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A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration : AI and Its Application to Complex Industries

By: (Author) Jiangjun Tang , (Author) Junfei Qiao , (Author) Mexico) Yu Mexico City Wen

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Ksh 20,000.00

Format: Hardback or Cased Book

ISBN-10: 1394354010

ISBN-13: 9781394354016

Publisher: John Wiley & Sons Inc

Imprint: Wiley-IEEE Press

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Dec 28th, 2025

Print length: 400 Pages

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An expert discussion of intelligent optimization control in complex industrial processes In A Hardware-in-Loop Digital Twin Approach to Intelligent Optimization of Municipal Solid Waste Incineration, 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 to 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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