The freezing point depression (FPD) of uvaia pulp with and without additives -10, 16, 22 and 28% of maltodextrin (MD), was measured using a simple apparatus consisting of two major sections: a freezing vessel and a data acquisition system. The thermal conductivity of the pulps was also investigated as a function of the frozen water fraction and temperature using a coaxial dualcylinder apparatus. Above the initial freezing point, thermal conductivity fitted the polynomial equations well. Below the freezing point, thermal conductivity was strongly affected by both the frozen water fraction and the temperature. Simple equations in terms of the frozen water fraction and temperature could be fitted to the experimental data for freezing point depression and thermal conductivity.Indexing terms: Freezing point depression, uvaia pulp, thermal conductivity.
RESUMOO ponto de início de congelamento (FPD) da polpa de uvaia com e sem aditivos -10, 16, 22 e 28% de maltodextrina (MD), foi medido por um aparelho simples, que consiste de duas seções principais: um vaso de congelamento e um sistema de aquisição de dados. A condutividade térmica foi calculada em função da fração de água congelada e da temperatura, usando um aparelho cilíndrico coaxial duplo. Foram empregadas equações polinomiais para descrever o comportamento do ponto inicial de congelamento e da condutividade térmica. Abaixo do ponto de congelamento, a condutividade térmica foi fortemente afetada pela fração de água congelada e pela temperatura. Equações simples em termos da fração de água congelada e da temperatura podem ser ajustadas aos dados experimentais no cálculo do início do ponto de congelamento e da condutividade térmica.Termos para indexação: Ponto de início do congelamento, polpa de uvaia, condutividade térmica.
This research aims to compare the classical thin-layer models, stepwise fit regression method (SRG) and artificial neural networks (ANN) in the modelling of drying kinetics of shrimp shell and crab exoskeleton. Thus, drying curves were obtained using a convective dryer (3.0 m/s) at temperatures of 30.45 and 60 o C. The results showed a decreasing tendency for the drying time as the temperature increased for both materials. Drying curves modelling of both materials showed fitted results with R 2 adj>0.998 and MRE<13.128% for some thin-layer models. On the other hand, by SRG a simple model could be obtained as a function of time and temperature, with the greatest accuracy being found in the modelling of experimental data of crab exoskeleton, with MRE<10.149%. Finally, the ANNs were employed successfully in the modelling of drying kinetics, showing high prediction quality with the trained recurrent ANN models.
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