[1]张伟,乔俊飞.神经网络的污水处理过程多目标优化控制方法[J].智能系统学报,2016,11(5):594-599.[doi:10.11992/tis.201512022]
　ZHANG Wei,QIAO Junfei.Multi-objective optimization control for wastewatertreatment processing based on neural network[J].CAAI Transactions on Intelligent Systems,2016,11(5):594-599.[doi:10.11992/tis.201512022]
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神经网络的污水处理过程多目标优化控制方法

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 11 期数: 2016年第5期 页码: 594-599 栏目: 学术论文—机器学习 出版日期: 2016-11-01
Title:
Multi-objective optimization control for wastewatertreatment processing based on neural network
作者:
张伟1,2,3, 乔俊飞1,3
1. 北京工业大学 电子信息与控制工程学院, 北京 100124;
2. 河南理工大学 电气工程与自动化学院, 河南 焦作 454000;
3. 计算智能与智能系统北京市重点实验室, 北京 100124
Author(s):
ZHANG Wei1,2,3, QIAO Junfei1,3
1. College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo 454000, China;
3. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing 100124, China
关键词:
多目标优化; 神经网络; 能量消耗; 污水处理; 基准仿真模型BSM1
Keywords:
multi-objective optimization; neural network; energy consumption; wastewater treatment; benchmark simulation model
分类号:
TP18
DOI:
10.11992/tis.201512022
摘要:
针对污水处理过程能耗过高问题，提出一种基于神经网络的动态多目标优化控制方法。该方法对污水处理过程中的曝气能耗和泵送能耗同时优化，通过NSGA-II进化算法实现溶解氧浓度和硝态氮浓度设定值的动态寻优，由PID控制实现底层跟踪。采用神经网络在线建模方法构造污水处理过程多目标优化模型，解决了优化变量与性能指标间没有精确数学描述的问题。基于国际基准仿真平台BSM1的实验表明，与PID控制、单目标优化控制方法相比，多目标优化控制在保证出水水质达标的前提下可以获得更优的节能效果。
Abstract:
To solve the energy-extensive consumption problem of the wastewater treatment process (WWTP), a dynamic multi-objective optimization control strategy is proposed in this paper.The proposed method simultaneously optimizes the aerate energy and pumped energy consumption of WWTP, and the set-points of dissolved oxygen concentration and nitrate level can be optimized dynamically using the NSGA-Ⅱ evolutionary algorithm. The proportion-integral-derivative(PID) is chosen to realize the tracking control task for the low layer. To overcome the difficulty of establishing an optimal model for WWTP, an online neural network modeling method was proposed for constructing the multi-objective optimization model, which solves the problem that there is no accurate mathematical description with the optimization variables and performance indexes. The simulation results, based on the international benchmark simulation model No. 1, demonstrate that compared with the PID and the single-objective optimization methods, energy consumption can be significantly reduced by using the proposed method while still assuring water quality.
参考文献/References:
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[2] OLSSON G. ICA and me-a subjective review[J]. Water research, 2012, 46(6):1585-1624.
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[4] HOLENDA B, DOMOKOS E, RéDEY á, et al. Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control[J]. Computers & chemical engineering, 2008, 32(6):1270-1278.
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[9] 韩广, 乔俊飞, 韩红桂, 等. 基于Hopfield神经网络的污水处理过程优化控制[J]. 控制与决策, 2014, 29(11):2085-2088. HAN Guang, QIAO Junfei, HAN Honggui, et al. Optimal control for wastewater treatment process based on Hopfield neural network[J]. Control and decision, 2014, 29(11):2085-2088.
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[12] CADET C, BéTEAU J F, HERNANDEZ S C. Multicriteria control strategy for cost/quality compromise in wastewater treatment plants[J]. Control engineering practice, 2004, 12(3):335-347.
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[14] ZHANG Rui, XIE Wenming, YU Hanqing, et al. Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method[J]. Bioresource technology, 2014, 157:161-165.
[15] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ[J]. IEEE transactions on evolutionary computation, 2002, 6(2):182-197.
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备注/Memo

收稿日期:2015-12-12。
基金项目:国家自然科学基金项目(61533002,61203099)； 北京市自然科学基金项目(4122006)；
作者简介:张伟,女,1978年生,副教授,主要研究方向为污水处理系统的智能控制与优化控制;乔俊飞,男,1968年生,教授,博士生导师,主要研究方向为智能信息处理、智能控制理论与应用。获教育部科技进步奖一等奖和北京市科学技术奖三等奖各1项。发表学术论文近100篇,其中被SCI检索18篇,EI检索60篇,获得授权发明专利20项.
通讯作者:张伟.E-mail:zwei1563@126.com

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