A hybrid lumped parameter/neural network model for spouted bed drying of pastes with inert particles

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorFreire, J. T.-
Autor(es): dc.creatorFreire, F. B,-
Autor(es): dc.creatorFerreira, M. C.-
Autor(es): dc.creatorNascimento, B. S.-
Data de aceite: dc.date.accessioned2026-02-09T12:21:53Z-
Data de disponibilização: dc.date.available2026-02-09T12:21:53Z-
Data de envio: dc.date.issued2017-05-30-
Data de envio: dc.date.issued2017-05-30-
Data de envio: dc.date.issued2012-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/13077-
Fonte completa do material: dc.identifierhttp://www.tandfonline.com/doi/abs/10.1080/07373937.2012.684085-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1159797-
Descrição: dc.descriptionThe current study analyzed the suitability of a hybrid CST/neural network model to describe the highly coupled heat and mass transfer during paste drying in a spouted bed. In the present approach, the main information was the moisture content predictions in the powder. The model was based on global energy and water mass balances in the liquid and the gaseous phases. In this model, the inter-phase coupling term r, which reflects both water evaporation and particle coating, was described by an artificial neural network. Artificial neural networks are efficient computing models which are extensively used whenever theoretical models fail to properly represent a given phenomena and reliable data basis of the main variables involved is available. Simulations were done in MatLab. The drying experiments for model verification were carried out in a conical semi-pilot scale spouted bed, from which measurements of gas and solid phase moisture were done. The good agreement between calculated and measured powder moisture content suggested that the well-mixed hypothesis could be applied for paste drying in a spouted bed. The robustness of the model with respect to changes in feed flow rates and other operating conditions showed the merits of using a trained neural network.-
Idioma: dc.languageen-
Publicador: dc.publisherTaylor & Francis Group-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceDrying Technology-
Palavras-chave: dc.subjectModeling-
Palavras-chave: dc.subjectNeural networks-
Palavras-chave: dc.subjectPaste drying-
Palavras-chave: dc.subjectSpouted bed drying-
Palavras-chave: dc.subjectRedes neurais-
Palavras-chave: dc.subjectPasta de secagem-
Título: dc.titleA hybrid lumped parameter/neural network model for spouted bed drying of pastes with inert particles-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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