摘要
以粒子群算法为基础,针对浆液pH、浆液体积流量和脱硫系统负荷等参数辨识得到适用于石灰石-石膏湿法脱硫系统的多输入双输出模型。通过前馈-反馈复合控制消除干扰变量的影响,并结合预测函数(PFC)和模糊反馈校正设计出应用于脱硫系统的预测模糊控制器(PFPID)。结果表明:该控制方式可将浆液pH维持在5.6左右,并使出口SO2质量浓度低于30 mg/m3,为控制方法的改进提供了理论依据。
A multi-input dual-output model applicable to the limestone-gypsum wet desulfurization system of a power plant was obtained by identifying the following parameters based on particle swarm optimization(PSO)algorithm,such as the slurry pH value,the slurry flow rate and the load of the desulfurization system,etc.Then,a predictive fuzzy proportional-integral-differential(PFPID)controller was designed for the desulfurization system using feedforward-feedback composite control to eliminate the influence of interference variables,combined with predictive functional control(PFC)and fuzzy state feedback control.Results show that the method proposed can help to maintain the pH value of slurry at about 5.6,and keep the outlet SO2 concentration to be lower than 30 mg/m3,which may serve as a reference for the improvement of desulfurization control methods.
作者
范昊鹏
夏凤毅
包军宇
杨佳晨
FAN Haopeng;XIA Fengyi;BAO Juryu;YANG Jiachen(School of Quality and Safety Engineering,China Jiliang University,Hangzhou 310016,China;Institute of Environmental Science,China Jiliang University,Hangzhou 310016,China)
出处
《动力工程学报》
CAS
CSCD
北大核心
2020年第10期808-814,共7页
Journal of Chinese Society of Power Engineering
基金
金华市科技局计划重大资助项目(2010-1-089)。
关键词
湿法脱硫
模型辨识
粒子群算法
预测模糊控制
前馈-反馈复合控制
wet desulfurization
model identification
particle swarm optimization
predictive fuzzy control
feedforward-feedback composite control
