Using artificial neural networks (ANNs) method in investigation and estimation of some drying characteristics of eggplant and turnip in a combined microwave - convective dryer

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.