上海交通大学学报

上海交通大学学报 >

2020 , Vol. 54 >Issue 9: 916 - 923

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2020.170

学报（中文）

城市污水处理过程出水氨氮优化控制

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  • 北京工业大学 信息学部; 计算智能与智能系统北京市重点实验室, 北京 100124
韩红桂(1983-),男,江苏省泰州市人,教授,现主要从事城市污水处理过程建模、优化和控制研究.电话(Tel.): 010-67391631;E-mail: rechardhan@sina.com.

收稿日期: 2019-07-30

  网络出版日期: 2020-10-10

基金资助

国家重点研发计划(2018YFC1900800-5);国家自然科学基金(61890931);北京高校卓越青年科学家(BJJWZYJH01201910005020)

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Optimal Control of Effluent Ammonia Nitrogen for Municipal Wastewater Treatment Process

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  • Faculty of Information Technology; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China

Received date: 2019-07-30

  Online published: 2020-10-10

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摘要

为了改善城市污水处理过程出水氨氮的处理效果,提出一种城市污水处理过程出水氨氮优化控制方法.首先,基于机理特性分析影响出水氨氮浓度的性能指标,建立一种基于自适应核函数的性能指标与控制变量的关联模型,并通过粒子群优化算法获取控制变量溶解氧浓度的优化设定值;其次,设计自适应模糊神经网络控制器,完成溶解氧浓度优化设定值的跟踪控制;最后,将出水氨氮优化控制方法应用于基准仿真平台BSM1.实验结果表明,该优化控制方法不仅提高了出水氨氮的去除效果,而且有效降低了能耗.

关键词： 城市污水处理过程; 出水氨氮; 优化控制; 自适应模糊神经网络控制器

本文引用格式

韩红桂, 杨士恒, 张璐, 乔俊飞 . 城市污水处理过程出水氨氮优化控制[J]. 上海交通大学学报, 2020 , 54(9) : 916 -923 . DOI: 10.16183/j.cnki.jsjtu.2020.170

Abstract

To improve the treatment effect of effluent ammonia nitrogen in municipal wastewater treatment process, an optimal control method was proposed in this paper. First, the performance index of effluent ammonia nitrogen concentration was analyzed by using the mechanism characteristics. Then, a relationship model with the adaptive kernel function between the performance index and the control variables was established. Next, a particle swarm optimization algorithm was used to obtain the optimal solutions of dissolved oxygen concentration. After that, an adaptive fuzzy neural network controller was designed to complete the tracking control of dissolved oxygen concentration. Finally, the proposed optimal control method was applied to the benchmark simulation model No.1 (BSM1). The results demonstrated that the proposed optimal control method can not only improve the treatment effect of effluent ammonia nitrogen, but also effectively reduce the energy consumption.

Key words： municipal wastewater treatment process; effluent ammonia nitrogen; optimal control; adaptive fuzzy neural network controller

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