JWSS - Journal of Water and Soil Science

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Traditional paddy dryer systems in Iran cause considerable losses in rice production due to non-uniform drying. In order to decrease the amount of kernel fissuring and to increase the drying rate, fluidized bed method was applied in this study for rough rice drying at temperatures higher than normal. An experimental dryer was used for drying the samples. The drying experiments were set up to find kernel fissuring percentage and the drying times under three conditions: fixed, minimum, and full fluidized bed conditions at temperatures of 40, 60 and 80^oC. Results showed that the amount of kernel fissuring, at minimum fluidization compared to fixed bed condition, decreased 57%, 68% and 75% at temperatures of 40, 60 and 80^oC, respectively. This reduction at full fluidization compared to fixed bed condition, at the above temperatures, was 40%, 54% and 65%. The minimum fluidization method took the lowest and the fixed bed method took the highest drying time. It was concluded that the minimum fluidization drying method had the lowest fissuring and drying times at all experimental temperatures.

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Rapid drying can increase brittleness of and induce internal cracks in the grain which predispose the product to breakage during subsequent activities. To fully understand the drying process requires an accurate description of the drying mechanism. Kernel equilibruim moisture content (EMC) is a property strongly related to agricultural products drying phenomena. Its accurate prediction can lead to optimisation of drying processes, especially in highly automated computer aided drying systems. In this study, a finite element formulation and solution of a set of coupled conductive heat and diffusive moisture transfer equations, to improve grain drying simulation of axisymmetric bodies are presented. Axisymmetric linear triangular elements with two degrees of freedom per node are used to discretize the rice grain in model for different equilibrium moisture content (from 7.5 to 0.12 d.b.%). For the purpose of this study, one medium grain, ‘Sepidrod CV.’, was used. During the thin layer drying, the drying air temperature of 69 °C and initial moisture content of 17.23 d.b.%, were adopted. A high relation has been observed when the output of model with 11.5 d.b.% EMC was compared to experimental data obtained by others. The least and most root mean square error analysis (RMSE) calculated for models at different EMC with experimental data were 0.0091 and 0.1025. The least and most mean relative deviation modulus were 1.394 and 5.129, respectively. Considering the mean errors of the models in relation to the obtained experimental data, the equilibrium moisture content for 11.5 d.b gave the best result.

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The objective of this research was to predict head rice yield (HRY) in fluidized bed dryer using artificial neural network approaches. Several parameters considered here as input variables for artificial neural network affect operation of fluidized bed dryers. These variables include: air relative humidity, air temperature, inlet air velocity, bed depth, initial moisture content, final moisture content and inlet air temperature. In aggregate, 274 drying experiments were conducted for creating training and testing patterns by a laboratory dryer. Samples were collected from dryer, and then dehulling and polishing operations were done using laboratory apparatus. HRY was measured at several different depths , average of which was considered as HRY for each experiment. Three networks and two training algorithms were used for training presented patterns. Results showed that the cascade forward back propagation algorithm with topology of 7- 13-7-1 and Levenberg-Marquardt training algorithm and activation function of Sigmoid Tangent predicted HRY with determination coefficient of 95.48% and mean absolute error 0.019 in different conditions of fluidized bed paddy drying method. Results showed that the input air temperature and final moisture content has the most significant effect on HRY.