{"product_id":"a-hardware-in-loop-digital-twin-approach-for-intelligent-optimization-of-municipal-solid-waste-incineration-ai-and-its-application-to-complex-industrial-processes-hardback-9781394354016","title":"A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration; AI and Its Application to Complex Industrial Processes (Hardback) 9781394354016","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eA Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAI and Its Application to Complex Industrial Processes\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eJiang Tang (Author), Wen Yu (Author), Junfei Qiao (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394354016, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 1 December 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e624 pages\u003cbr\u003e28 x 19 x 2.6 cm, 1.093 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn expert discussion of intelligent optimization control in complex industrial processes\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eA Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration: AI and Its Application to Complex Industrial Processes,\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cp\u003eAdditional topics include: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA thorough introduction to numerical simulation modeling of whole industrial processes\u003c\/li\u003e\n\u003cli\u003eComprehensive explorations of the design, implementation, and validation of hardware-in-loop digital twin platforms\u003c\/li\u003e\n\u003cli\u003ePractical discussions of AI-driven modeling, control, and optimization\u003c\/li\u003e\n\u003cli\u003eFulsome descriptions of the skills required to address challenges posed by complex industrial processes\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for environmental engineers and researchers, \u003ci\u003eA Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration \u003c\/i\u003ewill also benefit MSWI plant operators and managers, as well as AI and machine learning researchers and developers of environmental monitoring and control systems.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eList of Figures xvii\u003c\/p\u003e \u003cp\u003eList of Tables xxix\u003c\/p\u003e \u003cp\u003eAbout the Authors xxxiii\u003c\/p\u003e \u003cp\u003ePreface xxxv\u003c\/p\u003e \u003cp\u003eAbbreviations xxxvii\u003c\/p\u003e \u003cp\u003eSymbol Meaning xliii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Municipal Solid Waste Incineration (MSWI) Process and Optimal Control 1\u003c\/p\u003e \u003cp\u003e1.2 AI-Based Modeling and Monitoring 17\u003c\/p\u003e \u003cp\u003e1.3 Control and Optimization Based on AI and DT 32\u003c\/p\u003e \u003cp\u003e1.4 Hardware-in-Loop DT for MSWI Processes 36\u003c\/p\u003e \u003cp\u003e1.5 Book’s Structure 42\u003c\/p\u003e \u003cp\u003ePart I 42\u003c\/p\u003e \u003cp\u003ePart II 45\u003c\/p\u003e \u003cp\u003ePart III 47\u003c\/p\u003e \u003cp\u003eReferences 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Modeling and Monitoring Based on AI 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Numerical Simulation and Modeling Analysis on Whole Industrial Process by Coupling Multiple Software 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Simulated Plant and Simulation Modeling 69\u003c\/p\u003e \u003cp\u003e2.2 Modeling Strategy with Virtual Data-driven 92\u003c\/p\u003e \u003cp\u003e2.3 Modeling Implementation for Whole Process 94\u003c\/p\u003e \u003cp\u003e2.4 Numerical Simulation and Modeling Results 103\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 124\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Conventional Pollutant Deep Modeling Using Virtual Data and Real Data Hybrid-Driven 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Virtual–Real Data-Driven Conventional Pollutant Modeling 129\u003c\/p\u003e \u003cp\u003e3.2 Real Data Hybrid-Driven Modeling Implementation 133\u003c\/p\u003e \u003cp\u003e3.3 Deep Modeling Results and Discussion 142\u003c\/p\u003e \u003cp\u003e3.4 Conclusion 157\u003c\/p\u003e \u003cp\u003eReferences 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Trace Pollutant Modeling Using the Selective Ensemble Algorithm 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Selective Ensemble Modeling Strategy 163\u003c\/p\u003e \u003cp\u003e4.2 Trace Pollutant Modeling Implementation 168\u003c\/p\u003e \u003cp\u003e4.3 Data-Driven Ensemble Modeling Results and Discussion 176\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 201\u003c\/p\u003e \u003cp\u003eReferences 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Trace Pollutant Modeling Based on Semi-supervised Random Forest Optimization 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Data-Driven Trace Pollutant Semi-supervised Random Forest Optimization Modeling Strategy 205\u003c\/p\u003e \u003cp\u003e5.2 Data-Driven Trace Pollutant Modeling Implementation 212\u003c\/p\u003e \u003cp\u003e5.3 Experimental Verification 221\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 238\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Combustion State Identification Using ViT-IDFC with Global Flame Feature 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Combustion