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Submit a ProposalDigital Twins and ESG
 | Edited by Surajit Mondal, Adesh Kumar, and Mudassir Khan Copyright: 2025 | Expected Pub Date:2025// ISBN: 9781394303212 | Hardcover | 434 pages Price: $225 USD  |
One Line Description
Digital Twins and ESG provides essential insight on how integrating cutting-edge Digital Twin technology with ESG practices can transform the understanding of sustainability and propel businesses towards a more transparent, accountable, and responsible future.
Audience
Sustainability managers, ESG analysts, corporate responsibility officers, students, faculty, and environmental engineers interested in technological integrations with environmental, social, and governance practices
Sustainability managers, ESG analysts, corporate responsibility officers, students, faculty, and environmental engineers interested in technological integrations with environmental, social, and governance practices
Description
Digital Twins and ESG introduces the dynamic world of ESG practices, emphasizing the pivotal role technology plays in shaping and advancing sustainability goals. It introduces readers to the multifaceted world of Digital Twin technology, offering a comprehensive understanding of its historical development and diverse applications across industries. This volume will intricately examine the integration of Digital Twins in ESG metrics and reporting frameworks. Artificial intelligence, machine learning, and blockchain technologies are also discussed as key enablers for achieving ESG goals, providing readers with a glimpse into the potential advancements and breakthroughs that lie ahead. Through detailed analyses and case studies, readers will gain insights into how Digital Twins enhance data collection, monitoring, and reporting, elevating transparency and accountability. Digital Twins and ESG serves as a rallying call, urging businesses to embrace Digital Twins as an integral component of their ESG strategies, ultimately paving the way for a more sustainable and responsible future.
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Digital Twins and ESG introduces the dynamic world of ESG practices, emphasizing the pivotal role technology plays in shaping and advancing sustainability goals. It introduces readers to the multifaceted world of Digital Twin technology, offering a comprehensive understanding of its historical development and diverse applications across industries. This volume will intricately examine the integration of Digital Twins in ESG metrics and reporting frameworks. Artificial intelligence, machine learning, and blockchain technologies are also discussed as key enablers for achieving ESG goals, providing readers with a glimpse into the potential advancements and breakthroughs that lie ahead. Through detailed analyses and case studies, readers will gain insights into how Digital Twins enhance data collection, monitoring, and reporting, elevating transparency and accountability. Digital Twins and ESG serves as a rallying call, urging businesses to embrace Digital Twins as an integral component of their ESG strategies, ultimately paving the way for a more sustainable and responsible future.
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Author / Editor Details
Surajit Mondal, PhD is teaching at the University of Petroleum and Energy Studies. He has published eight books, over 50 international research articles as an author or co-author, and 85 patents, 20 of which were granted. His research interests include optimization of renewable energy, biofuels, and solar thermal technologies.
Surajit Mondal, PhD is teaching at the University of Petroleum and Energy Studies. He has published eight books, over 50 international research articles as an author or co-author, and 85 patents, 20 of which were granted. His research interests include optimization of renewable energy, biofuels, and solar thermal technologies.
Adesh Kumar, PhD is an associate professor in the Department of Electrical and Electronics Engineering at the University of Petroleum and Energy Studies with over 15 years of experience. He has published five books and over 100 research papers in international journals and conferences in addition to chairing over 200 conference sessions. His research interests include embedded systems, digital image processing, and telecommunications.
Mudassir Khan, PhD is a postdoctoral fellow at Multimedia University and a co-supervisor for Postgraduate Studies at Lincoln University College with over 13 years of experience. He has published over 80 research articles and more than 10 editorial books and authored four books, inspiring many in technology and education. His research interests include big data, deep learning, and AI, particularly in medical imaging.
