Online-Growing Neural Network Control for Dissolved Oxygen Concentration

被引：13
作者：
Qiao, Junfei [1 ,2 ]
Su, Yin [1 ,2 ]
Yang, Cuili [1 ,2 ]
机构：
[1] Beijing Univ Technol, Beijing Lab Intelligent Environm Protect, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
[2] Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
来源：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2023年 / 19卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Process control; Mathematical models; Wastewater treatment; Recurrent neural networks; Artificial neural networks; Wastewater; Thermal stability; Dissolved oxygen (DO) concentration; neural network (NN) control; online-growing mechanism; wastewater treatment process (WWTP); WATER TREATMENT PROCESS;
D O I：
10.1109/TII.2022.3200471
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The activated sludge method is one of the most commonly used methods for the wastewater treatment process (WWTP). Among them, the dissolved oxygen (DO) concentration is a key factor affecting microbial metabolism and wastewater treatment effectiveness. However, due to the nonlinearity and dynamicity of WWTP, it is difficult to precisely control the DO concentration by traditional control methods. To solve this problem, the online-growing pipelined recurrent wavelet neural network (OG-PRWNN) control is proposed to improve the DO control accuracy. First, the online growing mechanism is designed to adjust the number of modules of the controller by measuring the control performance. Then, the structure of the controller is automatically determined to meet the different operating conditions of the WWTP. Second, the online algorithm of parameters incorporating adaptive learning rates is designed to train the OG-PRWNN to meet the control requirements. In addition, the stability of the OG-PRWNN controller is analyzed by the Lyapunov stability theorem. Finally, the performance of the controller is verified by the benchmark simulation model of WWTP. Simulation results show that the OG-PRWNN controller can obtain better control accuracy.
引用
收藏
页码：6794 / 6803
页数：10
相关论文
共 28 条
[1]   Combined L2-Stable Feedback and Feedforward Aeration Control in a Wastewater Treatment Plant [J].
Chistiakova, Tatiana ;
Wigren, Torbjorn ;
Carlsson, Bengt .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2020, 28 (03) :1017-1024
[2]   Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure [J].
Chu, Yundi ;
Fei, Juntao ;
Hou, Shixi .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (04) :1297-1309
[3]   Hierarchical partial update generalized functional link artificial neural network filter for nonlinear active noise control [J].
Dinh Cong Le ;
Zhang, Jiashu ;
Li, Defang .
DIGITAL SIGNAL PROCESSING, 2019, 93 :160-171
[4]   Event-triggered PID control for wastewater treatment plants [J].
Du, Shengli ;
Yan, Qiushuo ;
Qiao, Junfei .
JOURNAL OF WATER PROCESS ENGINEERING, 2020, 38
[5]   Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network [J].
Guo, Zhiwei ;
Du, Boxin ;
Wang, Jianhui ;
Shen, Yu ;
Li, Qiao ;
Feng, Dong ;
Gao, Xu ;
Wang, Heng .
RSC ADVANCES, 2020, 10 (23) :13410-13419
[6]   Wastewater treatment control method based on adaptive recurrent fuzzy neural network [J].
Han G.-T. ;
Qiao J.-F. ;
Han H.-G. .
Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2016, 33 (09) :1252-1258
[7]  
[韩改堂 Han Gaitang], 2016, [化工学报, CIESC Journal], V67, P954
[8]   Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network [J].
Han, Hong-Gui ;
Zhang, Lu ;
Hou, Ying ;
Qiao, Jun-Fei .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (02) :402-415
[9]   Cooperative Fuzzy-Neural Control for Wastewater Treatment Process [J].
Han, Honggui ;
Liu, Hongxu ;
Li, Jiaming ;
Qiao, Junfei .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (09) :5971-5981
[10]   Data-Driven Multiobjective Predictive Control for Wastewater Treatment Process [J].
Han, Honggui ;
Liu, Zheng ;
Hou, Ying ;
Qiao, Junfei .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (04) :2767-2775
123