Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials

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/).

Detalles Bibliográficos
Autores: Guirado Albeira, Elena, Ruiz Martínez, Jaime D., Campoy Naranjo, Manuel, Leiva Fernández, Carlos
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/174904
Acceso en línea:https://hdl.handle.net/11441/174904
https://doi.org/10.3390/pr13040933
Access Level:acceso abierto
Palabra clave:Bottom ash
Fly ash
Fire resistance
Mechanical properties
Machine learning
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spelling Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant MaterialsGuirado Albeira, ElenaRuiz Martínez, Jaime D.Campoy Naranjo, ManuelLeiva Fernández, CarlosBottom ashFly ashFire resistanceMechanical propertiesMachine learningThis 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/).Significant amounts of coal fly and bottom ash are generated globally each year, with especially large quantities of bottom ash accumulating in landfills. In this study, fly ash and bottom ash were used to create fire-resistant materials. A mix of 30 wt% gypsum, 9.5 wt% vermiculite, and 0.5 wt% polypropylene fibers was used, maintaining a constant water-to-solid ratio, with varying fly ash/bottom ash ratios (40/20, 30/30, and 20/40). The density, as well as various mechanical properties (compressive strength, flexural strength, and surface hardness), fire insulation capacity, and leaching behavior of both ashes were evaluated. When comparing the 40/20 and 20/40 compositions, a slight decrease in density was observed; however, compressive strength dropped drastically by 80%, while flexural strength decreased slightly due to the action of the polypropylene fibers, and fire resistance dropped by 8%. Neither of the ashes presented any environmental concerns from a leaching standpoint. Additionally, historical data from various materials with different wastes in previous works were used to train different machine learning models (random forest, gradient boosting, artificial neural networks, etc.). Compressive strength and fire resistance were predicted. Simple parameters (density, water/solid ratio and composition for compressive strength and thickness and the composition for fire resistance) were used as input in the models. Both regression and classification algorithms were applied to evaluate the models’ ability to predict compressive strength. Regression models for fire resistance reached r2 up to about 0.85. The classification results for the fire resistance rating (FRR) showed high accuracy (96%). The prediction of compressive strength is not as good as the fire resistance prediction, but compressive strength classification reached up to 99% accuracy for some models.MDPIIngeniería Química y AmbientalMinisterio de Ciencia e Innovación (MICIN). España2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/174904https://doi.org/10.3390/pr13040933reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésProcesses, 13 (4), 933.PID2023-147971OB-C32https://www.mdpi.com/2227-9717/13/4/933info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1749042026-06-17T12:51:07Z
dc.title.none.fl_str_mv Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
title Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
spellingShingle Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
Guirado Albeira, Elena
Bottom ash
Fly ash
Fire resistance
Mechanical properties
Machine learning
title_short Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
title_full Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
title_fullStr Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
title_full_unstemmed Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
title_sort Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
dc.creator.none.fl_str_mv Guirado Albeira, Elena
Ruiz Martínez, Jaime D.
Campoy Naranjo, Manuel
Leiva Fernández, Carlos
author Guirado Albeira, Elena
author_facet Guirado Albeira, Elena
Ruiz Martínez, Jaime D.
Campoy Naranjo, Manuel
Leiva Fernández, Carlos
author_role author
author2 Ruiz Martínez, Jaime D.
Campoy Naranjo, Manuel
Leiva Fernández, Carlos
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Química y Ambiental
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Bottom ash
Fly ash
Fire resistance
Mechanical properties
Machine learning
topic Bottom ash
Fly ash
Fire resistance
Mechanical properties
Machine learning
description 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/).
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/174904
https://doi.org/10.3390/pr13040933
url https://hdl.handle.net/11441/174904
https://doi.org/10.3390/pr13040933
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Processes, 13 (4), 933.
PID2023-147971OB-C32
https://www.mdpi.com/2227-9717/13/4/933
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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