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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
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    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.
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    Citations

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    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.

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