Neural networks for the heat and mass transfer prediction during drying of cassava and mango.
Hernández-Pérez J. A., García-Alvarado M. A., Trystram G., Heyd B.
Author Affiliation: Joint Research Unit Food Process Engineering (Cemagref, ENSIA, INAPG, INRA) ENSIA, 1 avenue des Olympiades, 91744 Massy Cedex, France.
Innovative Food Science & Emerging Technologies 5 : 57-64
Abstract : A predictive model for heat and mass transfer using artificial neural network is proposed in order to obtain on-line predictions of temperature and moisture kinetics during the drying of cassava and mango. The model takes into account shrinkage of the product as a function of moisture content. Two separate feedforward networks with one hidden layer were used (for cassava and mango, respectively). The best fitting with the training data set was obtained with three neurons in the hidden layer, which made possible to predict heat and mass transfer with accuracy, at least as good as the experimental error, over the whole experimental range. On the validation data set, simulations and experimental kinetics test were in good agreement. The developed model can be used for on-line state estimation and control of drying processes.