Modeling of Eggplant Drying Process by Infrared System using Genetic Algorithm- Artificial Neural Network Method

In this study, the thin-layer drying behavior of eggplant slices in an infrared dryer (IR) was investigated. The effect of infrared lamp power (150, 250 and 375 watt), the distance of sample from lamp (5, 10 and 15 cm), samples thickness (0.5 and 1 cm) and drying time on drying of eggplant slices were examined. The results of infrared drying of eggplant showed that with increasing in lamp power and decreases in sample distance from the heat source, the drying rate increases. With increase in infrared power from 150 to 375 watts, weight loss increased from 31.08 to 92.44%. With increase in lamp distance from 5 to 15 cm, weight loss decreased from 92.44 to 31.15%. In this study, process modeling was done with the genetic algorithm–artificial neural network (GA-ANN) method with 4 inputs (power and lamp distance, sample thickness and drying time) and 1 output for prediction of weight reduction. The GA-ANN modeling results showed a network with 14 neurons in one hidden layer with using sigmoid function can be well predict the weight loss in eggplant drying by infrared system (R=0.99). Sensitivity analysis results by optimum ANN showed the infrared power was the most sensitive factor for controlling the weight loss of samples.