9]. However, after 6 h of contact time, the adsorption did not reach equilibrium. The adsorption performance was expected to further increase beyond 6 h of contact time and to reach the saturation of nitrate on the active sites at equilibrium. 50 Removal efficiency (%)
45 40 35 30 25 20 15 10 5 0
0
1
2
3 4 Time (hr)
Fig. 1 Effect of contact time on nitrate adsorption onto SCGs
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0 100
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500
Initial nitrate concentration (mg/L) Fig. 2 Effect of initial nitrate concentration on nitrate adsorption onto SCGs
3.2 Effect of Initial Nitrate Concentration The initial nitrate concentration was prepared in the range of 100–500 mg/L to observe its effect on the adsorption process, while keeping 0.6 g of SCGs in the solution constant. Figure 2 shows the effect of initial nitrate concentration on nitrate adsorption onto SCGs. The nitrate removal efficiency began to increase from 39 to 78% with increasing the initial nitrate concentration. Moreover, the linear increase of qe was observed from 3.9094 to 39.0474 mg/g, which showed that nitrate adsorption onto SCGs was also increased with increasing initial nitrate concentration. The highest qe with 39.0474 mg/g occurred at 500 mg/L of initial nitrate concentration. This behavior could be due to the increased driving force and concentration gradient at increased nitrate concentration, which consequently improved the removal efficiency [10].
3.3 Effect of Temperature The adsorption experiment was also performed at different operating temperatures in the range of 298–328 K. Figure 3 shows the effect of temperature on nitrate removal efficiency by SCGs. The result shows an increase of removal efficiency from 56 to 63%, as the operating temperature increases from 298 to 308 K. However, the removal efficiency was decreased from 63 to 12% when a higher temperature from 308 to 328 K was used. The increased removal efficiency can be explained by the decreasing thickness at the adsorbent’s boundary layer, resulting in lesser resistance for the interaction between the adsorbent and adsorbate. Consequently, this
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60 50 40 30 20 10 0 298
308 318 Temperature (K)
328
Fig. 3 Effect of temperature on nitrate adsorption using SCGs
condition enhanced the ability of the adsorbent to interact with the adsorbate during the adsorption process [11]. However, the adsorption performance was decreased when the operating temperature was increased to 328 K. This could be due to the deactivation, or denaturation of the active sites on the adsorbent surface. The weak bond between the adsorbate and the surface of the SCGs could have been broken due to high temperature, resulting in low availability of active sites. Therefore, the optimum temperature for nitrate adsorption onto SCGs was at 308 K.
3.4 Adsorption Isotherm Analysis The analysis results of the initial nitrate concentration are used to plot the linear form of both isotherm models, as shown in Fig. 4. Based on the R2 obtained for both models, the Freundlich model fitted better for the trend of nitrate adsorption onto SCGs. This finding was supported by the findings reported by Yagub et al. [12]. Theoretically, the Freundlich isotherm model assumes that adsorption capacity is directly proportional to nitrate concentration, which occurs on the heterogeneous surfaces of the adsorbents. Table 1 shows the isotherm constants and correlation coefficients, as analyzed from the experimental data. The k f and n constants were 44.5759 and 4.1754, respectively. These results showed that the greater the k f value, the higher the sorption efficiency of the adsorbent. Meanwhile, the calculated n was greater than 1, indicating that the process of nitrate removal using SCGs was a favorable adsorption [13].
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2.1
Fig. 4 a Langmuir and b Freundlich isotherm models of nitrate adsorption onto SCGs
2.05
log qe
2 1.95 1.9
y = 0.2395x + 1.6491 R² = 0.9802
1.85 1.8 1.75 0.5
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2
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0.01 0.005 0 0
0.1
0.2
0.3
1/Ce
(b)
Table 1 Isotherm constants for nitrate adsorption onto SCGs Adsorbate Nitrate (NO3
Langmuir −)
Freundlich
q0
b
RL
R2
108.6957
0.3183
0.0305
0.9699
kf
n
R2
44.5759
4.1754
0.9802
3.5 Adsorption Kinetics Analysis In this research, the adsorption kinetics was analyzed based on the pseudo-first and pseudo-second order models to describe the nitrate adsorption behavior onto SCGs. As shown in Fig. 5, the linear pseudo-first order model is well fitted to the results of the adsorption experiment. The high linear regression value, R2 = 0.9652, proved that the adsorption of nitrate onto SCGs can best be described by the pseudo-first order model. The low R2 = 0.6814 value observed in the pseudo-second order model failed to fit into the model. Notably, the pseudo-first order model assumes that the adsorption rate is directly proportional to the amount of active sites on the adsorbent [14]. Pagalan et al. [7] also reported that the adsorption of aniline yellow dye using SCG followed the pseudo-first order kinetics model. Additionally, the kinetics parameters were identified based on the linear equation obtained from the graph that fitted the pseudo-first order model. It was found that k 1 was 0.041/min and qe was 1.9131 mg/ g for the adsorption of nitrate using SCGs.
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Fig. 5 a Pseudo-first and b pseudo-second order kinetics models of nitrate adsorption onto SCGs
0.6
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y = -2.3458x + 611.54 R² = 0.6814
0
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(b)
3.6 Thermodynamic Analysis Using the results of the effect of temperature, the analyzed thermodynamic parameters are summarized in Table 2. The ΔH ◦ and ΔS ◦ values were analyzed based on the slope and intercept of the ln K c versus 1/T plot. The adsorption process has a positive ΔH ◦ value (0.0008 J/mol), which explained the endothermic nature of the interaction. Meanwhile, the positive ΔS ◦ value (0.0291 J/mol K) described the affinity towards the adsorption process, and the randomness of the solid-solution interface that increased during the adsorption process [15]. The positive ΔG ◦ value showed that the adsorption process required an external energy source for the interactions. Table 2 Thermodynamic parameters of nitrate adsorption onto SCGs ΔG ◦ (kJ/ mol)
Adsorbate
C 0 (mg/L)
T (K)
qe (mg/g)
K c (L/g)
Nitrate (NO3 − )
100
298
5.624
0.1285
5.0835
308
6.277
0.1686
4.5582
318
3.839
0.0623
7.3379
328
1.219
0.0139
11.6632
ΔH ◦ (J/ mol)
ΔS ◦ (J/ mol K)
0.0008
0.0291
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4 Conclusion This study has investigated the efficiency of SCGs as adsorbents for nitrate adsorption in a batch process. The adsorption process was studied at different process conditions: contact time, initial nitrate concentration, and temperature. Based on the parametric study, a higher removal efficiency was observed at a longer contact time for nitrate to be exposed to SCGs. Meanwhile, when the initial nitrate concentration (100–500 mg/L) was increased, nitrate removal efficiency was also increased. The optimum operating temperature for the batch experiment was found to be 308 K. The experimental data were further evaluated for the adsorption isotherms, kinetics, and thermodynamic. It was concluded that the adsorption behavior was fitted best by the Freundlich isotherm model, which indicated that nitrate adsorption could occur on heterogeneous surface sites. The adsorption kinetics of nitrate onto SCGs can be explained by the pseudo-first order model with R2 = 0.9699. The positive values of ΔH ◦ and ΔG ◦ indicated that the adsorption of nitrate onto SCGs was endothermic and that an external energy source was required. Meanwhile, the positive value of ΔS ◦ (0.0291 J/mol K) explained the randomness at the solid-solution interface, which increased during the adsorption process. This study has successfully proven that SCGs could potentially be used as alternative adsorbents to remove nitrate from water. Acknowledgements The authors would like to acknowledge Universiti Malaysia Perlis for the use of facilities and the Ministry of Higher Education Malaysia for the Long-Term Research Grant Scheme (LRGS) 1/2018 (UTAR-4411/S01) (UTM PY/2020/03532) to conduct this study.
References 1. Satayeva AR, Howell CA, Korobeinyk AV, Jandosov J, Inglezakis VJ, Mansurov ZA (2018) Investigation of rice husk derived activated carbon for removal of nitrate contamination from water. Sci Total Environ 630:1237–1245 2. Mohan H, Vadivel S, Rajendran S (2022) Removal of harmful algae in natural water by semiconductor photocatalysis—A critical review. Chemosphere 302:134827 3. World Health Organization: Nitrate and nitrite in drinking-water. Background Document for Development of WHO Guidelines for Drinking-Water Quality. https://www.who.int. Accessed 10 July 22 4. Fernandes AS, Mello FVC, Thode FS, Carpes RM, Honório JG, Marques MRC, Felzenszwalb I, Ferraz ERA (2017) Impacts of discarded coffee waste on human and environmental health. Ecotoxicol Environ Saf 141:30–36 5. Mohamed KN, Yee LL (2019) Removal of Fe Ion from polluted water by reusing spent coffee grounds. Pertan J Sci Technol 27:1077–1090 6. Sukhbaatar B, Yoo B, Lim JH (2021) Metal-free high-adsorption-capacity adsorbent derived from spent coffee grounds for methylene blue. RSC Adv 11:5118–5127 7. Pagalan E, Sebron M, Gomez S, Salva SJ, Ampusta R, Macarayo AJ, Joyno C, Ido A, Arazo R (2020) Activated carbon from spent coffee grounds as an adsorbent for treatment of water contaminated by aniline yellow dye. Ind Crops Prod 145:111953
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8. Kumari D, Singh R (2018) Pretreatment of lignocellulosic wastes for biofuel production: a critical review. Renew Sustain Energy Rev 90:877–891 9. Ahsan MA, Jabbari V, Islam MT, Kim H, Hernandez-Viezcas JA, Lin Y, Díaz-Moreno CA, Lopez J, Gardea-Torresdey J, Noveron JC (2018) Green synthesis of a highly efficient biosorbent for organic, pharmaceutical, and heavy metal pollutants removal: engineering surface chemistry of polymeric biomass of spent coffee waste. J Water Process Eng 25:309–319 10. Mondal NK, Ghosh P, Sen K, Mondal A, Debnath P (2019) Efficacy of onion peel towards removal of nitrate from aqueous solution and field samples. Environ Nanotechnol Monitor Manag 11:100222 11. Anastopoulos I, Bhatnagar A, Hameed BH, Ok YS, Omirou M (2017) A review on wastederived adsorbents from sugar industry for pollutant removal in water and wastewater. J Mol Liq 240:179–188 12. Yagub MT, Sen TK, Afroze S, Ang HM (2014) Dye and its removal from aqueous solution by adsorption: a review. Adv Coll Interface Sci 209:172–184 13. Ying Pei L, Suhaidi AN, Zulkifli SM, Hassim SH, Kanakaraju D, Chin LY (2017) Modified spent tea leaves as bioadsorbent for methyl orange dye removal. Pertan J Sci Technol 25:73–84 14. Badawi MA, Negm NA, Abou Kana MTH, Hefni HH, Abdel Moneem MM (2017) Adsorption of aluminum and lead from wastewater by chitosan-tannic acid modified biopolymers: isotherms, kinetics, thermodynamics and process mechanism. Int J Biol Macromol 99:465–476 15. Low LW, Teng TT, Ahmad A, Morad N, Wong YS (2011) A novel pretreatment method of lignocellulosic material as adsorbent and kinetic study of dye waste adsorption. Water Air Soil Pollut 218:293–306
Tamarind Seed Modified by CuFe Layered for Caffeine Removal from Aqueous Solution Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Muhammad Azan Tamar Jaya, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad
Abstract Caffeine is considered as one of the emerging contaminants (ECs) and has been utilized extensively in beverages industry. This contaminant finds its ways to enter water bodies and causing severe effects on humans and aquatic organisms. Therefore, this study explored the potential of tamarind seed (TS) as activated carbon (TSAC) in adsorbing caffeine from an aqueous solution. TS was carbonized using a conventional furnace, layered with CuFe salts at different impregnation ratio (IR) and lastly, heated with a microwave oven. The concentration of caffeine was measured using UV-Vis spectrophotometer at a maximum wavelength of 274 nm. Characterization analysis showed that TSAC posed BET surface area and average pore diameter of 498.53 m2 /g and 2.19 nm, respectively. When IR increased from 0.5 to 1.5 g/g, adsorption uptakes and caffeine removal increased from 56.19 to 65.48 mg/g, and M. F. M. Yusop · A. Z. Abdullah · M. A. Ahmad (B) School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] M. F. M. Yusop e-mail: [email protected] A. Z. Abdullah e-mail: [email protected] N. K. E. M. Yahaya · J. Karim · M. A. M. Yusoff National Water Research Institute of Malaysia, Jalan Putra Permai, 43300 Seri Kembangan, Lot 5377 Seri Kembangan, Selangor, Malaysia e-mail: [email protected] J. Karim e-mail: [email protected] M. A. M. Yusoff e-mail: [email protected] M. A. T. Jaya Kolej GENIUS Insan, Universiti Sains Islam Malaysia, NegeriSembilan, 71800 Nilai, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_17
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from 60.86 to 69.84%, respectively. The isotherm study revealed that caffeine-TSAC adsorption system was best described by Freundlich isotherm due to high R2 value of 0.9991 and low average error of 7.39%. The maximum adsorption capacity, Qm obtained from Langmuir isotherm was found to be 214.25 mg/g. Keywords Adsorption process · Activated carbon · Microwave heating · Caffeine · CuFe layered
1 Introduction Rapid industrial development in various sectors has been causing a wide range of pollutants to enter the environment through wastewater. These pollutants include synthetic dyes such as [1, 2], heavy metals such as [3, 4], pesticides [5], and others. Besides these pollutants, emerging contaminants (ECs) is another class of pollutants that impose negative effects on human and the environment, but they are not regulated yet, or just regulated not long ago. Examples of ECs include pharmaceutical residues, pesticides, personal care product residues, bisphenol-A (BPA), bisphenol-B (BPB), bisphenol-S (BPS), and so on. Even at a very low concentration of 0.001 mg/L, ECs are the source of generational effects and biological disruption [6]. Caffeine is another example of ECs and is scientifically known as 1,3,7-trimethylxanthine which originated from the methylxanthine family. Although caffeine is popular with coffee, this substance can be found in other plants as well such as tea leaves and cocoa fruit. A large amount of caffeine is accumulated in the seeds, leaves, and fruits section where they act as a natural pesticide to paralyze and kill insects that feed on these plants. The existence of caffeine in water bodies can be harmful to aquatic creatures and the ecosystem due to its stability towards degradation. At high concentrations, caffeine can cause irritability, mutation effects such as inhibition of DNA, tremors, anxiety, bone mass loss through calcium mobilization from cells, and a risk factor for cardiovascular diseases [7]. Many technologies have been developed to treat ECs containing wastewater, but none can hold a candle to the adsorption process. The adsorption process can be performed by using a wide range of adsorbents including char, clay, bio-sorbent, and activated carbon (AC). According to Yusop et al. [8], the adsorption process via AC has too many benefits including uncomplicated in design, fast process, convenience in operation, and high immunity to toxicity levels. Researchers around the world are actively converting biomass and agricultural wastes into AC since these resources are practically unlimited and cheap, such as acacia sawdust [9], cocoa shell [10], jackfruit peel [11], teak wood [12], durian seed [13], sludge biomass [14], olive stone [15], periwinkle shell [16], Intsia bijuga sawdust [17], and lemongrass leaf [18]. This study aimed to produce tamarind seed-based AC (TSAC) via microwave irradiation technique to treat caffeine from an aqueous solution. Tamarind seed comes from the Tamarindus indica tree which belongs to the Fabaceae family and is native to tropical Africa. This tree produces tamarind fruit, which is sour in taste and has
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been used extensively in cooking in Southeast Asian countries, especially Malaysia. The seed of this fruit, which is black in colour and hard in texture, has no function and is often discarded into the environment. Another aim of this study was to modify the surface of TSAC with CuFe salts layered at different impregnation ratios (IR). Surface modification of AC is a promising method to tune surface characteristics [19]. Many studies have shown that adsorbents supported with metals such as Cu, Fe, Mg, and Zn produced higher adsorption uptakes of pollutants [20].
