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Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process

  • S.I. : Neuro, fuzzy and their Hybridization
  • Published:
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Abstract

Since wastewater treatment processes (WTP) are generally accompanied with intense coupling and fuzziness, conventional biochemical mechanisms-based methods cannot comprehensively express the WTP due to limited computational ability. In response to the challenge caused by fuzziness, this paper proposes a hybrid control and prediction system for modeling WTP with the fuse of Activated Sludge model, Convolutional neural network and Long short-term memory neural networks (AS-CL) with knowledge and data-driven characteristics. Moreover, the activated sludge model is employed to model the wastewater treatment process based on the perspective of knowledge. Besides, the hybrid neural network that combines convolutional neural network and long short-term memory model is adopted to model the WTP from the perspective of data. Then, a multi-layer perception model is set up to realize collaborative awareness of data and knowledge. Lastly, the proposed AS-CL has been evaluated by a real-world data-set collected from a real sewage treatment plant. The results show that compared with typical existing methods, the proposal improves modeling efficiency. With the collaborative modeling scheme, influence from fuzziness on WTP can be reduced to some extent. Compared with five benchmark methods of the two evaluation indexes, the results of AS-CL show that the average performance of this method exceeds 7% of the baseline.

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Acknowledgements

This research was supported by National Key Research and Development Program of China (2016YFE0205600), Science and Technology Research Project of Chongqing Municipal Education Commission (KJZD-M202000801), Natural Science Foundation of Chongqing Science & Technology Commission (cstc2020jcyj-msxmX0721), and Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202000810), Project of Chongqing Technology and Business University (ZDPTTD201917, KFJJ2019053), and Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044.

In addition, we would like to thank the lecturer Zhiwei Guo from Chongqing Technology and Business University, as he had given a number of professional comments during the process of writing and revision. And we also would like to thank the Engineer Dong Feng from Chongqing Sino French Environmental Excellence Research & Development Center Co. Ltd., as he provided experimental datasets from real-world wastewater treatment plants for evaluation.

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Authors and Affiliations

  1. National Research Base of Intelligent Manufacturing Service, Chongqing Key Laboratory of Catalysis & New Environmental Materials, Chongqing Technology and Business University, Chongqing, 400067, China

    Xuhong Cheng, Zhiwei Guo, Yu Shen & Xu Gao

  2. Chongqing South-to-Thais Environmental Protection Technology Research Institute Co., Ltd., Chongqing, 400069, China

    Yu Shen

  3. Global Information and Telecommunication Institute, Waseda University, Tokyo, Japan

    Keping Yu

  4. Chongqing Sino French Environmental Excellence Research & Development Center Co., Ltd., Chongqing, 400067, China

    Xu Gao

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  1. Xuhong Cheng
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Correspondence to Yu Shen or Keping Yu.

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Cheng, X., Guo, Z., Shen, Y. et al. Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process. Neural Comput & Applic 35, 7185–7206 (2023). https://doi.org/10.1007/s00521-021-06499-1

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