信息与控制2024，Vol.53Issue(2)：250-260,272,12.DOI:10.13976/j.cnki.xk.2023.2568
基于超标抑制策略的污水处理过程多目标优化控制
Multi-objective Optimization Control of Wastewater Treatment Process Based on Overshoot Suppression Strategy
摘要
Abstract
To solve the problem of exceeding standard concentrations of ammonia nitrogen and total nitro-gen in the wastewater treatment process,we propose a multi-objective optimization control method based on an exceeding standard suppression strategy.In the proposed method,we establish a pre-diction model for effluent ammonia nitrogen and total nitrogen concentrations by utilizing the time-series modeling capability of the long short-term memory network.We optimize the water quality and energy consumption using a dynamic population multi-neighborhood and multi-objective evolu-tionary algorithm based on decomposition.The optimal set values of nitrate nitrogen and dissolved oxygen concentrations are determined by combining the output of the prediction model,and the set values are tracked and controlled.When the water quality exceeds the standard,we adopt an inhi-bition strategy to control the external return flow and external carbon sources,adjust the concentra-tion of dissolved oxygen,and optimize the concentration of nitrate nitrogen twice to suppress the peak value.The proposed optimization control method is verified on the Benchmark Simulation Model No.l platform.The results show that the proposed method can effectively restrain the con-centration of ammonia nitrogen and total nitrogen from exceeding the standard and that the excee-ding time and energy consumption are significantly lower than the comparison control method.关键词
污水处理过程/超标抑制策略/长短期记忆网络/预测模型/二次优化Key words
wastewater treatment process/exceeding standard suppres-sion strategy/long short-term memory network/prediction model/quadratic optimization分类
信息技术与安全科学引用本文复制引用
刘传玉,熊伟丽..基于超标抑制策略的污水处理过程多目标优化控制[J].信息与控制,2024,53(2):250-260,272,12.基金项目
国家自然科学基金项目(61773182) （61773182）
国家重点研发计划子课题(2018YFC1603705-03) （2018YFC1603705-03）
广东省科技专项资金项目(2020ST010) （2020ST010）
江苏高校"青蓝工程"项目 （）
