This article compares diffusion models used to describe seedless grape drying at low temperature. The models were analyzed, assuming the following characteristics of the drying process: boundary conditions of the firs...This article compares diffusion models used to describe seedless grape drying at low temperature. The models were analyzed, assuming the following characteristics of the drying process: boundary conditions of the first and the third kind;constant and variable volume, V;constant and variable effective mass diffusivity, D;constant convective mass transfer coefficient, h. Solutions of the diffusion equation (analytical and numerical) were used to determine D and h for experimental data of seedless grape drying. Comparison of simulations of drying kinetics indicates that the best model should consider: 1) shrinkage;2) convective boundary condition;3) variable effective mass diffusivity. For the analyzed experimental dataset, the best function to represent the effective mass diffusivity is a hyperbolic cosine. In this case, the statistical indicators of the simulation can be considered excellent (the determination coefficient is R2 = 0.9999 and the chi-square is χ2 = 3.241 × 10–4).展开更多
文摘This article compares diffusion models used to describe seedless grape drying at low temperature. The models were analyzed, assuming the following characteristics of the drying process: boundary conditions of the first and the third kind;constant and variable volume, V;constant and variable effective mass diffusivity, D;constant convective mass transfer coefficient, h. Solutions of the diffusion equation (analytical and numerical) were used to determine D and h for experimental data of seedless grape drying. Comparison of simulations of drying kinetics indicates that the best model should consider: 1) shrinkage;2) convective boundary condition;3) variable effective mass diffusivity. For the analyzed experimental dataset, the best function to represent the effective mass diffusivity is a hyperbolic cosine. In this case, the statistical indicators of the simulation can be considered excellent (the determination coefficient is R2 = 0.9999 and the chi-square is χ2 = 3.241 × 10–4).
