Recursive Bilinear Subspace Modeling and Model-free Adaptive Control of Wastewater Treatment

被引：0
|
作者：
Zhang S. [1 ]
Zhou P. [1 ]
机构：
[1] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 07期
基金：
中国国家自然科学基金;
关键词：
model-free adaptive control (MFAC); multi-parameter sensitivity analysis (MPSA); recursive bilinear subspace identification (RBLSI); Wastewater treatment;
D O I：
10.16383/j.aas.c190514
中图分类号：
学科分类号：
摘要：
Conventional model-based approaches are unable to control the nitrate nitrogen concentration and dissolved oxygen concentration of biochemistry reaction effectively, which are the two most critical variables that determine the quality of effluent in wastewater treatment process. In this paper, a recursive bilinear subspace identification (RBLSI) modeling and model-free adaptive control method for wastewater treatment based on data-driven modeling and control technology was proposed. Firstly, according to the nonlinear time-varying dynamic characteristics of wastewater treatment process, a recursive bilinear model with parameter adaptability for biochemical reaction process of wastewater treatment was established by using the least square recursive bilinear subspace identification method. Secondly, the model-free adaptive control (MFAC) method based on the multi-parameter sensitivity analysis (MPSA) and genetic algorithm−particle swarm optimization (GA-PSO) algorithm was used to directly control the nitrate concentration and dissolved oxygen concentration in a data-driven mode based on the established data-driven model. Finally, data experiments and comparative analysis show the effectiveness and superiority of the proposed method. © 2022 Science Press. All rights reserved.
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页码：1747 / 1759
页数：12
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