[1]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.
QIAO Junfei,PANG Zefang,HAN Honggui.Neural network optimal control for wastewater treatment processbased on APSO[J].CAAI Transactions on Intelligent Systems,2012,7(5):429-436.
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QIAO Junfei,PANG Zefang,HAN Honggui.Neural network optimal control for wastewater treatment processbased on APSO[J].CAAI Transactions on Intelligent Systems,2012,7(5):429-436.
基于改进粒子群算法的污水处理过程神经网络优化控制
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
7
期数:
2012年第5期
页码:
429-436
栏目:
学术论文—智能系统
出版日期:
2012-10-25
- Title:
- Neural network optimal control for wastewater treatment processbased on APSO
- 文章编号:
- 1673-4785(2012)05-0429-08
- Keywords:
- wastewater treatment; intelligent control; optimal control; particle swarm optimzation; neural network
- 分类号:
- TP18
- 文献标志码:
- A
- 摘要:
- 针对活性污泥法污水处理过程高能耗的问题，综合考虑污水处理出水水质和生化反应参数之间的关系，文中设计了一种智能优化控制系统.该系统以国际水协(IWA)开发的基准仿真模型BSM1为研究对象，利用改进粒子群算法优化BSM1第2分区的硝态氮浓度和第5分区的溶解氧浓度、混合液悬浮物固体浓度的设定值；同时利用感知器神经网络预测污水处理过程的输出，在出水水质达标的前提下降低污水处理能耗.仿真实验结果表明，系统总能耗相比闭环控制策略降低4.614%，该神经网络智能优化控制系统能够有效降低污水处理的能耗.
- 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 standarts. The simulation results show that, comparing to the clooseloop 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.
- 参考文献/References:
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备注/Memo
收稿日期： 2012-05-21.
网络出版日期：2012-09-25.
基金项目：国家自然科学基金重点资助项目(61034008)； 北京市自然科学基金资助项目(4122006)； 北京市教育委员会科技计划项目(KZ201010005005).
通信作者：韩红桂. E-mail: isibox@sina.com.
作者简介：乔俊飞，男，1968年生，教授，博士生导师，博士，主要研究方向为智能控制、污水处理过程建模与优化控制、智能系统分析与设计.主持完成或承担国家自然科学基金项目4项、国家“863”计划项目2项、教育部博士点基金与北京市自然科学基金等省部级项目共9项.2011年获得教育部高等学校科学研究优秀成果奖科学技术进步奖一等奖.发表学术论文100余篇，其中被SCI检索12篇，EI检索60余篇，获得国家发明专利授权9项、软件著作权8项.
逄泽芳，女，1986年生，硕士研究生，主要研究方向为智能控制理论、方法与应用.
韩红桂，男，1983年生，讲师，主要研究方向为神经网络自组织设计及城市污水处理过程的建模和控制.
更新日期/Last Update:
2012-11-13