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Furnace Temperature Model Predictive Control Based on Particle Swarm Rolling Optimization for Municipal Solid Waste Incineration

Author

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  • Hao Tian

    (School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Jian Tang

    (School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Tianzheng Wang

    (School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

Abstract

Precise control of furnace temperature (FT) is crucial for the stable, efficient operation and pollution control of the municipal solid waste incineration (MSWI) process. To address the inherent nonlinearity and uncertainty of the incineration process, a FT control strategy is proposed. Firstly, by analyzing the process characteristics of the MSWI process in terms of FT control, the secondary air flow is selected as the manipulated variable to control the FT. Secondly, an FT prediction model based on the Interval Type-2 Fuzzy Broad Learning System (IT2FBLS) is developed, incorporating online parameter learning and structural learning algorithms to enhance prediction accuracy. Next, particle swarm rolling optimization (PSRO) is used to solve the optimal control law sequence to ensure optimization efficiency. Finally, the stability of the proposed method is validated using Lyapunov theory, confirming the controller’s reliability in practical applications. Experiments based on actual operational data confirm the method’s effectiveness.

Suggested Citation

  • Hao Tian & Jian Tang & Tianzheng Wang, 2024. "Furnace Temperature Model Predictive Control Based on Particle Swarm Rolling Optimization for Municipal Solid Waste Incineration," Sustainability, MDPI, vol. 16(17), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7670-:d:1470941
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    References listed on IDEAS

    as
    1. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    2. Leihua Feng & Feng Yang & Wei Zhang & Hong Tian, 2019. "Model Predictive Control of Duplex Inlet and Outlet Ball Mill System Based on Parameter Adaptive Particle Swarm Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, March.
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