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Comparative Study of Wastewater Treatment Plant Feature Selection for COD Prediction

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 749))

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Abstract

The focus of this study is on the instrumentation used in wastewater treatment stations, with the ultimate goal of creating virtual sensors in the future. In this stage, a specific correlation case is being examined to determine the appropriate and sufficient variables needed to predict chemical oxygen demand (COD) in a wastewater treatment plant. Initially, four methods are used to select the features with all the variables being monitored. This is followed by applying three regression techniques to evaluate the performance of the previously selected variables. All cases yielded a satisfactory prediction of COD, which indicates that virtual sensors can be implemented in the future with a high degree of predictability and effectiveness.

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Acknowledgement

Míriam Timiraos’s research was supported by the “Xunta de Galicia” (Regional Government of Galicia) through grants to industrial PhD (http://gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04_IN606D_2022_ 2692965.

Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference: FPU21/00932.

CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).

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Authors and Affiliations

  1. University of A Coruña, CTC, CITIC, Department of Industrial Engineering, Calle Mendizábal s/n, Campus de Esteiro, 15471, Ferrol, Spain

    Míriam Timiraos, Antonio Díaz-Longueira, Álvaro Michelena, Francisco Zayas-Gato, José-Luis Casteleiro-Roca, Esteban Jove, Héctor Quintián, Óscar Fontenla-Romero & José Luis Calvo-Rolle

  2. Fundación Instituto Tecnológico de Galicia, Department of Water Technologies, National Technological Center, Cantón Grande 9, Planta 3, 15003, A Coruña, Spain

    Míriam Timiraos

Authors
  1. Míriam Timiraos
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  2. Antonio Díaz-Longueira
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  3. Álvaro Michelena
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  4. Francisco Zayas-Gato
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  5. José-Luis Casteleiro-Roca
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  6. Esteban Jove
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  7. Héctor Quintián
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  8. Óscar Fontenla-Romero
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  9. José Luis Calvo-Rolle
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Corresponding author

Correspondence to Míriam Timiraos .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Cite this paper

Timiraos, M. et al. (2023). Comparative Study of Wastewater Treatment Plant Feature Selection for COD Prediction. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_15

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