Modeling of sesame seed dehydration energy requirements by a soft-computing approach

Thermodynamic models and Soft-computing method of Neural Network (NN) were used for computation of sesame seed dehydration energy (heat and entropy). The NN method for prediction of the Equilibrium Moisture Content (EMC) of sesame seed was utilized. The heat of sorption of sesame seed is predicted by a mathematical model. After well training of the NN model, predictive power of the model was found to be high (R=0.99). A regression model was also developed for prediction of entropy of sorption. At moisture content of about 11% (d.b.) the heat and entropy of sorption of sesame seed were decreased, smoothly, and they became highest at moisture content of about 8% (d.b.). Dehydration energy values of sesame seeds was very low compared with the other agricultural products. Computation of dehydration energy would be useful in the simulation of dried sesame seed storage.