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Table of Contents
Preface
1. Digital Twins: Driving Innovation Through Virtual Optimization – A Systematic Review
Supriya, Vansh Joshi, Yash Singhal, Mudit Kumar and Prath Sharma
1.1 Introduction
1.1.1 Evaluation of Digital Twins Technology
1.1.2 Evolution and Technological Components
1.1.3 Key Technological Components
1.2 Literature Review
1.3 Applications and Use Cases
1.4 Challenges and Limitations
1.5 Future Trends and Research Directions
1.6 Conclusion
References
2. Core Principles and Applications of Digital Twins
Nitin Cholkar and Nitin Mahankale
2.1 Introduction
2.2 History and Evolution of Digital Twins
2.3 Key Components of Digital Twins
2.4 Types of Digital Twins
2.5 Digital Twin Architecture
2.6 Interaction between Physical and Digital Components
2.7 Data Flow and Management
2.8 Digital Twin Frameworks
2.9 Role of Cloud Computing in Digital Twins
2.10 Data Collection and Integration
Conclusion
References
3. ESG 2.0: Evolving Foundations and Strategies
Nitin Mahankale and Nitin Cholkar
3.1 Introduction to ESG and Its Evolution
3.2 Scope
3.3 From ESG 1.0 to ESG 2.0 Evolution
3.4 Key Drivers and Trends Influencing ESG 2.0
3.5 The Core Areas of ESG 2.0
3.6 Conclusion
References
4. Fundamentals of Digital Twins
Vineet Kumar and Ujjwal
4.1 Introduction
4.2 Evolution of Digital Twins
4.3 Types of Digital Twins
4.3.1 Static Digital Twins
4.3.2 Dynamic Digital Twins
4.3.2.1 Component or Parts of Digital Twins
4.3.2.2 Asset Digital Twins
4.3.2.3 Product Digital Twins
4.3.2.4 System Digital Twins or Unit DT
4.3.2.5 Process Digital Twins
4.4 Classification of DT Based on Their Applications
4.4.1 Urban Digital Twins (UDT)
4.4.2 Virtual Factory Replica
4.4.3 Historical Digital Twins (HDT)
4.5 Steps Involved in Creating DT
4.5.1 Steps Involved in Executable Digital Twins
4.6 Characteristics of DT
4.7 Advantages of DT
4.8 Disadvantages of DT
4.9 Key Enablers
4.9.1 Internet of Things (IoT)
4.9.2 Cloud Computing
4.9.3 Extended Reality (XR)
4.9.4 Data-Driven Modeling
4.9.5 Machine Vision (MV)
4.9.6 Industrial Robots (IR)
4.10 Applications of DT
4.10.1 User Cases
4.10.1.1 User Case 1: Steel Manufacturing
4.10.1.2 User Case 2: Konecranes
4.10.1.3 User Case 3: Construction Project
4.10.1.4 User Case 4: Use of Digital Twins in Manufacturing Industry
4.10.1.5 User Case 5: Flight Simulator
4.10.1.6 Use Case 6: Automotive Industry
4.10.1.7 Use Case 7: Utilities
References
5. Machine Learning Lending a Hand to ESG: A Case Study on CO2 Emissions
Supriya, Vriti Amit Khandelwal, Kushagar Sharma and Vasu Sharma
5.1 Introduction
5.1.1 Machine Learning Lending a Hand
5.2 Evolution of ESG
5.3 Applications of ESG
5.4 Challenges and Limitations of ESG
5.5 Case Study
5.6 Conclusion
References
6. A Comprehensive Review on Digital Initiatives Fostering Improvements in Solar PV Systems
Sivasankari Sundaram, Almas and Pavan Darbha
6.1 Introduction
6.1.1 Digital Model Development
6.1.2 Deployment of Digital Model
6.1.3 Digital Transformation
6.1.4 Challenges in Digital Transformation/Digitalization of Energy Generation Unit
6.1.5 Importance of Solar Leading to Digital Transformation
