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Dissolved Oxygen Model Predictive Control for Activated Sludge Process Model Based on the Fuzzy C-means Cluster Algorithm

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  • Intelligent Control and Applications
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Abstract

In this work, the problem of predictive control of dissolved oxygen for the activated sludge process model with high nonlinearity and strong coupling is addressed. Firstly, the determination of the structure of fuzzy rules is displayed established upon Activated sludge model 1 (ASM1). Besides, the fuzzy space is divided through the clustering algorithm of fuzzy C-means. The corresponding parameters are estimated by means of the well-known least squares method. Subsequently, a fuzzy predictive model of dissolved oxygen is established by using the historical data. The aim is to design a predictive controller that is capable of performing the online track of dissolved oxygen attributed to better dynamic response and steadier output in different weather. Ultimately, the availability and validity of the developed technique are verified by a comparison example.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Information, Anhui University of Technology, Anhui, 243032, China

    Minghe Li, Saifei Hu, Jing Wang & Hao Shen

  2. College of Mathematic Science, Liaocheng university, Liaocheng, 252000, China

    Jianwei Xia

  3. School of Automation and Electrical Engineering, Linyi University, Linyi, 276005, China

    Jing Wang

  4. College of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

    Xiaona Song

Authors
  1. Minghe Li
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  6. Hao Shen
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Corresponding author

Correspondence to Jing Wang.

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Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Choon Ki Ahn under the direction of Editor Jay H. Lee. This work was supported by the National Natural Science Foundation of China under Grants 61873002, 61703004, 61973199, the National Natural Science Foundation of Anhui Province under Grants 1808085QA18.

Minghe Li received her M.S. degree in Electrical engineering from Hefei University of Technology in 1995. Since 1993, she has been with Anhui University of Technology, China, where she is currently a Professor. Her current research interests include industrial process, networked control systems, complex system modeling and control.

Saifei Hu received his B.Sc. degree in Automation from Industrial and Commercial College, Anhui University of Technology, Ma'anshan, China, in 2017 and where he is now an M.S. candidate. His current research interests include algorithm optimization, modeling and predictive control of wastewater treatment systems.

Jianwei Xia received his Ph.D. degree in Automatic Control from Nanjing University of Science and Technology in 2007. From 2010 to 2012, he worked as a Postdoctoral Research Associate in the School of Automation, Southeast University, Nanjing, China. From 2013 to 2014, he worked as a Postdoctoral Research Associate in the Department of Electrical Engineering, Yeungnam University, Kyongsan, Korea. His research topics are robust control, stochastic systems and neural networks, etc.

Jing Wang received her Ph.D. degree in Electric Power System and Automation from Hohai University in 2019. Since 2011, she has been with Anhui University of Technology, China, where she is currently an Associate Professor. Her current research interests include nonlinear control, complex networks, power systems.

Xiaona Song received her Ph.D. degree in Control Theory and Control Engineering from Nanjing University of Science and Technology, Nanjing, China, in 2011. From Feb. 2009 to Aug. 2009 and Apr. 2016 to Apr. 2017, she was a visiting scholar with the Department of Electrical Engineering, Utah State University and Southern Illinois University Carbondale, respectively. Since 2011, she has been with Henan University of Science and Technology, Luoyang, China, where she is currently an Associate Professor with the School of Information Engineering. Her current research interests include Markov jump distributed parameter systems, complex networks, fractional-order systems and control, fuzzy control, nonlinear control.

Hao Shen received his Ph.D. degree in control theory and control engineering from Nanjing University of Science and Technology, Nanjing, China, in 2011. From February 2013 to March 2014, he was a Post-Doctoral Fellow with the Department of Electrical Engineering, Yeungnam University, Korea. Since 2011, he has been with Anhui University of Technology, China, where he is currently a Professor and a Doctoral Supervisor. His current research interests include stochastic hybrid systems, complex networks, fuzzy systems and control, nonlinear control. Prof. Shen has served on the technical program committee for several international conferences. He has been an Associate Editor/Guest Editor for several international journals, including IEEE ACCESS, Journal of The Franklin Institute, Applied Mathematics and Computation, Transactions of the Institute Measurement and Control and Mathematical Problems in Engineering. Prof. Shen was a recipient of the Highly Cited Researcher Award by Clarivate Analytics (formerly, Thomson Reuters) in 2019.

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Li, M., Hu, S., Xia, J. et al. Dissolved Oxygen Model Predictive Control for Activated Sludge Process Model Based on the Fuzzy C-means Cluster Algorithm. Int. J. Control Autom. Syst. 18, 2435–2444 (2020). https://doi.org/10.1007/s12555-019-0438-1

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