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Data-driven management for fuzzy sewage treatment processes using hybrid neural computing

Neural Computing and Applications (2021)Cite this article

Abstract

With the growing public attention on sustainable development and green ecosystems, the efficient management of fuzzy sewage treatment processes (FSTPs) has been a major concern in academia. Characterized by strong abstraction and analysis abilities, data mining technologies provide a novel perspective to solve this problem. In recent years, data-driven management for FSTP has been widely investigated, resulting in a number of typical approaches. However, almost all existing technical approaches consider FSTP a unidirectional, sequential process, ignoring the bidirectional temporality caused by backflow operations. Therefore, we propose a data-driven management mechanism for FSTP based on hybrid neural computing (IM-HNC for short). This mechanism attempts to capture the bidirectional time-series features of FSTP with the aid of a bidirectional long short-term memory model, and further introduces a convolutional neural network to construct feature spaces with a stronger expression capability. Empirically, we implement a series of experiments on three datasets under different parameter settings to test the efficiency and robustness of the proposed IM-HNC. The experimental results manifest that the IM-HNC has an average performance improvement of approximately 5% compared to the baselines.

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Acknowledgments

This research was supported by National Key Research and Development Program of China (2016YFE0205600), Chongqing basic research and frontier exploration project of China (cstc2018jcyjAX0638), Chongqing Natural Science Foundation of China (cstc2019jcyj-msxmX0747), Scientific Program of Chongqing Technology and Business University (ZDPTTD201917, KFJJ2018071, 1952027), and Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044.

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Authors and Affiliations

  1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 400067, China

    Wenru Zeng, Zhiwei Guo, Yu Shen & Xu Gao

  2. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

    Ali Kashif Bashir

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

    Keping Yu

  4. Department of Information Systems in Rabigh, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Yasser D. Al-Otaibi

  5. Chongqing Sino French Environmental Excellence Research and Development Center Co. Ltd, Chongqing, 400042, China

    Xu Gao

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  1. Wenru Zeng
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  2. Zhiwei Guo
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  3. Yu Shen
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  4. Ali Kashif Bashir
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Correspondence to Keping Yu or Xu Gao.

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Zeng, W., Guo, Z., Shen, Y. et al. Data-driven management for fuzzy sewage treatment processes using hybrid neural computing. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-020-05655-3

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  • DOI: https://doi.org/10.1007/s00521-020-05655-3

Keywords

  • Data-driven management
  • Fuzzy sewage treatment process
  • Hybrid neural computing
  • Bidirectional time-series features
  • Green ecosystems