6.2 Digital Model Applications in the Field of Solar PV Industry
6.2.1 Solar Power Prediction Models
6.2.1.1 Machine Learning Based Digital Models for Solar Power Forecasting
6.2.1.2 Artificial Neural Network Based Digital Models for Solar Power Forecasting
6.2.1.3 Hybrid Digital Models for Solar Power Prediction/Forecasting
6.2.2 Solar Irradiance Prediction Techniques
6.2.2.1 Support Vector Machine Based Machine Learning Approach
6.2.2.2 Random Forest Machine Learning Algorithm
6.2.3 Digital Models for Prediction of Ground Based Solar Irradiance
6.2.4 Digital Model Applications for Maximization of Solar PV System Efficiency
6.2.4.1 Optimization Based Digital Model Algorithms Rendering Improved System Performance
6.2.5 Digital Models for Prediction of Parameters which Affect the Operation & Maintenance of the PV Plant
6.3 Digital Twin of Solar PV Plant
6.4 Conclusion
References
7. The Convergence of AI, ML, and Digital Twins in Shaping the Future of ESG
Aaryan Gupta, Preeti Narooka, Ishani Lohar and Mansi Amarnani
7.1 Introduction
7.2 Understanding Digital Twins
7.2.1 Definition and Evolution of Digital Twins
7.2.2 Key Features and Functionalities
7.2.3 Applications Across Different Industries
7.3 AI and ML Technologies
7.3.1 Overview of AI and ML
7.3.2 Key Techniques and Methodologies
7.3.3 Recent Advancements and Trends
7.4 Integration of AI, ML, and Digital Twins
7.4.1 How AI and ML Enhance Digital Twin Capabilities
7.4.2 Use Cases of AI-Driven Digital Twins in ESG Contexts
7.4.3 Examples of Successful Integrations in Various Sectors
7.5 Impact on ESG Metrics and Reporting
7.5.1 How AI and Digital Twins Improve ESG Metrics Accuracy and Reporting
7.5.2 Real-Time Data Collection, Monitoring, and Analysis
7.5.3 Case Studies Demonstrating Enhanced ESG Reporting Through These Technologies
7.6 Innovations and Future Directions
7.6.1 Emerging Trends and Technologies in Digital Twins, AI, and ML
7.6.2 Potential Future Developments and Their Implications for ESG
7.6.3 Predictions for How These Technologies Will Continue to Shape ESG Practices
7.7 Challenges and Considerations
7.7.1 Technical and Ethical Challenges in Integrating AI, ML, and Digital Twins
7.7.2 Data Privacy and Security Concerns
7.7.3 Addressing Limitations and Ensuring Effective Implementation
7.8 Conclusion
7.8.1 Summary of Key Insights and Findings
7.8.2 The Transformative Potential of the Convergence
7.8.3 Recommendations for Businesses and Policymakers
References
8. A Roadmap for Sustainable Industry: Merging ESG, Digital Twin, and Circular Economy Practices
Adith Kumar and Harshit Tiwari
8.1 Introduction
8.2 Environmental, Social and Governance (ESG)
8.2.1 Implementation of ESG in PQRS Manufacturing
8.3 Digital Twin Technology
8.3.1 Implementation of the Digital Twin Technology to PQRS’ Operation
8.4 Circular Economy
8.4.1 Analysis of the Circular Economy System
8.4.2 Deciding to Go Circular for PQRS
8.5 Life Cycle Assessment (LCA)
8.5.1 The History of LCA from 1970 to 2000
8.5.2 The Presence of LCA: A Decade of Development
8.5.3 LCA Future (2010−2020): Decade of Life Cycle Sustainability Analysis
8.5.4 Applications of LCA
8.5.5 Modeling Using OpenLCA
8.5.6 Modeling Using OpenLCA for PQRS
