Research on Multivariable Control of Boilers Based on Model Predictive Control (MPC) and Genetic Algorithm

This paper proposes a model predictive control (MPC) method based on genetic algorithm optimization for the problem of multivariable control of boilers. By constructing a dynamic mathematical model and an optimization control strategy for the boiler system, the problems of high energy consumption, slow response and insufficient anti-interference in traditional control methods are solved. In the experimental design, a comparative analysis of traditional MPC and GA-MPC was conducted in combination with a dynamic simulation platform. The results show that GA-MPC is significantly superior to traditional MPC in steady-state energy consumption optimization, can effectively reduce energy consumption by about 28.5%, and maintain good control accuracy and rapid response capabilities in the dynamic adjustment stage. At the same time, GA-MPC shows higher robustness and stability when dealing with complex multivariable systems, proving that the global optimization capability of genetic algorithms significantly improves the performance of MPC. The research results of this paper provide an efficient optimization strategy for boiler control systems, which has important theoretical value and engineering application significance.