2 Materials and Methods 2.1 Materials Chemicals employed in this study namely caffeine (C8 H10 N4 O2 ), iron (III) nitrate nonahydrate (FeH18 N3 O18 ), and copper (II) nitrate trihydrate (Cu(NO2 )2 0.3 H2 O) were bought from Sigma Aldrich Sdn. Bhd. Activating gas of carbon dioxide, CO2 (99.80%), and inert gas of nitrogen, N2 (99.99%) were supplied by Air Product, Malaysia, and all solutions in this experiment were prepared using deionized water.
2.2 Preparation of TSAC Tamarind seed (TS) in its raw form was collected from a night market operated at Nibong Tebal, Pulau Pinang. In order to remove any impurities on it, TS was washed with plenty of tap water. After that, the wet TS was placed inside an oven for the drying process to take place at 110 °C for 1 day. Dried TS was introduced with a carbonization process at 500 °C for 1 h, under the flow of inert nitrogen, N2 gas at a constant flow rate of 150 cm3 /min, in a vertical tubular furnace. Once this process was done, the furnace was let to cool down to room temperature under N2 gas, before the sample was collected. The collected sample is now known as char. The char was impregnated with copper and ferum, CuFe salts with impregnation ratio, IR of 2:1:1 for char: (Cu(NO2 )2 0.3 H2 O): (FeH18 N3 O18 ). The impregnation process occurred at 110 °C for 24 h inside an oven. Next, the impregnated char was activated using microwave irradiation technique at radiation power and radiation time of 616 W and 20 min, respectively, under N2 gas flow at 150 cm3 /min. Once the activation process is done, the resulted sample was known as tamarind seed-based AC, TSAC [2, 20].
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2.3 Equilibrium Study The adsorption system of caffeine-TSAC was studied in terms of the IR effect on caffeine uptakes and caffeine removal. An accurately weighed 0.2 g of TSAC was added into 0.2 L of caffeine solution with starting concentration of 50 mg/L. Other parameters such as solution temperature and water bath rotation speed were fixed at 30 °C and 80 rpm, respectively. After 24 h, a small amount of caffeine solution was taken out as a sample using a syringe [1, 2]. This sample was measured for its caffeine concentration using UV-Vis spectrophotometry (Model Shimadzu UV1800, Japan) at a wavelength of 274 nm [21]. The quantity of caffeine removed by TSAC at equilibrium was determined from the following equation: qe =
(Co − Ce )V m
(1)
where qe , C o , C e , V, and m are caffeine uptakes at equilibrium (mg/g), initial concentration of caffeine (mg/L), the equilibrium concentration of caffeine (mg/L), the volume of solution (mL), and mass of TSAC used (g). On contrary, the caffeine removal is calculated as follows: Removal(%) =
(Co − Ce ) × 100% Ce
(2)
2.4 Isotherm Study The relationship between the caffeine concentration in the bulk phase and caffeine concentration in the solid phase can be verified from the isotherm study. Two isotherm models namely Langmuir and Freundlich were applied, and their equations are given as follows, respectively: Langmuir [22]: Q m K L Ce 1 + K L Ce
(3)
qe = K F Ce1/n F
(4)
qe = Freundlich [23]:
where Qm is the Langmuir maximum monolayer adsorption capacity; K L is the constant of Langmuir; K F and nF are constants of Freundlich. These non-linear equations were solved with the aid of Microsoft Excel Solver version 2016. The best-fitted isotherm model was picked based on the highest correlation coefficient,
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R2 and the lowest root mean squared error (RMSE). RMSE can be calculated as follows: / n 2 1 ∑ qe,exp,n − qe,cal,n RMSE = (5) n − 1 n=1 where n refers to the number of data points.
3 Results and Discussion 3.1 Characteristics of Samples It was found that the BET surface area of TS increased from 1.52 to 125.42 m2 /g after the carbonization process took place. The heat treatment from the carbonization process promotes the thermal degradation process which removes moisture and volatile components in TS [3, 4]. As a result, these components evaporated and leave the sample, providing the necessary space for the pores network to form. After chemical activation with CuFe and microwave heating, the BET surface increased tremendously from 125.42 m2 /g in TS char to 498.53 m2 /g in TSAC. The microwave irradiation technique further enhances the thermal degradation process especially polar volatile compounds such as moisture, cellulose, and lignin in TS char. On the other hand, the metals of CuFe move freely to penetrate deep into the skeleton of TS char, thus causing more pores to be developed [8]. Both thermal degradation and CuFe penetration contributed to the increment of total pore volume from 0.06 cm3 /g in TS char to 0.37 cm3 /g in TSAC and an increment in average pore diameter from 2.11 nm in TS char to 2.19 nm in TSAC. Based on IUPAC classification, the pores in TSAC belong to the mesopores region (2–50 nm) which is suitable for water treatment applications. This can be explained by the fact that the micropores region of AC (50 nm) decreases the contact area between AC’s surface and adsorbate molecules, thus enhancing the desorption process [25].
3.2 Adsorption Study—Effect of IR on Caffeine Uptakes and Removal Figure 1a, b show the plots of caffeine adsorption uptakes and percentage removal for the caffeine-TSAC adsorption system at 30 °C, respectively. It was found that both caffeine uptakes and caffeine percentage removal increased from 56.19 to 65.48 mg/ g and from 60.86 to 69.84%, respectively when the IR of CuFe increased from 0.50 g/
M. F. M. Yusop et al.
80
80
70
63.05
65.48
56.19
60 50
Caffeine removal (%)
Adsorption capacity, Qe (mg/g)
216
0
0.5
1
1.5
2
66.42
70
69.84
60.86 60 50
0
0.5
1
1.5
2
Impregnation ratio, IR (g/g)
Impregnation ratio, IR (g/g)
(b)
(a)
Fig. 1 Plots of a adsorption uptakes and b caffeine removal by TSAC
g (2:1:1 for char:Cu:Fe) to 1.50 g/g (2:1.5:1.5 for char:Cu:Fe). At higher IR, more metals of CuFe were available to penetrate the skeleton matrix of TSAC at a higher intensity [8]. As a result, more pores were developed, hence, providing more active sites to be occupied by caffeine molecules. Consequently, adsorption capacity and percentage removal of caffeine by TSAC were increased.
3.3 Adsorption Isotherm
Adsorption uptake of caffeine at equilibrium, Qe (mg/g)
Figure 2 shows the isotherm plot for Langmuir and Freundlich isotherms whilst Table 1 summarized the isotherm parameters obtained in this study. Both Langmuir and Freundlich models were found to produce high R2 values of 0.9997 and 0.9991, respectively. However, caffeine adsorption onto TSAC can be concluded to follow Freundlich due to its lower RMSE and average error percentage of 4.93 and 7.39%. Therefore, it signified that caffeine molecules formed a multilayer coverage on TSAC with main forces contributed by physisorption. The maximum adsorption capacity gathered from Langmuir isotherm, Qm was found to be 214.25 mg/g. This adsorption process was confirmed to be favourable since the n value is between 1 and 10 [4].
80
Experimental data 70
Langmuir 60
Freundlich
50 40 12
14
16
18
Concentration of caffeine at equilibrium, Ce (mg/L)
Fig. 2 Isotherm plots for caffeine-TSAC adsorption system at 30 °C
20
Tamarind Seed Modified by CuFe Layered for Caffeine Removal … Table 1 Summary of isotherm parameters for caffeine-TSAC adsorption system at 30 °C
Langmuir
217
Freundlich
Qm (mg/g)
214.25
K L (L/mg)
0.02
K F (mg/g) (L/mg)1/n
38.77
n
6.00
RMSE
12.88
RMSE
4.93
Average error (%)
15.90
Average error (%)
7.39
R2
0.9991
R2
0.9997
4 Conclusion By employing the carbonization process, CuFe salts layered and microwave heating, TS was successfully converted to TSAC to remove caffeine in an aqueous solution with Langmuir maximum monolayer capacity, Qm of 214.25 mg/g. A relatively moderate BET surface area of 498.53 m2 /g was obtained for TSAC with average pore diameter of 2.19 nm, which lies in the mesopore region. Adsorption study at different IR levels revealed that both caffeine uptakes and caffeine removal by TSAC increased when IR increased. In fact, the highest caffeine uptakes of 65.48 mg/g and highest caffeine removal of 69.84% were obtained when IR value was at the highest level of 1.50. Isotherm data fitted Freundlich model the best due to high R2 of 0.9991 and low RMSE value of 4.93. This signified the major role of the physisorption in multilayer coverage adsorption of caffeine onto TSAC. Acknowledgements This research was funded by the Ministry of Higher Education Malaysia through the Fundamental Research Grant Scheme (project code: FRGS/1/2021/TK0/USM/01/3) and the Postdoctoral Fellowship Scheme from Universiti Sains Malaysia.
