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