A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process

Jian Tang (), Hao Tian and Tianzheng Wang
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Jian Tang: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Hao Tian: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Tianzheng Wang: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China

Sustainability, 2024, vol. 16, issue 17, 1-35

Abstract: Municipal solid waste incineration (MSWI) is essential for tackling urban environmental challenges and facilitating renewable energy recycling. The MSWI process has characteristics of multiple variables, strong coupling, and complex nonlinearity, requiring advanced process control (APC) technology. Although there have been several reviews on the modeling and control of the MSWI process, there is a lack of focus on model predictive control (MPC), a widely used APC technology. This article aims to comprehensively review MPC strategies in the MSWI process. First, it describes MSWI process technology in detail, examining control issues and objectives to highlight the complexity and challenges in controller design while providing an overview of MPC methods and their benefits. Second, it reviews incinerator modeling for control, including traditional modeling techniques and machine learning technologies such as fuzzy neural networks. Third, it reviews the controllers used for MSWI process, emphasizing the advantages of MPC over existing control methods. Fourth, it discusses the current status of MPC design and online updates, covering the need for an accurate dynamic predictive model and objective function and the online updates components such as predictive modeling, rolling optimization, and feedback correction. Finally, the study concludes with a summary of the findings.

Keywords: municipal solid waste incineration; model predictive control; machine learning; intelligent optimization algorithms; advanced process control (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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