EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management
dc.contributor.author | Velásquez, David | |
dc.contributor.author | Vallejo, Paola | |
dc.contributor.author | Toro, Mauricio | |
dc.contributor.author | Odriozola, Juan | |
dc.contributor.author | Moreno, Aitor | |
dc.contributor.author | Naveran, Gorka | |
dc.contributor.author | Giraldo, Michael | |
dc.contributor.author | Maiza, Mikel | |
dc.contributor.author | Sierra Araujo, Basilio | |
dc.date.accessioned | 2024-05-14T17:48:14Z | |
dc.date.available | 2024-05-14T17:48:14Z | |
dc.date.issued | 2024-04-24 | |
dc.identifier.citation | Sustainability 16(9) : (2024) // Article ID 3578 | es_ES |
dc.identifier.issn | 2071-1050 | |
dc.identifier.uri | http://hdl.handle.net/10810/67950 | |
dc.description.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. | es_ES |
dc.description.sponsorship | Universidad EAFIT and the Vicomtech Foundation, under the project EDAR 4.0, partly funded this research. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | |
dc.subject | data-driven modeling | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | industry 4.0 | es_ES |
dc.subject | visual analytics | es_ES |
dc.subject | wastewater management | es_ES |
dc.subject | wastewater treatment plant (WWTP) | es_ES |
dc.title | EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2024-05-10T13:18:29Z | |
dc.rights.holder | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2071-1050/16/9/3578 | es_ES |
dc.identifier.doi | 10.3390/su16093578 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala |
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Except where otherwise noted, this item's license is described as © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).