Furnace Temperature and Steam Flow Control for the Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI), as an efficient treatment technology, has been widely adopted in waste management. However, the complexity and multivariable coupling characteristics of the MSWI process pose substantial challenges for its control. This article proposes a multi-input multi-output (MIMO) control approach for furnace temperature (FT) and steam flow (SF). First, the MSWI process in terms of FT and SF is descripted. Next, a MIMO controlled-object model was constructed using the MIMO-LRDT method with manipulated variables as put features. Finally, a parallel fuzzy broad learning system (FBLS) controller was designed for control FT and SF. Experimental results based on process data from a real MSWI plant in Beijing validate the effectiveness of the proposed approach.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/icaisisas64483.2025.11051838
Primary Topic
Iron and Steelmaking Processes
Type
article
Field-Weighted Citation Impact
0.00

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Controls
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article

Furnace Temperature and Steam Flow Control for the Municipal Solid Waste Incineration Process

Aijun Yan, Ye Hua, Jian Tang, Weiwei Yang et al.
Iron and Steelmaking Processes
article

Furnace Temperature and Steam Flow Control for the Municipal Solid Waste Incineration Process

Aijun Yan, Ye Hua, Jian Tang, Weiwei Yang, Hao Tian, Guo Li
article en

Abstract

Municipal solid waste incineration (MSWI), as an efficient treatment technology, has been widely adopted in waste management. However, the complexity and multivariable coupling characteristics of the MSWI process pose substantial challenges for its control. This article proposes a multi-input multi-output (MIMO) control approach for furnace temperature (FT) and steam flow (SF). First, the MSWI process in terms of FT and SF is descripted. Next, a MIMO controlled-object model was constructed using the MIMO-LRDT method with manipulated variables as put features. Finally, a parallel fuzzy broad learning system (FBLS) controller was designed for control FT and SF. Experimental results based on process data from a real MSWI plant in Beijing validate the effectiveness of the proposed approach.

Beijing University of Technology (CN)
National Natural Science Foundation of China, National Science and Technology Major Project
Openalex Percentile: Top 84%
Iron and Steelmaking Processes
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