Event-Triggered Self-Organizing T-S Fuzzy Neural Network Controller for Furnace Temperature in Municipal Solid Waste Incineration Process

Municipal Solid Waste Incineration (MSWI) is increasingly becoming an essential technological choice in the field of municipal solid waste management. To address the challenge of precise temperature control in MSWI furnaces, a control strategy based on an event-triggered self-organizing Takagi-Sugeno fuzzy neural network (ETSO-TSFNN) has been proposed. The event-triggered self-organizing Takagi-Sugeno fuzzy neural network controller (ETSO-TSFNNC) integrates an event-triggering mechanism and a self-organizing network mechanism, reducing the frequency of control actions and allowing for adaptive adjustment of the network structure. Conducting experiments with actual operational data validated the effectiveness of the proposed controller. The results indicate that the ETSO-TSFNNC can respond swiftly and manage furnace temperatures with high precision and adaptability.

I. Introduction

With the acceleration of technological innovation and municipal development, there is a significant increase in municipal population density, which has led to a sharp rise in the production of municipal solid waste (MSW) [1] [2]. This growth trend poses an increasingly severe challenge to the sustainability of municipal environments and the health of residents [3]. Therefore, how to achieve efficient and environmentally friendly solid waste management is a current issue that needs to be addressed. MSWI technology, as a means to achieve harmless and reduction treatment of solid waste, and with the potential for energy recovery, is gradually becoming an important technological option in the field of municipal waste treatment [4].