1. <button id="qm3rj"><thead id="qm3rj"></thead></button>
      <samp id="qm3rj"></samp>
      <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source>

      <video id="qm3rj"><code id="qm3rj"></code></video>

        1. <tt id="qm3rj"><track id="qm3rj"></track></tt>
            1. 2.845

              2023影響因子

              (CJCR)

              • 中文核心
              • EI
              • 中國科技核心
              • Scopus
              • CSCD
              • 英國科學(xué)文摘

              留言板

              尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問(wèn)題, 您可以本頁(yè)添加留言。我們將盡快給您答復。謝謝您的支持!

              姓名
              郵箱
              手機號碼
              標題
              留言?xún)热?/th>
              驗證碼

              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

              蘇尹, 楊翠麗, 喬俊飛. 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. 自動(dòng)化學(xué)報, 2024, 50(6): 1199?1209 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(jīng)網(wǎng)絡(luò )的污水處理過(guò)程多變量控制. 自動(dòng)化學(xué)報, 2024, 50(6): 1199?1209 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): 1199?1209 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): 1199?1209 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
              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

              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 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: 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.
              • 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(shí)DO控制結果

                Fig.  5  Control results of DO under different wavelet functions

                圖  6  SRWNN小波節點(diǎn)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(xiàn)

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

                圖  12  $Q_a$變化曲線(xiàn)

                Fig.  12  The change curves of $Q_a$

                圖  13  SRWNN小波節點(diǎn)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(xiàn)

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

                圖  19  $Q_a$變化曲線(xiàn)

                Fig.  19  The change curves of $Q_a$

                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.DONO
                IAEISEDEV_MAXIAEISEDEV_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: “$*$”表示原文中的結果, “—”表示無(wú)相應數據.
                下載: 導出CSV

                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.DONO
                IAEISEDEV_MAXIAEISEDEV_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: “$*$”表示原文中的結果, “—”表示無(wú)相應數據.
                下載: 導出CSV
                1. <button id="qm3rj"><thead id="qm3rj"></thead></button>
                  <samp id="qm3rj"></samp>
                  <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source>

                  <video id="qm3rj"><code id="qm3rj"></code></video>

                    1. <tt id="qm3rj"><track id="qm3rj"></track></tt>
                        亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页
                      1. [1] Tang W Z, Pei Y S, Zheng H, Zhao Y, Shu L M, Zhang H. Twenty years of China's water pollution control: Experiences and challenges. Chemosphere, 2022, 295: Article No. 133875 doi: 10.1016/j.chemosphere.2022.133875
                        [2] 楊翠麗, 武戰紅, 韓紅桂, 喬俊飛. 城市污水處理過(guò)程優(yōu)化設定方法研究進(jìn)展. 自動(dòng)化學(xué)報, 2020, 46(10): 2092?2108

                        Yang Cui-Li, Wu Zhan-Hong, Han Hong-Gui, Qiao Jun-Fei. Perspectives on optimal setting methods for municipal wastewater treatment processes. Acta Automatica Sinica, 2020, 46(10): 2092?2108
                        [3] Lizarralde I, Fernández-Arévalo T, Brouckaert C, Vanrolleghem P, Ikumi D S, Ekama G A, et al. A new general methodology for incorporating physico-chemical transformations into multi-phase wastewater treatment process models. Water Research, 2015, 74: 239?256 doi: 10.1016/j.watres.2015.01.031
                        [4] 潘南全. 基于前饋控制模型的生化反應池曝氣控制優(yōu)化. 工控制計算機, 2015, 28(1): 58?60

                        Pang Nan-Quan. Optimization aeration control of reaction tank based on feed forward control model. Industrial Control Computer, 2015, 28(1): 58?60
                        [5] Du S L, Yan Q S, Qiao J F. Event-triggered PID control for wastewater treatment plants. Journal of Water Process Engineering, 2022, 47: Article No. 102765 doi: 10.1016/j.jwpe.2020.101659
                        [6] 曾春霞, 董宗哲, 何濤. 模糊代數PID控制在污水處理溶解氧控制系統的應用. 化工自動(dòng)化及儀表, 2021, 48(6): 528?534 doi: 10.3969/j.issn.1000-3932.2021.06.003

