Modeling the kinetics of thin-layer drying of button mushroom by hot air using genetic algorithm - artificial neural network

Modeling the kinetics of drying of button mushroom in a hot air dryer by genetic algorithm– artificial neural network (GA-ANN) was investigated. The effects of hot air temperature at three levels 50, 60 and 70 °C, the air flow rate at three levels 1, 2 and 3 m/s on drying of button mushroom were examined. The results of hot air drying of button mushroom showed that with increasing the temperature and air velocity in hot air dryer, the drying rate increases. The increasing of the dryer temperature from 50 to 70 °C, and air velocity from 1 to 3 m/s, weight loss increased 12.2 and 12.0 %, respectively. Modeling the kinetics of thin-layer drying of button mushroom was done with the GA-ANN method with 3 inputs such as air temperature, flow rate and drying time and 1 output for predicting of weight loss. The modeling results showed a network with 16 neurons in one hidden layer with using hyperbolic tangent activation function and Levenberg–Marquardt can be well predict the weight loss in button mushroom drying by hot air (R=0.999). Sensitivity analysis results by optimum network showed that the dryer temperature was the most sensitive factor to controlling the weight loss of samples.