References 1. Yusop MFM, Jaya MAT, Idris I, Abdullah AZ, Ahmad MA (2023) Optimization and mass transfer simulation of remazol brilliant blue R dye adsorption onto meranti wood based activated carbon. Arab J Chem 16(5):104683 2. Yusop MFM, Aziz A, Ahmad MA (2022) Conversion of teak wood waste into microwaveirradiated activated carbon for cationic methylene blue dye removal: optimization and batch studies. Arab J Chem 15(9):104081 3. Yusop MFM, Jaya EMJ, Din ATM, Bello OS, Ahmad MA (2022) Single-stage optimized microwave-induced activated carbon from coconut shell for cadmium adsorption. Chem Eng Technol 45:1943–1951 4. Yusop MFM, Jaya EMJ, Ahmad MA (2022) Single-stage microwave assisted coconut shell based activated carbon for removal of Zn(II) ions from aqueous solution—Optimization and batch studies. Arab J Chem 15(8):104011 5. Aziz A, Nasehir Khan MN, Yusop MFM, Jaya EMJ, Jaya MAT, Ahmad MA (2021) Singlestage microwave assisted coconut shell based activated carbon for removal of dichlorodiphenyltrichloroethane (DDT) from aqueous solution: optimization and batch studies. Int J Chem Eng 9331386
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6. Chen L, Hu C, Tsui MMP, Wan T, Peterson DR, Shi Q, Lam PKS, Au DWT, Lam JCW, Zhou B (2018) Multigenerational disruption of the thyroid endocrine system in marine medaka after a life-cycle exposure to perfluorobutanesulfonate. Environ Sci Technol 52(7):4432–4439 7. Torres F (2014) Crisis y estrategias de los inmigrantes en España: el acento latino. Rev CIDOB d’Afers Int 106–107(7):215–236 8. Yusop MFM, Ahmad MA, Rosli NA, Gonawan FN, Abdullah SJ (2021) Scavenging malachite green dye from aqueous solution using durian peel based activated carbon. Malays J Fundament Appl Sci 17(1):95–103 9. Yusop MFM, Aziz HA, Ahmad MA (2017) Scavenging remazol brilliant blue R dye using microwave-assisted activated carbon from acacia sawdust: equilibrium and kinetics studies. AIP Conf Proc 1892:040018 10. Ahmad F, Daud WMAW, Ahmad MA, Radzi R, Azmi AA (2013) The effects of CO2 activation on porosity and surface functional groups of cocoa (Theobroma cacao)—Shell based activated carbon. J Environ Chem Eng 1(3):378–388 11. Yusop MFM, Abdullah AZ, Ahmad MA (2023) Malachite green dye adsorption by jackfruit based activated carbon: Optimization, mass transfer simulation and surface area prediction. Diam Relat Mater 136:109991 12. Yusop MFM, Khan MNN, Zakaria R, Abdullah AZ, Ahmad MA (2023) Mass transfer simulation on remazol brilliant blue R dye adsorption by optimized teak wood based activated carbon. Arab J Chem 16(6):104780 13. Ahmad MA, Hamid SRA, Yusop MFM, Aziz HA (2017) Optimization of microwave-assisted durian seed based activated carbon preparation conditions for methylene blue dye removal. AIP Conf Proc 1892:040019 14. Ahmad MA, Yusop MFM, Awang S, Yahaya NKEM, Rasyid MA, Hasan H (2021) Carbonization of sludge biomass of water treatment plant using continuous screw type conveyer pyrolyzer for methylene blue removal. IOP Conf Ser: Earth Environ Sci 765:012112 15. Alslaibi TM, Abustan I, Ahmad MA, Foul AA (2014) Microwave irradiated and thermally heated olive stone activated carbon for nickel adsorption from synthetic wastewater: a comparative study. AIChE J 60(1):237–250 16. Bello OS, Ahmad MA (2011) Removal of Remazol Brilliant Violet-5R dye using periwinkle shells. Chem Ecol 27(5):481–492 17. Khasri A, Ahmad MA (2018) Adsorption of basic and reactive dyes from aqueous solution onto Intsia bijuga sawdust-based activated carbon: batch and column study. Environ Sci Pollut Res Int 25(31):31508-31519 18. Ahmad MA, Ahmed NB, Adegoke KA, Bello OS (2021) Adsorptive potentials of lemongrass leaf for methylene blue removal. Chem Data Collect 31:100578 19. Huang W-H, Lee D-J, Huang C (2021) Modification on biochars for applications: a research update. Biores Technol 319:124100 20. Zubair M, Aziz HA, Ihsanullah I, Ahmad MA, Al-Harthi MA (2021) Biochar supported CuFe layered double hydroxide composite as a sustainable adsorbent for efficient removal of anionic azo dye from water. Environ Technol Innov 23:101614 21. Quesada HB, De Araújo TP, Cusioli LF, De Barros MASD, Gomes RG, Bergamasco R (2022) Caffeine removal by chitosan/activated carbon composite beads: adsorption in tap water and synthetic hospital wastewater. Chem Eng Res Des 184:1–12 22. Langmuir I (1918) The adsorption of gases on plane surfaces of glass, mica and platinum. J Am Chem Soc 40(9):1361–1403 23. Freundlich H (1906) Over the adsorption in solution. J Phys Chem 57(385471):1100–1107 24. Pelekani C, Snoeyink VL (2000) Competitive adsorption between atrazine and methylene blue on activated carbon: the importance of pore size distribution. Carbon 38(10):1423–1436 25. Li L, Quinlivan PA, Knappe DRU (2002) Effects of activated carbon surface chemistry and pore structure on the adsorption of organic contaminants from aqueous solution. Carbon 40(12):2085–2100
Synthesis of Pineapple Peel Based Activated Carbon Via Microwave Irradiation Technique for Methylene Blue Dye Removal Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Iylia Idris, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad
Abstract Dye effluents generated from industries were escaping from inefficient wastewater treatment systems and entering the water bodies, thus causing environmental and ecological problems due to their highly toxic properties. In this study, the pineapple peel-based activated carbon (PPAC) was prepared via physical activation through carbon dioxide (CO2 ) gasification and heated using a microwave oven (440 W activation power for 5 min) to treat methylene blue (MB) dye from synthetic solution. PPAC was disclosed to pose 544.07 m2 /g for BET surface area, 285.32 m2 / g for mesopores surface area and 0.3035 cm3 /g for total pore volume. The average pore diameter for PPAC was revealed to be 2.23 nm. The MB adsorption uptakes by PPAC increased from 19.53 to 75.18 mg/g whereas MB removal dropped from 78.13 M. F. M. Yusop · A. Z. Abdullah · M. A. Ahmad (B) School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] M. F. M. Yusop e-mail: [email protected] A. Z. Abdullah e-mail: [email protected] N. K. E. M. Yahaya · J. Karim · M. A. M. Yusoff National Water Research Institute of Malaysia, Lot 5377, Jalan Putra Permai, 43300 Seri Kembangan, Selangor, Malaysia e-mail: [email protected] J. Karim e-mail: [email protected] M. A. M. Yusoff e-mail: [email protected] I. Idris School of Chemical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_18
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to 25.06%, when MB starting concentration was elevated from 25 to 300 mg/L. The MB adsorption equilibrium toward PPAC was excellently fitted by the Langmuir isotherm model with optimum single layer adsorption capacity, Qm of 84.76 mg/g. Meanwhile, the kinetic studies of MB adsorption onto PPAC were best represented by pseudo-second order (PSO) kinetic models. Keywords Adsorption process · Activated carbon · Microwave heating · Methylene blue · CO2 gasification
1 Introduction Methylene blue (MB) dye, a basic dye is a common dye utilized in textile industries and many researchers have made an effort to treat this type of dye [1–3] as they can escape from the wastewater treatment plant and existed in water bodies. MB dye belongs to the cationic group and this type of dye dissociated in water to form positive ions and is attracted to the negative polar region of water molecules, thus making its separation from water to be harder. The existence of MB dye in the environment was despised by societies due to harmful diseases that it can cause to humans namely jaundice, cyanosis, methemoglobinemia, tissue necrosis, and quadriplegia [4, 5]. Furthermore, synthetic dyes can survive in water bodies for a long period due to its reluctant properties towards chemical and biological degradation [6]. Dyes removal technologies can be divided into three main groups namely biological method such as activated sludge, chemical method such as oxidation and physical method such as adsorption process. The process of contaminants adsorption by utilizing activated carbon (AC) can be considered the most reliable one due to many advantages that it offers including relatively easy process, fast and is capable of eliminating a wide range of contaminants including heavy metals [7–9], pesticides [10] and most importantly, dyes [11, 12]. For the past two decades, researchers around the world are actively converting agricultural wastes or biomass into AC to reduce reliance on non-renewable bituminous coal as AC’s precursor. These biomasses-based ACs such as biomass sludgebased AC [13], acacia sawdust-based AC [14], jengkol peel-based AC [15], and many others were proven to be beneficial in removing different groups of dyes. Hence, in this study, an attempt was made to produce pineapple peel-based AC (PPAC) to treat MB dye. In literature, several researchers succeeded in using pineapple peel based adsorbent to adsorb heavy metals like chromium (IV) [16], nickel, zinc, and copper [17]. Pineapple comes the third place, after banana and citrus as the most famous tropical fruit around the globe, due to its outstanding taste of the juice, one of a kind flavour and contains many nutrients [18]. In terms of plantation area, Malaysia made it to the top 20 countries with total pineapple production of 335,725 MT in 2014, which resembled 0.01% of the total global pineapple production [19]. In Malaysia and other Southeast Asia countries, pineapple is eaten raw, cooked in dishes, and converted into jam and canned product. Unfortunately, the peel of pineapple has a
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very limited application, thus they are discarded in a landfill. Converting pineapple peel into AC would benefit the environment and solve its disposal problem, indirectly.
2 Materials and Methods 3 Materials and Preparation of PPAC Pineapple peel (PP) was collected from the fruit stall located in Nibong Tebal, Pulau Pinang. The adsorbate of MB dye was supplied from Sigma Aldrich Sdn. Bhd. The inactive gas of nitrogen, N2 and reactive gas of carbon dioxide, CO2 with the purity of 99.90% and 99.50%, respectively, were purchased from MOX Sdn. Bhd. Upon receiving PP, it was immediately cleaned with plenty of tap water to eliminate all impurities on it. Next, the wet PP was dried in an oven at a temperature of 110 °C for 24 h. Dried PP was placed inside a modified microwave. The dried PP was then radiated for 5 min at 440 watts while being fed with 150 cm3 /min of CO2 gas. CO2 was chosen as the activating gas because it is easier to control as compared to steam which behaves more robust under microwave activation, thus rupturing the pores of the sample. Since the activation time was very short, the amount of CO2 used was minimal and did not contribute to CO2 release into the atmosphere significantly. Once the microwave heating process ended, the temperature of the sample was reduced to room temperature while N2 gas purging through it. At room temperature, the sample which is now known as PPAC was collected and kept inside an air-tight container to be used later for the adsorption process. Figure 1 shows the schematic diagram of the activation process.
3.1 Batch Equilibrium Study The study on the MB-PPAC adsorption system was evaluated in terms of the effect of MB initial concentration and contact time. A stock solution with a concentration of 1000 mg/L was made by combining 1.0 g of MB dye and 1000 mL of deionized water. Deionized water was used to dilute this stock solution to manufacture MB solutions Gas inlet
Fig. 1 Schematic diagram of activation process
Gas outlet
Power controller Time controller Quartz glass Sample
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with 6 known initial concentrations of 25, 50, 100, 200, 250, and 300 mg/L. After that, each conical flask was inserted with 0.20 g of PPAC. The solution temperature and rotation speed were fixed at 30 °C and 80 rpm, respectively. MB concentration was quantified through UV-Visible spectrophotometry (Model Shimadzu UV-1800, Japan) at a wavelength of 664 nm. The quantity of MB uptakes at equilibrium and MB percentage removal were computed using the following equations, respectively: qe =
(Co − Ce )V m
Removal (%) =
(Co − Ce ) × 100% Ce
(1) (2)
where qe is the MB dye adsorption uptake at equilibrium (mg/g), C o and C e are MB dye concentrations at starting and equilibrium states, respectively, V is the volume of the MB dye solution and m is the mass of PPAC used (g).
3.2 Isotherm Study Isotherm study is important to be conducted to verify the connection between MB dye molecules in the bulk phase and solid phase. Equations for the models of Langmuir [20] and Freundlich [21]: Ce 1 1 = + Ce qe qm K L qm
(3)
where qm is the Langmuir optimum single layer adsorption capacity (mg/g) and K L is the Langmuir constant (L/mg). logqe = logK f +
1 logCe n
(4)
where K f is the constant of Freundlich, and 1/n is the adsorption intensity.
3.3 Kinetic Study Kinetic study was performed to obtain valuable information such as the rate constant. Two most popular kinetic models namely pseudo-first order (PFO) [22] and pseudosecond order (PSO) [23] were utilized in this study and these equations are given as follows, respectively: ln(qe − qt ) = lnqe − k1 t
(5)
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t 1 t = + qt k2 qe2 qe
(6)
where qe and qt are MB dye uptakes at equilibrium (mg/g) and at time t (mg/ g), respectively, whilst k 1 and k 2 are rate constant for PFO (min−1 ) and PSO (g mg−1 min−1 ), respectively.
4 Results and Discussion 4.1 Characteristics of Samples Table 1 shows the characteristics of the surface area together with pore of the samples, Table 2 presents the proximate analysis of the samples whereas SEM images of the samples are presented in Fig. 2. Data in Table 1 divulged that microwave activation succeeded in increasing the BET surface area, mesopore surface area, and total pore volume from 1.15 to 544.07 m2 /g, from 0.35 to 285.32 m2 /g, and from 0.0037 to 0.3035 cm3 /g, respectively. CO2 gasification during the microwave irradiation technique was effective in increasing the average pore diameter from 1.67 nm which belong in the micropore region to 2.23 nm which belong in the mesopore region. Similar results were obtained by [8, 11, 24] where CO2 gasification enlarged the mean pore diameter of the sample. Based on Table 2, the percentage of moisture and volatile matter in the precursor dropped significantly from 15.32 to 6.2% and from 62.14 to 23.92%, respectively. During the activation process, water and components that made up the volatile matter (cellulose, hemicellulose, and lignin) became unstable and evaporated. Consequently, the percentage of fixed carbon rose tremendously from 20.45 to 67.92%. A high percentage of fixed carbon is desired since fixed carbon build up the matrix structure of PPAC. Low ash content in PPAC is good since ash contains no pores and did not contribute during the adsorption process. Based on Fig. 1, the SEM image of the precursor revealed that its surface has no pores. On contrary, the SEM image of PPAC divulged that its surface was filled up with many pores, which were formed during microwave activation and the CO2 gasification process. Table 1 Surface area data and pores information of studied samples Samples Precursor PPAC
BET surface area (m2 /g)
Mesopores surface area (m2 /g)
Total pore volume (cm3 /g)
Average pore diameter (nm)
1.15
0.35
0.0037
1.67
544.07
285.32
0.3035
2.23
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Table 2 Data on proximate analysis of studied samples Samples Precursor PPAC
Proximate analysis (%) Moisture
Volatile matter
Fixed carbon
Ash
15.32
62.14
20.45
2.09
6.28
23.92
67.92
1.88
Fig. 2 SEM images of a precursor peel and b PPAC with magnification 1000X
4.2 Batch Equilibrium Adsorption Figure 3a, b present the graphs of MB amount adsorbed and MB elimination percentage by PPAC for various initial concentrations at 30 °C, respectively. Based on these figures, MB amount adsorbed and MB elimination percentage increased as the adsorption time increased, especially from t = 0 h until t = 7 h. Starting from t = 8 to t = 24 h, insignificant increments of MB adsorption uptakes and percentage removal were noticed. At the equilibrium state, the amount of MB dye molecules being adsorbed and desorbed from PPAC was equal in magnitude. Figure 3a showed that MB adsorption uptakes rose from 19.53 to 75.18 mg/g as the starting concentration of MB was increased from 25 to 300 mg/L. Conversely, based on Fig. 3b, higher MB initial concentration produced lower MB percentage removal and vice versa. Specifically, MB percentage removal dropped from 78.13 to 25.06% when MB initial concentration increased from 25 to 300 mg/L. This result was expected since the ratio of MB molecules to active sites in PPAC was low at lower MB starting concentrations, therefore less competition occurred between MB molecules to be adsorbed by PPAC. These results were also seen in other studies that involved the MB adsorption onto biomass-based AC [4, 24].
