Dual-Layer Optimization Control for Furnace Temperature Setting and Tracking in Municipal Solid Waste Incineration Process

In the global trend towards a sustainable circular economy, incineration technology is widely used for the treatment of municipal solid waste (MSW), as it effectively achieves waste harmlessness, reduction, and energy recovery. During the MSW incineration (MSWI) process, the furnace temperature (FT) is closely linked to pollutant emission concentrations. Therefore, precise control and stable monitoring of the FT are essential for minimizing pollution emissions. However, existing studies generally treat the optimization of FT setpoint value and tracking control as separate issues, lacking a unified optimization framework that can link environmental objectives with control parameters in an online, automatic, and closed-loop manner. To address these issues, a dual-layer optimization control method for FT setting and tracking, aimed at minimizing pollutant concentrations, is proposed. In the first layer, the optimization targets the lowest possible NOx and CO₂ emission concentrations, using a genetic algorithm (GA) to determine optimal FT setpoints. In the second layer, the optimization minimizes the Integral of Time-weighted Absolute Error (ITAE) as the performance index, optimizing the parameters of multi-loop PID controllers via an improved GA. Additionally, an innovative shared-memory judgment mechanism is proposed to transmit process data in real time. Based on residual dynamic correction of the optimization function, an effective double-loop closed control architecture is established. Experimental validation shows that, compared to traditional methods, the optimized control system exhibits faster setpoint value tracking, smaller steady-state errors, and stronger anti-interference capabilities, leading to a significant reduction in pollutant emissions. This study provides a new approach for intelligent optimization control in MSWI with substantial application prospects.