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    1. Data och IT
    2. Systemvetenskap och AI
    3. Artificiell intelligens

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

    AI and Its Application to Complex Industrial Processes

    AvJiang Tang,Wen Yu

    Inbunden, Engelska, 2025

    1 462 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Beskrivning

    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 processesComprehensive explorations of the design, implementation, and validation of hardware-in-loop digital twin platformsPractical discussions of AI-driven modeling, control, and optimizationFulsome descriptions of the skills required to address challenges posed by complex industrial processesPerfect 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.

    Produktinformation

    • Utgivningsdatum:2025-12-01
    • Mått:237 x 158 x 41 mm
    • Vikt:1 093 g
    • Format:Inbunden
    • Språk:Engelska
    • Antal sidor:624
    • Förlag:John Wiley & Sons Inc
    • ISBN:9781394354016

    Utforska kategorier

    • Artificiell intelligens inom Data och IT
    • Miljöteknik inom Naturvetenskap och teknik

    Mer om författaren

    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.

    Innehållsförteckning

    • List of Figures xviiList of Tables xxixAbout the Authors xxxiiiPreface xxxvAbbreviations xxxviiSymbol Meaning xliii1 Introduction 11.1 Municipal Solid Waste Incineration (MSWI) Process and Optimal Control 11.2 AI-Based Modeling and Monitoring 171.3 Control and Optimization Based on AI and DT 321.4 Hardware-in-Loop DT for MSWI Processes 361.5 Book’s Structure 42Part I 42Part II 45Part III 47References 48Part I Modeling and Monitoring Based on AI 672 Numerical Simulation and Modeling Analysis on Whole Industrial Process by Coupling Multiple Software 692.1 Simulated Plant and Simulation Modeling 692.2 Modeling Strategy with Virtual Data-driven 922.3 Modeling Implementation for Whole Process 942.4 Numerical Simulation and Modeling Results 1032.5 Conclusion 124References 1253 Conventional Pollutant Deep Modeling Using Virtual Data and Real Data Hybrid-Driven 1293.1 Virtual–Real Data-Driven Conventional Pollutant Modeling 1293.2 Real Data Hybrid-Driven Modeling Implementation 1333.3 Deep Modeling Results and Discussion 1423.4 Conclusion 157References 1604 Trace Pollutant Modeling Using the Selective Ensemble Algorithm 1634.1 Selective Ensemble Modeling Strategy 1634.2 Trace Pollutant Modeling Implementation 1684.3 Data-Driven Ensemble Modeling Results and Discussion 1764.4 Conclusion 201References 2015 Trace Pollutant Modeling Based on Semi-supervised Random Forest Optimization 2055.1 Data-Driven Trace Pollutant Semi-supervised Random Forest Optimization Modeling Strategy 2055.2 Data-Driven Trace Pollutant Modeling Implementation 2125.3 Experimental Verification 2215.4 Conclusion 238References 2396 Combustion State Identification Using ViT-IDFC with Global Flame Feature 2436.1 Combustion State Identification and Global Flame Feature 2436.2 State Monitoring Implementation Using ViT-IDFC 2496.3 Experimental Results 2566.4 Conclusion 273References 2737 Online Combustion Status Recognition of Using IDFC based on Convolutional Multi-Layer Feature Fusion 2777.1 Convolutional Multi-layer Feature Fusion Based Online Combustion Identification 2777.2 Convolutional-Feature-IDFC-Based Implementation 2807.3 State Monitoring Results and Discussion 2897.4 Conclusion 298References 298Part II Control and Optimization Based on AI and Digital Twin 3018 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network (IT2FNN) for Furnace Temperature Control 3038.1 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network Control Strategy 3038.2 BO-Based Interval Type-2 Fuzzy Neural Network Control 3098.3 Simulation Results 3208.4 Conclusion 339References 3409 Interval Type-2 Fuzzy Control with Multiple Event Triggers for Furnace Temperature Control 3459.1 Type-2 Fuzzy Broad Control with Multiple Event Triggers 3459.2 METM-Based Interval Type-2 Fuzzy Broad Control 3519.3 Stability Analysis 3589.4 Simulation Results 3629.5 Conclusion 376References 37710 Intelligent Optimal Control of Furnace Temperature Using Multi-loop Controller and PSO Optimization 38110.1 Multi-loop Controller Using PSO Optimization 38110.2 Data-Driven Furnace Temperature Optimization 39210.3 Simulation Results 40010.4 Conclusion 415References 41611 Data-Driven Multi-objective Intelligent Optimal Control of Industrial Process 41911.1 Multiple Objectives Multiple Controlled Variables Optimization 41911.2 Data-Driven Multiple Controlled Variables Optimization Implementation 42911.3 Simulation Results 43711.4 Conclusion 453References 454Part III Hardware-in-loop Digital Twin Platform Design and Validation 45712 Description of Hardware-in-Loop Digital Twin Platform Requirements for Industrial Process 45912.1 Overview 45912.2 Laboratory Research on Platform Functionality Requirements 45912.3 Industrial Applications on Platform Functionality Requirements 46112.4 Platform Functional Requirements from a Flex Reconfiguration Perspective 46312.5 Conclusion 46613 Design and Realization of Hardware-in-Loop Digital Twin Platform 46713.1 Digital Twin Functional Design 46713.2 Hardware-in-Loop Structural Design 46813.3 Hardware Setup 47713.4 Software Design 47913.5 Platform Realization 48714 Testing and Validation of Hardware-in-Loop Digital Twin Platform 49514.1 System Effectiveness Testing and Verification 49514.2 Laboratory Scene Intelligent Algorithm Testing and Validation 50014.3 Intelligent Algorithm Transplantation Application in Industrial Scenarios 51215 Summary and Outlook of Hardware-in-Loop Digital Twin Platform 51915.1 Summary 51915.2 Future AI Algorithm Research and Validation End-Edge-Cloud Platform 520Index 537