                        Zeng Chun-Xia, Dong Zong-Zhe, He Tao. Application of fuzzy algebra PID control in dissolved oxygen control system of wastewater treatment. Control and Instruments in Chemical Industry, 2021, 48(6): 528?534 doi: 10.3969/j.issn.1000-3932.2021.06.003
                        [7] 劉鎖清, 劉少虹, 李軍紅, 彭偉娟. 基于模糊自整定PID串級控制的廢水處理PH值控制. 自動(dòng)化技術(shù)與應用, 2019, 38(2): 22?27 doi: 10.3969/j.issn.1003-7241.2019.02.006

                        Liu Suo-Qing, Liu Shao-Hong, Li Jun-Hong, Peng Wei-Juan. Wastewater treatment PH value control based on fuzzy self-tuning PID cascade control. Techniques of Automation and Applications, 2019, 38(2): 22?27 doi: 10.3969/j.issn.1003-7241.2019.02.006
                        [8] Hoang B L, Tien D N, Luo F, Nguyen P H. Dissolved oxygen control of the activated sludge wastewater treatment process using Hedge Algebraic control. In: Proceedings of the 7th International Conference on Biomedical Engineering and Informatics. Dalian, China: IEEE, 2014. 827?832
                        [9] 許進(jìn)超, 楊翠麗, 喬俊飛, 馬士杰. 基于自組織模糊神經(jīng)網(wǎng)絡(luò )溶解氧控制方法研究. 智能系統學(xué)報, 2018, 13(6): 905?912 doi: 10.11992/tis.201801019

                        Xu Jin-Chao, Yang Cui-Li, Qiao Jun-Fei, Ma Shi-Jie. Dissolved oxygen concentration control method based on self-organizing fuzzy neural network. CAAI Transactions on Intelligent Systems, 2018, 13(6): 905?912 doi: 10.11992/tis.201801019
                        [10] Wang D, Ha M M, Qiao J F. Data-driven iterative adaptive critic control toward an urban wastewater treatment plant. IEEE Transactions on Industrial Electronics, 2021, 68(8): 7362?7369 doi: 10.1109/TIE.2020.3001840
                        [11] Nawaz A, Arora A S, Yun C M, Lee J J, Lee M. Development of smart AnAmmOx system and its agile operation and decision support for pilot-scale WWTP. Soft Computing Techniques in Solid Waste and Wastewater Management. Amsterdam: Elsevier, 2021. 7423?7454
                        [12] 韓紅桂, 張璐, 盧薇, 喬俊飛. 城市污水處理過(guò)程動(dòng)態(tài)多目標智能優(yōu)化控制研究. 自動(dòng)化學(xué)報, 2021, 47(3): 620?629

                        Han Hong-Gui, Zhang Lu, Lu Wei, Qiao Jun-Fei. Research on dynamic multiobjective intelligent optimal control for municipal wastewater treatment process. Acta Automatica Sinica, 2021, 47(3): 620?629
                        [13] 喬俊飛, 韓改堂, 周紅標. 基于知識的污水生化處理過(guò)程智能優(yōu)化方法. 自動(dòng)化學(xué)報, 2017, 43(6): 1038?1046

                        Qiao Jun-Fei, Han Gai-Tang, Zhou Hong-Biao. Knowledge-based intelligent optimal control for wastewater biochemical treatment process. Acta Automatica Sinica, 2017, 43(6): 1038?1046
                        [14] Vega P, Revollar S, Francisco M, Martín J M. Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs. Computers and Chemical Engineering, 2014, 68: 78?95 doi: 10.1016/j.compchemeng.2014.03.027
                        [15] Han H G, Liu H X, Li J M, Qiao J F. Cooperative fuzzy-neural control for wastewater treatment process. IEEE Transactions on Industrial Informatics, 2021, 17(9): 5971?5981 doi: 10.1109/TII.2020.3034335
                        [16] El-Sousy F F M, Abuhasel K A. Adaptive nonlinear disturbance observer using a double-loop self-organizing recurrent wavelet neural network for a two-axis motion control system. IEEE Transactions on Industry Applications, 2018, 54(1): 764?786 doi: 10.1109/TIA.2017.2763584
                        [17] 王桐, 邱劍彬, 高會(huì )軍. 隨機非線(xiàn)性系統基于事件觸發(fā)機制的自適應神經(jīng)網(wǎng)絡(luò )控制. 自動(dòng)化學(xué)報, 2019, 45(1): 226?233

