Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 degrees C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (D-eff) of moisture in CD drying (1.95x10(-7)-4.09x10(-7) m(2)/s) was found to be lower than that observed in MD (2.94x10(-7)-8.21x10(-7) m(2)/s). The activation energy (Ea) values of CD drying and MD drying were 122.28-125 kJ/mol and 14.01-15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R-2 values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.

Automated summary

The study compared the effects and performance of convective drying and microwave drying methods for dehydration of apples. The results showed that microwave drying technology was more efficient than convective drying, performing better overall and showing significant differences in aspects such as moisture diffusion rate, activation energy, and specific energy consumption. Artificial neural network models effectively predicted the moisture ratio of apple slices under different drying methods.