IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i16p6348-d395665.html
   My bibliography  Save this article

Intelligent System for the Predictive Analysis of an Industrial Wastewater Treatment Process

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

Considering the exponential growth of today’s industry and the wastewater results of its processes, it needs to have an optimal treatment system for such effluent waters to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that are progressively increasing their demand. This leads to the need to innovate in the control and management information systems of the systems responsible to treat these residual waters in search of improvement. This paper proposes the development of an intelligent system that uses the data from the process and makes a prediction of its behavior to provide support in decision making related to the operation of the wastewater treatment plant (WWTP). To carry out the development of this system, a multilayer perceptron neural network with 2 hidden layers and 22 neurons each is implemented, together with process variable analysis, time-series decomposition, correlation and autocorrelation techniques; it is possible to predict the chemical oxygen demand (COD) at the input of the bioreactor with a one-day window and a mean absolute percentage error (MAPE) of 10.8%, which places this work between the adequate ranges proposed in the literature.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6348-:d:395665
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/16/6348/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/16/6348/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Estela Bee Dagum, 2010. "Time Series Modelling and Decomposition," Statistica, Department of Statistics, University of Bologna, vol. 70(4), pages 433-457.
    2. Juan Manuel Ponce Romero & Stephen H. Hallett & Simon Jude, 2017. "Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector," Sustainability, MDPI, vol. 9(12), pages 1-19, November.
    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. 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. 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.
    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.

    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. Pedrini, Giulio & Cappiello, Giuseppe, 2022. "The impact of training on labour productivity in the European utilities sector: An empirical analysis," Utilities Policy, Elsevier, vol. 74(C).
    2. Cahn, Amir & Katz, David & Ghermandi, Andrea & Prevos, Peter, 2023. "Adoption of data-as-a-service by water and wastewater utilities," Utilities Policy, Elsevier, vol. 81(C).
    3. Bhandari, Pratik & Creighton, Douglas & Gong, Jinzhe & Boyle, Carol & Law, Kris M.Y., 2023. "Evolution of cyber-physical-human water systems: Challenges and gaps," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    4. Cahn, Amir & Katz, David & Ghermandi, Andrea & Prevos, Peter, 2023. "Initiating data-as-a-service adoption in water utilities: A service design approach," Utilities Policy, Elsevier, vol. 84(C).
    5. Gabriel Koman & Martin Holubcik & Milan Kubina, 2018. "Descriptive representation about transformation of company by using current technologies and tools for analytical processing and evaluation of diverse data," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 19(1), pages 89-101.
    6. Jorge Alejandro Silva, 2022. "Implementation and Integration of Sustainability in the Water Industry: A Systematic Literature Review," Sustainability, MDPI, vol. 14(23), pages 1-28, November.

    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:12:y:2020:i:16:p:6348-:d:395665. 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.