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dc.contributor.author	Velázquez Martí, Borja	es_ES
dc.contributor.author	Bonini Neto, Alfredo	es_ES
dc.contributor.author	Nuñez Retana, Daniel	es_ES
dc.contributor.author	Carrillo Parra, Artemio	es_ES
dc.contributor.author	Guerrero-Luzuriaga, Sebastian	es_ES
dc.date.accessioned	2025-02-27T19:03:09Z
dc.date.available	2025-02-27T19:03:09Z
dc.date.issued	2024-07	es_ES
dc.identifier.issn	0961-9534	es_ES
dc.identifier.uri	http://hdl.handle.net/10251/214926
dc.description.abstract	[EN] The difficulty of measuring the drying rate of biomass under hot air convection conditions due to the influence of multiple factors, such as environmental conditions and material properties; and the problems associated with the variability of desiccation curves under changing conditions makes the use of mass transfer models based on diffusion and convection generally quite inaccurate. The research proposes the use of neural networks to determine the average drying speed (g removed water in unit of biomass material (kg) in unit time (s)), highlighting its ability to handle complex and variable data, as well as its adaptability and robustness. After 62 iterations, the R 2 of the training process reached values of 0.93. Subsequent validation provided an R 2 of 0.88. The mean square error was less than 10 -3 g dryed water kg -1 biomass s -1 . Traditional mass transfer models applied to drying processes were compared with experimental data. It has been proven that the values of the convection coefficient in mass transfer are overestimated when obtained from the Sherwood number. Values of this coefficient applied to wood are 30 times lower due to capillary phenomena and electrostatic forces between the material and the water particles.	es_ES
dc.language	Inglés	es_ES
dc.publisher	Elsevier	es_ES
dc.relation.ispartof	Biomass and Bioenergy	es_ES
dc.rights	Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)	es_ES
dc.subject	Neuronal networks applications	es_ES
dc.subject	Biomass drying	es_ES
dc.subject	Biomass processing	es_ES
dc.subject	Drying kinetics	es_ES
dc.subject.classification	INGENIERIA AGROFORESTAL	es_ES
dc.title	Determination of biomass drying speed using neural networks	es_ES
dc.type	Artículo	es_ES
dc.identifier.doi	10.1016/j.biombioe.2024.107260	es_ES
dc.rights.accessRights	Abierto	es_ES
dc.contributor.affiliation	Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural	es_ES
dc.description.bibliographicCitation	Velázquez Martí, B.; Bonini Neto, A.; Nuñez Retana, D.; Carrillo Parra, A.; Guerrero-Luzuriaga, S. (2024). Determination of biomass drying speed using neural networks. Biomass and Bioenergy. 189. https://doi.org/10.1016/j.biombioe.2024.107260	es_ES
dc.description.accrualMethod	S	es_ES
dc.relation.publisherversion	https://doi.org/10.1016/j.biombioe.2024.107260	es_ES
dc.type.version	info:eu-repo/semantics/publishedVersion	es_ES
dc.description.volume	189	es_ES
dc.relation.pasarela	S\520286	es_ES
dc.contributor.funder	Universitat Politècnica de València	es_ES
dc.subject.ods	09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación	es_ES
dc.subject.ods	12.- Garantizar las pautas de consumo y de producción sostenibles	es_ES
upv.costeAPC	3750	es_ES

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