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80
Fig. 3 Plots of a MB adsorption uptakes and b MB removal by PPAC
25 mg/L
qt (mg/g)
60
50 mg/L 100 mg/L
40
200 mg/L 20
250 mg/L 300 mg/L
0 0
5
10
15
20
25
t (h)
(a) MB removal (%)
80 25 mg/L 60
50 mg/L
40
100 mg/L 200 mg/L
20
250 mg/L
0
300 mg/L 0
5
10
15
20
25
t (h)
(b)
4.3 Adsorption Isotherm The isotherm plots of Langmuir and Freundlich are presented in Fig. 4a, b, respectively. Adsorption of MB dye onto PPAC was excellently fitted by Langmuir isotherm, since this isotherm produced highest R2 value of 0.9771. This signified that a monolayer coverage of MB dye molecules had taken place on PPAC’s surface with maximum monolayer adsorption capacity, Qm of 84.76 mg/g. Adsorption of MB dye onto adsorbents such as acacia wood-based AC [4] and teak wood-based AC [24] were also found to follow Langmuir isotherm. The adsorption system of MBPPAC was found to be a favourable process due to the n value obtained from the Freundlich model 2.69, which lies between 1 and 10 [8].
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2.0
3.5 1.8
2.5
log qe
Ce/qe (g/L)
3.0 2.0 1.5 1.0
1.6 1.4 1.2
0.5
R2 = 0.9220
R2 = 0.9771
0.0 0
100
200
300
1.0 0.50
1.50
Ce
log Ce
(a)
(b)
2.50
Fig. 4 Isotherm plots of a Langmuir and b Freundlich for MB-PPAC adsorption system
4.4 Adsorption Kinetic The linearized plots of kinetic models are given in Fig. 5a, b for PFO and PSO, respectively at 30 °C. PFO produced a higher average R2 value of 0.9963 as compared to PSO with 0.9909. However, PFO failed to predict the experimental data, thus creating a high value for an average error of 92.75%. On contrary, PSO was found to produce a much lower average error percentage of 15.02%, therefore implying PSO was the best kinetic model in representing the MB-PPAC adsorption system. It is also discovered that PSO accurately described the adsorption system of MB dye by teak wood-based AC [24] and acacia wood-based AC [4]. Rate constant, k 2 was observed to be lower at higher MB initial concentration mainly because at higher MB concentration, the competition between MB molecules to be adsorbed by PPAC was high, hence, producing a slower adsorption process.
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6
ln (qe-qt)
5 4
25 mg/L 50 mg/L 100 mg/L 200 mg/L 250 mg/L 300 mg/L
3 2 1 0
0
1
2
3
4
t
(a) 0.20
t/qt
0.15
25 mg/L 50 mg/L 100 mg/L 200 mg/L 250 mg/L 300 mg/L
0.10 0.05 0.00
0
1
2
3
4
t
(b) Fig. 5 Graphs of kinetic models: a PFO and b PSO for MB-PPAC adsorption system
5 Conclusion Pineapple peel was successfully transformed into PPAC through a one-step microwave irradiation technique, conducted at 440 W (radiation power) and 5 min (radiation time). PPAC was found to pose a BET surface area of 544.07 m2 /g, mesopores surface area of 285.32 m2 /g, total pore volume of 0.3035 cm3 /g, and average pore diameter of 2.23 nm. PPAC was able to eliminate MB dye in an aqueous solution with a maximum single layer adsorption capacity of 84.76 mg/g. The batch equilibrium study revealed that as the MB starting concentration altered from 25 to 300 mg/L, MB amount adsorbed increased from 19.53 to 75.18 mg/g, and MB elimination percentage decreased from 78.13 to 25.06%. MB adsorption onto PPAC was best described by the Langmuir model, thus signifying monolayer coverage type of adsorption. In terms of kinetic study, PSO fitted the kinetic behavior the most accurate. Acknowledgements This research was supported by the Ministry of Higher Education Malaysia through the Fundamental Research Grant Scheme (project code: FRGS/1/2021/TK0/USM/01/3) and Postdoctoral Fellowship Scheme from Universiti Sains Malaysia.
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References 1. Yusop MFM, Abdullah AZ, Ahmad MA (2023) Malachite green dye adsorption by jackfruit based activated carbon: optimization, mass transfer simulation and surface area prediction. Diam Relat Mater 136:109991 2. Ahmad MA, Hamid SRA, Yusop MFM, Aziz HA (2017) Optimization of microwave-assisted durian seed based activated carbon preparation conditions for methylene blue dye removal. AIP Conf Proc 1892:040019 3. Khasri A, Ahmad MA (2018) Adsorption of basic and reactive dyes from aqueous solution onto Intsia bijuga sawdust-based activated carbon: batch and column study. Environ Sci Pollut Res Int 25(31):31508–31519 4. Yusop MFM, Ahmad MA, Rosli NA, Manaf MEA (2021) Adsorption of cationic methylene blue dye using microwave-assisted activated carbon derived from acacia wood: optimization and batch studies. Arab J Chem 14(6):103122 5. Kushwaha AK, Gupta N, Chattopadhyaya MC (2014) Removal of cationic methylene blue and malachite green dyes from aqueous solution by waste materials of Daucus carota. J Saudi Chem Soc 18(3):200–207 6. Yusop MFM, Khan MNN, Zakaria R, Abdullah AZ, Ahmad MA (2023) Mass transfer simulation on remazol brilliant blue R dye adsorption by optimized teak wood based activated carbon. Arab J Chem 16(6):104780 7. Yusop MFM, Jaya EMJ, Din ATM, Bello OS, Ahmad MA (2022) Single-stage optimized microwave-induced activated carbon from coconut shell for cadmium adsorption. Chem Eng Technol 45:1943-1951 8. Yusop MFM, Jaya EMJ, Ahmad MA (2022) Single-stage microwave assisted coconut shell based activated carbon for removal of Zn(II) ions from aqueous solution—Optimization and batch studies. Arab J Chem 15(8):104011 9. Alslaibi TM, Abustan I, Ahmad MA, Foul AA (2014) Microwave irradiated and thermally heated olive stone activated carbon for nickel adsorption from synthetic wastewater: a comparative study. AIChE J 60(1):237–250 10. Aziz A, Nasehir Khan MN, Yusop MFM, Jaya EMJ, Jaya MAT, Ahmad MA (2021) Singlestage microwave assisted coconut shell based activated carbon for removal of dichlorodiphenyltrichloroethane (DDT) from aqueous solution: optimization and batch studies. Int J Chem Eng 9331386 11. Yusop MFM, Ahmad MA, Rosli NA, Gonawan FN, Abdullah SJ (2021) Scavenging malachite green dye from aqueous solution using durian peel based activated carbon. Malays J Fundam Appl Sci 17(1):95–103 12. Yusop MFM, Jaya MAT, Idris I, Abdullah AZ, Ahmad MA (2023) Optimization and mass transfer simulation of remazol brilliant blue R dye adsorption onto meranti wood based activated carbon. Arab J Chem 16(5):104683 13. Ahmad MA, Yusop MFM, Awang S, Yahaya NKEM, Rasyid MA, Hasan H (2021) Carbonization of sludge biomass of water treatment plant using continuous screw type conveyer pyrolyzer for methylene blue removal. IOP Conf Ser: Earth Environ Sci 765:012112 14. Yusop MFM, Aziz HA, Ahmad MA (2017) Scavenging remazol brilliant blue R dye using microwave-assisted activated carbon from acacia sawdust: equilibrium and kinetics studies. AIP Conf Proc 1892:040018 15. Mohd Ramli MR, Shoparwe NF, Ahmad MA (2022) Methylene blue removal using activated carbon adsorbent from Jengkol Peel: kinetic and mass transfer studies. Arab J Sci Eng (In press) 16. Shakya A, Agarwal T (2019) Removal of Cr(VI) from water using pineapple peel derived biochars: adsorption potential and re-usability assessment. J Mol Liq 293:111497 17. Iamsaard K, Weng C-H, Yen L-T, Tzeng J-H, Poonpakdee C, Lin Y-T (2022) Adsorption of metal on pineapple leaf biochar: key affecting factors, mechanism identification, and regeneration evaluation. Biores Technol 344:126131
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18. Chen Y, Zhang H, Feng X, Ma L, Zhang Y, Dai H (2021) Lignocellulose nanocrystals from pineapple peel: preparation, characterization and application as efficient pickering emulsion stabilizers. Food Res Int 150:110739 19. Najeeb MI, Sultan MTH, Andou Y, Shah AUM, Eksiler K, Jawaid M, Ariffin AH (2020) Characterization of silane treated Malaysian Yankee Pineapple AC6 leaf fiber (PALF) towards industrial applications. J Market Res 9(3):3128–3139 20. Langmuir I (1918) The adsorption of gases on plane surfaces of glass, mica and platinum. J Am Chem Soc 40(9):1361–1403 21. Freundlich H (1906) Over the adsorption in solution. J Phys Chem 57(385471):1100–1107 22. Lagergren SK (1898) About the theory of so-called adsorption of soluble substances. Sven Vetenskapsakad Handingarl 24:1–39 23. Ho YS, McKay G (1988) Sorption of dye from aqueous solution by peat. Chem Eng J 70(2):115– 124 24. Yusop MFM, Aziz A, Ahmad MA (2022) Conversion of teak wood waste into microwaveirradiated activated carbon for cationic methylene blue dye removal: optimization and batch studies. Arab J Chem 15(9):104081
Preparation of Edamame Bean Pod Based Activated Carbon for Methylene Blue Dye Adsorption Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad
Abstract Methylene blue (MB) dye is a basic and cationic dye. It is a widely used dye in the textile industry. However, its existence in water bodies was despised due to its harmful properties. Hence, an effort was made in this study to produce edamame bean pod-based activated carbon (EBPAC) through microwave heating coupled with carbon dioxide, CO2 gasification, at radiation power and radiation time of 470 W and 4 min, respectively. The adsorption equilibrium study revealed that when the MB starting concentration changed from 25 to 300 mg/L, MB amount adsorbed rose from 18.26 to 97.06 mg/g, whereas MB removal decreased from 87.66 to 38.83%. Isotherm study disclosed that the MB-EBPAC adsorption system accurately followed the Freundlich model which signified multilayer coverage of MB dye on the layer surface of EBPAC. The Langmuir maximum single layer adsorption capacity, Qm was determined to be 118.31 mg/g. In the kinetic study, the kinetic behaviour was excellently obeyed pseudo-first order (PFO) kinetic model.
M. F. M. Yusop · A. Z. Abdullah · M. A. Ahmad (B) School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] M. F. M. Yusop e-mail: [email protected] A. Z. Abdullah e-mail: [email protected] N. K. E. M. Yahaya · J. Karim · M. A. M. Yusoff National Water Research Institute of Malaysia, Lot 5377, Jalan Putra Permai, 43300 Seri Kembangan, Selangor, Malaysia e-mail: [email protected] J. Karim e-mail: [email protected] M. A. M. Yusoff e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_19
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Keywords Adsorption process · Activated carbon · Microwave heating · Methylene blue
1 Introduction Dyes are produced and employed in many industries such as textiles, paper, magazines, plastic, and foodstuffs to cater to the demand from societies. Despite being attractive to the eyes, dyes that do not undergo proper wastewater treatment can enter the environment through industrial effluent, especially a basic ionic dye like methylene blue (MB). This dye dissolves in water at a higher degree due to the attraction between the negative polar region of water molecules and the positive ions of MB, hence causing the treatment of this dye to be more difficult [1]. Several diseases have been linked with MB dye upon exposure to humans namely cyanosis, tissue necrosis, quadriplegia, jaundice, and methemoglobinemia [2]. Even at a very little concentration, dyes can reduce sunlight penetration and affect the photosynthesis process of aquatic plants. It is widely known that the utilization of activated carbon (AC) in the adsorption process has been one of the most efficient technologies to treat different types of pollutants including dyes [3–5], heavy metals [6, 7], pesticides [8], and so on. To further reduce the total production cost and increase the adsorption efficiency of AC, many researchers have been exploring microwave heating techniques to replace the conventional heating process to activate the precursor [9–11]. This method is proven to be significantly faster, thus consuming fewer resources and energy, without compromising the quality of the resulting AC. For the past two decades, biomass or agricultural wastes have been a popular choice among researchers to be converted into AC. Biomass such as jackfruit peel [12], cocoa shell [13], pomelo peels [14], meranti sawdust [15], and durian peel [16] posed good characteristics such as moderately high in fixed carbon and low in ash contents. Therefore, this study was interested to produce AC from edamame bean pod (EBPAC) through a one-step microwave heating method to scavenge MB dye from an aqueous solution. Edamame bean is a type of soybean that can be consumed raw and usually is cooked via the stir-fry method. However, the edamame pods that house the beans have no specific application, and thus are discarded into the environment. Therefore, the production of EBPAC from edamame bean pods can reduce the disposal problem of this waste and transform them into something beneficial for societies.
