基于PSO-IWOA改进算法的CFB锅炉燃烧系统建模
Modeling of CFB Boiler Combustion System based on PSO-IWOA Optimization Algorithm
王琦 1刘祥 1张静 1荆蕊蕊1
作者信息
- 1. 山西大学 自动化与软件学院,山西 太原 030006
- 折叠
摘要
针对现有CFB锅炉建模方法需要调节参数过多、精度不足的问题,提出一种基于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,提高了算法的全局搜索能力、收敛速度和寻优精度.应用PSO-WOA算法对CFB锅炉燃烧系统进行建模,并通过实验进行模型有效性验证.实验结果表明:PSO-IWOA算法能建立较为精确的燃烧系统模型,该算法将模型精度提高了86.51%.
Abstract
To solve the problem that existing CFB boiler modeling methods need to adjust too many pa-rameters and have insufficient accuracy,a modeling method of particle swarm optimization improved whale optimization algorithm(PSO-IWOA)is proposed,which uses chaotic mapping to initialize the pop-ulation,adopts dynamic spiral updating,improves the convergence factor and introduces the particle swarm adaptive inertia weight.The method improves the global search ability,convergence speed and op-timization accuracy of the algorithm.The combustion system of CFB boiler is modeled by PSO-WOA algo-rithm,and the effectiveness of the model is verified by experiments.The experimental results show that PSO-IWOA algorithm can establish a more accurate combustion system model,and the model accuracy is improved by 86.51%.
关键词
循环流化床/燃烧系统/鲸鱼算法/系统模型/快速辨识Key words
circulating fluidized bed(CFB)/combustion system/whale optimization algorithm/system model/rapid identification引用本文复制引用
基金项目
国家自然科学基金联合基金(U1610116)
出版年
2024
NETL
NSTL
万方数据