8.6 Conclusion
References
9. Waste Biomass to Bioenergy with Regulatory Framework for a Sustainable Economy
Debajyoti Bose, Riya Bhattacharya, Rashmi Raj, L. S. Pranathi Ganti, Abhijeeta Sarkar and Margavelu Gopinath
9.1 Introduction
9.2 Biowaste Sources
9.2.1 Woodland
9.2.2 Food
9.2.3 Animal Waste
9.2.4 Municipal Waste
9.2.5 Industrial Waste
9.3 Bioengineering Techniques for Biowaste Conversion to Bioenergy
9.4 Social Impact of Bioenergy Products
9.4.1 Biohydrogen
9.4.2 Biogas
9.4.3 Bioethanol and Biobutanol
9.4.4 Biodiesel
9.5 Bio-Circular Economy for Sustainable Bioenergy Production
9.6 Future Perspective
References
10. An Automated System to Identify and Detect the Faults in Bottle Cap Production and Visual Inspection Using Raspberry Pi
Shaik Asif Hussain, Mudassir Khan, J. Chinna Babu, Shaik Javeed Hussain and Yu-Chen Hu
10.1 Introduction
10.2 Existing Techniques and Proposed Approach
10.2.1 Scale Invariant Feature Transform (SIFT)
10.2.2 Histogram of Oriented Gradients (HOG)
10.2.3 Haar Cascade
10.3 Design and Implementation
10.3.1 Flowchart
10.4 System Validation
10.4.1 Types, States, and Movements of Caps
10.5 Conclusion
References
11. DDoS Detection Using Semi-Supervised Machine Learning Algorithms
Ajmeera Kiran, Mudassir Khan, J. Chinna Babu, B. P. Santosh Kumar and Mohammad Mazhar Nezami
11.1 Introduction
11.2 Existing System
11.3 Proposed System
11.4 Conclusion
References
12. Enhanced Encryption and Digital Signature Scheme Utilizing EC-Based Encryption and Multi-Chaotic Pseudo Random Generation
Mudassir Khan, S.Z. Parveen, Shaik Karimullah and Barga Mohammed Mujahid
12.1 Introduction
12.2 Literature Review
12.3 Proposed Methodology
12.4 Simulation Results
12.5 Conclusion and Future Scope
References
13. Utilization of Waste for Production of Nanomaterials: An Industry 4.0 Approach of Waste to Wealth
Dip Jyoti Sardar, Parna Ganguli, Arpita Ghosh and Surabhi Chaudhuri
13.1 Introduction
13.2 Critical Review
13.2.1 Carbon-Based Nanomaterials
13.2.1.1 Production of Carbon Nanofibers from Organochlorine Waste
13.2.1.2 Production of Carbon Nanomaterials from Polyethylene Waste
13.2.1.3 Carbon Based Nanomaterials from Bioethanol Industry
13.2.1.4 Carbon-Based Nanomaterials from Rice Waste
13.2.1.5 Production of SiO2 Nanoparticles from Agro-Waste
13.2.2 Microbiological Production of Nano-Cellulose from Agro-Waste
13.2.3 Synthesis of Aluminum Nanomaterials from Al2O3 Waste
13.2.3.1 Synthesis of Aluminum Oxide Nanomaterials Using Electrochemical Sludge Treatment
13.2.3.2 Synthesis of Aluminum Nanomaterials Using Hydrothermal Treatment
of Alumina Waste
13.2.4 Synthesis of Mesoporous Zeolite Nanomaterials (MZN) from Glass Fiber Waste
13.2.5 Synthesis of Nanomaterials from Discarded Ore
13.2.6 Production of Magnetic Nanomaterials
13.2.6.1 Production from Industrial Waste (IOW-R)
13.2.6.2 Production of Magnetic Fe-Oxide NPs from Waste Iron
13.2.6.3 Production of Metal Oxide Nanomaterials from E-Waste
13.2.7 Recovery of Nano-Zero Valent Copper Particles from Automobile and Steel Industry Waste
13.2.8 Production of Cobalt Ferrite Nanoparticles Using Battery Waste
13.3 Conclusions, Recommendations, Future Perspectives
