智能系统学报2012，Vol.7Issue(5)：429-436,8.DOI:10.3969/j.issn.1673-4785.201205034
基于改进粒子群算法的污水处理过程神经网络优化控制
Neural network optimal control for wastewater treatment process based on APSO
摘要
Abstract
Due to the high energy consumption of activated sludge wastewater treatment process, a new intelligent optimal control system is designed in this paper by considering the effluent quality and the relationship between the biochemical reaction parameters. This control system is used for the benchmark simulation model (BSM1) proposed by the International Water Association (IWA). The APSO is utilized to optimize the dissolved oxygen and MLSS levels in the fifth compartment and the nitrate level in the second anoxic tank. Meanwhile, the outputs of BSM1 are predicted by the neural network, and the energy consumption is cut down whthin the effluent water quality standar-ts. The simulation results show that, comparing to the cloose-loop control strategy, the totle energy consumption of this proposed optimal control system is lowered by 4. 614% , the neural network optimal control strategy can significantly reduce the energy consumption of activated sludge wastewater treatment process.关键词
污水处理/智能控制/优化控制/粒子群算法/神经网络Key words
wastewater treatment/ intelligent control/ optimal control/ particle swarm optimzation/ neural network分类
信息技术与安全科学引用本文复制引用
乔俊飞,逄泽芳,韩红桂..基于改进粒子群算法的污水处理过程神经网络优化控制[J].智能系统学报,2012,7(5):429-436,8.基金项目
国家自然科学基金重点资助项目(61034008) （61034008）
北京市自然科学基金资助项目(4122006) （4122006）
北京市"创新人才建设计划"项目(PHR201006103) （PHR201006103）
教育部新世纪优秀人才支持计划项目(NCET-08-0616) （NCET-08-0616）
北京市教育委员会科技计划项目(KZ201010005005). （KZ201010005005）
