جستجوی مقالات مرتبط با کلیدواژه « moisture ratio » در نشریات گروه « مهندسی شیمی، نفت و پلیمر »

In this research, the process of drying the quince fruit and the effect of various parameters such as the drying air speed, time, temperature and thickness on moisture ratio were studied. 7 mathematical models were fitted to the data obtained from 27 series of experiments and the best model was selected. Modeling was also performed by artificial neural network. In this modeling, the effect of all input parameters on the drying process was investigated simultaneously. The selective network structure was considered multi-layer perceptron with the back-propagation algorithm. By researching the number of different hidden layer neurons and different transfer functions, 9 neurons and "logsig” transfer function were used for the hidden layer and "purelin” transfer function for the output layer. Modeling by artificial neural network predicted the simultaneous effect of the four input parameters with very high accuracy. The results showed that ANN modeling had better accuracy than the best mathematical model.

In present study, the thin- layer drying of orange slices in a laboratory scale hot-air dryer has been modeled. Drying experiments were conducted at three different temperatures of 50, 60 and 70°C, and two air velocities of 1.0 and 2.0 m/s. The statistical results of data showed the change of drying temperature and air velocity had significant effects on moisture ratio (p<0.05) but interaction effect of air velocity and temperature had insignificant effect on moisture ratio. Based on the results, the minimum moisture ratio of dried orange slices was obtained 5.3% when the dryer air temperature and velocity were 70°C and 2.0 m/s, respectively. After the end of experiments, the experimental data were fitted to the 7 well-known drying models. According to fitting results, Page’s model with determination coefficient R2-3 showed better performance to predict the moisture ratio. Also, this study used a feed forward back propagation neural network in order to estimate orange slices moisture ratio, based on the temperature, air velocity and time as input variables. In order to design this model, two main activation functions called tanh and purlin, widely used in engineering calculations, were applied in hidden and output layer, respectively. The artificial neural network with 3-20-1 topology and Levenberg-Marquardt training algorithm provided best results with the maximum determination coefficient (0.9994) and minimum Root of Mean Square Error (1.009×10-3) values. The results indicated the artificial neural network model was more accurate than Page’s model for prediction of moisture ratio of orange slices during drying process.

Paddy drying is one of the most important and critical post-harvest processes of rice in order to prevent the growth of germs and fungi; which enhances the product storage time. In this study, drying process of Tarom Hashemi was evaluated in a continuous band microwave dryer and in a domestic microwave dryer. The obtained results indicated that the moisture ratio decreased by increasing the paddy thickness from 6 to 18 mm and the microwave power levels from 10 to 30%, whereas the seed breakage increased. The drying ratio in the domestic microwave dryer was always less than that in the continuous band microwave dryer except at the 10% input power level, whereas the seed breakage percent was always more in the domestic microwave dryer in comparison with the continuous band microwave dryer. The results indicated that the moisture diffusion coefficient was larger in drying with continuous band microwave dryer in comparison with the domestic microwave dryer at 10% microwave power levels, whereas this coefficient was less at the 50% microwave power. As a final conclusion, the use of household microwave dryer for drying the paddy is not recommended due to high percentage of seed breakage. In contrast using a continuous band microwave dryer with low input power levels (e.g., 10%) is recommended on a large scale due to the rapid moisture reduction and low seed breakage percentage.