References
14. Factors Affecting Sanitation Behavior Among Rural Communities in Low and Middle-Income Countries: A Critical Review
Arpita Ghosh, Puneet Sharma and Parul Malik
14.1 Introduction
14.2 Research Methodology
14.3 Literature Review
14.3.1 Sanitation Behavior
14.3.2 Contextual Factors Influencing Sanitation Behavior Among Rural Communities
14.3.2.1 Societal Level
14.3.2.2 Community Level
14.3.2.3 Household/Interpersonal Level
14.3.2.4 Individual Level
14.3.3 Psychosocial Factors Influencing Sanitation
Behavior Among Rural Communities
14.3.3.1 Societal and Community Level
14.3.3.2 Household/Interpersonal Level
14.3.3.3 Individual Level
14.3.3.4 Habitual Level
14.3.4 Technological Factors Influencing Sanitation Behavior Among Rural Communities
14.3.4.1 Societal and Community Level
14.3.4.2 Household/Interpersonal Level
14.3.4.3 Individual Level and Habitual Level
14.4 Recommendations and Conclusion
References
15. Assessing the Comparison of Environmental and Social Governance (ESG) Performance of Tourism Companies Worldwide
Arpita Ghosh, Abhishek Kumar and Ananya Das
15.1 Introduction
15.2 Methodology
15.3 Literature Discussion
15.4 Secondary Data and Discussion
15.5 Conclusion and Recommendation
References
16. Ion Beam Induced PMMA-Based Corrosion-Resistant Hydrophobic Coating on a Metal Substrate
Udit Gupta, Kailash Pandey, Rakesh Jain and Rajeev Gupta
16.1 Introduction
16.2 Experimental
16.3 Results and Discussion
16.3.1 Wettability Measurement
16.3.2 UV-Vis Spectroscopy Study
16.3.3 Raman Study
16.3.4 Mechanical Properties
16.4 Conclusion
References
Index
Back to Top
Preface
1. Digital Twins: Driving Innovation Through Virtual Optimization – A Systematic Review
Supriya, Vansh Joshi, Yash Singhal, Mudit Kumar and Prath Sharma
1.1 Introduction
1.1.1 Evaluation of Digital Twins Technology
1.1.2 Evolution and Technological Components
1.1.3 Key Technological Components
1.2 Literature Review
1.3 Applications and Use Cases
1.4 Challenges and Limitations
1.5 Future Trends and Research Directions
1.6 Conclusion
References
2. Core Principles and Applications of Digital Twins
Nitin Cholkar and Nitin Mahankale
2.1 Introduction
2.2 History and Evolution of Digital Twins
2.3 Key Components of Digital Twins
2.4 Types of Digital Twins
2.5 Digital Twin Architecture
2.6 Interaction between Physical and Digital Components
2.7 Data Flow and Management
2.8 Digital Twin Frameworks
2.9 Role of Cloud Computing in Digital Twins
2.10 Data Collection and Integration
Conclusion
References
3. ESG 2.0: Evolving Foundations and Strategies
Nitin Mahankale and Nitin Cholkar
3.1 Introduction to ESG and Its Evolution
3.2 Scope
3.3 From ESG 1.0 to ESG 2.0 Evolution
3.4 Key Drivers and Trends Influencing ESG 2.0
3.5 The Core Areas of ESG 2.0
3.6 Conclusion
References
4. Fundamentals of Digital Twins
Vineet Kumar and Ujjwal
4.1 Introduction
4.2 Evolution of Digital Twins
4.3 Types of Digital Twins
4.3.1 Static Digital Twins
4.3.2 Dynamic Digital Twins
4.3.2.1 Component or Parts of Digital Twins
4.3.2.2 Asset Digital Twins
4.3.2.3 Product Digital Twins
4.3.2.4 System Digital Twins or Unit DT
4.3.2.5 Process Digital Twins
4.4 Classification of DT Based on Their Applications