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2 Materials and Methods 2.1 Materials and Preparation of EBPAC The precursor of edamame bean pod (EBP) was collected from a local store situated in Nibong Tebal, Pulau Pinang. Methylene blue (MB) dye was purchased from Sigma Aldrich Sdn. Bhd. Nitrogen gas, N2 (99.90% purity) and carbon dioxide, CO2 gas (99.50% purity) were obtained from MOX Sdn. Bhd. EBP was soaked inside water for 2 min before being rinsed to remove all dirt and impurities on them. Wet EBP was dried at 110 °C for 24 h inside an oven. Dried EBP was inserted inside a modified microwave and was radiated at 470 W for 4 min, under the continuous flow of activating gas of CO2 , flowing at 150 cm3 /min. Next, the temperature of the sample was reduced to 40 °C, with N2 gas still purging through, before the sample was taken out. The resulting sample is now known as edamame bean pod-based AC (EBPAC).
2.2 Batch Equilibrium Study In the batch equilibrium study, the effects of MB initial concentration and contact time in affecting MB adsorption uptakes and MB percentage removal were verified. The stock solution of MB dye (1000 mg/L) was diluted to prepare MB solution with six different concentrations between 25 and 300 mg/L in conical flasks. These conical flasks were assembled in a water bath shaker and 0.20 g of EBPAC was inserted into them. The water bath shaker’s shaking speed and temperature were set to 80 rpm and 30 °C, respectively. The MB solution sample was withdrawn with a syringe to be tested with a UV–Vis spectrometer (Model Shimadzu UV-1800, Japan) for its concentration. During the sample withdrawal process, the tip of the syringe was attached with syringe filter nylon (0.22 µm) to separate EBPAC from the sample solution. The wavelength of MB dye was set at 664 nm. MB uptakes at equilibrium, qe together with its percentage removal was calculated using the following formulas, respectively: qe =
(Co − Ce )V m
Removal(%) =
(Co − Ce ) × 100% Ce
(1) (2)
where C o is the MB starting concentration (mg/L), C e is the MB concentration at equilibrium state (mg/L), qe is the amount of MB adsorbed by EBPAC (mg/g), V is the volume of MB solution (mL) and m refers to the EBPAC’s mass utilized (g).
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2.3 Isotherm Study Various information can be obtained from the isotherm parameters. Therefore, an isotherm study was performed by utilizing Langmuir [17] and Freundlich [18] models. Their equations are shown as follows, respectively: Ce 1 1 = + Ce qe qm K L qm logqe = logK f +
1 logCe n
(3) (4)
where qm and K L refer to the Langmuir maximum monolayer adsorption capacity (mg/g) and Langmuir constant (L/mg), respectively. K f is the constant of Freundlich, and 1/n denotes the adsorption intensity.
2.4 Kinetic Study Useful information like the rate of the adsorption process can be obtained from the kinetic study. Hence, this study has employed pseudo-first order (PFO) [19] and pseudo-second order (PSO) [20] kinetic models. The formulas for PFO and PSO are given as follows: ln(qe − qt ) = lnqe − k1 t
(5)
t 1 t = + 2 qt k 2 qe qe
(6)
where qe and qt are the amount of MB dye adsorbed by EBPAC at equilibrium (mg/ g) and at time t (mg/g), respectively, whereas k 1 is the rate constant for PFO (min−1 ) and k 2 is the rate constant for PSO (g mg−1 min−1 ).
3 Results and Discussion 3.1 Batch Equilibrium Adsorption The plots of MB adsorption uptakes and percentage removal by EBPAC for different MB initial concentrations are given in Fig. 1a, b, respectively. Both of these figures revealed that as the contact time increased, MB amount adsorbed, and MB elimination percentage increased as well. After some time, MB amount adsorbed, and MB
Preparation of Edamame Bean Pod Based Activated Carbon …
qt (mg/g)
100
MB removal (%)
Fig. 1 Plots of a MB amount adsorbed and b MB elimination percentage by EBPAC at 30 °C
235
50 0
100 80 60 40 20 0
0
4
8
12
16 t (hr) (a)
20
24
25 mg/L 50 mg/L 100 mg/L 200 mg/L 250 mg/L 300 mg/L
25 mg/l 50 mg/l 100 mg/l 200 mg/l 250 mg/l 300 mg/l
0
4
8
12 16 t (hr) (b)
20
24
elimination percentage became constant, implying that an equilibrium state has been achieved. At this state, no more MB dye can be adsorbed by EBPAC because EBPAC had already attained its exhaustion point. It was spotted that lower MB dye solution (25, 50, and 100 mg/L) reached the point of equilibrium faster which is around 5–6 h. On contrary, higher MB dye solution (200, 250, and 300 mg/L) took 22 h to reach the same equilibrium point. Based on Fig. 1a, as the MB initial concentration rose from 25 to 300 mg/L, MB adsorption uptakes increased too, after 22 h, from 18.26 to 97.06 mg/g. Conversely, Fig. 1b divulged that after 22 h MB removal decreased from 87.66 to 38.83%, as the MB starting concentration changed from 25 to 300 mg/L. At elevated MB starting concentration, the ratio of MB molecules to available active sites (MB dye:active sites) was high, therefore lower MB removal was obtained.
3.2 Adsorption Isotherm Models of isotherm namely Langmuir and Freundlich are given in Fig. 2a, b, respectively. Due to the highest R2 value of 0.9533, it can be verified that the Freundlich model described MB adsorption onto EBPAC the best. Freundlich model suggests that multilayer coverage of MB dye molecules occurred on the heterogeneous surface of EBPAC. The Langmuir maximum monolayer adsorption capacity for the studied adsorption process was calculated to be 118.31 mg/g (Eq. 3). This value can be considered relatively low as compared to other studies of MB adsorption by teak wood based-AC (567.52 mg/g) [1] and acacia wood-based AC (338.29 mg/g) [2]. This result was expected since EBPAC was synthesized by using a single activation method only, without a prior carbonization process, therefore reducing its adsorption
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2.0
1.8
log qe
Ce/qe (g/L)
1.5 1.0 0.5
R2 = 0.9349
0.0 0
100
200
1.6 1.4
R2 = 0.9533
1.2 1.0 0.0
1.0
2.0
Ce (mg/L)
log Ce
(a)
(b)
3.0
Fig. 2 Isotherm plots of a Langmuir and b Freundlich for MB-EBPAC adsorption system
capacity for MB dye. This adsorption process was found to be favourable since the n value was 2.29 (Eq. 4), a value that lies between 1 and 10 [9].
3.3 Adsorption Kinetic Models of kinetic plots in the linearized forms are given in Fig. 3a, b, respectively. Table 1 shows the summary of R2 and error percentage for PFO and PSO. PFO was divulged to represent the kinetic behaviour of the MB-EBPAC adsorption system the best, due to its higher average R2 value of 0.9770 and lower average error percentage of 3.36%. A similar result where PFO fitted the adsorption process the best was also noticed in the study of adsorption of malachite green dye by durian peel-based AC [16]. Determination of the reaction order gave an insight into how changing the adsorbate concentration would change the rate of the adsorption process.
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0.25 0.20
4
0.15 3
t/qt
ln (qe - qt)
237
2
0.10 0.05 0.00
1 0
1
2
0
3
1
25 mg/L 100 mg/L 250 mg/L
2
3
t (h)
t (h)
50 mg/L 200 mg/L 300 mg/L
25 mg/L 100 mg/L 250 mg/L
50 mg/L 200 mg/L 300 mg/L
(a)
(b)
Fig. 3 Kinetic plots of a PFO and b PSO models for MB-EBPAC adsorption system at 30 °C
Table 1 Summary of R2 and error percentage for PFO and PSO Concentration (mg/L)
PFO
PSO
R2
Error (%)
R2
25
0.9881
8.16
0.9047
3.23
50
0.9006
4.88
0.9942
28.04
Error (%)
100
0.9997
0.30
0.9915
55.89
200
0.9912
4.62
0.9620
42.28
250
0.9927
1.92
0.9204
72.12
300
0.9895
0.26
0.9950
22.85
Average
0.9770
3.36
0.9613
37.40
4 Conclusion Edamame bean pod was successfully activated to be EBPAC via microwave irradiation technique under the gasification effect of CO2 gas. Radiation power and radiation time to produce EBPAC were set to be 470 W and 4 min. The resulting EBPAC was effectively removing MB dye from aqueous solution with Langmuir maximum monolayer adsorption capacity of 118.31 mg/g. As MB starting concentration altered from 25 to 300 mg/L, MB amount adsorbed elevated from 18.26 to 97.06 mg/g whereas MB removal dropped from 87.66 to 38.83%. Isotherm study revealed that the adsorption process followed the Freundlich model whereas the kinetic study divulged that the kinetic data followed PFO.
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Acknowledgements This research was financed by the Ministry of Higher Education Malaysia through the Fundamental Research Grant Scheme (project code: FRGS/1/2021/TK0/USM/01/3) and Postdoctoral Fellowship Scheme from Universiti Sains Malaysia.
References 1. Yusop MFM, Aziz A, Ahmad MA (2022) Conversion of teak wood waste into microwaveirradiated activated carbon for cationic methylene blue dye removal: optimization and batch studies. Arab J Chem 15(9):104081 2. Yusop MFM, Ahmad MA, Rosli NA, Manaf MEA (2021) Adsorption of cationic methylene blue dye using microwave-assisted activated carbon derived from acacia wood: optimization and batch studies. Arab J Chem 14(6):103122 3. Ahmad MA, Ahmed NB, Adegoke KA, Bello OS (2021) Adsorptive potentials of lemongrass leaf for methylene blue dye removal. Chem Data Collect 31:100578 4. Yusop MFM, Khan MNN, Zakaria R, Abdullah AZ, Ahmad MA (2023) Mass Transfer Simulation on Remazol Brilliant Blue R Dye Adsorption by Optimized Teak Wood Based Activated Carbon. Arab J Chem 16(6):104780 5. Bello OS, Ahmad MA (2011) Removal of Remazol Brilliant Violet-5R dye using periwinkle shells. Chem Ecol 27(5):481–492 6. Yusop MFM, Jaya EMJ, Din ATM, Bello OS, Ahmad MA (2022) Single-stage optimized microwave-induced activated carbon from coconut shell for cadmium adsorption. Chem Eng Technol 45:1943–1951 7. Alslaibi TM, Abustan I, Ahmad MA, Foul AA (2014) Microwave irradiated and thermally heated olive stone activated carbon for nickel adsorption from synthetic wastewater: a comparative study. AIChE J 60(1):237–250 8. Aziz A, Nasehir Khan MN, Yusop MFM, Jaya EMJ, Jaya MAT, Ahmad MA (2021) Singlestage microwave assisted coconut shell based activated carbon for removal of dichlorodiphenyltrichloroethane (DDT) from aqueous solution: optimization and batch studies. Int J Chem Eng 9331386 9. Yusop MFM, Jaya EMJ, Ahmad MA (2022) Single-stage microwave assisted coconut shell based activated carbon for removal of Zn(II) ions from aqueous solution—optimization and batch studies. Arab J Chem 15(8):104011 10. Yusop MFM, Aziz HA, Ahmad MA (2017) Scavenging remazol brilliant blue R dye using microwave-assisted activated carbon from acacia sawdust: equilibrium and kinetics studies. AIP Conf Proc 1892:040018 11. Ahmad MA, Hamid SRA, Yusop MFM, Aziz HA (2017) Optimization of microwave-assisted durian seed based activated carbon preparation conditions for methylene blue dye removal. AIP Conf Proc 1892:040019 12. Yusop MFM, Abdullah AZ, Ahmad MA (2023) Malachite green dye adsorption by jackfruit based activated carbon: Optimization, mass transfer simulation and surface area prediction. Diam Relat Mater 136:109991 13. Ahmad F, Daud WMAW, Ahmad MA, Radzi R, Azmi AA (2013) The effects of CO2 activation on porosity and surface functional groups of cocoa (Theobroma cacao)—shell based activated carbon. J Environ Chem Eng 1(3):378–388 14. Bello OS, Ahmad MA, Semire B (2015) Scavenging malachite green dye from aqueous solutions using pomelo (Citrus grandis) peels: kinetic, equilibrium and thermodynamic studies. Desalin Water Treat 56(2):521–535 15. Yusop MFM, Jaya MAT, Idris I, Abdullah AZ, Ahmad MA (2023) Optimization and Mass Transfer Simulation of Remazol Brilliant Blue R Dye Adsorption onto Meranti Wood Based Activated Carbon. Arab J Chem 16(5):104683
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16. Yusop MFM, Ahmad MA, Rosli NA, Gonawan FN, Abdullah SJ (2021) Scavenging malachite green dye from aqueous solution using durian peel based activated carbon. Malays J Fundam Appl Sci 17(1):95–103 17. Langmuir I (1918) The adsorption of gases on plane surfaces of glass, mica and platinum. J Am Chem Soc 40(9):1361–1403 18. Freundlich H (1906) Over the adsorption in solution. J Phys Chem 57(385471):1100–1107 19. Lagergren SK (1898) About the theory of so-called adsorption of soluble substances. Sven Vetenskapsakad Handingarl 24:1–39 20. Ho YS, McKay G (1988) Sorption of dye from aqueous solution by peat. Chem Eng J 70(2):115– 124
Activated Carbon Adsorbent Using Desiccated Coconut Residue for Removing Methylene Blue Dye Mohamad Firdaus Mohamad Yusop, Nasehir Khan E. M. Yahaya, Jamilah Karim, Muhammad Azroie Mohamed Yusoff, Ahmad Zuhairi Abdullah, and Mohd Azmier Ahmad
Abstract Basic dye like methylene blue (MB) poses a serious threat to human and aquatic organisms. Activated carbon (AC) is one type of promising adsorbents utilized in the adsorption process to treat wastewater from various contaminants including dyes. Therefore, the objective of this study was to produce desiccated coconut residue-based activated carbon (DCRAC) by employing the microwave irradiation technique together with carbon dioxide, CO2 gasification, to treat MB dye from an aqueous solution. Activation power and time used were 440 W and 4 min respectively, which yielded DCRAC with Langmuir maximum single layer adsorption capacity, Qm of 256.41 mg/g. Batch equilibrium study disclosed that MB adsorption uptakes and MB removal increased from 20.98 to 185.55 mg/g, and decreased from 83.91 to 61.85%, respectively when MB starting concentration increased from 25 to 300 mg/L. MB-DCRAC adsorption system obeyed Freundlich model and pseudo-second order (PSO) model in terms of isotherm and kinetic studies, respectively.
