Neural Network Approaches for Prediction of Pistachio Drying Kinetics

Abstract

Abstract Thin-layer drying characteristics of whole pistachio were investigated by using a hot air convective dryer at a constant airflow velocity of 2 m.s-1 and air temperature in the range of 40-70°C. The experimental drying data were fitted to the eight well-known drying models i.e. the Newton, Page, Modified Page, Henderson and Pabis, Logarithmic, Diffusion, Thomson and New models. A predictive model using artificial neural network was proposed in order to obtain on-line predictions of moisture kinetics during drying of pistachio nut. For monitoring the drying process of pistachio, different activation function of neural networks such as tangent hyperbolic (tanh) and logarithmic sigmoid (logsig) were utilized. Drying time and air drying temperature were considered as network input and moisture ratio was as network output. The result indicated that tanh activation function gave better results than logsig activation function for monitoring the moisture ratio. Generally, perceptron neural network with logsig activation function as a goodness activation function was able to predict moisture ratio with 7 neuron in first and second hidden layer with R2 value equal 0.994. Investigation of validation data demonstrated that the predicted and experimental dying data were in good agreement. Comparing the R2 (coefficient of determination) and MAE using the developed ANN model it was concluded that the neural network could be used for on-line state estimation of drying characteristics and control of drying processes.