                        Wang Tong, Qiu Jian-Bin, Gao Hui-Jun. Event-triggered adaptive neural network control for a class of stochastic nonlinear systems. Acta Automatica Sinica, 2019, 45(1): 226?233
                        [18] Liu Y J, Li J, Tong S C, Chen C L P. Neural network control-based adaptive learning design for nonlinear systems with full-state constraints. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(7): 1562?1571 doi: 10.1109/TNNLS.2015.2508926
                        [19] Lee C H, Chang H H. Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems. Neural Computing and Applications, 2014, 24(5): 1035?1045 doi: 10.1007/s00521-012-1326-2
                        [20] Lin C H. A novel hybrid recurrent wavelet neural network control of permanent magnet synchronous motor drive for electric scooter. Turkish Journal of Electrical Engineering and Computer Sciences, 2014, 22(4): 1056?1075
                        [21] 張偉, 喬俊飛, 李凡軍. 溶解氧濃度的直接自適應動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò )控制方法. 控制理論與應用, 2015, 32(1): 115?121 doi: 10.7641/CTA.2014.40311

                        Zhang Wei, Qiao Jun-Fei, Li Fan-Jun. Direct adaptive dynamic neural network control for dissolved oxygen concentration. Control Theory & Applications, 2015, 32(1): 115?121 doi: 10.7641/CTA.2014.40311
                        [22] El-Sousy F F M, Abuhasel K A, Self-organizing recurrent fuzzy wavelet neural network-based mixed $ {H_2/H_\infty } $ adaptive tracking control for uncertain two-axis motion control system. In: Proceedings of the IEEE Industry Applications Society Annual Meeting. Addison, USA: IEEE, 2015. 1?14
                        [23] Han H G, Zhang L, Hou Y, Qiao J F. Nonlinear model predictive control based on a self-organizing recurrent neural network. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2): 402?415 doi: 10.1109/TNNLS.2015.2465174
                        [24] Ku C C, Lee K Y. Diagonal recurrent neural networks for dynamic systems control. IEEE Transactions on Neural Networks, 1995, 6(1): 144?156 doi: 10.1109/72.363441
                        [25] 韓廣, 喬俊飛, 薄迎春. 溶解氧濃度的前饋神經(jīng)網(wǎng)絡(luò )建??刂品椒? 控制理論與應用, 2013, 30(5): 585?591 doi: 10.7641/CTA.2013.20773

                        Han Guang, Qiao Jun-Fei, Bo Ying-Chun. Feedforward neural network modeling and control for dissolved oxygen concentration. Control Theory & Applications, 2013, 30(5): 585?591 doi: 10.7641/CTA.2013.20773
                        [26] Qiao J F, Han G T, Han H G, Chai W. Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network. International Journal of Intelligent Computing and Cybernetics, 2017, 10(2): 94?110 doi: 10.1108/IJICC-12-2016-0069
                        [27] Qiao J F, Han G T, Han H G, Yang C L, Chai W. Decoupling control for wastewater treatment process based on recurrent fuzzy neural network. Asian Journal of Control, 2019, 21(3): 1270?1280 doi: 10.1002/asjc.1844
                        [28] 韓改堂, 喬俊飛, 韓紅桂. 基于自適應遞歸模糊神經(jīng)網(wǎng)絡(luò )的污水處理控制. 控制理論與應用, 2016, 33(9): 1252?1258 doi: 10.7641/CTA.2016.50965

                        Han Gai-Tang, Qiao Jun-Fei, Han Hong-Gui. Wastewater treatment control method based on adaptive recurrent fuzzy neural network. Control Theory and Applications, 2016, 33(9): 1252?1258 doi: 10.7641/CTA.2016.50965
                      2. 加載中
                      3. 圖(19) / 表(2)
                        計量
                        • 文章訪(fǎng)問(wèn)數:  330
                        • 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

                        目錄

                          /

                          返回文章
                          返回