知識和數據驅動的污水處理反硝化脫氮過程協同優化控制

韓紅桂; 王玉爽; 劉崢; 孫浩源; 喬俊飛

doi:10.16383/j.aas.c230695

知識和數據驅動的污水處理反硝化脫氮過程協同優化控制

doi: 10.16383/j.aas.c230695

韓紅桂^{1, 2, 3,},
王玉爽^{1, 2, 3,},
劉崢^{1, 2, 3,},
孫浩源^{1, 2, 3,},
喬俊飛^{1, 2,}

1.
北京工業大學信息學部北京 100124
2.
計算智能與智能系統北京市重點實驗室北京 100124
3.
數字社區教育部工程研究中心北京 100124

基金項目: 國家自然科學基金(62125301, 62021003, 62103010, 62303024), 國家重點研發計劃(2022YFB3305800-5), 中國博士后科學基金(2022M720319), 北京市自然科學基金(KZ202110005009), 青年北京學者項目(037), 北京市博士后工作經費資助項目(2023-zz-91)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

王玉爽：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, 協同優化控制. E-mail: wangyushuang@emails.bjut.edu.cn

劉崢：北京工業大學信息學部講師. 主要研究方向為神經網絡, 智能系統, 過程系統的建模和控制. E-mail: liuzheng@bjut.edu.cn

孫浩源：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網絡化控制, 隨機采樣控制. E-mail: sunhaoyuan@bjut.edu.cn

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: adqiao@bjut.edu.cn

計量
- 文章訪問數: 96
- HTML全文瀏覽量: 45
- 被引次數: 0
出版歷程
- 收稿日期: 2023-11-09
- 錄用日期: 2024-01-10
- 網絡出版日期: 2024-03-25

Knowledge-data-driven Cooperative Optimal Control for Wastewater Treatment Denitrification Process

HAN Hong-Gui^{1, 2, 3
,},
WANG Yu-Shuang^{1, 2, 3
,},
LIU Zheng^{1, 2, 3
,},
SUN Hao-Yuan^{1, 2, 3
,},
QIAO Jun-Fei^{1, 2
,}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124
3.
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124

Funds: Supported by National Natural Science Foundation of China (62125301, 62021003, 62103010, 62303024), National Key Research and Development Program of China (2022YFB3305800-5), China Postdoctoral Science Foundation (2022M720319), Beijing Natural Science Foundation (KZ202110005009), Youth Beijing Scholars Program (037), and Beijing Postdoctoral Research Foundation (2023-zz-91)

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

WANG Yu-Shuang　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, and cooperative optimal control

LIU Zheng　Lecturer at the Faculty of Information Technology, Beijing University of Technology. His research interest covers neural networks, intelligent systems, and modeling and control in process systems

SUN Hao-Yuan　Lecturer at the Faculty of Information Technology, Beijing University of Technology. His research interest covers networked control of municipal wastewater treatment process, and stochastic sampled-data control

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

摘要: 為有效提升城市污水處理過程的脫氮效果, 提出一種知識和數據驅動的反硝化脫氮過程協同優化控制(Knowledge-data-driven cooperative optimal control, KDDCOC). 所提方法主要有以下兩個方面: 首先, 建立一種基于自適應知識核函數的協同優化控制目標模型, 動態描述出水水質(Effluent quality, EQ)以及泵送能耗(Pumping energy consumption, PE)、關鍵變量的協同關系; 其次, 提出一種知識引導的協同優化算法(Knowledge guide-based cooperative optimization algorithm, KGCO), 快速準確求解硝態氮(Nitrate nitrogen, S_NO)優化設定值, 提高KDDCOC的響應速度. KDDCOC利用比例?積分?微分(Proportional-integral-derivative, PID)控制器對硝態氮優化設定值進行跟蹤, 將提出的KDDCOC應用于城市污水處理過程基準仿真模型 1 號(Benchmark simulation model 1, BSM1); 實驗結果表明, 該方法能夠提高出水水質, 降低運行能耗, 有效改善脫氮效果.
- 反硝化脫氮過程 /
- 知識和數據驅動 /
- 協同優化控制 /
- 自適應知識核函數 /
- 知識引導的協同優化算法
Abstract: In order to effectively improve the performance of the wastewater treatment denitrification process, a knowledge-data-driven cooperative optimal control (KDDCOC) is proposed. The main work of this paper includes the following two points: First, a cooperative optimal control objective model, based on adaptive knowledge kernel function, is designed to dynamically describe the cooperative relationship between effluent quality (EQ), pumping energy consumption (PE), and key variables; Second, a knowledge guide-based cooperative optimization algorithm (KGCO) is proposed to quickly and accurately obtain the optimum set points of nitrate nitrogen and improve the response speed of KDDCOC. A proportional-integral-derivative controller is used to track the optimum set points of nitrate nitrogen. The proposed KDDCOC is applied to the benchmark simulation model 1 for wastewater treatment process. The experimental results indicate that KDDCOC can improve the effluent quality, reduce the energy consumption, and effectively improve the removal of nitrogen.

