A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration

AI and Its Application to Complex Industrial Processes

Jiang Tang, Wen Yu, Junfei Qiao (Autoren)

Buch | Hardcover

624 Seiten

2025
Wiley-IEEE Press (Verlag)
978-1-394-35401-6 (ISBN)

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An expert discussion of intelligent optimization control in complex industrial processes

In A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration: AI and Its Application to Complex Industrial Processes, a team of distinguished researchers delivers an innovative new approach to integrating virtual mechanism data generated through coupled numerical simulation and orthogonal experimental design with real historical data. The book explains how to create a heterogenous ensemble prediction model for carbon monoxide emissions in municipal solid waste incineration (MSWI) processes.

The authors focus on intelligent optimization control of MSWI processes based on hardware-in-loop DT platforms. They demonstrate AI-driven modeling, control, optimization algorithms in real-world applications, including virtual-real data hybrid-driven deep modeling and intelligent optimal controls based on multiple objectives.

Additional topics include:

A thorough introduction to numerical simulation modeling of whole industrial processes
Comprehensive explorations of the design, implementation, and validation of hardware-in-loop digital twin platforms
Practical discussions of AI-driven modeling, control, and optimization
Fulsome descriptions of the skills required to address challenges posed by complex industrial processes

Perfect for environmental engineers and researchers, A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration will also benefit MSWI plant operators and managers, as well as AI and machine learning researchers and developers of environmental monitoring and control systems.

Jian Tang, PhD, is a Professor and Researcher with the Department of Artificial Intelligence and Automation in the Faculty of Information Technology at the Beijing University of Technology. Wen Yu, PhD, is a Professor and Head of Department of the Departamento de Control Automatico at CINVESTAV-IPN (National Polytechnic Institute) in Mexico City, Mexico. Junfei Qiao, PhD, is a Professor with the Beijing University of Technology and Director of Beijing Laboratory of Smart Environmental Protection in Beijing, China.

List of Figures xvii

List of Tables xxix

About the Authors xxxiii

Preface xxxv

Abbreviations xxxvii

Symbol Meaning xliii

1 Introduction 1

1.1 Municipal Solid Waste Incineration (MSWI) Process and Optimal Control 1

1.2 AI-Based Modeling and Monitoring 17

1.3 Control and Optimization Based on AI and DT 32

1.4 Hardware-in-Loop DT for MSWI Processes 36

1.5 Book’s Structure 42

Part I 42

Part II 45

Part III 47

References 48

Part I Modeling and Monitoring Based on AI 67

2 Numerical Simulation and Modeling Analysis on Whole Industrial Process by Coupling Multiple Software 69

2.1 Simulated Plant and Simulation Modeling 69

2.2 Modeling Strategy with Virtual Data-driven 92

2.3 Modeling Implementation for Whole Process 94

2.4 Numerical Simulation and Modeling Results 103

2.5 Conclusion 124

References 125

3 Conventional Pollutant Deep Modeling Using Virtual Data and Real Data Hybrid-Driven 129

3.1 Virtual–Real Data-Driven Conventional Pollutant Modeling 129

3.2 Real Data Hybrid-Driven Modeling Implementation 133

3.3 Deep Modeling Results and Discussion 142

3.4 Conclusion 157

References 160

4 Trace Pollutant Modeling Using the Selective Ensemble Algorithm 163

4.1 Selective Ensemble Modeling Strategy 163

4.2 Trace Pollutant Modeling Implementation 168

4.3 Data-Driven Ensemble Modeling Results and Discussion 176

4.4 Conclusion 201

References 201

5 Trace Pollutant Modeling Based on Semi-supervised Random Forest Optimization 205

5.1 Data-Driven Trace Pollutant Semi-supervised Random Forest Optimization Modeling Strategy 205

5.2 Data-Driven Trace Pollutant Modeling Implementation 212

5.3 Experimental Verification 221

5.4 Conclusion 238

References 239

6 Combustion State Identification Using ViT-IDFC with Global Flame Feature 243

6.1 Combustion State Identification and Global Flame Feature 243

6.2 State Monitoring Implementation Using ViT-IDFC 249

6.3 Experimental Results 256

6.4 Conclusion 273

References 273

7 Online Combustion Status Recognition of Using IDFC based on Convolutional Multi-Layer Feature Fusion 277

7.1 Convolutional Multi-layer Feature Fusion Based Online Combustion Identification 277

7.2 Convolutional-Feature-IDFC-Based Implementation 280

7.3 State Monitoring Results and Discussion 289

7.4 Conclusion 298

References 298

Part II Control and Optimization Based on AI and Digital Twin 301

8 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network (IT2FNN) for Furnace Temperature Control 303

8.1 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network Control Strategy 303

8.2 BO-Based Interval Type-2 Fuzzy Neural Network Control 309

8.3 Simulation Results 320

8.4 Conclusion 339

References 340

9 Interval Type-2 Fuzzy Control with Multiple Event Triggers for Furnace Temperature Control 345

9.1 Type-2 Fuzzy Broad Control with Multiple Event Triggers 345

9.2 METM-Based Interval Type-2 Fuzzy Broad Control 351

9.3 Stability Analysis 358

9.4 Simulation Results 362

9.5 Conclusion 376

References 377

10 Intelligent Optimal Control of Furnace Temperature Using Multi-loop Controller and PSO Optimization 381

10.1 Multi-loop Controller Using PSO Optimization 381

10.2 Data-Driven Furnace Temperature Optimization 392

10.3 Simulation Results 400

10.4 Conclusion 415

References 416

11 Data-Driven Multi-objective Intelligent Optimal Control of Industrial Process 419

11.1 Multiple Objectives Multiple Controlled Variables Optimization 419

11.2 Data-Driven Multiple Controlled Variables Optimization Implementation 429

11.3 Simulation Results 437

11.4 Conclusion 453

References 454

Part III Hardware-in-loop Digital Twin Platform Design and Validation 457

12 Description of Hardware-in-Loop Digital Twin Platform Requirements for Industrial Process 459

12.1 Overview 459

12.2 Laboratory Research on Platform Functionality Requirements 459

12.3 Industrial Applications on Platform Functionality Requirements 461

12.4 Platform Functional Requirements from a Flex Reconfiguration Perspective 463

12.5 Conclusion 466

13 Design and Realization of Hardware-in-Loop Digital Twin Platform 467

13.1 Digital Twin Functional Design 467

13.2 Hardware-in-Loop Structural Design 468

13.3 Hardware Setup 477

13.4 Software Design 479

13.5 Platform Realization 487

14 Testing and Validation of Hardware-in-Loop Digital Twin Platform 495

14.1 System Effectiveness Testing and Verification 495

14.2 Laboratory Scene Intelligent Algorithm Testing and Validation 500

14.3 Intelligent Algorithm Transplantation Application in Industrial Scenarios 512

15 Summary and Outlook of Hardware-in-Loop Digital Twin Platform 519

15.1 Summary 519

15.2 Future AI Algorithm Research and Validation End-Edge-Cloud Platform 520

Index 537

Erscheinungsdatum	14.11.2025
Sprache	englisch
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik ► Elektrotechnik / Energietechnik
	Technik ► Umwelttechnik / Biotechnologie
ISBN-10	1-394-35401-0 / 1394354010
ISBN-13	978-1-394-35401-6 / 9781394354016
Zustand	Neuware