Abstract—Artificial neural network (ANN) based model of transient simultaneous heat and mass transfer was used for the prediction of some thermo-physical during reconstitution of gari into thick paste. Temperature changes in the paste and moisture losses were recorded over a period of two hours while the granules are being reconstituted. Data on convective heat and mass transfer coefficients were obtained during reconstitution of gari into paste. In developing the ANN model, several configurations were evaluated. The mean square error (MSE), mean absolute error (MAE) and sum square error (SSE) were used to compare the performances of the various ANN configurations. The best ANN configuration included two hidden layers, with twenty-five neurons in each hidden layer was able to produce convective heat and mass transfer coefficients values with MSE, MAE and SSE of 0.000016, 0.0029 and 0.0085%, respectively, and had R2 of 0.992. The effectiveness of the empirical results was compared with the developed ANN model and these are valid for heat and mass transfer data obtained for the reconstitution characteristics of gari paste.
Index Terms—Artificial neural network (ANN), convective heat and mass transfer coefficient, Gari granules, modeling, reconstitution, thick paste.
S. S. Sobowale is with the Department of Food Science and Technology, Moshood Abiola polytechnic, Abeokuta, Ogun State, Nigeria (e-mail: sunsam_2003@yahoo.co.uk).
S. O. Awonorin and T. A. Shittu are with the Department of Food Science and Technology, Federal University of Technology, Abeokuta, Ogun State, Nigeria.
E. S. A. Ajisegiri is with the Department of Agricultural Engineering, Federal University of Technology, Abeokuta, Ogun State, Nigeria.
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Cite: S. S. Sobowale, S. O. Awonorin, T. A. Shittu, and E. S. A. Ajisegiri, "Artificial Neural Network (ANN) of Simultaneous Heat and Mass Transfer Model during Reconstitution of Gari Granules into Thick Paste," International Journal of Chemical Engineering and Applications vol. 5, no. 6, pp. 462-467, 2014.
