Home Itens selecionados Provedores de dados OAI Créditos Sobre Ajuda

			Busca avançada
	Provedor de dados Ciênc. Tecnol. Aliment. (2) Autor BEIGI,Mohsen (2) AHMADI,Iman (1) Palavra-chave Apple (1) Artificial neural networks (1) Celeriac (1) Drying kinetics (1) Energy efficiency (1) Moisture diffusion (1) Multilayer feed forward back propagation (1) Thermodynamic parameters (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Ano 2019 (1) 2016 (1) País Brazil (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Energy efficiency and moisture diffusivity of apple slices during convective drying Ciênc. Tecnol. Aliment. BEIGI,Mohsen. Abstract The present study aimed at investigating the influences of drying air temperature and flow rate on energy parameters and dehydration behaviour of apple slices. For this purpose, apple slices were dried in a convective dryer at air temperatures of 50, 60 and 70 °C, and air velocities of 1, 1.5 and 2 m s–1. Dehydration rate increased as the air temperature and flow rate increased from 50 to 70 °C and 1 to 2 m s–1, respectively. The effective moisture diffusivity was determined to be in the range of 6.75×10–10-1.28×10–9 m2 s–1. Results of data analysis showed that the maximum energy consumption (23.94 kW h) belonged to 50 °C and 2 m s–1 and the minimum (13.89 kW h) belonged to 70 °C and 1 m s–1 treatment. Energy efficiency values were in the range of... Tipo: Info:eu-repo/semantics/article Palavras-chave: Apple; Moisture diffusion; Thermodynamic parameters; Energy efficiency. Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000100145 Artificial neural networks modeling of kinetic curves of celeriac (Apium graveolens L.) in vacuum drying Ciênc. Tecnol. Aliment. BEIGI,Mohsen; AHMADI,Iman. Abstract The objective of this study was to predict celeriac drying curves using artificial neural networks (ANNs). The experimental data for vacuum drying kinetics of celeriac slices reported by other researcher in the previously published article was used. The air temperature, chamber pressure and time values were used as ANN inputs. To predict the moisture content, the multilayer feed forward back propagation neural network, as a well-known network, was used. The network with Levenberg-Marquardt learning algorithm, hyperbolic tangent sigmoid transfer function, and 3-6-9-1 topology provided the superior results. Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural networks; Celeriac; Drying kinetics; Multilayer feed forward back propagation. Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612019000500035 Registros recuperados: 2 Primeira ... 1 ... Última

Empresa Brasileira de Pesquisa Agropecuária - Embrapa Todos os direitos reservados, conforme Lei n° 9.610 Política de Privacidade Área restrita		Embrapa Parque Estação Biológica - PqEB s/n° Brasília, DF - Brasil - CEP 70770-901 Fone: (61) 3448-4433 - Fax: (61) 3448-4890 / 3448-4891 SAC: https://www.embrapa.br/fale-conosco