EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management

David Velásquez (), Paola Vallejo, Mauricio Toro, Juan Odriozola, Aitor Moreno, Gorka Naveran, Michael Giraldo, Mikel Maiza and Basilio Sierra
Additional contact information
David Velásquez: RID on Information Technologies and Communications Research Group (GIDITIC), Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia
Paola Vallejo: RID on Information Technologies and Communications Research Group (GIDITIC), Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia
Mauricio Toro: RID on Information Technologies and Communications Research Group (GIDITIC), Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia
Juan Odriozola: Department of Data Intelligence for Energy and Industrial Processes, Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain
Aitor Moreno: Department of R&D, Ibermática, Cercas Bajas, 7 int.-Office 2, 01001 Vitoria-Gasteiz, Spain
Gorka Naveran: Department of R&D, Giroa-Veolia, Laida Bidea, Building 407, 48170 Zamudio, Spain
Michael Giraldo: Industry, Materials and Energy Area, Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia
Mikel Maiza: Department of Data Intelligence for Energy and Industrial Processes, Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain
Basilio Sierra: Department of Computer Science and Artificial Intelligence, University of Basque Country, Manuel Lardizabal Ibilbidea, 1, 20018 Donostia-San Sebastián, Spain

Sustainability, 2024, vol. 16, issue 9, 1-17

Abstract: Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness.

Keywords: data-driven modeling; machine learning; Industry 4.0; visual analytics; wastewater management; 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: 2024
References: View references in EconPapers View complete reference list from CitEc
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