基于自组织递归小波神经网络的污水处理过程多变量控制

苏尹; 杨翠丽; 乔俊飞

doi:10.16383/j.aas.c220679

基于自组织递归小波神经网络的污水处理过程多变量控制

doi: 10.16383/j.aas.c220679

苏尹^{1, 2,},
杨翠丽^1,,
乔俊飞^1,

1.
北京工业大学信息学部计算智能与智能系统北京重点实验室北京 100124
2.
嘉兴大学信息科学与工程学院嘉兴 314001

基金项目: 国家自然科学基金 (61890930-5, 62021003, 61973010), 国家重点研发计划(2021ZD0112302) 资助

详细信息

作者简介:
苏尹：嘉兴大学信息科学与工程学院讲师. 2023年获得北京工业大学控制科学与工程专业博士学位. 主要研究方向为基于神经网络的城市污水处理过程预测及过程控制. E-mail: suy@zjxu.edu.cn

杨翠丽：北京工业大学信息学部副教授. 2008年获得中国石油大学(东营)工学学士学位, 2010年获得天津大学理学硕士学位, 2014年获得香港城市大学博士学位. 主要研究方向为计算智能, 污水处理过程的建模与控制. E-mail: clyang5@bjut.edu

乔俊飞：北京工业大学信息学部教授. 分别于1992年和1995年获得辽宁工业大学控制工程学士和硕士学位, 1998年获得东北大学博士学位. 主要研究方向为神经网络, 智能系统, 自适应系统和过程控制. 本文通信作者. E-mail: adqiao@bjut.edu.cn

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出版历程
- 收稿日期: 2022-08-29
- 网络出版日期: 2024-03-28
- 刊出日期: 2024-06-20

Multivariate Control of Wastewater Treatment Process Based on Self-organized Recurrent Wavelet Neural Network

SU Yin^{1, 2
,},
YANG Cui-Li^1
,,
QIAO Jun-Fei^1
,

1.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
College of Information Science and Engineering, Jiaxing University, Jiaxing 314001

Funds: Supported by National Natural Science Foundation of China (61890930-5, 62021003, 61973010) and National Key Research and Development Program of China (2021ZD0112302)

More Information

Author Bio:
SU Yin　Lecturer at the College of Information Science and Engineering, Jiaxing University. She received her Ph.D. degree in control science and engineering from Beijing University of Technology in 2023. Her research interest covers neural network-based urban wastewater treatment process prediction and process control

YANG Cui-Li　Associate professor at the Faculty of Information Technology, Beijing University of Technology. She received her bachelor degree from China University of Petroleum (Dongying) in 2008, master degree from Tianjin University in 2010, and Ph.D. degree from City University of Hong Kong, Hong Kong, China, in 2014. Her research interest covers computational intelligence, and modeling and control for wastewater treatment process

QIAO Jun-Fei　Professor at the Faculty of Information Technology, Beijing University of Technology. He received his bachelor and master degrees in control engineering from Liaoning Technical University in 1992 and 1995, respectively, and his Ph.D. degree from Northeastern University in 1998. His research interest covers neural networks, intelligent systems, self-adaptive systems, and process control. Corresponding author of this paper

摘要

摘要: 污水处理过程(Wastewater treatment process, WWTP)是一个包含多个生化反应的复杂过程, 具有非线性和动态特性. 因此, 实现污水处理过程的精准控制是一项挑战. 为解决这个问题, 提出一种基于自组织递归小波神经网络(Self-organized recurrent wavelet neural network, SRWNN)的污水处理过程多变量控制. 首先, 针对污水处理过程的动态特性, 根据小波基的激活强度设计一种自组织机制来动态调整递归小波神经网络控制器的结构, 提高控制的性能. 然后, 采用结合自适应学习率的在线学习算法, 实现控制器的参数学习. 此外, 通过李雅普诺夫稳定性定理证明此控制器的稳定性. 最后, 采用基准仿真平台进行仿真验证, 实验结果表明, 此控制方法可以有效提高污水处理过程的控制绝对误差积分(Integral of absolute error, IAE)和积分平方误差(Integral of squared error, ISE)的精度.
- 神经网络控制 /
- 污水处理过程 /
- 自组织机制 /
- 多变量控制
Abstract: The wastewater treatment process (WWTP) is a complex process containing multiple biochemical reactions with nonlinear and dynamic characteristics. Therefore, it is a challenge to achieve accurate control of the wastewater treatment process. To solve this problem, a multi-variable control of wastewater treatment process based on the self-organized recurrent wavelet neural network (SRWNN) is proposed. Firstly, to deal with the dynamicity of wastewater treatment process, according to the firing strength of the wavelet base, the self-organizing mechanism is designed to dynamically adjust the structure of the recurrent wavelet neural network controller to improve the control performance. Then, an online learning algorithm combined with adaptive learning rate is used to learn the parameters of controller. In addition, the stability of the controller is proved by the Lyapunov stability theorem. Finally, the benchmark simulation platform is used to conduct simulation. The experimental results show that this control method can effectively improve the integral of absolute error (IAE) and integral of squared error (ISE) of the wastewater treatment process.
- Neural network control /
- wastewater treatment process (WWTP) /
- self-organized mechanism /
- multi-variable control

