A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process DOI Open Access
Jian Tang, Hao Tian, Tianzheng Wang

и другие.

Sustainability, Год журнала: 2024, Номер 16(17), С. 7650 - 7650

Опубликована: Сен. 3, 2024

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, complex nonlinearity, requiring advanced control (APC) technology. Although there have been several reviews on the modeling process, a lack focus model predictive (MPC), widely used APC This article aims to comprehensively review MPC strategies in process. First, it describes technology detail, examining issues objectives highlight complexity controller design while providing an overview methods their benefits. Second, incinerator control, including traditional techniques machine learning technologies such as fuzzy neural networks. Third, controllers emphasizing advantages over existing methods. Fourth, discusses current status online updates, covering need accurate dynamic objective function updates components modeling, rolling optimization, feedback correction. Finally, study concludes with summary findings.

Язык: Английский

A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process DOI Open Access
Jian Tang, Hao Tian, Tianzheng Wang

и другие.

Sustainability, Год журнала: 2024, Номер 16(17), С. 7650 - 7650

Опубликована: Сен. 3, 2024

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, complex nonlinearity, requiring advanced control (APC) technology. Although there have been several reviews on the modeling process, a lack focus model predictive (MPC), widely used APC This article aims to comprehensively review MPC strategies in process. First, it describes technology detail, examining issues objectives highlight complexity controller design while providing an overview methods their benefits. Second, incinerator control, including traditional techniques machine learning technologies such as fuzzy neural networks. Third, controllers emphasizing advantages over existing methods. Fourth, discusses current status online updates, covering need accurate dynamic objective function updates components modeling, rolling optimization, feedback correction. Finally, study concludes with summary findings.

Язык: Английский

Процитировано

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