Modular Feed Forward Networks to Predict Sugar Diffusivity from Date Pulp Part I. Model Validation

In Tunisia, some 15,000 tons of fructose could be produced annually from second quality dates presently being left to rot. Extraction is the first step in producing sugar from these dates, and sugar diffusivity from the date paste governs the process. The objective of this project was therefore to measure in the laboratory, the sugar diffusivity of three date varieties (Manakher, Lemsi, and Alligue) under three different temperatures (50, 65 and 80°C), and from this data, develop an artificial neural network (ANN) model to predict sugar extraction. For each date variety, the laboratory procedure consisted of soaking a layer of date paste in water at one of the three temperatures and observing water sugar concentration at 20 mm from the date layer, every 15 min over a period of 240 min. This experimental data was then used to develop the ANN model, where several configurations were evaluated. Date sugar concentration with time was found to be significantly influenced by temperature and variety. The Lemsi variety allowed for the highest sugar extraction of 75% at 80°C. The optimal ANN model was found to be a network with two hidden layers and seven neurones in both the upper and lower levels of each hidden layers. This optimal model was capable of predicting sugar diffusivity from the different date varieties with a mean square error of 0.0037 and an 8.0% Error. The results show very good agreement between the predicted and the desired values of sugar diffusivity (R2 = 0.98). The coefficient of determination was also very good (R2 > 0.95), due to a small prediction error.