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

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, 2021, vol. 13, issue 8, 1-14

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.

Keywords: artificial neural network (ANN); chemical oxygen demand (COD); data-driven decision making (DDDM); Industry 4.0; machine learning (ML); optimization; 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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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