基于知识的污水生化处理过程智能优化方法

乔俊飞; 韩改堂; 周红标

doi:10.16383/j.aas.2017.c170088

基于知识的污水生化处理过程智能优化方法

doi: 10.16383/j.aas.2017.c170088

乔俊飞^1,2, ,,
韩改堂^1,2,,
周红标^1,2,

1.
北京工业大学信息学部北京 100124
2.
计算智能与智能系统北京市重点实验室北京 100124

基金项目:

国家杰出青年科学基金项目 61225016

国家自然科学基金 61533002

详细信息

作者简介:
韩改堂北京工业大学博士研究生.主要研究方向为计算智能与智能系统, 神经网络结构设计和优化.E-mail:hangaitang@emails.bjut.edu.cn

周红标北京工业大学博士研究生.主要研究方向为神经网络结构设计与优化, 多目标优化和过程控制系统.E-mail:hyitzhb@163.com

通讯作者:
乔俊飞北京工业大学教授.主要研究方向为智能控制, 神经网络分析与设计.E-mail:junfeq@bjut.edu.cn

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出版历程
- 收稿日期: 2017-02-20
- 录用日期: 2017-03-30
- 刊出日期: 2017-06-20

Knowledge-based Intelligent Optimal Control for Wastewater Biochemical Treatment Process

QIAO Jun-Fei^{1,2
, ,},
HAN Gai-Tang^{1,2
,},
ZHOU Hong-Biao^{1,2
,}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124

Funds:

National Natural Science Funds for Distinguished Young Scholar 61225016

National Natural Science Foundation of China 61533002

More Information

Author Bio:
Ph. D. candidate at Beijing University of Technology. His research interest covers computational intelligence and intelligent systems, analysis and design of neural networks

Ph. D. candidate at Beijing University of Technology. His research interest covers analysis and design of neural networks, multi-objective optimization, and process control systems

Corresponding author: QIAO Jun-Fei Professor at Beijing University of Technology. His research interest covers intelligent control, analysis and design of neural networks. Corresponding author of this paper

摘要

摘要: 针对污水处理过程控制能耗过大和水质超标严重等问题，本文提出一种基于知识的污水生化处理过程智能优化控制方法.该方法通过记忆多目标智能优化算法的动态处理信息，建立环境变量参数与最优解之间的知识模型.优化算法利用知识库中非支配解的引导，结合定向局部区域寻优以及随机全局寻优策略，提高了算法的收敛性，获取了更高质量的解.最后基于国际通用平台BSM1进行实验验证.结果表明，与其他优化算法相比，该方法能够在保证出水水质的前提下产生更少的能量消耗.
- 污水处理过程 /
- 能耗 /
- 多目标优化 /
- 知识引导
Abstract: In order to solve the problems of excessive energy consumption and serious water quality in wastewater treatment process, a wastewater treatment process intelligent optimization control method based on knowledge is proposed. Knowledge model of environment variable parameters and optimal solutions are built by memorizing the dynamic processing information of the multi-objective intelligent optimization algorithm. The optimization algorithm is guided by the non-dominated solution in the knowledge base, and combines the oriented local area search and the stochastic global search strategy to improve the convergence of the algorithm and obtains a higher quality solution. Finally, experiment verification is performed on the international common simulation platform BSM1. Results show that the proposed method can reduce energy consumption under the premise of ensuring the quality of the effluent.
- Wastewater treatment process /
- energy consumption /
- multi-objective optimization /
- knowledge guidance
注释:

1) 本文责任编委郭戈

HTML全文

图 1 污水处理系统工艺布局图

Fig. 1 Wastewater treatment system process layout

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图 2 基于KBMOPSO的多目标优化控制方法

Fig. 2 Multiple objective optimization control system based on KBMOPSO

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图 3 KBMOPSO程序流程图

Fig. 3 The program flow chart of KBMOPSO

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图 4 FNN结构图

Fig. 4 The structure of FNN

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图 5 晴朗天气入水流量

Fig. 5 Influent flow in dry weather

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图 6 晴朗天气入水组分

Fig. 6 Influent component in dry weather

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图 7 案例数

Fig. 7 The case numbers

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图 8 能耗建模效果

Fig. 8 Model performance of EC

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图 9 出水水质建模效果

Fig. 9 Model performance of EQ

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图 10 Pareto最优解集及最优折衷解确定

Fig. 10 Pareto optimal solutions and identify of optimal

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图 11 设定值优化结果及跟踪效果

Fig. 11 Optimization and tracking results of the set point values

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图 12 出水水质参数变化情况

Fig. 12 The change of water quality parameters

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表 1 参数描述

Table 1 Parameters description

符号	描述
D_{o_set}	溶解氧设定值
S_{no_set}	硝态氮设定值
CT	控制器
MI	测量设备
Z₀	入水组分
Z_a	内回流组分
Z_r	外回流组分
Z_w	污泥组分
Z_e	出水组分
Q₀	入水流量
Q_a	内回流
Q_r	外回流
Q_w	污泥流
Q_e	出水流量

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表 2 不同算法性能比较

Table 2 Performance comparison for difierent algorithm

	S_{O, 5}	S_{NO, 2}	AE	PE	Energy	Up/Down	Fines	Up/Down
PID^[8]	2^*	1^*	841.1^*	86.2^*	927.3^*	-	5129.5	-
DDAOC^[8]	1.5799^*	1.087^*	758.2^*	89.8^*	848.0^*	↓8.50%^*	5185.6	↑1.79%^*
Hopfleld^[9]	-	-	814.8	63.4	878.2	↓5.30%^*	-	-
SOOC^[14]	-	-	852.6	53.8	906.4	↓2.25%^*	-	-
DMOOC^[14]	-	-	849.2	30.4	879.6	↓5.14%^*	5440.9	↑6.07%^*
MOPSO	1.5012	1.077	840.4	35.7	876.1	↓5.52%	5409.1	↑5.17%
KBMOPSO	1.3999	1.294	763.2	102.7	865.9	↓6.62%	5092.4	↓0.7%
^*表示参考了原文给出的结果

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参考文献(22)

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基于知识的污水生化处理过程智能优化方法

doi: 10.16383/j.aas.2017.c170088

通讯作者:
乔俊飞北京工业大学教授.主要研究方向为智能控制, 神经网络分析与设计.E-mail:junfeq@bjut.edu.cn

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Knowledge-based Intelligent Optimal Control for Wastewater Biochemical Treatment Process

Corresponding author: QIAO Jun-Fei Professor at Beijing University of Technology. His research interest covers intelligent control, analysis and design of neural networks. Corresponding author of this paper

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基于知识的污水生化处理过程智能优化方法

doi: 10.16383/j.aas.2017.c170088

通讯作者: 乔俊飞 北京工业大学教授.主要研究方向为智能控制, 神经网络分析与设计.E-mail:junfeq@bjut.edu.cn

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出版历程

Knowledge-based Intelligent Optimal Control for Wastewater Biochemical Treatment Process

Corresponding author: QIAO Jun-Fei Professor at Beijing University of Technology. His research interest covers intelligent control, analysis and design of neural networks. Corresponding author of this paper

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出版历程

目录

通讯作者:
乔俊飞北京工业大学教授.主要研究方向为智能控制, 神经网络分析与设计.E-mail:junfeq@bjut.edu.cn