Digital Twins and ESG

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.

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.

Preface xv

1 Digital Twins: Driving Innovation Through Virtual Optimization – A Systematic Review 1
Supriya, Vansh Joshi, Yash Singhal, Mudit Kumar and Parth Sharma

1.1 Introduction 1

1.1.1 Evaluation of Digital Twins Technology 3

1.1.2 Evolution and Technological Components 5

1.1.3 Key Technological Components 6

1.2 Literature Review 7

1.3 Applications and Use Cases 10

1.4 Challenges and Limitations 12

1.5 Future Trends and Research Directions 13

1.6 Conclusion 14

References 14

2 Core Principles and Applications of Digital Twins 17
Nitin Cholkar and Nitin Mahankale

2.1 Introduction 18

2.2 History and Evolution of Digital Twins 18

2.3 Key Components of Digital Twins 20

2.4 Types of Digital Twins 21

2.5 Digital Twin Architecture 24

2.6 Interaction between Physical and Digital Components 26

2.7 Data Flow and Management 28

2.8 Digital Twin Frameworks 29

2.9 Role of Cloud Computing in Digital Twins 31

2.10 Data Collection and Integration 32

Conclusion 35

References 36

3 ESG 2.0: Evolving Foundations and Strategies 39
Nitin Mahankale and Nitin Cholkar

3.1 Introduction to ESG and Its Evolution 39

3.2 Scope 40

3.3 From ESG 1.0 to ESG 2.0 Evolution 40

3.4 Key Drivers and Trends Influencing ESG 2.0 41

3.5 The Core Areas of ESG 2.0 43

3.6 Conclusion 53

References 54

4 Fundamentals of Digital Twins 57
Vineet Kumar and Ujjwal

4.1 Introduction 58

4.2 Evolution of Digital Twins 59

4.3 Types of Digital Twins 60

4.3.1 Static Digital Twins 60

4.3.2 Dynamic Digital Twins 61

4.3.2.1 Component or Parts of Digital Twins 61

4.3.2.2 Asset Digital Twins 61

4.3.2.3 Product Digital Twins 61

4.3.2.4 System Digital Twins or Unit DT 62

4.3.2.5 Process Digital Twins 62

4.4 Classification of DT Based on Their Applications 62

4.4.1 Urban Digital Twins (UDT) 62

4.4.2 Virtual Factory Replica 62

4.4.3 Historical Digital Twins (HDT) 63

4.5 Steps Involved in Creating DT 63

4.5.1 Steps Involved in Executable Digital Twins 64

4.6 Characteristics of DT 64

4.7 Advantages of DT 64

4.8 Disadvantages of DT 65

4.9 Key Enablers 65

4.9.1 Internet of Things (IoT) 65

4.9.2 Cloud Computing 66

4.9.3 Extended Reality (XR) 66

4.9.4 Data-Driven Modeling 66

4.9.5 Machine Vision (MV) 66

4.9.6 Industrial Robots (IR) 67

4.10 Applications of DT 67

4.10.1 Use Cases 67

4.10.1.1 Use Case 1: Steel Manufacturing 67

4.10.1.2 Use Case 2: Konecranes 68

4.10.1.3 Use Case 3: Construction Project 68

4.10.1.4 Use Case 4: Use of Digital Twins in Manufacturing Industry 69

4.10.1.5 Use Case 5: Flight Simulator 69

4.10.1.6 Use Case 6: Automotive Industry 69

4.10.1.7 Use Case 7: Utilities 71

References 71

5 Machine Learning Lending a Hand to ESG: A Case Study on CO 2 Emissions 75
Supriya, Vriti Amit Khandelwal, Kushagar Sharma and Vasu Sharma

5.1 Introduction 75

5.1.1 Machine Learning Lending a Hand 77

5.2 Evolution of ESG 78

5.3 Applications of ESG 80

5.4 Challenges and Limitations of ESG 82

5.5 Case Study 83

5.6 Conclusion 86

References 87

6 A Comprehensive Review on Digital Initiatives Fostering Improvements in Solar PV Systems 89
Sivasankari Sundaram, Almas and Pavan Darbha

6.1 Introduction 90

6.1.1 Digital Model Development 90

6.1.2 Deployment of Digital Model 91

6.1.3 Digital Transformation 91

6.1.4 Challenges in Digital Transformation/Digitalization of Energy Generation Unit 92

6.1.5 Importance of Solar Leading to Digital Transformation 92

6.2 Digital Model Applications in the Field of Solar PV Industry 94

6.2.1 Solar Power Prediction Models 95

6.2.1.1 Machine Learning Based Digital Models for Solar Power Forecasting 95

6.2.1.2 Artificial Neural Network Based Digital Models for Solar Power Forecasting 96

6.2.1.3 Hybrid Digital Models for Solar Power Prediction/Forecasting 104

6.2.2 Solar Irradiance Prediction Techniques 107

6.2.2.1 Support Vector Machine Based Machine Learning Approach 107

6.2.2.2 Random Forest Machine Learning Algorithm 108

6.2.3 Digital Models for Prediction of Ground Based Solar Irradiance 109

6.2.4 Digital Model Applications for Maximization of Solar PV System Efficiency 114

6.2.4.1 Optimization Based Digital Model Algorithms Rendering Improved System Performance 114

6.2.5 Digital Models for Prediction of Parameters which Affect the Operation & Maintenance of the PV Plant 117

6.3 Digital Twin of Solar PV Plant 119

6.4 Conclusion 121

References 123

7 The Convergence of AI, ML, and Digital Twins in Shaping the Future of ESG 129
Aaryan Gupta, Preeti Narooka, Ishani Lohar and Mansi Amarnani

