Artificial neural network modeling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves

The present study involved integration of artificial neural network (ANN) with genetic algorithm (GA) for predicting the optimized process parameters required for fluidized bed drying of green tea leaves. It had a layer each for input and output with linear activation function and two hidden layers with a sigmoid function. The feed forward back propagation method was used to train the developed model. The input parameters used by ANN for generalizing the drying process were temperature (50-80 degrees C) and air flow velocity (7-9.5 m/s), and the output parameters were drying time, total color difference (TCD), and total phenolic content (TPC). The weights and bias values of trained ANN were used by GA to estimate the fitness function, which maximizes the TPC and minimizes the drying time and TCD. The optimum process condition of independent variables (80 degrees C and 9 m/s) obtained from the hybrid ANN/GA was validated, and agreeable relationship between actual and predicted values with relative standard deviation (SD) of 5.7, 0.46, and 0.22 was found for drying time, TCD and TPC of dried leaves, respectively. Hence, under this optimal drying condition, best quality green tea can be obtained within the limits defined.