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                    數據和知識驅動的城市污水處理過程多目標優化控制

                    韓紅桂 張琳琳 伍小龍 喬俊飛

                    韓紅桂, 張琳琳, 伍小龍, 喬俊飛. 數據和知識驅動的城市污水處理過程多目標優化控制. 自動化學報, 2021, 47(x): 1?9 doi: 10.16383/j.aas.c210098
                    引用本文: 韓紅桂, 張琳琳, 伍小龍, 喬俊飛. 數據和知識驅動的城市污水處理過程多目標優化控制. 自動化學報, 2021, 47(x): 1?9 doi: 10.16383/j.aas.c210098
                    Han Hong-Gui, Zhang Lin-Lin, Wu Xiao-Long, Qiao Jun-Fei. Data-knowledge driven multiobjective optimal control for municipal wastewater treatment process. Acta Automatica Sinica, 2021, 47(x): 1?9 doi: 10.16383/j.aas.c210098
                    Citation: Han Hong-Gui, Zhang Lin-Lin, Wu Xiao-Long, Qiao Jun-Fei. Data-knowledge driven multiobjective optimal control for municipal wastewater treatment process. Acta Automatica Sinica, 2021, 47(x): 1?9 doi: 10.16383/j.aas.c210098

                    數據和知識驅動的城市污水處理過程多目標優化控制

                    doi: 10.16383/j.aas.c210098
                    基金項目: 國家重點研發項目(2018YFC1900800-5), 國家自然科學基金(61890930-5, 61903010, 62021003), 北京市卓越青年科學家計劃項目(BJJWZYJH01201910005020), 北京市教育委員會科技計劃重點項目(KZ202110005009)資助
                    詳細信息
                      作者簡介:

                      韓紅桂:北京工業大學信息學部教授. 主要研究方向為城市污水處理過程智能優化控制, 神經網絡結構設計與優化. 本文通信作者. E-mail: rechardhan@bjut.edu.cn

                      張琳琳:北京工業大學信息學部博士研究生. 主要研究方向為城市污水處理過程智能優化控制. E-mail: zhangllsy@163.com

                      伍小龍:北京工業大學信息學部講師. 主要研究方向為城市污水處理過程智能特征建模與智能控制. E-mail: lewis_wxl@sina.com

                      喬俊飛:北京工業大學信息學部教授. 主要研究方向為城市污水處理過程智能優化控制, 神經網絡結構設計與優化. E-mail: junfeq@bjut.edu.cn

                    Data-Knowledge Driven Multiobjective Optimal Control for Municipal Wastewater Treatment Process

                    Funds: Supported by National Key Research and Development Project (2018YFC1900800-5), National Science Foundation of China (61890930-5, 61903010, 62021003), Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020), and Beijing Natural Science Foundation (KZ202110005009)
                    More Information
                      Author Bio:

                      HAN Hong-Gui Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent optimal control of municipal wastewater treatment process, structure design and optimization of neural networks. Corresponding author of this paper

                      ZHANG Lin-Lin Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers intelligent optimal control of municipal wastewater treatment process

                      WU Xiao-Long Lecturer at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent feature modeling and intelligent control of municipal wastewater treatment process

                      QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent optimal control of municipal wastewater treatment process, structure design and optimization of neural networks

                    • 摘要: 城市污水處理過程優化控制是降低能耗的有效手段, 然而, 如何提高出水水質的同時降低能耗依然是當前城市污水處理過程面臨的挑戰. 圍繞上述挑戰, 文中提出了一種數據和知識驅動的多目標優化控制(Data-knowledge driven multiobjective optimal control, DK-MOC)方法. 首先, 建立了出水水質、能耗以及系統運行狀態的表達關系, 獲得了運行過程優化目標模型. 其次, 提出了一種基于知識遷徙學習的動態多目標粒子群優化算法, 實現了控制變量優化設定值的自適應求解. 最后, 將提出的DK-MOC應用于城市污水處理過程基準仿真模型1 (Benchmark simulation model no. 1, BSM1). 結果表明該方法能夠實時獲取控制變量的優化設定值, 提高了出水水質, 并且有效降低了運行能耗.
                    • 圖  1  DK-MOC的流程圖

                      Fig.  1  The flow chart of DK-MOC

                      圖  2  控制變量設定值求解步驟

                      Fig.  2  The solution procedure of set values of control variables

                      圖  3  干旱天氣下出水水質建模結果

                      Fig.  3  The modeling results of effluent quality in dry weather

                      圖  4  干旱天氣下能耗建模結果

                      Fig.  4  The modeling results of energy consumption in dry weather

                      圖  5  干旱天氣下平均出水水質值

                      Fig.  5  Average values of effluent quality in dry weather

                      圖  6  干旱天氣下平均能耗值

                      Fig.  6  Average values of energy consumption in dry weather

                      圖  7  干旱天氣下溶解氧濃度跟蹤性能

                      Fig.  7  Tracking performance of SO in dry weather

                      圖  8  干旱天氣下硝態氮濃度跟蹤性能

                      Fig.  8  Tracking performance of SNO in dry weather

                      圖  9  干旱天氣下硝態氮和溶解氧濃度跟蹤誤差

                      Fig.  9  Tracking errors of SNO and SO in dry weather

                      表  1  干旱天氣下能耗和出水水質的測量精度

                      Table  1  The measurement accuracy of energy consumption and effluent quality in dry weather

                      模型 EC EQ
                      RMSE PA RMSE PA
                      AKF 0.0082 98.71% 0.0079 97.06%
                      GA-ANN 0.0123 93.36% 0.0124 95.52%
                      LSSVM 0.0115 96.21% 0.0139 94.31%
                      AFNN 0.0079 98.99% 0.0070 98.73%
                      下載: 導出CSV

                      表  2  陰雨天氣下能耗和出水水質的測量精度

                      Table  2  The measurement accuracy of energy consumption and effluent quality in rainy weather

                      模型 EC EQ
                      RMSE PA RMSE PA
                      AKF 0.0093 96.60% 0.0833 97.37%
                      GA-ANN 0.0146 95.72% 0.1002 94.32%
                      LSSVM 0.0138 95.74% 0.0940 96.76%
                      AFNN 0.0089 98.88% 0.0776 98.89%
                      下載: 導出CSV

                      表  3  DK-MOC的出水水質結果

                      Table  3  Effluent quality results obtained by DK-MOC

                      水質指標 干旱天氣 陰雨天氣 排放標準
                      SNH/mg·L?1 3.44 3.89 <4
                      Ntot/mg·L?1 17.41 17.01 <18
                      TSS/mg·L?1 12.57 13.51 <30
                      COD/mg·L?1 47.75 46.53 <100
                      BOD/mg·L?1 2.71 2.93 <10
                      下載: 導出CSV

                      表  4  不同優化控制策略的比較結果

                      Table  4  Comparison results of different optimal control strategies

                      優化控制策略 干旱天氣 陰雨天氣
                      EC
                      kW·h
                      EQ
                      Kg Poll.units
                      EC
                      kW·h
                      EQ
                      Kg Poll.units
                      AFNN+NSGAII+PID 4015 8867 4969 9049
                      AFNN+CMOPSO+PID 4770 8915 4869 9293
                      AFNN+DMOPSO+PID 3848 7695 3870 7944
                      AFNN+KT-DSOPSO+PID 3727 9062 3892 9479
                      DK-MOC 3641 7179 3821 8106
                      下載: 導出CSV
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                    • 收稿日期:  2021-02-10
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