M. F. M. Yusop · A. Z. Abdullah · M. A. Ahmad (B) School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] M. F. M. Yusop e-mail: [email protected] A. Z. Abdullah e-mail: [email protected] N. K. E. M. Yahaya · J. Karim · M. A. M. Yusoff National Water Research Institute of Malaysia, Lot 5377, Jalan Putra Permai, 43300 Seri Kembangan, Selangor, Malaysia e-mail: [email protected] J. Karim e-mail: [email protected] M. A. M. Yusoff e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_20
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Keywords Adsorption process · Activated carbon · Microwave heating · Methylene blue · Dye
1 Introduction In the tertiary level of wastewater treatment, the adsorption process using activated carbon (AC) is a popular method due to its key advantages such as ease in operation, relatively low operational cost, and effectiveness. Over the years, AC has been spotted to be very versatile in adsorbing different types of contaminants which include dyes [1–3], pesticides [4, 5], heavy metals [6–8], and many others. In terms of the production method of AC, many researchers have been adopting microwave irradiation techniques [9–11]. Unlike the heating method using a conventional furnace, microwave heating provides a faster activation time, thus reducing the cost of resources and energy consumed [12, 13]. To further reduce the total cost of AC production, scientists around the world are actively transforming biomass and agricultural wastes into AC such as teak wood [14], corn fibers [15], acacia sawdust [16], intsia bijunga sawdust [17], olive stone [18], periwinkle shell [19], durian seed [20], cocoa shell [21] and others. The utilization of these wastes can reduce the dependency on bituminous coal as AC’s precursors. Dyes can be categorized as cationic, anionic, and non-ionic. An example of cationic dye is methylene blue dye which also belongs to the basic dye group. When dissociated in water, MB generates cations and dissolves in water at a higher degree due to its attraction toward the polar side of water molecules [2]. Once MB dye penetrates into the environment, it can exist there for decades due to its resistance to chemical and biological degradation. MB dye is only safe to be used for therapeutic purposes when the concentration is low ( 0.05. At the same time, the ratios of 1:1, 2:1, and 3:1 showed significant differences at P < 0.05. As a conclusion, the LSD analysis test showed that substrates that contained either 25 and 33.3% amount of SMC (1:2 and 1:3) showed a faster mycelium growth rate and were comparable to those of the control. Therefore, it can be deduced that SMC, depending on the formulation with RSD, can be used as the raw material in the preparation of mushroom substrates without adversely affecting the mushroom growth. Besides that, the time required for completion of mycelium running showed significant differences in each different formulation of a substrate. According to Table 1, the running time for the mycelium to completely colonize on a substrate with both the ratios of 2:1 and 3:1 was the slowest, which took approximately more than 30 days. It is also predicted that the ratio of 3:1 will take a longer time than 2:1 based on their respective mycelium growth rates. However, the running time for mycelium on a substrate of RSD (control) and the ratio of SMC mixed with RSD at 1:1, 1:2, and 1:3 was at almost equal rates. The fastest running time of mycelium was observed from both the 1:2 and 1:3, whereby the number of days recorded was 23 days, followed by
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Table 3 Effect of different formulation substrates on mycelium growth rate and completion of mycelium running of grey oyster mushroom Substrates
Mycelium growth rate (cm day−1 )
Time required for completion of mycelium running (days)
100% RSD (control)
0.8246 ± 0.0010a
24
50% SMC+50% RSD (1:1)
0.7375 ± 0.0039b
25
33.3% SMC+66.7% RSD (1:2)
0.8184 ±
0.0055a
23
25% SMC+75% RSD (1:3)
0.8281 ± 0.0043a
23
66.7% SMC+33.3% RSD (2:1)
0.5783 ± 0.0012c
>30
75% SMC+25% RSD (3:1)
0.5414 ± 0.0009d
>30
Notes Values are means of 3 replicates. Mean (n = 3)±standard deviation a–d Means within a column followed by same letters are not significantly different at 5% level (P > 0.05) by using LSD test
the control at 24 days. It took 25 days to fully colonize the substrate bags. Therefore, it can be observed that the number of days for completing the mycelium running on both 1:2 and 1:3 ratios was significantly faster as compared to that for the control. Based on the results obtained, it can also be observed that as the amount of SMC in the substrates increases, the mycelium growth rates for P. pulmonarius spawn will decrease. This might be due to excessive nitrogen content found in the SMC. The high nitrogen content in the substrates may have adversely affected the growth. Seephueak et al. reported that an excess of nitrogen inhibits the breakdown of lignin, thus slowing or even inhibiting the development of mycelium and reducing basidiocarp formation [7]. The higher percentage of SMC wet weight obtained at both the 2:1 and 3:1 ratio causes the moisture content of the substrates to increase. The high moisture content in the SMC is probably due to the high content of the absorbent material in the compost resulting from the composting process. According to Ahmad Zakil et al. (2020), the excessive moisture level in the substrate makes it difficult for the mycelium to breathe, inhibiting sweating and allowing bacteria and worms to thrive. Low moisture levels, on the other hand, might lead to the death of the fruiting body [8]. As a result, the mycelium growth rate on substrates containing 66.7 and 75% of SMC (2:1 and 3:1) decreases, although the other ratios such as 1:1, 1:2, and 1:3 showed comparable results to the control.
3.3 Fruit Body Weight from Different Formulation Substrates The ultimate result of this study is the weight of the fruiting body after about 23– 25 days of colonization. Table 2 shows that the rubber sawdust and also the control maintain as the best substrate so far for high yield production. However, surprisingly
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the replacement with 25 and 33.3% produced is not significantly different from the control. Even replacement by 50% of SMC still can produce fruiting body at slightly lower than 25 and 33.3%. The more rubber sawdust was replaced by SMC the lower is the fruiting body production. This means that rubber sawdust is still the most favorite substrate by oyster mushroom. It is expected that the lignocellulosic material in the sawdust plays a certain role in the growth eventhough the mycelium has to secrete an enzyme to digest those materials. There is a need in the present for minimum amount of lignocellulosic material to promote the mycelium growth and fruiting body production. Enzymes secreted by mushrooms are able to convert cellulose and hemicellose to other carbohydrates that are favorable for mushrooms [9]. SMC may serve as an immediate nutrition for the mushroom, yet the lignocellulosic material is influenced in unknown ways in promoting growth. Within the three flushes of production, there is a consistency of yield reduction shown by SMC mixed substrate similar to the control. This indicates that oyster mushroom consumption on SMC is by more or less similar to how rubber sawdust was consumed. It is known that SMC served as an immediate nutrient for the mushroom, however, reduction in fruiting body trend shows that they are consumed in gradual and not immediately as expected since SMC containing C and N are more in simple form resulting from degradation of organic compounds through enzymatic activities [10] (Table 2). Table 4 Fruit body weight for different substrates at three times production Substrates
Mushroom weight by cycles
Total yield
1st
2nd
100% RSD (control)
73.14 ± 0.0021a
70.44 ± 0.0047a 67.19 ± 0.0035a 210.77a
3rd
50% SMC+50% RSD (1:1)
59.78 ± 0.0032b
40.96 ± 0.0053d 31.93 ± 0.0046d 132.67c
33.3% SMC+66.7% RSD 70.99 ± 0.0043ab 65.24 ± 0.0022c 52.21 ± 0.0042c 188.44b (1:2) 25% SMC+75% RSD (1:3)
71.23 ± 0.0013ab 67.29 ± 0.0034b 60.22 ± 0.0035b 198.74ab
66.7% SMC+33.3% RSD 25.51 ± 0.0056c (2:1)
23.12 ± 0.032e
20.24 ± 0.0037e 68.87d
75% SMC + 25% RSD (3:1)
19.78 ± 0.0019f
15.35 ± 0.0043f
27.13 ± 0.0044d
62.26e
Notes Values are means of 3 replicates. Mean (n = 3) ±sandard deviation a–d Means within a column followed by same letters are not significantly different at 5% level (P > 0.05) by using LSD test
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4 Conclusion In this study, the spawn of P. pulmonarius in the substrate formulation ratios of 1:3 and 1:2 followed by 1:1 showed good results for the mycelium growth rate as compared to the 100% RSD substrate that served as a control. However, as the percentage of SMC in the substrates increases above 50%, the mycelium growth rates for P. pulmonarius spawn had decreased. Mushroom production gained from formulation ratios of 1:3 and 1:2 is just slightly lower than the control. This study has proven that SMC, depending on the formulation with RSD, can be used as the raw material in the preparation of mushroom substrates without adversely affecting the mushroom growth. Acknowledgements The authors would like to thank the Research Management Centre (RMC) of Universiti Malaysia Perlis and Fermwaste Sdn. Bhd. for supporting this work through UniversityPrivate Matching Fund (UniPRIMA) 9001-00713 (UniMAP) and 9002-00147 (Industry).
