MODELING ISOSTERIC HEAT OF SOYA BEAN FOR DESORPTION ENERGY ESTIMATION USING NEURAL NETWORK APPROACH

Sorption isotherm of soya bean (Glycine max (L.) Merr.) was obtained by the dynamic experimental method. Artificial Neural Networks (ANNs) were used for modeling soya bean equilibrium moisture content (EMC). Thermodynamic equations and trained ANN for prediction of two thermodynamic properties of net isosteric heat and entropy of soya bean were utilized. The ANN models were better compared with mathematical models. In this study, the isosteric heat and entropy of sorption of soya bean were separately predicted by two power models as a EMC function. Predictive power of the models was high (R2 ^ 0.99). At the moisture content above 11% (dry basis, db), isosteric heat and entropy of sorption of soya bean were smoothly decreased, while they were highest at moisture content about 8% (db). Isosteric heat and entropy would be useful in the storage simulation of dried soya bean. The ANN model predicts soya bean EMC more accurately than mathematical models. Hence, better equations could be developed for the prediction of heat of sorption and entropy based on data from the ANN model.