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              基于自組織遞歸小波神經網絡的污水處理過程多變量控制

              蘇尹 楊翠麗 喬俊飛

              蘇尹, 楊翠麗, 喬俊飛. 基于自組織遞歸小波神經網絡的污水處理過程多變量控制. 自動化學報, 2024, 50(6): 1001?1011 doi: 10.16383/j.aas.c220679
              引用本文: 蘇尹, 楊翠麗, 喬俊飛. 基于自組織遞歸小波神經網絡的污水處理過程多變量控制. 自動化學報, 2024, 50(6): 1001?1011 doi: 10.16383/j.aas.c220679
              Su Yin, Yang Cui-Li, Qiao Jun-Fei. Multivariate control of wastewater treatment process based on self-organized recurrent wavelet neural network. Acta Automatica Sinica, 2024, 50(6): 1001?1011 doi: 10.16383/j.aas.c220679
              Citation: Su Yin, Yang Cui-Li, Qiao Jun-Fei. Multivariate control of wastewater treatment process based on self-organized recurrent wavelet neural network. Acta Automatica Sinica, 2024, 50(6): 1001?1011 doi: 10.16383/j.aas.c220679

              基于自組織遞歸小波神經網絡的污水處理過程多變量控制

              doi: 10.16383/j.aas.c220679
              基金項目: 國家自然科學基金 (61890930-5, 62021003, 61973010), 國家重點研發計劃(2021ZD0112302) 資助
              詳細信息
                作者簡介:

                蘇尹:嘉興大學信息科學與工程學院講師. 2023年獲得北京工業大學控制科學與工程博士學位. 主要研究方向為基于神經網絡的城市污水處理過程預測及過程控制. E-mail: suy@zjxu.edu.cn

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

                喬俊飛:北京工業大學信息學部教授. 分別于1992年和1995年獲得遼寧工業大學控制工程學士和碩士學位, 1998年獲得東北大學博士學位. 主要研究方向為神經網絡, 智能系統, 自適應系統和過程控制. 本文通信作者. E-mail: adqiao@bjut.edu.cn

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

              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 Institute 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: 污水處理過程(Wastewater treatment process, WWTP)是一個包含多個生化反應的復雜過程, 具有非線性和動態特性. 因此, 實現污水處理過程的精準控制是一項挑戰. 為解決這個問題, 提出一種基于自組織遞歸小波神經網絡(Self-organizing recurrent wavelet neural network, SRWNN)的污水處理過程多變量控制. 首先, 針對污水處理過程的動態特性, 根據小波基的激活強度設計一種自組織機制來動態調整遞歸小波神經網絡控制器的結構, 提高控制的性能. 然后, 采用結合自適應學習率的在線學習算法, 實現控制器的參數學習. 此外, 通過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行仿真驗證, 實驗結果表明, 此控制方法可以有效提高污水處理過程的控制絕對積分誤差和平方誤差積分的精度.
              • 圖  1  活性污泥法

                Fig.  1  Activated sludge method

                圖  2  控制框圖

                Fig.  2  Control block diagram

                圖  3  SRWNN結構圖

                Fig.  3  The structure of SRWNN

                圖  4  控制流程圖

                Fig.  4  The flow chart of control

                圖  5  不同小波函數時DO控制結果

                Fig.  5  Control results of DO under different wavelet functions

                圖  6  SRWNN小波節點變化圖

                Fig.  6  Change of SRWNN wavelet node

                圖  7  晴天工況下DO控制結果

                Fig.  7  Control results of DO under sunny condition

                圖  8  晴天工況下NO控制結果

                Fig.  8  Control results of NO under sunny condition

                圖  9  陰雨工況下DO控制結果

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

                圖  10  陰雨工況下NO控制結果

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

                圖  11  $K_{La5}$變化曲線

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

                圖  12  $Q_a$變化曲線

                Fig.  12  The change curves of $Q_a$

                圖  13  SRWNN小波節點變化圖

                Fig.  13  Change of SRWNN wavelet node

                圖  14  晴天工況下DO控制結果

                Fig.  14  Control results of DO under sunny condition

                圖  15  晴天工況下NO控制結果

                Fig.  15  Control results of NO under sunny condition

                圖  16  陰雨工況下DO控制結果

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

                圖  17  陰雨工況下NO控制結果

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

                圖  18  $K_{La5}$變化曲線

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

                圖  19  $Q_a$變化曲線

                Fig.  19  The change curves of $Q_a$

                表  1  不同控制方法在恒定設定值時的性能比較

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

                工況控制器No.DONO
                IAEISE$ \text{Dev}^{\text{max}} $IAEISE$ \text{Dev}^{\text{max}} $
                晴天SRWNN35.66×10?41.63×10?60.00870.00367.61×10?50.0114
                RWNN50.00173.26×10?50.05260.00203.06×10?50.0540
                NNOMC100.0390*5.31×10?4*0.0725*0.0490*7.18×10?4*0.1630*
                RARFNNC40.0073*1.61×10?4*0.0104*0.0126*2.83×10?4*0.1050*
                DRFNNC60.0079*1.82×10?4*0.0154*0.0085*3.25×10?4*0.0176*
                陰雨SRWNN40.00411.75×10?40.10420.01019.80×10?40.1291
                RWNN50.00512.21×10?40.14340.01171.40×10?30.2244
                PID0.00161.90×10?30.20380.03178.23×10?30.3233
                注: “$ \star $”表示原文中的結果, “—”表示無相應數據.
                下載: 導出CSV

                表  2  不同控制方法在變化設定值時的性能比較

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

                工況控制器No.DONO
                IAEISE$ \text{Dev}^{\text{max}} $IAEISE$ \text{Dev}^{\text{max}} $
                晴天SRWNN30.00673.68×10?60.01560.00611.64×10?40.0067
                RWNN50.00872.62×10?40.11560.01262.30×10?30.1116
                PID0.01272.38×10?30.10380.02714.90×10?30.2184
                陰雨SRWNN30.00471.10×10?40.05380.00653.18×10?40.1527
                RWNN50.00691.92×10?40.06440.00884.58×10?40.1781
                RFNNC0.0240*2.40×10-3*0.08630.0260*1.00×10?3*0.1881*
                注: “$ \star $”表示原文中的結果, “—”表示無相應數據.
                下載: 導出CSV
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