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

Hao Tian, Jian Tang () and Tianzheng Wang
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Hao Tian: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Jian Tang: 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, 1-23

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.

Keywords: municipal solid waste incineration (MSWI); furnace temperature (FT); model predictive control (MPC); interval type-2 fuzzy broad learning system (IT2FBLS); particle swarm rolling optimization (PSRO) (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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