Prediction of carrot cubes drying kinetics during fluidized bed drying by artificial neural network

This article presents static and recurrent artificial neural networks (ANNs) to predict the drying kinetics of carrot cubes during fluidized bed drying. Experiments were performed on square−cubed carrot with dimensions of 4, 7 and 10 mm, air temperatures of 50, 60 and 70°C and bed depths of 3, 6 and 9 cm. Initially, static ANN was used to correlate the outputs (moisture ratio and drying rate) to the four exogenous inputs (drying time, drying air temperature, carrot cubes size, and bed depth). In the recurrent ANNs, in addition to the four exogenous inputs, two state input and output (moisture ratio or drying rate) were applied. A number of hidden neurons and training epoch were investigated in this study. The dying kinetics was predicted with R2 values of greater than 0.94 and 0.96 using static and recurrent ANNs, receptively.