摘要
针对燃煤机组脱硫系统运行过程中会产生过度的物料投入和设备电能消耗问题,将运行成本评价引入脱硫系统运行优化中。采集某660 MW燃煤机组脱硫系统的历史数据建立运行成本模型和出口污染物质量浓度预测模型。以脱硫系统可变操纵量为变量,以可变操纵量实际调节范围为约束,采用非支配排序遗传算法-II(non-dominated sorting genetic algorithm II,NSGA II)和多目标粒子群(multi-objective particle swarm optimization,MOPSO)算法的NSGA II-MOPSO算法建立混合优化模型得到可变操纵量的设定值,并与NSGA II和MOPSO算法的优化结果进行对比。仿真结果表明,通过对可变操纵量的优化,在实现出口污染物质量浓度达标的前提下,优化后的运行策略使得4个主要工况条件下的度电成本分别下降了9.38%、6.01%、3.69%和3.99%,具有一定的实际应用价值。
Aiming at solving the problem of excessive material input and equipment power consumption in desulfurization system of coal-fired power units,the operation cost evaluation is introduced into the operation optimization of the desulfurization system.Historical data of the desulfurization system of a 660 MW coal-fired power unit are collected to establish the operating cost model and the outlet pollutant mass concentration prediction model.By taking the variable manipulation amount of the desulfurization system as a variable,taking the actual adjustment range of variable manipulation amount as the constraints,and using the hybrid non-dominated sorting genetic algorithm II(NSGA II)combing with multi-objective particle swarm optimization(MOPSO)algorithm,a hybrid optimization model is established,and a set value of the variable manipulation amount is obtained.The optimization results are compared with that of the NSGA-II and MOPSO algorithm.The simulation results show that,by optimizing the variable manipulation amount,the new operation strategy can effectively reduce the cost per kW·h by 9.38%,6.01%,3.69%and 3.99%under four major working conditions,respectively,on the premise of the pollutants mass concentration at outlet meets the standard.The research has certain practical application value.
作者
李兆北
王印松
苏杰
LI Zhaobei;WANG Yinsong;SU Jie(Department of Automation,North China Electric Power University,Baoding 071003,China)
出处
《热力发电》
CAS
CSCD
北大核心
2022年第7期149-155,共7页
Thermal Power Generation
基金
国家自然科学基金重点项目(U1709211)。
关键词
湿法烟气脱硫
多目标优化
遗传算法
粒子群优化算法
运行策略
wet flue gas desulfurization
multi-objective optimization
genetic algorithm
particle swarm optimization algorithm
operation strategy
