جستجوی مقالات مرتبط با کلیدواژه « خشک کن مایکروویو » در نشریات گروه « صنایع غذایی »

Rice is one of the most important cereals and is the second-highest worldwide production after wheat and also is a good source of nutrients for humans. It plays an important role in the feeding of the many parts of the world including Iran. The harvested paddy rice has the high initial moisture content of nearly 25-28% (wet basis) that caused corruption. Therefore, in order to prevent corruption and safe storage, it must be dried to 10-13% moisture content. Drying is one of the oldest methods of preserving food and agricultural products that used to increase the food’s storage time. There are several methods for drying paddy rice that none of them are ideal and have several advantages and disadvantages that one of them that recently the use of it has been increased is microwave drying. Microwave drying uses electromagnetic radiations with the frequency range of 300 MHz to 300 GHz and the wavelengths of 1-0.01m. In microwave drying due to better energy concentration, moisture is removed more quickly so the drying time decreases. Due to the complex relationship between input and output variables in the drying process, the selection of the model that can estimate the drying behavior of the products is difficult. Hence, the use of intelligent modeling methods such as neural networks is the best choice.

In this research, in order to investigate the effect of microwave power on kinetics of rice drying, head rice yield and effective diffusivity coefficient of moisture, a continuous type of domestic microwave dryer ( DEM-281 QOT-PW) were used. This dryer has a microwave radiation chamber where the samples are put on it on the tray that was placed on a digital balance. The experiments were performed at three microwave power levels designated as 270, 360 and 450 W. Also, Shirudi paddy rice was used as the raw material and the drying rice process from the initial moisture content of 21% to the final moisture content of 11% is examined. In this study, the neural network toolbox of MATLAB 2017R was used to model the kinetics of rice drying in the microwave dryers. RBF and MLP have 3 layers including input, hidden and output layers. The input layer has two neurons that show the number of input variables that were time and microwave power and the output layer has one neuron that shows the number of output variables that was MR in this study. 70% and 30% of the data was used for training and testing the network, respectively. To estimate the ANN performance, mean square error (MSE) and the coefficient of determination (R2) was used.

The maximum and minimum drying time was 42 and 20 minutes in 270 and 450 watts, respectively. Also, the maximum and minimum effective diffusivity coefficient of moisture were 4.17 * 10^-9 and 1.82* 10^-9 in 450 and 270 watts, respectively. RBF network with Guassian transfer function and high neurons number and MLP network with Levenberg-Marquardt ( LM) learning algorithm and tan-sigmoid (tansig) transfer function with low neurons number were able to model the kinetics of drying as well as. In general, the drying time and head rice yield decreased but the effective diffusivity coefficient of moisture increased by increasing the microwave power. Also drying at different microwave power did not affect rice color and quality.  The results of the modeling of rice drying by using two different neural networks including MLP and RBF demonstrated that the MLP network with Levenberg-Mrrqurdt (LM) learning algorithm and tan-sigmoid (tansig) transfer function has the better performance than the RBF network with Gussian transfer function and the error and the correlation coefficient in MLP are less and higher than the RBF, respectively.

In this research work, in order to estimate the drying properties of eggplant and turnip in a combined microwave-convection dryer it was used artificial neural network method in order to estimate the Drying process was accomplished in three temperature levels (40, 55, and 70 °C), three inlet air velocity levels (0.5, 1.1 and 1.7 m/s) and three microwave power levels (270, 450 and 630 W) in a combined microwave-convection dryer that the three parameters were utilized as input in predicting the effective moisture diffusion coefficient and specific energy consumption in artificial neural network. A feed forward and cascade forward back propagation neural network with training functions of Levenberg - Marquardt (LM) and Bayesian Regulation (BR) for training of patterns. According to results, the highest value of the effective moisture diffusion coefficient for eggplant and turnip was obtained 3.39×10-9 and 3.05×10-9 m2/s, respectively. Results of ANN investigations showed that the optimum feed forward back propagation network with 3-20-20-2 topology and training function of Levenberg – Marquardt could predict the effective moisture diffusion coefficient and specific energy consumption with determination coefficients of 0.9821 and 0.9952 and mean square error of 0.00014 in various drying conditions of eggplant and turnip. Also the highest determination coefficients for prediction of drying rate and moisture ration obtained 0.9698 and 0.9988 with the value of mean square error of 0.0045 in cascade back propagation neural network with training algorithm of Levenberg – Marquardt.

In this study، the impact of different drying conditions (4 levels of microwave power 130، 260، 380 and 450 watts and 4 absolute pressure levels 200، 400، 600 and 800 mbar) on drying rate، effective diffusion coefficient، activation energy، and energy consumption of a microwave–vacuum drier for drying the mushroom slices were analyzed. According to the findings، effective diffusion coefficient for mushroom varied between 3. 21×10-9 to 20. 005×10-9 m2/s. In addition، the activation energy based on an Arrhenius based exponential equation was very close to each other for three different methods. In terms of energy consumption، the minimum and maximum quantities were about 0. 168 and 0. 48 kWh، respectively.

In the present study, the drying kinetics of non-fat yoghurt with constant thickness of 2.2 mm) in a microwave–vacuum dryer to the power of (35, 130, and 260 W) as well as 5 absolute pressure levels (60, 200, 400, 600, and 800 mbar) were evaluated. To choose the best, also 12 experimental/ semi-theoretical and/or empirical thin-layer drying models were examined. Based on the findings of this study various ranges of microwave power affects drying time where by increasing the microwave power (from 35 to 260 W), drying time significantly decreases from 90 min to 15) which is (600%). However, no significant effects in drying time were observed when reduced the absolute pressure in the system. RMSE was picked to be the most suitable model when the comparison runs between Logistic, Middilli and coworkers models for it contained maximum R2 and minimum χ2 so it could satisfactorily describe the drying kinetics of non-fat yoghurt. The effective moisture diffusivity was increased by increasing the microwave power from 4.42×10-10 to 2.83×10-9 m2s-1 while the pressure had zero effect on this value. In addition, the activation energy was calculated using the modified Arrhenius equation method.

In this study، the drying behavior and the effects of different drying conditions on the trend ofmoisture loss and effective diffusion coefficient in sweet and sour pomegranate were determined. Thebest drying model، energy consumption and activation energy values were also determined for thetwo varieties using microwave drying. Results of regression analysis of mathematical models pointsto the page model as the one best described the data. Effective diffusion coefficient of pomegranatearils was found to be 3. 43´10-10 to 32. 05´10-10 m2/s. Activation energy was determined to be 17. 22and 23. 83 kJ for sweet and sour pomegranates respectively. In addition، consumed energyconsumption drying of for sweet and sour pomegranate arils was 0. 167 to 0. 383 kW/kg respectively.