【摘要】 针对目前火电厂循环流化床(CFB)锅炉燃烧系统的数学模型辨识偏差较大等问题，提出一种改进的基于飞蛾扑火优化(MFO)算法和灰狼优化(GWO)算法的H-MFO-GWO算法。算法利用Tent混沌映射改善初始种群，并通过改进控制参数、引进螺旋更新策略和高斯变异加快算法收敛速度，提高寻优精度。通过与其它算法进行数值实验对比，验证上述算法优越性。选取350MW超临界CFB锅炉的实际运行数据建模，利用所提算法进行模型辨识，并验证模型精度。研究结果表明，该模型能较好地反映给煤量、一次风量和床温、主蒸汽压力之间的动态关系。以上研究为350MW超临界CFB锅炉燃烧系统的控制与优化奠定了良好的基础，也为系统模型辨识提供了新途径。更多还原

【Abstract】 An improved H-MFO-GWO algorithm based on Moth to Flame Optimization(MFO)algorithm and Gray Wolf Optimization(GWO)algorithm is proposed to solve the problems of large deviation in mathematical model identification of circulating fluidized bed(CFB)boiler combustion system in thermal power plants. The algorithm utilizes the Tent chaotic mapping to enhance the initial population and improves the convergence speed and optimization accuracy by enhancing control parameters, introducing spiral update strategy, and incorporating Gaussian mutation. The superiority of the algorithm is verified through numerical experiments comparing it with other algorithms. The actual operating data of a 350MW supercritical CFB boiler is selected for modeling, and the algorithm is used for model identification and verification of model accuracy. The validation results indicate that the model can effectively reflect the dynamic relationship between coal feed rate, primary air flow rate, bed temperature, and main steam pressure. This study establishes a solid foundation for the control and optimization of the combustion system in a 350MW supercritical CFB boiler, and provides a new approach for system model identification.更多还原