自动化学报2024，Vol.50Issue(6)：1221-1233,13.DOI:10.16383/j.aas.c230695
知识和数据驱动的污水处理反硝化脱氮过程协同优化控制
Knowledge-data-driven Cooperative Optimal Control for Wastewater Treatment Denitrification Process
摘要
Abstract
In order to effectively improve the performance of wastewater treatment denitrification process,a know-ledge-data-driven cooperative optimal control(KDDCOC)is proposed.The main work of this paper includes the following two points:First,a cooperative optimal control objective model,based on adaptive knowledge kernel func-tion,is designed to dynamically describe the cooperative relationship among effluent quality(EQ),pumping energy consumption(PE),and key variables;Second,a knowledge guide-based cooperative optimization algorithm(KGCO)is proposed to quickly and accurately obtain the optimal set-points of nitrate nitrogen(SNO).Then,the response speed of KDDCOC is improved.A proportional-integral-derivative(PID)controller is used to track the optimal set-points of nitrate nitrogen.The proposed KDDCOC is applied to the benchmark simulation model No.1(BSM1)of wastewater treatment process.The experimental results indicate that KDDCOC can improve the effluent quality and the efficiency of denitrification,reduce the energy consumption.关键词
污水处理反硝化脱氮过程/知识和数据驱动/协同优化控制/自适应知识核函数/知识引导的协同优化算法Key words
Wastewater treatment denitrification process/knowledge-data-driven/cooperative optimal control/ad-aptive knowledge kernel function/knowledge guide-based cooperative optimization algorithm(KGCO)引用本文复制引用
韩红桂,王玉爽,刘峥,孙浩源,乔俊飞..知识和数据驱动的污水处理反硝化脱氮过程协同优化控制[J].自动化学报,2024,50(6):1221-1233,13.基金项目
国家自然科学基金(62125301,62021003,62103010,62303024),国家重点研发计划(2022YFB3305800-5),中国博士后科学基金(2022M720319),北京市自然科学基金(KZ202110005009),青年北京学者项目(037),北京市博士后工作经费资助项目(2023-zz-91)资助 Supported by National Natural Science Foundation of China(62125301,62021003,62103010,62303024),National Key Re-search and Development Program of China(2022YFB3305800-5),China Postdoctoral Science Foundation(2022M720319),Beijing Natural Science Foundation(KZ202110005009),Youth Beijing Scholars Program(037),and Beijing Postdoctoral Research Foundation(2023-zz-91) （62125301,62021003,62103010,62303024）
