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Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1752))

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Abstract

Water is a fundamental human resource and its scarcity is reflected in social, economic and environmental problems. Water used in human activities must be treated before reusing or returning to nature. This treatment takes place in wastewater treatment plants (WWTPs), which need to perform their functions with high quality, low cost, and reduced environmental impact. This paper aims to identify failures in real-time, using streaming data to provide the necessary preventive actions to minimize damage to WWTPs, heavy fines and, ultimately, environmental hazards. Convolutional and Long short-term memory (LSTM) autoencoders (AEs) were used to identify failures in the functioning of the dissolved oxygen sensor used in WWTPs. Five faults were considered (drift, bias, precision degradation, spike and stuck) in three different scenarios with variations in the appearance order, intensity and duration of the faults. The best performance, considering different model configurations, was achieved by Convolutional-AE.

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Acknowledgments

This research was supported by the ERDF and national funds through the project SYNAPPS (CENTRO-01-0247-FEDER-046978). We also acknowledge the support of the EC project CHIST-ERA-19-XAI-012, and project CHIST-ERA/0004/2019 funded by FCT.

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Authors and Affiliations

  1. Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal

    Rodrigo Salles & Rita P. Ribeiro

  2. Institute of Systems and Robotics, University of Coimbra, 3030-290, Coimbra, Portugal

    Rodrigo Salles & Jérôme Mendes

  3. INESC TEC, 4200-465, Porto, Portugal

    Rita P. Ribeiro & João Gama

  4. Faculty of Economics, University of Porto, 4200-464, Porto, Portugal

    João Gama

Authors
  1. Rodrigo Salles
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  2. Jérôme Mendes
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  3. Rita P. Ribeiro
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  4. João Gama
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Corresponding author

Correspondence to Rodrigo Salles .

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Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

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  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

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  16. ICAR-CNR, Rende, Italy

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  17. University of Pisa, Pisa, Italy

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  18. Aalen University of Applied Sciences, Aalen, Germany

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  19. Warsaw University of Technology, Warszaw, Poland

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  20. KU Leuven, Leuven, Belgium

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  28. University of Bari Aldo Moro, Bari, Italy

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  29. University of Bari Aldo Moro, Bari, Italy

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  30. University of Lisbon, Lisbon, Portugal

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  31. University of Lisbon, Lisbon, Portugal

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  32. Northwestern University, Chicago, USA

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  33. Roche, Basel, Switzerland

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Salles, R., Mendes, J., Ribeiro, R.P., Gama, J. (2023). Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-23618-1_4

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  • Online ISBN: 978-3-031-23618-1

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