化工学报 ›› 2021, Vol. 72 ›› Issue (4): 2167-2177.DOI: 10.11949/0438-1157.20201068

• 过程系统工程 • 上一篇    下一篇

基于多策略自适应差分进化算法的污水处理过程多目标优化控制

赵杨1,2(),熊伟丽1,2()   

  1. 1.江南大学轻工过程先进控制教育部重点实验室,江苏 无锡 214122
    2.江南大学物联网工程学院,江苏 无锡 214122
  • 收稿日期:2020-07-30 修回日期:2020-11-08 出版日期:2021-04-05 发布日期:2021-04-05
  • 通讯作者: 熊伟丽
  • 作者简介:赵杨(1997—),男,硕士研究生,zhaoyang970730@163.com
  • 基金资助:
    国家自然科学基金项目(61773182);国家重点研发计划子课题(2018YFC1603705-03);江苏高校 “青蓝工程”资助项目

Multi-objective optimization control of wastewater treatment process based on multi-strategy adaptive differential evolution algorithm

ZHAO Yang1,2(),XIONG Weili1,2()   

  1. 1.Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
    2.School of the Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2020-07-30 Revised:2020-11-08 Online:2021-04-05 Published:2021-04-05
  • Contact: XIONG Weili

摘要:

针对污水处理过程中的能耗过高和出水水质不达标等问题,提出一种基于多策略自适应差分进化算法的污水处理过程多目标优化控制方法。首先,在常规跟踪控制结构的基础上,增加对第3、4单元溶解氧浓度的跟踪控制,扩大了能耗和出水水质的优化调节范围。然后,设计一种多策略自适应差分进化算法(MSADE),该算法采用多策略融合变异和排序优选方法,选取合适的变异策略和较优的随机个体引导种群变异,并根据进化过程信息自适应地更新交叉率,以提升算法的收敛性和pareto解的多样性。最后,将MSADE算法与PID控制器相结合,并构建以能耗和出水水质为优化目标的多目标优化问题,实现对溶解氧和硝态氮浓度设定值的动态寻优和跟踪控制。基于国际基准仿真平台BSM1进行验证,结果表明所提的多目标优化控制方法能有效降低污水处理过程的能耗并提升出水水质。

关键词: 污水处理, 多目标优化控制, 差分进化算法, 自适应更新, 多策略

Abstract:

To address the problems of high energy consumption and substandard effluent quality in the wastewater treatment process, a multi-objective optimization control method for wastewater treatment process based on multi-strategy adaptive differential evolution algorithm is proposed. Firstly, the tracking control of the dissolved oxygen concentration of the 3rd and 4th units is added under the conventional differential tracking control framework, which aims to expand the optimal adjustment range of energy consumption and effluent water quality. Then, a multi-strategy adaptive differential evolution algorithm (MSADE) is proposed, which selects proper mutation strategy and random individuals to guide the population mutation by combining the multi-strategy fusion mutation technique and sorting optimization method. Further, the convergence of proposed algorithm and the diversity of the pareto solution can be greatly improved by updating the crossover rate adaptively according to the evolution process information. Finally, the MSADE algorithm and the PID controller are combined, and a novel multi-objective optimization method is built with a good balance of energy consumption and effluent quality, realizing the dynamic optimization process and the tracking control of setting values of dissolved oxygen and nitrate nitrogen concentration. The simulation results on the international benchmark simulation platform BSM1 show that the proposed method can effectively reduce energy consumption and improve effluent quality in the wastewater treatment process.

Key words: wastewater treatment, multi-objective optimization control, differential evolution algorithm, update adaptively, multi-strategy

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