Showing 2 results for Amiri Chaijan
R. Amiri Chaijan, M. H. Khosh-Taghaza,
Volume 7, Issue 4 (winter 2004)
Abstract
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 80oC. 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 80oC, 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.
R Amiri Chaijan, M Khosh Taghaza, Gh Montazer, S Minaee, M Alizadeh,
Volume 13, Issue 48 (7-2009)
Abstract
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.
