A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration (eBook)
627 Seiten
Wiley-IEEE Press (Verlag)
978-1-394-35403-0 (ISBN)
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.
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 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.
| Erscheint lt. Verlag | 31.10.2025 |
|---|---|
| Sprache | englisch |
| Themenwelt | Technik ► Bauwesen |
| Technik ► Elektrotechnik / Energietechnik | |
| Schlagworte | Artificial Intelligence • Digital Twin (DT) • ensemble learning • hybrid data-driven • Industrial Control • industrial modeling • Industrial Optimization • municipal solid waste incineration (MSWI) • numerical simulation • Semi-Supervised Learning |
| ISBN-10 | 1-394-35403-7 / 1394354037 |
| ISBN-13 | 978-1-394-35403-0 / 9781394354030 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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