4.4.1 Urban Digital Twins (UDT)
4.4.2 Virtual Factory Replica
4.4.3 Historical Digital Twins (HDT)
4.5 Steps Involved in Creating DT
4.5.1 Steps Involved in Executable Digital Twins
4.6 Characteristics of DT
4.7 Advantages of DT
4.8 Disadvantages of DT
4.9 Key Enablers
4.9.1 Internet of Things (IoT)
4.9.2 Cloud Computing
4.9.3 Extended Reality (XR)
4.9.4 Data-Driven Modeling
4.9.5 Machine Vision (MV)
4.9.6 Industrial Robots (IR)
4.10 Applications of DT
4.10.1 User Cases
4.10.1.1 User Case 1: Steel Manufacturing
4.10.1.2 User Case 2: Konecranes
4.10.1.3 User Case 3: Construction Project
4.10.1.4 User Case 4: Use of Digital Twins in Manufacturing Industry
4.10.1.5 User Case 5: Flight Simulator
4.10.1.6 Use Case 6: Automotive Industry
4.10.1.7 Use Case 7: Utilities
References
5. Machine Learning Lending a Hand to ESG: A Case Study on CO2 Emissions
Supriya, Vriti Amit Khandelwal, Kushagar Sharma and Vasu Sharma
5.1 Introduction
5.1.1 Machine Learning Lending a Hand
5.2 Evolution of ESG
5.3 Applications of ESG
5.4 Challenges and Limitations of ESG
5.5 Case Study
5.6 Conclusion
References
6. A Comprehensive Review on Digital Initiatives Fostering Improvements in Solar PV Systems
Sivasankari Sundaram, Almas and Pavan Darbha
6.1 Introduction
6.1.1 Digital Model Development
6.1.2 Deployment of Digital Model
6.1.3 Digital Transformation
6.1.4 Challenges in Digital Transformation/Digitalization of Energy Generation Unit
6.1.5 Importance of Solar Leading to Digital Transformation
6.2 Digital Model Applications in the Field of Solar PV Industry
6.2.1 Solar Power Prediction Models
6.2.1.1 Machine Learning Based Digital Models for Solar Power Forecasting
6.2.1.2 Artificial Neural Network Based Digital Models for Solar Power Forecasting
6.2.1.3 Hybrid Digital Models for Solar Power Prediction/Forecasting
6.2.2 Solar Irradiance Prediction Techniques
6.2.2.1 Support Vector Machine Based Machine Learning Approach
6.2.2.2 Random Forest Machine Learning Algorithm
6.2.3 Digital Models for Prediction of Ground Based Solar Irradiance
6.2.4 Digital Model Applications for Maximization of Solar PV System Efficiency
6.2.4.1 Optimization Based Digital Model Algorithms Rendering Improved System Performance
6.2.5 Digital Models for Prediction of Parameters which Affect the Operation & Maintenance of the PV Plant
6.3 Digital Twin of Solar PV Plant
6.4 Conclusion
References
7. The Convergence of AI, ML, and Digital Twins in Shaping the Future of ESG
Aaryan Gupta, Preeti Narooka, Ishani Lohar and Mansi Amarnani
7.1 Introduction
7.2 Understanding Digital Twins
7.2.1 Definition and Evolution of Digital Twins
7.2.2 Key Features and Functionalities
7.2.3 Applications Across Different Industries
7.3 AI and ML Technologies
7.3.1 Overview of AI and ML
7.3.2 Key Techniques and Methodologies
7.3.3 Recent Advancements and Trends
7.4 Integration of AI, ML, and Digital Twins
7.4.1 How AI and ML Enhance Digital Twin Capabilities
7.4.2 Use Cases of AI-Driven Digital Twins in ESG Contexts
7.4.3 Examples of Successful Integrations in Various Sectors
7.5 Impact on ESG Metrics and Reporting
7.5.1 How AI and Digital Twins Improve ESG Metrics Accuracy and Reporting
