توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
۱Prediction of Dehydration Characteristics of Tarom Garlic slices in Microwave–convection Combination
اطلاعات انتشار: پنجمین کنگره بین المللی مهندسی شیمی، سال ۱۳۸۶
تعداد صفحات: ۱۱
The convection and microwave–convection drying of garlic cloves were carried out in a laboratory scale microwave dryer, which was developed for this study. On both techniques the sample sizes were about 1.09 gr each, and with thicknesses of 5 and 7 mm. Experiments were carried out at temperature of 40oC, 100oC and 140C at air velocity of 1.0sm in microwave–convection method using powers of 100, 180 and 300 W. The effect of air temperature and sample thickness and in microwave–convection the effect of power, on dehydration characteristics of garlic slices was examined. The transport of water during dehydration was described by Fick’s equation and the effective diffusivity was between 2.9 and 31 1010×sm in microwave–convection method and, between 0.7 and 5.6 10×sm in convection method. The effect of temperature on the effective diffusivity was described by the Arrhenius–type relationship. The activation energy in the microwave–convective drying ranged between 2.54 and 14.67kJand in convective drying was between 16.38 and 18.84molkJ.On both methods, the experimental dehydration data of garlic slices were fitted to the five well–known semi–theoretical drying models, i.e. the Henderson and Pabis, two term, Lewis, page and verma et al.models. The accuracies of the models were measured using the coefficient of determination(R), root mean square error (RMSE) and sum of square error (SSE). All five models are acceptable for describing dehydration characteristics of garlic slices, however based on statistical analysis, the page, the verma and the two–term model showed a better performance to predict dehydration characteristics. In this paper, after examining above mentioned models, a mathematical model was developed and showed better fitness to the experimental data compared to the other models.<\div>
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