Prediction of white mulberry drying kinetics in microwave- convective dryer: A comparative study between mathematical model, artificial neural network and ANFIS

The aim of this study was to determine the kinetics, effective moisture diffusivity, activation energy, specific energy consumption, and also predict the moisture content of white mulberry during the drying process with microwave-hot air dryer using mathematical models, Artificial Neural Networks (ANN) and Neuro-Fuzzy Inference System (ANFIS). Drying process was accomplished in three temperature levels (40, 55, and 70°C), three inlet air velocity levels (0.5, 1 and 1.5 m/s) and three microwave power levels (270, 450 and 630 W). To estimate the moisture ratio of white mulberry, 10 mathematical models, ANN and ANFIS were used to fit the experimental data of thin-layer drying. The results showed, the maximum and minimum effective moisture diffusivity during drying was calculated 3.56×10-9 and 3.86×10-10 m2/s, respectively. Also, the minimum and maximum effective moisture diffusivity during drying was achieved 48.54 and 1380.88 Mj/kg, respectively. Among the mathematical models under study, the Page model was the best model for describing the behavior of the thin layer of white mulberry drying. The mean square error (MSE) values for the mathematical models, ANN, and ANFIS were 0.00059, 0.0052 and 0.0044, respectively. Therefore, the ANFIS model with the highest Correlation Coefficient (R2=0.99995), the least percentage of mean relative error (ε=1.84) and mean square error (MSE=0.0044) were used to evaluate the moisture ratio in comparison with other methods implemented in this research Selected as the best model