基于多目标粒子群算法的污水处理智能优化控制

发布时间：2018-06-19 22:47

本文选题：污水处理过程 + 智能优化控制　；参考：《化工学报》2017年04期

【摘要】：为了满足污水处理过程出水水质排放达标的同时降低运行能耗,提出了一种基于多目标粒子群的污水处理多目标智能优化控制方法。首先,通过分析污水处理运行数据,建立了基于自适应回归核函数的污水处理能耗和出水水质模型;其次,设计出一种污水处理多目标优化方法,利用多目标粒子群优化算法同时对污水处理自适应能耗和出水水质模型进行优化,获得溶解氧和硝态氮浓度的优化设定值;最后,利用PID控制器对溶解氧和硝态氮浓度优化设定值进行跟踪控制,实现了污水处理过程的多目标优化控制。基于污水处理基准仿真平台BSM1的实验结果显示,该多目标优化控制方法不但能够保证出水水质达标,而且能有效降低污水处理过程的能耗。
[Abstract]:In order to meet the requirements of effluent quality and reduce energy consumption, a multi-objective intelligent optimization control method for wastewater treatment based on multi-objective particle swarm optimization was proposed. Firstly, by analyzing the operation data of sewage treatment, a model of wastewater treatment energy consumption and effluent quality based on adaptive regression kernel function is established. Secondly, a multi-objective optimization method for sewage treatment is designed. The multi-objective particle swarm optimization algorithm is used to optimize the adaptive energy consumption and effluent quality model of wastewater treatment at the same time, and the optimal setting values of dissolved oxygen and nitrate concentration are obtained. The pid controller is used to track and control the optimal setting values of dissolved oxygen and nitrate concentration, and the multi-objective optimal control of sewage treatment process is realized. The experimental results based on the simulation platform BSM1 show that the multi-objective optimal control method can not only ensure the quality of effluent reaches the standard, but also effectively reduce the energy consumption in the process of wastewater treatment.
【作者单位】：北京工业大学信息学部;
【基金】：国家自然科学基金项目(61622301,61533002) 北京市教育委员会科研计划项目(KZ201410005002,km201410005001)~~
【分类号】：X703;TP18

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