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图 1 活性污泥法

Fig. 1 Activated sludge method

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图 2 控制框图

Fig. 2 Control block diagram

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图 3 SRWNN结构图

Fig. 3 The structure of SRWNN

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图 4 控制流程图

Fig. 4 The flow chart of control

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图 5 不同小波函数时DO控制结果

Fig. 5 Control results of DO under different wavelet functions

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图 6 SRWNN小波节点变化图

Fig. 6 Change of SRWNN wavelet node

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图 7 晴天工况下DO控制结果

Fig. 7 Control results of DO under sunny condition

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图 8 晴天工况下NO控制结果

Fig. 8 Control results of NO under sunny condition

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图 9 阴雨工况下DO控制结果

Fig. 9 Control results of DO under cloudy and rain conditions

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图 10 阴雨工况下NO控制结果

Fig. 10 Control results of NO under cloudy and rain conditions

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图 11 $K_{La5}$变化曲线

Fig. 11 The change curves of $K_{La5}$

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图 12 $Q_a$变化曲线

Fig. 12 The change curves of $Q_a$

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图 13 SRWNN小波节点变化图

Fig. 13 Change of SRWNN wavelet node

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图 14 晴天工况下DO控制结果

Fig. 14 Control results of DO under sunny condition

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图 15 晴天工况下NO控制结果

Fig. 15 Control results of NO under sunny condition

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图 16 阴雨工况下DO控制结果

Fig. 16 Control results of DO under cloudy and rain conditions

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图 17 阴雨工况下NO控制结果

Fig. 17 Control results of NO under cloudy and rain conditions

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图 18 $K_{La5}$变化曲线

Fig. 18 The change curves of $K_{La5}$

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图 19 $Q_a$变化曲线

Fig. 19 The change curves of $Q_a$

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表 1 不同控制方法在恒定设定值时的性能比较

Table 1 Performance comparison of different control methods at constant set-point

工况	控制器	No.	DO			NO
工况	控制器	No.	IAE	ISE	DEV_MAX	IAE	ISE	DEV_MAX
晴天	SRWNN	3	5.66×10⁻⁴	1.63×10⁻⁶	0.0087	0.0036	7.61×10⁻⁵	0.0114
	RWNN	5	0.0017	3.26×10⁻⁵	0.0526	0.0020	3.06×10⁻⁵	0.0540
	NNOMC	10	0.0390*	5.31×10⁻⁴*	0.0725*	0.0490*	7.18×10⁻⁴*	0.1630*
	RARFNNC	4	0.0073*	1.61×10⁻⁴*	0.0104*	0.0126*	2.83×10⁻⁴*	0.1050*
	DRFNNC	6	0.0079*	1.82×10⁻⁴*	0.0154*	0.0085*	3.25×10⁻⁴*	0.0176*
阴雨	SRWNN	4	0.0041	1.75×10⁻⁴	0.1042	0.0101	9.80×10⁻⁴	0.1291
	RWNN	5	0.0051	2.21×10⁻⁴	0.1434	0.0117	1.40×10⁻³	0.2244
	PID	—	0.0016	1.90×10⁻³	0.2038	0.0317	8.23×10⁻³	0.3233
注: “$*$”表示原文中的结果, “—”表示无相应数据.

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表 2 不同控制方法在变化设定值时的性能比较

Table 2 Performance comparison of different control methods at changed set-point

工况	控制器	No.	DO			NO
工况	控制器	No.	IAE	ISE	DEV_MAX	IAE	ISE	DEV_MAX
晴天	SRWNN	3	0.0067	3.68×10⁻⁶	0.0156	0.0061	1.64×10⁻⁴	0.0067
	RWNN	5	0.0087	2.62×10⁻⁴	0.1156	0.0126	2.30×10⁻³	0.1116
	PID	—	0.0127	2.38×10⁻³	0.1038	0.0271	4.90×10⁻³	0.2184
阴雨	SRWNN	3	0.0047	1.10×10⁻⁴	0.0538	0.0065	3.18×10⁻⁴	0.1527
	RWNN	5	0.0069	1.92×10⁻⁴	0.0644	0.0088	4.58×10⁻⁴	0.1781
	RFNNC	—	0.0240*	2.40×10⁻³*	0.0863	0.0260*	1.00×10⁻³*	0.1881*
注: “$*$”表示原文中的结果, “—”表示无相应数据.

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