Optimization of Desulfurization Control System for Thermal Power Plant Based on Improved Model Predictive Control

Thermal power plants produce large quantities of SO2 and other toxic gases during power generation, which cause serious pollution to the atmosphere and ecological environment. How to better control the desulfurization process to improve and optimize the emission of harmful gases has become a difficult point in the thermal system, this paper uses a control scheme based on the model predictive control (MPC) algorithm and improves it. Firstly, the model of the desulfurization system was established through system identification. Secondly, the MPC algorithm was used to optimize the pH value of absorber tower slurry and the outlet net flue gas SO2 concentration. Finally it was compared with the PID algorithm in the Matlad platform. From the experimental results, it can be concluded that the MPC algorithm can eliminate pH oscillations in order to reduce SO2 emissions and improve the blindness of human control operations. The improved MPC algorithm reduced the outlet net flue gas SO2 concentration by 8% compared to the MPC algorithm.