7.5.2 Real-Time Data Collection, Monitoring, and Analysis
7.5.3 Case Studies Demonstrating Enhanced ESG Reporting Through These Technologies
7.6 Innovations and Future Directions
7.6.1 Emerging Trends and Technologies in Digital Twins, AI, and ML
7.6.2 Potential Future Developments and Their Implications for ESG
7.6.3 Predictions for How These Technologies Will Continue to Shape ESG Practices
7.7 Challenges and Considerations
7.7.1 Technical and Ethical Challenges in Integrating AI, ML, and Digital Twins
7.7.2 Data Privacy and Security Concerns
7.7.3 Addressing Limitations and Ensuring Effective Implementation
7.8 Conclusion
7.8.1 Summary of Key Insights and Findings
7.8.2 The Transformative Potential of the Convergence
7.8.3 Recommendations for Businesses and Policymakers
References
8. A Roadmap for Sustainable Industry: Merging ESG, Digital Twin, and Circular Economy Practices
Adith Kumar and Harshit Tiwari
8.1 Introduction
8.2 Environmental, Social and Governance (ESG)
8.2.1 Implementation of ESG in PQRS Manufacturing
8.3 Digital Twin Technology
8.3.1 Implementation of the Digital Twin Technology to PQRS’ Operation
8.4 Circular Economy
8.4.1 Analysis of the Circular Economy System
8.4.2 Deciding to Go Circular for PQRS
8.5 Life Cycle Assessment (LCA)
8.5.1 The History of LCA from 1970 to 2000
8.5.2 The Presence of LCA: A Decade of Development
8.5.3 LCA Future (2010−2020): Decade of Life Cycle Sustainability Analysis
8.5.4 Applications of LCA
8.5.5 Modeling Using OpenLCA
8.5.6 Modeling Using OpenLCA for PQRS
8.6 Conclusion
References
9. Waste Biomass to Bioenergy with Regulatory Framework for a Sustainable Economy
Debajyoti Bose, Riya Bhattacharya, Rashmi Raj, L. S. Pranathi Ganti, Abhijeeta Sarkar and Margavelu Gopinath
9.1 Introduction
9.2 Biowaste Sources
9.2.1 Woodland
9.2.2 Food
9.2.3 Animal Waste
9.2.4 Municipal Waste
9.2.5 Industrial Waste
9.3 Bioengineering Techniques for Biowaste Conversion to Bioenergy
9.4 Social Impact of Bioenergy Products
9.4.1 Biohydrogen
9.4.2 Biogas
9.4.3 Bioethanol and Biobutanol
9.4.4 Biodiesel
9.5 Bio-Circular Economy for Sustainable Bioenergy Production
9.6 Future Perspective
References
10. An Automated System to Identify and Detect the Faults in Bottle Cap Production and Visual Inspection Using Raspberry Pi
Shaik Asif Hussain, Mudassir Khan, J. Chinna Babu, Shaik Javeed Hussain and Yu-Chen Hu
10.1 Introduction
10.2 Existing Techniques and Proposed Approach
10.2.1 Scale Invariant Feature Transform (SIFT)
10.2.2 Histogram of Oriented Gradients (HOG)
10.2.3 Haar Cascade
10.3 Design and Implementation
10.3.1 Flowchart
10.4 System Validation
10.4.1 Types, States, and Movements of Caps
10.5 Conclusion
References
11. DDoS Detection Using Semi-Supervised Machine Learning Algorithms
Ajmeera Kiran, Mudassir Khan, J. Chinna Babu, B. P. Santosh Kumar and Mohammad Mazhar Nezami
11.1 Introduction
11.2 Existing System
11.3 Proposed System
11.4 Conclusion
References
12. Enhanced Encryption and Digital Signature Scheme Utilizing EC-Based Encryption and Multi-Chaotic Pseudo Random Generation
Mudassir Khan, S.Z. Parveen, Shaik Karimullah and Barga Mohammed Mujahid
12.1 Introduction
