Digital Twins and ESG.

Format:

Book

Author/Creator:

Mondal, Surajit.

Contributor:

Kumar, Adesh.

Khan, Mudassir.

Language:

English

Subjects (All):

Digital twins (Computer simulation)--Environmental aspects.

Digital twins (Computer simulation).

Physical Description:

1 online resource (349 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

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.

Contents:

Cover

Series Page

Title Page

Copyright Page

Contents

Preface

Chapter 1 Digital Twins: Driving Innovation Through Virtual Optimization - A Systematic Review

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

Chapter 2 Core Principles and Applications of Digital Twins

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

Chapter 3 ESG 2.0: Evolving Foundations and Strategies

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

Chapter 4 Fundamentals of Digital Twins

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 Use Cases

4.10.1.1 Use Case 1: Steel Manufacturing

4.10.1.2 Use Case 2: Konecranes

4.10.1.3 Use Case 3: Construction Project

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

4.10.1.5 Use Case 5: Flight Simulator

4.10.1.6 Use Case 6: Automotive Industry

4.10.1.7 Use Case 7: Utilities

Chapter 5 Machine Learning Lending a Hand to ESG: A Case Study on CO2 Emissions

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

Chapter 6 A Comprehensive Review on Digital Initiatives Fostering Improvements in Solar PV Systems

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 &amp

Maintenance of the PV Plant

6.3 Digital Twin of Solar PV Plant

6.4 Conclusion

Chapter 7 The Convergence of AI, ML, and Digital Twins in Shaping the Future of ESG

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

Chapter 8 A Roadmap for Sustainable Industry: Merging ESG, Digital Twin, and Circular Economy Practices

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

Chapter 9 Waste Biomass to Bioenergy with Regulatory Framework for a Sustainable Economy

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

Chapter 10 An Automated System to Identify and Detect the Faults in Bottle Cap Production and Visual Inspection Using Raspberry Pi

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

Chapter 11 DDoS Detection Using Semi-Supervised Machine Learning Algorithms

11.1 Introduction

11.2 Existing System

11.3 Proposed System

11.4 Conclusion

Chapter 12 Enhanced Encryption and Digital Signature Scheme Utilizing EC-Based Encryption and Multi-Chaotic Pseudo Random Generation

12.1 Introduction

12.2 Literature Review

12.3 Proposed Methodology

12.4 Simulation Results

12.5 Conclusion and Future Scope

Chapter 13 Utilization of Waste for Production of Nanomaterials: An Industry 4.0 Approach of Waste to Wealth

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-30324-6

1-394-30323-8

OCLC:

1530149031