References 1. Chong SF, Zakaria Z, Low JZ (2009) Investigation of chlamydospores and mycelium liquid spawn storage period of grey oyster mushroom (pleurotus pulmonarius) and effects on growth rate. Int J Adv Sci Eng Technol 7(2):50–52 2. Owaid MN, Al-Saeedi SSS, Sabaratnam V-A, Raman IAA, J. (2015) Growth performance and cultivation of four oyster mushroom species on sawdust and rice bran substrates. J Adv Biotechnol 4(3):424–429 3. Rasib NAA, Tompang ZZ, Rahman MF, Othman RA, H. (2015) Characterization of biochemical composition for different types of spent mushroom substrate in Malaysia. Malay J Anal Sci 19(1):41–45 4. Mortada AN, Bolhassan MH, Wahi R (2020) Physicochemical composition of spent oyster mushroom substrate. Malay J Anal Sci 24(6):848–854 5. Ratnasingam J, Ramasamy G, Ioras F, Kaner J, Wenming L (2012) Production potential of rubberwood in malaysia: its economic challenges. Notulae Botanicae Horti Agrobotanici ClujNapoca 40(2):317–322 6. Zailani SN, Shaheen AAF, Zainol NA (2021) Compost bed size influences the co-composting of cow dung and spent mushroom at mesophilic stage. IOP Conf Ser Earth Environ Sci 765:012074 7. Seephueak P, Preecha C, Seephueak W (2019) Effects of palm oil sludge as a supplement for Ganoderma lucidum (Fr.) Karst Cultivation. Songklanakarin J Sci Technol 41(2):292–298 8. Ahmad Zakil F, Muhammad Hassan KH, Mohd Sueb MS, Isha R (2020) Growth and yield of pleurotus ostreatus using sugarcane bagasse as an alternative substrate in Malaysia. IOP Conf Ser: Mater Sci Eng 736(2) 9. Jaturong K, Nakarin S, Kanaporn S, Watsana P, Pattana K, Kritsana J, Santhiti V, aisamorn L (2020) Cultivation of mushrooms and their lignocellulolytic enzyme production through the utilization of agroIndustrial waste. Molecules 25:2811. https://doi.org/10.3390/molecules251 22811 10. Jurado MM, Suárez-Estrella F, Vargas-García MC, López MJ, López-González JA, Moreno J (2014) Evolution of enzymatic activities and carbon fractions throughout composting of plant waste. J Environ Manag 133
Effect of Latex Coating on the Physical Properties of Calcium Alginate Beads Yee-Ming Peh, Chee-Seng Lew, Boon-Beng Lee, Farizul Hafiz Kasim, Akmal Hadi Ma’Radzi, Md Nabil Ab Adzim Saifuddin, Ahmad Radi Wan Yaakub, and Mohd Asri Yusoff
Abstract Alginate has been commonly applied in encapsulation due to its gelling capacity, biocompatibility, and environmentally friendly properties. Alginates can produce a thermally stable and biocompatible hydrogel in the presence of divalent cations such as calcium. However, the high porosity and low physical stability of calcium alginate beads can lead to encapsulation loss and degradation of encapsulated materials. It is speculated that latex coating on the beads can overcome the issues. Hence, this study aims to investigate the effect of latex coating on the physical properties of Ca-alginate beads. An extrusion dripping method was adopted to produce Ca-alginate beads. The beads were multilayer coated with a 5% latex solution. The size and shape of uncoated and coated beads were analyzed using 2D image analysis. The thickness of the latex coating layers was measured layer by layer Y.-M. Peh · C.-S. Lew · B.-B. Lee (B) · F. H. Kasim · A. H. Ma’Radzi · Md. Nabil Ab. Adzim Saifuddin · A. R. W. Yaakub Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] Y.-M. Peh e-mail: [email protected] F. H. Kasim e-mail: [email protected] A. H. Ma’Radzi e-mail: [email protected] Md. Nabil Ab. Adzim Saifuddin e-mail: [email protected] A. R. W. Yaakub e-mail: [email protected] B.-B. Lee · F. H. Kasim · A. H. Ma’Radzi · Md. Nabil Ab. Adzim Saifuddin Centre of Excellence for Biomass Utilization, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia M. A. Yusoff Koperasi Pengusaha Harumanis Y.A. Chuping Perlis Berhad, Lot 4136, Kg Baru Panggas, Jalan Kilang Gula, 02500 Kangar, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_24
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using a thickness gauge. The results indicated that as the number of coating layers increased, the size of the beads also increased. However, the sphericity of the beads is decreased as the number of layers of latex coating is increased. The thickness of the latex coating increased layer by layer, from 0.017 mm to 0.112 mm. In short, the calcium alginate beads can be coated by latex. The diameter of the bead is significantly increased after two layers of latex coating, and the sphericity of the beads is reduced considerably after two layers of latex coating. Keywords Calcium alginate beads · Latex coating · Bead diameter · Bead sphericity
1 Introduction Alginate is one of the most commonly used polymers in the controlled-release system through encapsulation technology due to its non-toxicity, low cost, ease of formation, biodegradability, and biocompatibility. It is a hydrophilic colloidal carbohydrate derived from brown seaweed. Its primary structures consist of linear binary copolymers of 1-4-linked α-D-mannuronic acid (M block) and β-L-guluronic acid (G block) monomers [1, 2]. Alginate has been employed in various encapsulating applications, including pharmaceutical, feed, food, biomedical, and bioprocess [3, 4]. It can produce a thermally stable and biocompatible hydrogel in the presence of di- or tri-cations such as calcium. Besides that, alginate beads can be easily made by dropping an alginate solution into a calcium chloride bath. However, Ca-alginate beads have low physical stability and are porous, which can cause encapsulation loss and degradation of encapsulated materials [5–7]. Some researchers have reported that alginate can form beads with improved structural and functional properties when coated with other biopolymers, such as natural latex, chitosan, gelatin, pectin, and starch [6–8]. A coating is a covering applied to the surface of an object, usually referred to as a substrate. The coating can help reduce the porosity and improve the mechanical properties of Ca-alginate beads, thereby increasing the encapsulation effectiveness [9]. Therefore, Ca-alginate beads can be coated with other biopolymers such as natural latex to overcome the limitations mentioned earlier. To date, there is a lack of study on the effect of coating material on the physical properties of Ca-alginate beads. Hence, this study is initiated to study the use of latex solution as a coating material to improve the physical properties (such as size and shape) of the Ca-alginate beads. Once the calcium alginate beads had been coated, the physical properties of the beads were characterized.
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2 Materials and Methods 2.1 Materials All chemicals used for the experiment were of analytical grade. The chemicals that were used in the study are sodium alginate (Kimitsu Chemical Industries, Japan), calcium chloride (Bendosen, Malaysia), calcium carbonate (Bendosen, Malaysia) and latex solution (Bendosen, Malaysia).
2.2 Preparation of Solutions Sodium alginate was dissolved uniformly in distilled water using an agitator motor to produce sodium alginate of 2% w/v and 3% w/v. In this study, calcium carbonate is used as the model gas-releasing agent. Calcium carbonate was added to each sodium alginate solution under constant stirring for homogeneous mixing. Besides that, 1.5 g of calcium chloride was dissolved in 100 mL of distilled water to produce 1.5% w/ v of calcium chloride solution.
2.3 Preparation of Calcium Alginate Beads The alginate beads were prepared by dropping the sodium alginate solution and calcium carbonate mixture via a syringe into a gelation bath made up of 1.5% w/ v calcium chloride. The beads formed were kept in the gelation bath to harden for 30 min. Then, the beads were filtered, rinsed with distilled water, and dried at room temperature. This step was repeated using 2%w/v sodium alginate solution and a syringe with a 25G hypodermic needle to produce smaller beads.
2.4 Coating of Calcium Alginate Beads A 5% w/v of coating solution was prepared by diluting the latex with distilled water. The prepared Ca-alginate beads were then dipped into the coating solution and dried. Multilayer coatings of the beads were prepared by dipping the single-coated Caalginate beads into coating solutions. The procedures were repeated to produce two, four-layer, six-layer, and eight-layer latex-coated Ca-alginate beads.
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2.5 Determination of Bead Size and Bead Shape The size and shape of the uncoated and coated beads were measured using 2D image analysis. The beads were first scattered on a piece of a transparent plastic Petri dish. Then, a white light source was applied at the bottom to form a contrasting background to the gel beads. The image of the beads was taken from the top view using a digital camera (Huawei, China). The captured images were then imported into image analysis software (Image J, USA) to evaluate the bead diameter. The shape of the beads was characterized by the sphericity factor (SF), which provides information about the roundness of the beads, and was calculated by Eq. (1): SF =
Dmax − Dmin Dmax + Dmin
(1)
Dmax is the maximum diameter, and Dmin is the minimum diameter perpendicular to Dmax . Beads with SF ≤ 0.05 could be considered spherical [10, 11].
2.6 Determination of Thickness of Latex Coating Layers The thickness of latex coatings layers was measured layer by layer using a thickness gauge (Mitutoyo, USA). The diameter of the uncoated bead was measured, and the thickness of the bead was taken after one layer of coating. These steps were repeated as other layers were coated onto the beads. Five replications were applied for each layer.
2.7 Statistical Analysis Numerical data were presented as the mean ± standard deviation (SD) of the replicated determinations. Mean values were analyzed using one-way ANOVA.
3 Results and Discussion 3.1 Effect of Alginate Concentration on Diameter and Sphericity Factor of Beads. The diameter and sphericity factors of the Ca-alginate beads are shown in Fig. 1. The results demonstrated an insignificant difference in the diameter of the beads by increasing the alginate concentration and the number of coating layers (Fig. 1a).
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A lower concentration of alginate solution produced a bigger variation of beads. This result may be explained by the fact that weak gelation had occurred in the low concentration of alginate solution, resulting in forming a loose alginate structure and hence producing a bigger variation of beads [11, 12]. Besides that, increasing the number of coating layers from two to eight layers resulted in an increase in the diameter of latex-coated Ca-alginate beads. For beads with 2% w/v alginate solution, the diameter of the beads increased from 3.819 ± 0.416 mm to 3.962 ± 0.7809 mm, while the diameter of beads with 3% w/v alginate solution increased from 4.122 ± 0.1964 to 4.221 ± 0.4473. However, the diameter increase is slight since the latex coating layer is very thin that it had little effect on the diameter. In addition, the sphericity factor of beads with 2% w/v alginate solution is lower than beads with 3% w/v (Fig. 1b). The results show that the beads’ sphericity factor increases as the alginate solution concentration increases. This may be due to the surface tension of the alginate solution. The surface tension of the alginate solution decreased as the concentration of the alginate solution increased. The droplet cannot form a perfect sphere before impacting the surface of the CaCl2 gelation solution
Diameter (mm)
5.00
a
4.00 3.00 2.00 1.00 0.00 0
2
4
6
8
Number of Latex Coating Layer 2% w/v Alginate
3% w/v Alginate
Sphericity Factor (SF)
0.08 0.07
b
0.06 0.05 0.04 0.03 0.02 0.01 0.00 0
2
4
6
8
Number of Latex Coating Layer 2% w/v Alginate
3% w/v Alginate
Fig. 1 Effect of alginate concentration on the diameter (a) and sphericity factor (b) of beads. Date represent means ± SE (n = 60)
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when the surface tension of the alginate solution is lower than that of water [11]. As a result, the beads produced might have a tear-drop shape. Also, from the results, two-layer coated Ca-alginate beads show the SF value to be less than 0.05 for both alginate solution concentrations. They can be considered spherical beads since the value of SF is below 0.05 [11]. These results are likely to be related to the latex coating layer, which is very thin. When more layer is added, the latex coating will become thick and uneven. Hence, increasing the number of layers of latex coating, the spherical nature of the beads decreases.
3.2 Effect of Dripping Tip Diameter on Diameter and Sphericity Factor of Beads Figure 2 shows the diameter and sphericity factor of the beads produced from 0.5 mm and 2 mm of dripping tip. The results indicated that the dripping tip diameter significantly affected the diameter and sphericity factor (SF) of beads. In this study, the diameter of the beads increased as the diameter of the dripping tip increased as shown in Fig. 2a. These findings are similar to previous studies [12]. However, the increase in the diameter of the beads with the increase in the number of latex coating layers is not significant. This may be due to the latex coatings layer, which is very thin and has no significant effect on the diameter of beads. Besides, the beads produced from the 2 mm dripping tip had a perfectly spherical shape, even with a latex coating (Fig. 2b). However, most of the beads produced from the 0.5 mm dripping tip had an SF value of more than 0.05; hence, it can be concluded that the beads had a less perfectly spherical shape.
3.3 Thickness of Latex Coating Layers The thickness of the latex coating layers is shown layer by layer in Fig. 3. Based on the result, it was clearly shown that the thickness of the latex coatings increased with the addition of the latex coating layers. Eight layers of latex coatings had the highest thickness of 0.112 ± 0.018 mm, while the single layer of latex coatings had the lowest thickness of 0.017 ± 0.005 mm. As expected, the thickness of the coated layer is increased as the number of the coating layers increased. This is because the amount of latex deposited on the beads is also increased.
Effect of Latex Coating on the Physical Properties of Calcium Alginate …
Diameter (mm)
5.00
285
a
4.00 3.00 2.00 1.00 0.00 0
2
8
Number of Latex Coating Layer
Sphericity Factor (SF)
2% w/v Alginate (0.5 mm dripping tip diameter) 2% w/v Alginate (2 mm dripping tip diameter) 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00
b
0
2
8
Number of Latex Coating Layer 2% w/v Alginate (0.5 mm dripping tip diameter) 2% w/v Alginate (2 mm dripping tip diameter)
Fig. 2 Effect of dripping tip diameter on diameter (a) and sphericity factor (b) of beads. Date represent means ± SE (n = 60)
Thickness (mm)
0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 0
1
2
3
4
5
6
7
8
9
Number of layer Fig. 3 The thickness of latex coatings. Data represent means ± SE (n = 5) (Experimental data: the beads are produced using 3% w/v alginate solution)
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4 Conclusions This study demonstrated the effect of latex coating on the physical properties of the Ca-alginate beads. It was found that the application of latex solution as a coating material had affected the size and shape of the Ca-alginate beads. Increasing the number of coating layers decreased the spherical shape of the beads but increased the diameter of the beads. Additionally, the thickness of the latex coating also increased layer by layer. Hence, it can be concluded that the latex solution can be used as a coating material for Ca-alginate beads. Further studies such as release and in vivo studies might be required to maximize the potential of the latex solution as a coating material for Ca-alginate beads. Acknowledgements The authors would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS/1/2021/TK0/UNIMAP/02/70) from the Ministry of Higher Education. The authors would also like to acknowledge the support from Universiti Malaysia Perlis.
