建立循环流化床(Circulating Fluidized Bed,CFB)锅炉燃烧系统的精确模型对提高锅炉燃烧系统控制性能尤为重要。针对现有建模方法需要调节参数过多、精度不足的问题,提出一种粒子群改进鲸鱼算法(Particle Swarm Optimization Improved Whale Optimization Algorithm,PSO-IWOA)的建模方法,即利用混沌映射初始化种群,采用动态螺旋更新、改进收敛因子、引入粒子群自适应惯性权重,提高算法的全局搜索能力、收敛速度和寻优精度。使用PSO-IWOA算法对CFB锅炉燃烧系统进行建模,并进行模型有效性验证。实验结果表明,PSO-IWOA算法能建立较为准确的燃烧系统模型,为系统模型的快速辨识提供了新方法。

It is very important to establish an accurate model of CFB boiler combustion system to improve the control performance of the boiler combustion system. Aiming at the problems that existing modeling methods need to adjust too many parameters and lack of accuracy, a modeling method of particle swarm optimization improved whale algorithm is proposed, that is, chaotic mapping is used to initialize the population, dynamic spiral updating is adopted, convergence factor is improved, and particle swarm adaptive inertia weight is introduced to improve the global search ability, convergence speed and optimization accuracy of the algorithm. The combustion system of CFB boiler is modeled by PSO-IWOA algorithm, and the validity of the model is verified. The experimental results show that PSO-IWOA algorithm can establish a more accurate combustion system model and provide a new method for rapid identification of system model.

TP391.9

国家自然科学基金项目（面上项目，重点项目，重大项目）