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EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management

Author

Listed:
  • David Velásquez

    (RID on Information Technologies and Communications Research Group (GIDITIC), Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia
    Industry, Materials and Energy Area, Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia
    Department of Data Intelligence for Energy and Industrial Processes, Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain
    Department of Computer Science and Artificial Intelligence, University of Basque Country, Manuel Lardizabal Ibilbidea, 1, 20018 Donostia-San Sebastián, Spain)

  • Paola Vallejo

    (RID on Information Technologies and Communications Research Group (GIDITIC), Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia)

  • Mauricio Toro

    (RID on Information Technologies and Communications Research Group (GIDITIC), Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia)

  • Juan Odriozola

    (Department of Data Intelligence for Energy and Industrial Processes, Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain)

  • Aitor Moreno

    (Department of R&D, Ibermática, Cercas Bajas, 7 int.-Office 2, 01001 Vitoria-Gasteiz, Spain)

  • Gorka Naveran

    (Department of R&D, Giroa-Veolia, Laida Bidea, Building 407, 48170 Zamudio, Spain)

  • Michael Giraldo

    (Industry, Materials and Energy Area, Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia)

  • Mikel Maiza

    (Department of Data Intelligence for Energy and Industrial Processes, Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain)

  • Basilio Sierra

    (Department of Computer Science and Artificial Intelligence, University of Basque Country, Manuel Lardizabal Ibilbidea, 1, 20018 Donostia-San Sebastián, Spain)

Abstract

Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness.

Suggested Citation

  • David Velásquez & Paola Vallejo & Mauricio Toro & Juan Odriozola & Aitor Moreno & Gorka Naveran & Michael Giraldo & Mikel Maiza & Basilio Sierra, 2024. "EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management," Sustainability, MDPI, vol. 16(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3578-:d:1382032
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    References listed on IDEAS

    as
    1. Shuai Shao & Dianzheng Fu & Tianji Yang & Hailin Mu & Qiufeng Gao & Yun Zhang, 2023. "Analysis of Machine Learning Models for Wastewater Treatment Plant Sludge Output Prediction," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    2. Lelys Bravo Guenni & Susan J. Simmons & R.A. Haggarty & C.A. Miller & E.M. Scott & F. Wyllie & M. Smith, 2012. "Functional clustering of water quality data in Scotland," Environmetrics, John Wiley & Sons, Ltd., vol. 23(8), pages 685-695, December.
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