Optimization of free gossypol removal from cottonseed meal by the extrusion process based on artificial neural network with genetic algorithm

The artificial neural network ( ANN) was used for the simulation of the degradation of free gossypol in cottonseed meal by the extrusion process.A three- layer back propagation neural network was optimized to predict the degradation of free gossypol.The result of 10- fold cross validation showed that the model of back propagation neural network giving the smallest mean square error ( MSE) was the ANN with the training function as traingdx at hidden layer with 8 neurons.And ANN predicted results were very close to the experimental results with correlation coefficient ( R2) of 0.9941 and RMSE of 0.4971.A genetic algorithm ( GA) based on an established neural network model was also used to optimizing de- gossypol process.The results of GA obtained showed that the optimal condition of de- gossypol by the extrusion process was temperature 131℃, water ratio 51%, rotational speed158 r / min, and feeding speed 136 kg / h, and in this condition the degradation rate of free gossypol was 90.50%, which was close to the result of GA predicted with the small average relative error of 1.38%. These results suggested that the GA based on a neural network model might be an excellent tool for optimizing cottonseed meal de- gossypol process.