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

被引：0
|
作者：
Han H.-G. [1 ]
Zhang L.-L. [1 ]
Wu X.-L. [1 ]
Qiao J.-F. [1 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Data-knowledge driven method; Dynamic multiobjective particle swarm optimization; Knowledge transfer learning method; Multiobjective optimal control; Municipal wastewater treatment process;
D O I：
10.16383/j.aas.c210098
中图分类号：
学科分类号：
摘要：
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. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2538 / 2546
页数：8
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