12.2 Literature Review
12.3 Proposed Methodology
12.4 Simulation Results
12.5 Conclusion and Future Scope
References
13. Utilization of Waste for Production of Nanomaterials: An Industry 4.0 Approach of Waste to Wealth
Dip Jyoti Sardar, Parna Ganguli, Arpita Ghosh and Surabhi Chaudhuri
13.1 Introduction
13.2 Critical Review
13.2.1 Carbon-Based Nanomaterials
13.2.1.1 Production of Carbon Nanofibers from Organochlorine Waste
13.2.1.2 Production of Carbon Nanomaterials from Polyethylene Waste
13.2.1.3 Carbon Based Nanomaterials from Bioethanol Industry
13.2.1.4 Carbon-Based Nanomaterials from Rice Waste
13.2.1.5 Production of SiO2 Nanoparticles from Agro-Waste
13.2.2 Microbiological Production of Nano-Cellulose from Agro-Waste
13.2.3 Synthesis of Aluminum Nanomaterials from Al2O3 Waste
13.2.3.1 Synthesis of Aluminum Oxide Nanomaterials Using Electrochemical Sludge Treatment
13.2.3.2 Synthesis of Aluminum Nanomaterials Using Hydrothermal Treatment
of Alumina Waste
13.2.4 Synthesis of Mesoporous Zeolite Nanomaterials (MZN) from Glass Fiber Waste
13.2.5 Synthesis of Nanomaterials from Discarded Ore
13.2.6 Production of Magnetic Nanomaterials
13.2.6.1 Production from Industrial Waste (IOW-R)
13.2.6.2 Production of Magnetic Fe-Oxide NPs from Waste Iron
13.2.6.3 Production of Metal Oxide Nanomaterials from E-Waste
13.2.7 Recovery of Nano-Zero Valent Copper Particles from Automobile and Steel Industry Waste
13.2.8 Production of Cobalt Ferrite Nanoparticles Using Battery Waste
13.3 Conclusions, Recommendations, Future Perspectives
References
14. Factors Affecting Sanitation Behavior Among Rural Communities in Low and Middle-Income Countries: A Critical Review
Arpita Ghosh, Puneet Sharma and Parul Malik
14.1 Introduction
14.2 Research Methodology
14.3 Literature Review
14.3.1 Sanitation Behavior
14.3.2 Contextual Factors Influencing Sanitation Behavior Among Rural Communities
14.3.2.1 Societal Level
14.3.2.2 Community Level
14.3.2.3 Household/Interpersonal Level
14.3.2.4 Individual Level
14.3.3 Psychosocial Factors Influencing Sanitation
Behavior Among Rural Communities
14.3.3.1 Societal and Community Level
14.3.3.2 Household/Interpersonal Level
14.3.3.3 Individual Level
14.3.3.4 Habitual Level
14.3.4 Technological Factors Influencing Sanitation Behavior Among Rural Communities
14.3.4.1 Societal and Community Level
14.3.4.2 Household/Interpersonal Level
14.3.4.3 Individual Level and Habitual Level
14.4 Recommendations and Conclusion
References
15. Assessing the Comparison of Environmental and Social Governance (ESG) Performance of Tourism Companies Worldwide
Arpita Ghosh, Abhishek Kumar and Ananya Das
15.1 Introduction
15.2 Methodology
15.3 Literature Discussion
15.4 Secondary Data and Discussion
15.5 Conclusion and Recommendation
References
16. Ion Beam Induced PMMA-Based Corrosion-Resistant Hydrophobic Coating on a Metal Substrate
Udit Gupta, Kailash Pandey, Rakesh Jain and Rajeev Gupta
16.1 Introduction
16.2 Experimental
16.3 Results and Discussion
16.3.1 Wettability Measurement
16.3.2 UV-Vis Spectroscopy Study
16.3.3 Raman Study
16.3.4 Mechanical Properties
16.4 Conclusion
References
Index
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