Determination of biomass drying speed using neural networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversitat Politècnica de València-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorJuárez University of the State of Durango-
Autor(es): dc.contributorUniversidad Nacional de Chimborazo-
Autor(es): dc.creatorVelázquez Martí, Borja-
Autor(es): dc.creatorBonini Neto, Alfredo-
Autor(es): dc.creatorNuñez Retana, Daniel-
Autor(es): dc.creatorCarrillo Parra, Artemio-
Autor(es): dc.creatorGuerrero-Luzuriaga, Sebastian-
Data de aceite: dc.date.accessioned2025-08-21T15:40:30Z-
Data de disponibilização: dc.date.available2025-08-21T15:40:30Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.biombioe.2024.107260-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/297978-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/297978-
Descrição: dc.descriptionThe 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 R2 of the training process reached values of 0.93. Subsequent validation provided an R2 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.-
Descrição: dc.descriptionDepartamento de Ingeniería Rural y Agroalimentaria Universitat Politècnica de València, Camino de Vera s/n-
Descrição: dc.descriptionDepartment of Biosystems Engineering School of Sciences and Engineering São Paulo State University (Unesp)-
Descrição: dc.descriptionInstitute of Silviculture and Wood Industry Juárez University of the State of Durango, Boulevard del Guadiana 501, Ciudad Universitaria, Research Tower, Durango-
Descrição: dc.descriptionCarrera de Agroindustria Universidad Nacional de Chimborazo, Km 1 ½ Vía Guano Campus “Edison Riera”-
Descrição: dc.descriptionDepartment of Biosystems Engineering School of Sciences and Engineering São Paulo State University (Unesp)-
Idioma: dc.languageen-
Relação: dc.relationBiomass and Bioenergy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBiomass drying-
Palavras-chave: dc.subjectBiomass processing-
Palavras-chave: dc.subjectDrying kinetics-
Palavras-chave: dc.subjectNeuronal networks applications-
Título: dc.titleDetermination of biomass drying speed using neural networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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