State Identification and Global Flame Feature 243\u003c\/p\u003e \u003cp\u003e6.2 State Monitoring Implementation Using ViT-IDFC 249\u003c\/p\u003e \u003cp\u003e6.3 Experimental Results 256\u003c\/p\u003e \u003cp\u003e6.4 Conclusion 273\u003c\/p\u003e \u003cp\u003eReferences 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Online Combustion Status Recognition of Using IDFC based on Convolutional Multi-Layer Feature Fusion 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Convolutional Multi-layer Feature Fusion Based Online Combustion Identification 277\u003c\/p\u003e \u003cp\u003e7.2 Convolutional-Feature-IDFC-Based Implementation 280\u003c\/p\u003e \u003cp\u003e7.3 State Monitoring Results and Discussion 289\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 298\u003c\/p\u003e \u003cp\u003eReferences 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Control and Optimization Based on AI and Digital Twin 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network (IT2FNN) for Furnace Temperature Control 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network Control Strategy 303\u003c\/p\u003e \u003cp\u003e8.2 BO-Based Interval Type-2 Fuzzy Neural Network Control 309\u003c\/p\u003e \u003cp\u003e8.3 Simulation Results 320\u003c\/p\u003e \u003cp\u003e8.4 Conclusion 339\u003c\/p\u003e \u003cp\u003eReferences 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Interval Type-2 Fuzzy Control with Multiple Event Triggers for Furnace Temperature Control 345\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Type-2 Fuzzy Broad Control with Multiple Event Triggers 345\u003c\/p\u003e \u003cp\u003e9.2 METM-Based Interval Type-2 Fuzzy Broad Control 351\u003c\/p\u003e \u003cp\u003e9.3 Stability Analysis 358\u003c\/p\u003e \u003cp\u003e9.4 Simulation Results 362\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 376\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Intelligent Optimal Control of Furnace Temperature Using Multi-loop Controller and PSO Optimization 381\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Multi-loop Controller Using PSO Optimization 381\u003c\/p\u003e \u003cp\u003e10.2 Data-Driven Furnace Temperature Optimization 392\u003c\/p\u003e \u003cp\u003e10.3 Simulation Results 400\u003c\/p\u003e \u003cp\u003e10.4 Conclusion 415\u003c\/p\u003e \u003cp\u003eReferences 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Data-Driven Multi-objective Intelligent Optimal Control of Industrial Process 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Multiple Objectives Multiple Controlled Variables Optimization 419\u003c\/p\u003e \u003cp\u003e11.2 Data-Driven Multiple Controlled Variables Optimization Implementation 429\u003c\/p\u003e \u003cp\u003e11.3 Simulation Results 437\u003c\/p\u003e \u003cp\u003e11.4 Conclusion 453\u003c\/p\u003e \u003cp\u003eReferences 454\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Hardware-in-loop Digital Twin Platform Design and Validation 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Description of Hardware-in-Loop Digital Twin Platform Requirements for Industrial Process 459\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Overview 459\u003c\/p\u003e \u003cp\u003e12.2 Laboratory Research on Platform Functionality Requirements 459\u003c\/p\u003e \u003cp\u003e12.3 Industrial Applications on Platform Functionality Requirements 461\u003c\/p\u003e \u003cp\u003e12.4 Platform Functional Requirements from a Flex Reconfiguration Perspective 463\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 466\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Design and Realization of Hardware-in-Loop Digital Twin Platform 467\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Digital Twin Functional Design 467\u003c\/p\u003e \u003cp\u003e13.2 Hardware-in-Loop Structural Design 468\u003c\/p\u003e \u003cp\u003e13.3 Hardware Setup 477\u003c\/p\u003e \u003cp\u003e13.4 Software Design 479\u003c\/p\u003e \u003cp\u003e13.5 Platform Realization 487\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Testing and Validation of Hardware-in-Loop Digital Twin Platform 495\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 System Effectiveness Testing and Verification 495\u003c\/p\u003e \u003cp\u003e14.2 Laboratory Scene Intelligent Algorithm Testing and Validation 500\u003c\/p\u003e \u003cp\u003e14.3 Intelligent Algorithm Transplantation Application in Industrial Scenarios 512\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Summary and Outlook of Hardware-in-Loop Digital Twin Platform 519\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Summary 519\u003c\/p\u003e \u003cp\u003e15.2 Future AI Algorithm Research and Validation End-Edge-Cloud Platform 520\u003c\/p\u003e \u003cp\u003eIndex 537\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52433823891736,"sku":"9781394354016","price":88.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394354016.jpg?v=1784854214","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/a-hardware-in-loop-digital-twin-approach-for-intelligent-optimization-of-municipal-solid-waste-incineration-ai-and-its-application-to-complex-industrial-processes-hardback-9781394354016","provider":"Freshly Printed Books","version":"1.0","type":"link"}