锅炉技术2023，Vol.54Issue(6)：1-7,7.
基于改进粒子群算法的SCR烟气脱硝系统模型辨识
Model Identification of SCR Flue Gas Denitration System Based on Improved Particle Swarm Optimization
摘要
Abstract
Due to the nonlinearity,large inertia and large delay of selective catalytic reduction(SCR)flue gas denitration system in coal-fired power plants,the traditional identification methods have the problems of slow convergence speed and low identification accuracy.In this paper,the ideas of adaptive dynamic inertia weight,adaptive variation of particles and natural selection are introduced on the basis of fundamental particle swarm algorithm.The improved particle swarm algorithm is applied to model identification of SCR flue gas denitration system of an ultra-supercritical 660 MW unit.Based on the field operation data of the unit,the transfer function models between the position opening of the regulating valve and the mass flow of ammonia,between the mass flow of ammonia and the concentration of NOx in the outlet,and between the concentration of NOx in the inlet and that in the outlet were established.The results of the study show that the particle swarm algorithm optimization improves the model identification accuracy,and effectively improves the defects of basic particle swarm algorithm which is easy to fall into the local optimum and the loss of population diversity in the later stage.Meanwhile,the simulation results demonstrate the effectiveness of the proposed method.关键词
SCR/烟气脱硝/模型辨识/传递函数/改进粒子群算法Key words
SCR/flue gas denitration/model identification/transfer function/improved particle swarm algorithm分类
资源环境引用本文复制引用
扶文浩,康英伟,赵子龙..基于改进粒子群算法的SCR烟气脱硝系统模型辨识[J].锅炉技术,2023,54(6):1-7,7.基金项目
国家自然科学基金项目(61573239) （61573239）
上海发电过程智能管控工程技术研究中心资助项目(14DZ2251100) （14DZ2251100）
