A novel approach for modelling and predicting the drying kinetics of couscous grains using artificial neural networks

This paper presents a new approach for modelling and predicting the evolution of moisture content (MC) in couscous grains during drying using artificial neural networks. Couscous grains with an initial moisture content of 42 ± 1% were dried in an oven using the gravimetric method. Experimental data on moisture content versus drying time were obtained for nine groups of measurements at relative humidities of 7, 32 and 75% at drying temperatures of 30, 40 and 50°C. Different parameters of the ANN model, such as three learning algorithms and four activation functions, as well as the number of neurons in the hidden layer, were tested and evaluated using the correlation coefficient (R2) and the mean squared error (MSE) to find the optimal structure without overfitting. The best performing ANN structure consisted of the log sigmoid function in the hidden layer, with 24 neurons and the linear function in the output layer, trained by the Levenberg-Marquardt backpropagation algorithm. A significant agreement of predictions with experimental data was observed, with an R2 value of 0.99999 and an MSE value of 8.2173 ×10-4.