HTML全文

圖 1 KDDCOC結構

Fig. 1 Schematic diagram of KDDCOC structure

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圖 2 KDDCOC流程圖

Fig. 2 Flow chart of KDDCOC

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圖 3 干燥天氣下$S_{{\rm{NO}}}$的優化控制結果和$S_{{\rm{NO}}}$的控制誤差

Fig. 3 Optimal control results of $S_{{\rm{NO}}}$ and control errors of $S_{{\rm{NO}}}$ in dry weather

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圖 4 干燥天氣下的$Q_a$優化控制結果

Fig. 4 Optimal control results of $Q_a$ in dry weather

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圖 5 干燥天氣下每天的EQ值

Fig. 5 The values of EQ daily in dry weather

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圖 7 干燥天氣下每天的TC值

Fig. 7 The values of TC daily in dry weather

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圖 6 干燥天氣下每天的PE值

Fig. 6 The values of PE daily in dry weather

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圖 8 干燥天氣下各出水組分的濃度

Fig. 8 Effluent parameters in the dry weather

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圖 9 干燥天氣下的每天的$ N_{{\rm{tot}}}$值

Fig. 9 The values of $ N_{{\rm{tot}}}$daily in dry weather

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圖 10 暴雨天氣下$S_{{\rm{NO}}}$的優化控制結果和$S_{{\rm{NO}}}$的控制誤差

Fig. 10 Optimal control results of $S_{{\rm{NO}}}$ and control errors of $S_{{\rm{NO}}}$ in storm weather

下載: 全尺寸圖片幻燈片

圖 16 暴雨天氣下的每天的$ N_{{\rm{tot}}}$值

Fig. 16 The values of $ N_{{\rm{tot}}}$daily in storm weather

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圖 11 暴雨天氣下的$Q_a$優化控制結果

Fig. 11 Optimal control results of $Q_a$ in storm weather

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圖 12 暴雨天氣下每天的EQ值

Fig. 12 The values of EQ daily in storm weather

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圖 13 暴雨天氣下每天的PE值

Fig. 13 The values of PE daily in storm weather

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圖 14 暴雨天氣下每天的TC值

Fig. 14 The values of TC daily in storm weather

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圖 15 暴雨天氣下各出水組分的濃度

Fig. 15 Effluent parameters in storm weather

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表 1 干燥天氣下不同優化控制方法的詳細性能

Table 1 Detailed performance of different optimal control methods in dry weather

天氣	方法	PE (kW·h)	EQ (kg poll.units)	TC (€)	IAE
干燥	KDDCOC	237	6 543	700.99	0.043
	${\rm{KDDCOC}}{\text{-}}\lambda_1$	251	6 631	712.55	0.057
	${\rm{KDDCO}}{\rm{C}}{\text{-}}\lambda_2$	262	6 595	711.11	0.061
	DMOPSO-OC^[20]	258	6 654	716.23	0.092
	DMOOC^[21]	284	6 619	717.85	0.142
	PID	295	6 768	734.92	0.210

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表 2 暴雨天氣下不同優化控制方法的詳細性能

Table 2 Detailed performance of different optimal control methods in storm weather

天氣	方法	PE (kW·h)	EQ (kg poll.units)	TC (€)	IAE
暴雨	KDDCOC	221	7 338	777.34	0.097
	${\rm{KDDCOC} }{\text{-} }\lambda_1$	232	7 449	790.60	0.112
	${\rm{KDDCOC} }{\text{-} }\lambda_2$	244	7 381	786.17	0.106
	DMOPSO-OC^[20]	239	7 645	811.58	0.123
	DMOOC^[21]	264	7 536	805.61	0.204
	PID	295	7 773	835.42	0.248

下載: 導出CSV

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