数据和知识驱动的城市污水处理过程多目标优化控制

韩红桂; 张琳琳; 伍小龙; 乔俊飞

doi:10.16383/j.aas.c210098

数据和知识驱动的城市污水处理过程多目标优化控制

doi: 10.16383/j.aas.c210098

1.
北京工业大学信息学部, 计算智能与智能系统北京市重点实验室北京 100124

基金项目: 国家重点研发项目(2018YFC1900800-5), 国家自然科学基金(61890930-5, 61903010, 62021003), 北京市卓越青年科学家计划项目(BJJWZYJH01201910005020), 北京市自然科学基金(KZ202110005009)资助

详细信息

作者简介:
韩红桂：北京工业大学信息学部教授. 主要研究方向为城市污水处理过程智能优化控制, 神经网络结构设计与优化. 本文通信作者. E-mail: rechardhan@bjut.edu.cn

张琳琳：北京工业大学信息学部博士研究生. 主要研究方向为城市污水处理过程智能优化控制. E-mail: zhangllsy@163.com

伍小龙：北京工业大学信息学部讲师. 主要研究方向为城市污水处理过程智能特征建模与智能控制. E-mail: lewis_wxl@sina.com

乔俊飞：北京工业大学信息学部教授. 主要研究方向为城市污水处理过程智能优化控制, 神经网络结构设计与优化. E-mail: junfeq@bjut.edu.cn

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出版历程
- 收稿日期: 2021-02-10
- 录用日期: 2021-05-28
- 网络出版日期: 2021-07-28
- 刊出日期: 2021-11-18

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

1.
Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124

Funds: Supported by National Key Research and Development Project (2018YFC1900800-5), National Natural 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 main research interest is 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

摘要: 城市污水处理过程优化控制是降低能耗的有效手段, 然而, 如何提高出水水质的同时降低能耗依然是当前城市污水处理过程面临的挑战. 围绕上述挑战, 文中提出了一种数据和知识驱动的多目标优化控制(Data-knowledge driven multiobjective optimal control, DK-MOC)方法. 首先, 建立了出水水质、能耗以及系统运行状态的表达关系, 获得了运行过程优化目标模型. 其次, 提出了一种基于知识迁徙学习的动态多目标粒子群优化算法, 实现了控制变量优化设定值的自适应求解. 最后, 将提出的DK-MOC应用于城市污水处理过程基准仿真模型1 (Benchmark simulation model No. 1, BSM1). 结果表明该方法能够实时获取控制变量的优化设定值, 提高了出水水质, 并且有效降低了运行能耗.
- 城市污水处理过程 /
- 数据和知识驱动方法, 多目标优化控制 /
- 知识迁徙学习 /
- 动态多目标粒子群优化
Abstract: The optimal control is an effective method to reduce energy consumption for municipal wastewater treatment process. However, it is still a challenge to improve the effluent qualities and reduce energy consumption simultaneously for the municipal wastewater treatment process. To solve this problem, a data-knowledge driven multiobjective optimal control (DK-MOC) method is proposed in this paper. First, the expression relationship among effluent qualities, energy consumption and system operation state is established to obtain the operational optimal objective model. Second, a dynamic multiobjective particle swarm optimization algorithm, based on knowledge transfer learning method, is proposed to obtain the optimal set-points of control variables adaptively. Finally, the proposed DK-MOC method is applied to the benchmark simulation model No. 1 (BSM1) of the municipal wastewater treatment process. The results demonstrate that this proposed method can obtain the optimal set-points of control variables online, which not only improve the effluent qualities, but also reduce the operation energy consumption effectively.

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图 1 DK-MOC的流程图

Fig. 1 The flow chart of DK-MOC

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图 2 控制变量设定值求解步骤

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

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图 3 干旱天气下出水水质建模结果

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

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图 4 干旱天气下能耗建模结果

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

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图 5 干旱天气下平均出水水质值

Fig. 5 Average values of effluent quality in dry weather

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图 6 干旱天气下平均能耗值

Fig. 6 Average values of energy consumption in dry weather

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图 7 干旱天气下溶解氧浓度跟踪性能

Fig. 7 Tracking performance of S_O in dry weather

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图 8 干旱天气下硝态氮浓度跟踪性能

Fig. 8 Tracking performance of S_NO in dry weather

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图 9 干旱天气下硝态氮和溶解氧浓度跟踪误差

Fig. 9 Tracking errors of S_NO and S_O in dry weather

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表 1 干旱天气下能耗和出水水质的测量精度

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

模型	EC		EQ
模型	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 %

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表 2 阴雨天气下能耗和出水水质的测量精度

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

模型	EC		EQ
模型	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 %

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表 3 DK-MOC的出水水质结果

Table 3 Effluent quality results obtained by DK-MOC

水质指标	干旱天气	阴雨天气	排放标准
S_NH(mg·L⁻¹)	3.44	3.89	<4
N_tot (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

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表 4 不同优化控制策略的比较结果

Table 4 Comparison results of different optimal control strategies

优化控制策略	干旱天气		阴雨天气
优化控制策略	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+AMOPSO+PID	3848	7695	3870	7 944
AFNN+KT-DSOPSO+PID	3727	9062	3892	9479
DK-MOC	3 641	7 179	3 821	8106

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