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

Luis Arismendy, Carlos Cárdenas, Diego Gómez, Aymer Maturana, Ricardo Mejía and Christian G. Quintero M.
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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

Sustainability, 2020, vol. 12, issue 16, 1-19

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.

Keywords: artificial neural network (ANN); chemical oxygen demand (COD); wastewater treatment plant (WWTP) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:16:p:6348-:d:395665

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