In order to improve the control accuracy of the dynamic matrix algorithm(DMC) for desulfurization systems and facilitate automatic optimization of controller parameters

we propose an adaptive hybrid particle swarm optimization algorithm to optimize the parameters in DMC. Firstly

we construct an adaptive hybrid particle swarm by incorporating adaptive weights and local factors into the particle swarm algorithm. Then verify the optimization performance of the adaptive hybrid particle swarm through the Griewank function. Subsequently

we build a DMC model and use the adaptive hybrid particle swarm optimization algorithm to iteratively optimize control time domain

optimization time domain

and other parameters of the DMC. Finally

we conduct control simulation and anti-interference testing on the desulfurization system using slurry density and unit load as interference factors. Taking the pH value of desulfurization tower slurry configured for a 600 MW unit in a certain power plant as the research object

we use the actual operating data of the power plant as input to verify the characteristics of the control system. The simulation results show that compared to traditional PID control and Smith predictive control

the adaptive hybrid particle swarm optimization DMC control offers a shorter rise time for the slurry pH value

more centralized control

and a smaller fluctuation range. The coverage rate reaches 99.41% within the set value ± 0.02 range.