An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Jian Tang (), Tianzheng Wang, Heng Xia and Canlin Cui
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Jian Tang: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Tianzheng Wang: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Heng Xia: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Canlin Cui: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Sustainability, 2024, vol. 16, issue 5, 1-41

Abstract: Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

Keywords: municipal solid waste incineration; optimal control; artificial intelligence; modeling; control; optimization; maintenance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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