IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i8p4311-d535046.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/8/4311/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/8/4311/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark McCormick, 2022. "An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process," Sustainability, MDPI, vol. 14(13), pages 1-23, June.
    2. Hokyeom Kim & Injun Choi & Jitaek Lim & Sanghyun Sung, 2022. "Business Process-Organizational Structure (BP-OS) Performance Measurement Model and Problem-Solving Guidelines for Efficient Organizational Management in an Ontact Work Environment," Sustainability, MDPI, vol. 14(21), pages 1-22, November.
    3. Chun-Ming Xu & Jia-Shuai Zhang & Ling-Qiang Kong & Xue-Bo Jin & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2022. "Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    4. Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4311-:d:535046. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.