基于自組織遞歸小波神經(jīng)網(wǎng)絡(luò )的污水處理過(guò)程多變量控制

蘇尹; 楊翠麗; 喬俊飛

doi:10.16383/j.aas.c220679

基于自組織遞歸小波神經(jīng)網(wǎng)絡(luò )的污水處理過(guò)程多變量控制

doi: 10.16383/j.aas.c220679

蘇尹^{1, 2,},
楊翠麗^1,,
喬俊飛^1,

1.
北京工業(yè)大學(xué)信息學(xué)部計算智能與智能系統北京重點(diǎn)實(shí)驗室北京 100124
2.
嘉興大學(xué)信息科學(xué)與工程學(xué)院嘉興 314001

基金項目: 國家自然科學(xué)基金 (61890930-5, 62021003, 61973010), 國家重點(diǎn)研發(fā)計劃(2021ZD0112302) 資助

詳細信息

作者簡(jiǎn)介:
蘇尹：嘉興大學(xué)信息科學(xué)與工程學(xué)院講師. 2023年獲得北京工業(yè)大學(xué)控制科學(xué)與工程專(zhuān)業(yè)博士學(xué)位. 主要研究方向為基于神經(jīng)網(wǎng)絡(luò )的城市污水處理過(guò)程預測及過(guò)程控制. E-mail: suy@zjxu.edu.cn

楊翠麗：北京工業(yè)大學(xué)信息學(xué)部副教授. 2008年獲得中國石油大學(xué)(東營(yíng))工學(xué)學(xué)士學(xué)位, 2010年獲得天津大學(xué)理學(xué)碩士學(xué)位, 2014年獲得香港城市大學(xué)博士學(xué)位. 主要研究方向為計算智能, 污水處理過(guò)程的建模與控制. E-mail: clyang5@bjut.edu

喬俊飛：北京工業(yè)大學(xué)信息學(xué)部教授. 分別于1992年和1995年獲得遼寧工業(yè)大學(xué)控制工程學(xué)士和碩士學(xué)位, 1998年獲得東北大學(xué)博士學(xué)位. 主要研究方向為神經(jīng)網(wǎng)絡(luò ), 智能系統, 自適應系統和過(guò)程控制. 本文通信作者. E-mail: adqiao@bjut.edu.cn

計量
- 文章訪(fǎng)問(wèn)數: 328
- HTML全文瀏覽量: 89
- PDF下載量: 108
- 被引次數: 0
出版歷程
- 收稿日期: 2022-08-29
- 網(wǎng)絡(luò )出版日期: 2024-03-28
- 刊出日期: 2024-06-27

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: 污水處理過(guò)程(Wastewater treatment process, WWTP)是一個(gè)包含多個(gè)生化反應的復雜過(guò)程, 具有非線(xiàn)性和動(dòng)態(tài)特性. 因此, 實(shí)現污水處理過(guò)程的精準控制是一項挑戰. 為解決這個(gè)問(wèn)題, 提出一種基于自組織遞歸小波神經(jīng)網(wǎng)絡(luò )(Self-organized recurrent wavelet neural network, SRWNN)的污水處理過(guò)程多變量控制. 首先, 針對污水處理過(guò)程的動(dòng)態(tài)特性, 根據小波基的激活強度設計一種自組織機制來(lái)動(dòng)態(tài)調整遞歸小波神經(jīng)網(wǎng)絡(luò )控制器的結構, 提高控制的性能. 然后, 采用結合自適應學(xué)習率的在線(xiàn)學(xué)習算法, 實(shí)現控制器的參數學(xué)習. 此外, 通過(guò)李雅普諾夫穩定性定理證明此控制器的穩定性. 最后, 采用基準仿真平臺進(jìn)行仿真驗證, 實(shí)驗結果表明, 此控制方法可以有效提高污水處理過(guò)程的控制絕對誤差積分(Integral of absolute error, IAE)和積分平方誤差(Integral of squared error, ISE)的精度.
- 神經(jīng)網(wǎng)絡(luò )控制 /
- 污水處理過(guò)程 /
- 自組織機制 /
- 多變量控制
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

HTML全文

圖 1 活性污泥法

Fig. 1 Activated sludge method

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圖 2 控制框圖

Fig. 2 Control block diagram

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圖 3 SRWNN結構圖

Fig. 3 The structure of SRWNN

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圖 4 控制流程圖

Fig. 4 The flow chart of control

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圖 5 不同小波函數時(shí)DO控制結果

Fig. 5 Control results of DO under different wavelet functions

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圖 6 SRWNN小波節點(diǎn)變化圖

Fig. 6 Change of SRWNN wavelet node

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圖 7 晴天工況下DO控制結果

Fig. 7 Control results of DO under sunny condition

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圖 8 晴天工況下NO控制結果

Fig. 8 Control results of NO under sunny condition

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圖 9 陰雨工況下DO控制結果

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

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圖 10 陰雨工況下NO控制結果

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

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圖 11 $K_{La5}$變化曲線(xiàn)

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

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圖 12 $Q_a$變化曲線(xiàn)

Fig. 12 The change curves of $Q_a$

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圖 13 SRWNN小波節點(diǎn)變化圖

Fig. 13 Change of SRWNN wavelet node

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圖 14 晴天工況下DO控制結果

Fig. 14 Control results of DO under sunny condition

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圖 15 晴天工況下NO控制結果

Fig. 15 Control results of NO under sunny condition

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圖 16 陰雨工況下DO控制結果

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

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圖 17 陰雨工況下NO控制結果

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

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圖 18 $K_{La5}$變化曲線(xiàn)

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

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圖 19 $Q_a$變化曲線(xiàn)

Fig. 19 The change curves of $Q_a$

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表 1 不同控制方法在恒定設定值時(shí)的性能比較

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

工況	控制器	No.	DO			NO
工況	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*
陰雨	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: “$*$”表示原文中的結果, “—”表示無(wú)相應數據.

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表 2 不同控制方法在變化設定值時(shí)的性能比較

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

工況	控制器	No.	DO			NO
工況	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
陰雨	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: “$*$”表示原文中的結果, “—”表示無(wú)相應數據.

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