7.1 Introduction 130

7.2 Understanding Digital Twins 130

7.2.1 Definition and Evolution of Digital Twins 130

7.2.2 Key Features and Functionalities 131

7.2.3 Applications Across Different Industries 131

7.3 AI and ML Technologies 133

7.3.1 Overview of AI and ml 133

7.3.2 Key Techniques and Methodologies 134

7.3.3 Recent Advancements and Trends 137

7.4 Integration of AI, ML, and Digital Twins 139

7.4.1 How AI and ML Enhance Digital Twin Capabilities 139

7.4.2 Use Cases of AI-Driven Digital Twins in ESG Contexts 140

7.4.3 Examples of Successful Integrations in Various Sectors 140

7.5 Impact on ESG Metrics and Reporting 141

7.5.1 How AI and Digital Twins Improve ESG Metrics Accuracy and Reporting 141

7.5.2 Real-Time Data Collection, Monitoring, and Analysis 143

7.5.3 Case Studies Demonstrating Enhanced ESG Reporting Through These Technologies 144

7.6 Innovations and Future Directions 145

7.6.1 Emerging Trends and Technologies in Digital Twins, AI, and ml 145

7.6.2 Potential Future Developments and Their Implications for ESG 146

7.6.3 Predictions for How These Technologies Will Continue to Shape ESG Practices 147

7.7 Challenges and Considerations 148

7.7.1 Technical and Ethical Challenges in Integrating AI, ML, and Digital Twins 148

7.7.2 Data Privacy and Security Concerns 149

7.7.3 Addressing Limitations and Ensuring Effective Implementation 151

7.8 Conclusion 154

7.8.1 Summary of Key Insights and Findings 154

7.8.2 The Transformative Potential of the Convergence 154

7.8.3 Recommendations for Businesses and Policymakers 155

References 156

8 A Roadmap for Sustainable Industry: Merging ESG, Digital Twin, and Circular Economy Practices 159
Adith Kumar and Harshit Tiwari

8.1 Introduction 160

8.2 Environmental, Social and Governance (ESG) 161

8.2.1 Implementation of ESG in PQRS Manufacturing 164

8.3 Digital Twin Technology 166

8.3.1 Implementation of the Digital Twin Technology to PQRS’ Operation 168

8.4 Circular Economy 169

8.4.1 Analysis of the Circular Economy System 171

8.4.2 Deciding to Go Circular for PQRS 174

8.5 Life Cycle Assessment (LCA) 175

8.5.1 The History of LCA from 1970 to 2000 176

8.5.2 The Presence of LCA: A Decade of Development 178

8.5.3 LCA Future (2010−2020): Decade of Life Cycle Sustainability Analysis 180

8.5.4 Applications of LCA 182

8.5.5 Modeling Using OpenLCA 183

8.5.6 Modeling Using OpenLCA for PQRS 184

8.6 Conclusion 186

References 187

9 Waste Biomass to Bioenergy with Regulatory Framework for a Sustainable Economy 189
Debajyoti Bose, Riya Bhattacharya, Rashmi Raj, L. S. Pranathi Ganti, Abhijeeta Sarkar and Margavelu Gopinath

9.1 Introduction 190

9.2 Biowaste Sources 191

9.2.1 Woodland 191

9.2.2 Food 193

9.2.3 Animal Waste 194

9.2.4 Municipal Waste 195

9.2.5 Industrial Waste 196

9.3 Bioengineering Techniques for Biowaste Conversion to Bioenergy 198

9.4 Social Impact of Bioenergy Products 201

9.4.1 Biohydrogen 201

9.4.2 Biogas 203

9.4.3 Bioethanol and Biobutanol 205

9.4.4 Biodiesel 208

9.5 Bio-Circular Economy for Sustainable Bioenergy Production 210

9.6 Future Perspective 220

References 222

10 An Automated System to Identify and Detect the Faults in Bottle Cap Production and Visual Inspection Using Raspberry Pi 227
Shaik Asif Hussain, Mudassir Khan, J. Chinna Babu, Shaik Javeed Hussain and Yu-Chen Hu

