Adaptive PID Control of Furnace Temperature in MSWI Process Based on Reinforcement Learning

This paper tackles the challenge of effectively regulating furnace temperature in the municipal solid waste incineration (MSWI) process. It proposes an approach that integrates reinforcement learning with traditional PID control. Firstly, an analysis of the characteristics of furnace temperature control is conducted to identify the key manipulated variables affecting it. Subsequently, a control strategy specifically tailored for furnace temperature is developed, employing online adaptive PID with a BPNN-fitted actor-critic network (BPNN-ACN-PID). Finally, the effectiveness of the proposed method is validated through control experiments using actual process data from a MSWI plant in Beijing.