Modeling of the Garification Process of Fermented Cassava Mash

Abstract

This study was carried out to analyze the garification process using Artificial Neural Network (ANN) based model of steady state simultaneous heat and mass transfer. Convective heat and mass transfer coefficients were obtained during garification process of fermented mash from cassava ages of different maturity. Empirical equations developed for heat, (hc) and mass, (hm) transfer coefficients [hc=0.017t2-0.388t+3.039, hm=0.042t2-0.914t+5.481]; with (R2>0.9) were best described by polynomial relationships. The optimum ANN model that produced convective heat and mass transfer coefficients for the garification process consisted of two hidden layers and twenty-five neurons in each hidden layer, with mean square error, mean absolute error, sum square error and R2 of 0.000015, 0.0030, 0.0082% and 0.995, respectively. The developed ANN model can be useful in the determination of heat and mass transfer rate for garification process and wide range of physical conditions. These results are equally important considerations for obtaining quality gari for commercial production.