10.1 Introduction 228

10.2 Existing Techniques and Proposed Approach 231

10.2.1 Scale Invariant Feature Transform (SIFT) 233

10.2.2 Histogram of Oriented Gradients (HOG) 233

10.2.3 Haar Cascade 234

10.3 Design and Implementation 234

10.3.1 Flowchart 237

10.4 System Validation 239

10.4.1 Types, States, and Movements of Caps 239

10.5 Conclusion 243

References 244

11 DDoS Detection Using Semi-Supervised Machine Learning Algorithms 247
Ajmeera Kiran, Mudassir Khan, J. Chinna Babu, B. P. Santosh Kumar and Mohammad Mazhar Nezami

11.1 Introduction 248

11.2 Existing System 252

11.3 Proposed System 255

11.4 Conclusion 266

References 266

12 Enhanced Encryption and Digital Signature Scheme Utilizing EC-Based Encryption and Multi-Chaotic Pseudo Random Generation 269
Mudassir Khan, S.Z. Parveen, Shaik Karimullah and Barga Mohammed Mujahid

12.1 Introduction 270

12.2 Literature Review 272

12.3 Proposed Methodology 276

12.4 Simulation Results 280

12.5 Conclusion and Future Scope 288

References 289

13 Utilization of Waste for Production of Nanomaterials: An Industry 4.0 Approach of Waste to Wealth 293
Dip Jyoti Sardar, Parna Ganguli, Arpita Ghosh and Surabhi Chaudhuri

13.1 Introduction 294

13.2 Critical Review 295

13.2.1 Carbon-Based Nanomaterials 304

13.2.1.1 Production of Carbon Nanofibers from Organochlorine Waste 304

13.2.1.2 Production of Carbon Nanomaterials from Polyethylene Waste 304

13.2.1.3 Carbon Based Nanomaterials from Bioethanol Industry 305

13.2.1.4 Carbon-Based Nanomaterials from Rice Waste 306

13.2.1.5 Production of SiO 2 Nanoparticles from Agro-Waste 306

13.2.2 Microbiological Production of Nano-Cellulose from Agro-Waste 307

13.2.3 Synthesis of Aluminum Nanomaterials from Al 2 O 3 Waste 307

13.2.3.1 Synthesis of Aluminum Oxide Nanomaterials Using Electrochemical Sludge Treatment 307

13.2.3.2 Synthesis of Aluminum Nanomaterials Using Hydrothermal Treatment of Alumina Waste 308

13.2.4 Synthesis of Mesoporous Zeolite Nanomaterials (MZN) from Glass Fiber Waste 308

13.2.5 Synthesis of Nanomaterials from Discarded Ore 309

13.2.6 Production of Magnetic Nanomaterials 309

13.2.6.1 Production from Industrial Waste (iow-r) 309

13.2.6.2 Production of Magnetic Fe-Oxide NPs from Waste Iron 310

13.2.6.3 Production of Metal Oxide Nanomaterials from E-Waste 310

13.2.7 Recovery of Nano-Zero Valent Copper Particles from Automobile and Steel Industry Waste 311

13.2.8 Production of Cobalt Ferrite Nanoparticles Using Battery Waste 312

13.3 Conclusions, Recommendations, Future Perspectives 312

References 313

14 Factors Affecting Sanitation Behavior Among Rural Communities in Low and Middle-Income Countries: A Critical Review 321
Arpita Ghosh, Puneet Sharma and Parul Malik

14.1 Introduction 322

14.2 Research Methodology 325

14.3 Literature Review 347

14.3.1 Sanitation Behavior 347

14.3.2 Contextual Factors Influencing Sanitation Behavior Among Rural Communities 348

14.3.2.1 Societal Level 348

14.3.2.2 Community Level 360

14.3.2.3 Household/Interpersonal Level 361

14.3.2.4 Individual Level 362

14.3.3 Psychosocial Factors Influencing Sanitation Behavior Among Rural Communities 362

14.3.3.1 Societal and Community Level 362

14.3.3.2 Household/Interpersonal Level 364

14.3.3.3 Individual Level 364

14.3.3.4 Habitual Level 366

14.3.4 Technological Factors Influencing Sanitation Behavior Among Rural Communities 366

14.3.4.1 Societal and Community Level 366

14.3.4.2 Household/Interpersonal Level 368

14.3.4.3 Individual Level and Habitual Level 368

14.4 Recommendations and Conclusion 370

References 372

15 Assessing the Comparison of Environmental and Social Governance (ESG) Performance of Tourism Companies Worldwide 379
Arpita Ghosh, Abhishek Kumar and Ananya Das

15.1 Introduction 380

15.2 Methodology 382

15.3 Literature Discussion 382

15.4 Secondary Data and Discussion 384

15.5 Conclusion and Recommendation 391

References 393

16 Ion Beam Induced PMMA-Based Corrosion-Resistant Hydrophobic Coating on a Metal Substrate 399
Udit Gupta, Kailash Pandey, Rakesh Jain and Rajeev Gupta

16.1 Introduction 399

16.2 Experimental 401

16.3 Results and Discussion 402

16.3.1 Wettability Measurement 402

16.3.2 UV-Vis Spectroscopy Study 404

16.3.3 Raman Study 405

16.3.4 Mechanical Properties 406

16.4 Conclusion 407

References 408

Index 411

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.

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.

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.