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A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach

Author

Listed:
  • Luis Arismendy

    (Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Carlos Cárdenas

    (Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Diego Gómez

    (Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Aymer Maturana

    (Department of Civil and Environmental Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Ricardo Mejía

    (Department of Civil and Environmental Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Christian G. Quintero M.

    (Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

Abstract

An important issue today for industries is optimizing their processes. Therefore, it is necessary to make the right decisions to carry out these activities, such as increasing the profit of businesses, improving the commercial strategies, and analyzing the industrial processes performance to produce better goods and services. This work proposes an intelligent system approach to prescribe actions and reduce the chemical oxygen demand (COD) in an equalizer tank of a wastewater treatment plant (WWTP) using machine learning models and genetic algorithms. There are three main objectives of this data-driven decision-making proposal. The first is to characterize and adapt a proper prediction model for the decision-making scheme. The second is to develop a prescriptive intelligent system based on expert’s rules and the selected prediction model’s outcomes. The last is to evaluate the system performance. As a novelty, this research proposes the use of long short-term memory (LSTM) artificial neural networks (ANN) with genetic algorithms (GA) for optimization in the WWTP area.

Suggested Citation

  • Luis Arismendy & Carlos Cárdenas & Diego Gómez & Aymer Maturana & Ricardo Mejía & Christian G. Quintero M., 2021. "A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4311-:d:535046
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    References listed on IDEAS

    as
    1. Berk, Lauren & Bertsimas, Dimitris & Weinstein, Alexander M. & Yan, Julia, 2019. "Prescriptive analytics for human resource planning in the professional services industry," European Journal of Operational Research, Elsevier, vol. 272(2), pages 636-641.
    2. Ahmed Ghoniem & Agha Iqbal Ali & Mohammed Al-Salem & Wael Khallouli, 2017. "Prescriptive analytics for FIFA World Cup lodging capacity planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1183-1194, October.
    3. Luis Arismendy & Carlos Cárdenas & Diego Gómez & Aymer Maturana & Ricardo Mejía & Christian G. Quintero M., 2020. "Intelligent System for the Predictive Analysis of an Industrial Wastewater Treatment Process," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
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    Cited by:

    1. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.

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