References 1. Donati I, Paoletti S, Donati I, Paoletti S (2009) Material properties of alginates. Alginates: biology and applications. Springer, Berlin, Heidelberg, pp 1–53 2. Ramdhan T, Ching SH, Prakash S, Bhandari B (2020) Physical and mechanical properties of alginate based composite gels. Trends Food Sci Technol 106:150–159 3. Chan ES, Yim ZH, Phan SH, Mansa RF, Ravindra P (2010) Encapsulation of herbal aqueous extract through absorption with Ca-alginate hydrogel beads. Food Bioprod Process 88(2–3):195–201 4. Patel N, Lalwani D, Gollmer S, Injeti E, Sari Y, Nesamony J (2016) Development and evaluation of a calcium alginate based oral ceftriaxone sodium formulation. Prog Biomater 5(2):117–133 5. Matricardi P, Meo CD, Coviello T, Alhaique F (2008) Recent advances and perspectives on coated alginate microspheres for modified drug delivery. Expert Opin Drug Deliv 5(4):417–425 6. Hosseini SM, Hosseini H, Mohammadifar MA, German JB, Mortazavian AM, Mohammadi A, khosravi-Darani K, Shojaee-Aliabadi S, Khaksar R (2014) Preparation and characterization of alginate and alginate-resistant starch microparticles containing nisin. Carbohydr Polym 103:573–580 7. Chan E-S, Wong S-L, Lee P-P, Lee J-S, Ti TB, Zhang Z, Poncelet D, Ravindra P, Phan S-H, Yim Z-H (2011) Effects of starch filler on the physical properties of lyophilized calcium-alginate beads and the viability of encapsulated cells. Carbohyd Polym 83(1):225–232 8. Riyajan S-A (2011) Development of neem capsule via biopolymer and natural rubber for its controlled release. Pesticides in the modern world—pesticides use and management, 233–258 9. Krasaekoopy W, Bhandari B, Deeth H (2004) The influence of coating materials on some properties of alginate beads and survivability of microencapsulated probiotic bacteria. Int Dairy J 14(8):737–743 10. Chan E-S, Lee B-B, Ravindra P, Poncelet D (2009) Prediction models for shape and size of ca-alginate macrobeads produced through extrusion-dripping method. J Colloid Interface Sci 338(1):63–72 11. Lee B-B, Ravindra P, Chan E-S (2013) Size and shape of calcium alginate beads produced by extrusion dripping. Chem Eng Technol 36(10):1627–1642
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Screening and Optimization Biosynthesis of Iron Nanoparticle Using Watermelon Rind as Reducing and Stabilizing Agent Rozaini Abdullah, Nurul Fazliana Ahmad, Sharifah Zati Hanani Syed Zuber, and Noraini Razali
Abstract This study aimed to screen and optimized the biosynthesis of iron nanoparticles (FeNPs) by utilizing watermelon rind extract (WMR) as the reducing and stabilizing agent. To determine the significant characteristics in terms of attaining a high yield of FeNPs, the screening of the factor influencing the biosynthesis FeNPs was then analyzed using a two-level factorial design with Design-Expert Software version 11. The factors were reactant concentration, incubation time, and incubation temperature, as well as the response, which was the yield of FeNPs. This response refers to the main target of this study which is to improve the yield of production FeNPs. The findings indicated that among all the factors investigated, reactant concentration, incubation time, and incubation temperature were the significant contributing factors impacting the yield of FeNPs with a p-value < 0.05. The experimental domain factors were effectively collected from the ANOVA analysis with a good linear regression; thus, these data could be augmented and to be used as the optimized condition. The optimal condition suggested by the Design Expert for the biosynthesis of FeNPs was 1 M of reactant concentration, 3 h of incubation time, and 30 °C of incubation temperature. Keywords Iron nanoparticles · Watermelon rind · Optimization · Reducing agent · Stabilizing agent
R. Abdullah (B) · N. F. Ahmad · S. Z. H. Syed Zuber Fakulti Kejuruteraan & Teknologi Kimia, Kompleks Pusat Pengajian Jejawi 3, Kawasan Perindustrian Jejawi, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia e-mail: [email protected] N. F. Ahmad e-mail: [email protected] S. Z. H. Syed Zuber e-mail: [email protected] N. Razali Kolej Kejuruteraan Kimia, Universiti Teknologi MARA (Cawangan Terengganu) Kampus Bukit Besi, 23200 Dungun, Terengganu, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Shukor et al. (eds.), Emerging Technologies for Future Sustainability, Green Energy and Technology, https://doi.org/10.1007/978-981-99-1695-5_25
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1 Introduction Nanotechnology is a constantly growing discipline that is used to create novel materials at the nanoscale in both science and technology [1]. Recently extracts from a variety of plant parts, including bark, leaves, fruit, stems, and seeds, have been employed successfully to synthesize metal nanoparticles [2]. In terms of the biosynthesis of nanoparticles, the use of plant extracts as biocompatible molecules is becoming more and more important because they are more affordable, contain phytoconstituents that act as reducing and stabilizing agents which are stable against environmental factors like high temperature, pH, and salt concentrations, and are more readily available and producible than other biomolecules like DNA, protein, peptides, and enzymes [1]. By using plant extracts as reducing or stabilizing agents that minimize the risk of contamination, magnetic nanoparticles could be produced via green chemistry. Because of their biodegradability and green attributes, plant extracts are recommended for the environmentally friendly synthesis of magnetic nanoparticles [3]. With an average annual production of 350,000 tonnes, watermelons are one of the most readily available and reasonably priced fruits in several South and Southeast Asian nations [4]. The red inside flesh of the watermelon is sweet and ripe and is used for juices and salads, but the outer rind is discarded because it is wasteful and has no marketable use. Citrulline, pectin, proteins, and carotenoids, all of which have a high concentration of hydroxyl (cellulose) and carboxylic (pectin) functional groups, and make up the watermelon rind (WMR) [5]. According to Neglo et al. [6], WMR possesses a good amount of phenolic content where polyphenol compounds contained in plant extracts could also act as reducing or stabilizing agents for the synthesis of palladium, silver, and iron nanoparticles (NPs) [7–9]. Previously, iron nanoparticles (FeNPs) had been synthesized using chemical and physical methods. Most harmful chemicals are used in chemical and physical methods including sol-gel and chemical reduction, which causes the generation of hazardous by-products from the reaction and pollution that results from the precursor. Therefore, technologies for creating NPs are being developed to manufacture nanoparticles that are clean, non-toxic, and favorable to the environment [9]. Recently, biological synthesis also known as the biosynthesis method offers an alternative way to the economic, simplicity, and effectiveness of particle forming [7]. Due to their numerous fascinating chemical or physical features, nanoparticles created through biosynthesis are becoming widely attractive. Researchers from all around the world are interested in the combination of biology and nanotechnology since it is necessary to synthesize materials with expanding numbers of development-friendly nanoparticles [10]. Therefore, this study will focus on the screening and optimization of biosynthesis of FeNPs by using a two-level factorial design via Design Expert Software version 11. The two-level factorial design had been selected to screen the range of each factor and evaluate the interaction between the factors that could not be defined via factor at one time (OFAT). With this approach, fewer experiments are required for
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a research topic, and information regarding interactions between various factors is also provided.
2 Materials and Methods 2.1 Material and Sample Preparation The fresh samples of watermelon rind (WMR) had been obtained from a selected store within Padang Besar, Perlis. FeCl3 from Sigma-Aldrich and all other reagents used were of analytical grade.
2.2 Extraction of WMR The WMR extraction had been extracted according to Ndikau et al. [11] with modification. Firstly, WMR would be thoroughly rinsed with double distilled water in the pre-treatment method to remove any fine dust particles and impurities adhering to the surface of WMR. After washing, WMR had been cut into a small piece and dried in an oven at 60 °C for 24 h to remove the moisture present in WMR. The dried WMR had been ground using a grinder to convert the WMR into smaller particle sizes. About 20 g of the crushed WMR had been diluted with 400 mL of distilled water in a 500 mL conical flask. The 500 mL conical flask had been placed in a water bath shaker (90 rpm) at 60 °C for 40 min to increase the yield of water-soluble polyphenols in the WMR extract. After 40 min, the 500 mL conical flask had been removed from the water-bath shaker and allowed to cool at room temperature. Then, the cold WMR aqueous extract had been filtered by using a Whatman filter paper (No. 1). The filtrated WMR extract had been stored at 4 °C in a screw cap bottle until further used.
2.3 Biosynthesis of FeNPs About 30 mL of FeCl3 solution in a concentration range of 0.10–1 M was slowly added into 10 mL of WMR aqueous extract on a continuous magnetic stirring hot plate (250 rpm) at a temperature of 30–80 °C for 1–3 h. The FeNPs solution had been centrifuged at 4000 rpm for 10 min and the solids would be washed with double distilled water. The solid of FeNPs had been dried in a vacuum oven (Memmert, V029) at 60 °C for 24 h and would be used for further characterization [12]. The amount of the FeNPs yield had been weighed and calculated using the following equation:
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Table 1 List of factors and their levels for the two-level factorial design Factors
Unit
Code
Low level
High level
Response
Reactant concentration
M
A
0.1
1.0
Yield of FeNPs (g)
Incubation time
Hour
B
1
3
Incubation temperature
°C
C
30
80
FeN Ps yield(g) = (Mass o f petri dish + FeN Ps) − Mass o f petri dish (1)
2.4 Experimental Design and Statistical Analysis The Design Expert Software version 11 has been used as a tool for the statistical design of the experiment to screen and optimize the factors involved in the biosynthesis of FeNPs production using WMR as a reduction and stabilizing agent. A two-level factorial design had been utilized to screen and optimize the effect of three independent factors, which were reactant concentration (A), incubation time (B), and incubation temperature (C), and the yield of biosynthesis of FeNPs was selected as the response in this study (Table 1). The aim of this study was to obtain the highest yield of biosynthesis FeNPs production. The analysis and experimental runs were done in triplicate, and the mean value was used to express the result. The p-value and significance of the model were examined using the analysis of variance (ANOVA).
2.5 Characterization of Synthesized FeNPs The FTIR study of the synthesized FeNPs was performed via Perkin Elmer Spectrum 65 where the sample analysis was prepared in the form of a KBr disc and measured with a resolution of 400–4000 cm−1 . The analysis of surface morphology was detected by using Scanning Electron Microscopy (SEM) (Jeol, JSM-6460LA) operated at an acceleration voltage of 10 kV, and analysis of crystallographic of synthesized FeNPs was analyzed via X-Ray Diffraction (XRD).
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3 Results and Discussion 3.1 ANOVA Analysis of Biosynthesis FeNPs A two-level factorial design was used to identify the significant factors and assess their impact on the biosynthesis of FeNPs. Prior to now, most of the literature frequently employed the one-factor-at-a-time (OFAT) approach to assess the effects of factors, which is not particularly helpful in screening and assessing the interaction between the factors [13]. Three blocks of experiments were run, in triplicate, and in the order recommended by the software. Experimental runs were randomized to lessen the effects of unexpected variability in observed responses [14]. Table 2 displays the findings from the 27 runs suggested by Design Expert Software version 11. It showed that the highest yield of FeNPs was obtained at 0.3234 g as the reactant concentration, incubation time, and incubation temperature were 1 M, 3 h, and 30 °C, respectively. Meanwhile, the lowest yield of FeNPs was recorded at 0.0161 g with 0.1 M of reactant concentration, 3 h of incubation time, and 80 °C of incubation temperature. The ANOVA analysis, normal probability plot of standard effects, and Pareto chart were analyzed to evaluate the significant factors in the biosynthesis of FeNPs using WMR as a reducing and stabilizing agent. To establish the quantitative information of each factor towards each response, an ANOVA analysis was conducted. The ANOVA analysis of the biosynthesis of FeNPs is shown in Table 3. This table shows that the p-value < 0.0001 indicated that the response model was highly significant. The R2 value of this study was 0.9980, showing that the data point fits the linear regression line. Due to a good regression model, these data were augmented for the optimization of the biosynthesis of FeNPs. The final model equation generated was expressed as the following Eq. (2). Y ield o f FeN Ps = 0.1130 + 0.0637A + 0.0253B − 0.0490C + 0.0221AB − 0.0273AC − 0.0175BC (2) Figure 1 illustrates the normal probability plots of each individual factor and their interactions with standardized effects. The significant factors have a normal distribution around the mean zero, which is represented by the straight line in the plot. however, important factors are situated beyond the line of sight. The significant factor is indicated by how much it deviates from the straight line, with the farther factor having the biggest significant impact [15]. In comparison to factors B and AB, factor A is located much farther from the line nearly zero. Even though the p-value of factor C and its interaction were less than 0.0001, these factors are not counted as significant effects due to these factors being smaller and centered around zero. It can be observed that the reactant concentration, incubation time, and its interaction highly influenced the yield of FeNPs compared to incubation temperature. As the concentration of FeCl3 increases and prolongs the incubation time, the amount of
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Table 2 Experimental result of two-level factorial designs for the biosynthesis of FeNPs Run
Factors
Response
A
B
C
Y1
Reactant concentration (M)
Incubation time (Hours)
Incubation temperature (°C)
Yield of FeNPs (g)
1
0.55
2
55
0.1539
2
0.10
1
80
0.0424
3
0.10
1
80
0.0322
4
0.55
2
55
0.1545
5
0.10
3
80
0.0165
6
0.10
1
30
0.0542
7
0.55
2
55
0.1549
8
1.00
1
30
0.1872
9
1.00
3
30
0.3191
10
0.10
3
30
0.0913
11
1.00
1
80
0.0683
12
0.10
1
30
0.0528
13
0.10
3
80
0.0161
14
1.00
1
80
0.0734
15
0.10
1
80
0.0428
16
0.10
3
30
0.0816
17
0.10
3
30
0.0938
18
1.00
1
30
0.1856
19
1.00
1
80
0.0781
20
1.00
3
30
0.3234
21
1.00
3
80
0.1262
22
0.10
1
30
0.0524
23
0.10
3
80
0.0163
24
1.00
3
80
0.1266
25
1.00
3
80
0.1295
26
1.00
1
30
0.1834
27
1.00
3
30
0.3195
FeNPs produced will also eventually increase. Besides that, incubation temperature was observed not to be a significant factor due to the polyphenol compound in the WMR extract would degrade at high temperatures (more than 80 °C). These observations also could be confirmed with the Pareto Chart (Fig. 2).
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Table 3 ANOVA analysis of factors that influenced the biosynthesis of FeNPs Source
Sum of squares
df
Mean square
F-value
p-value
Model
0.2072
6
0.0345
1560.04