Department of Chemical Engineering, University of Mazandaran, Babolsar, Iran
Abstract
In this study, adsorption data for the case of energetically heterogeneous solid surface are modeled using artificial neural network. A neural network with three hidden neurons, including the bias, was able to predict very accurately the temperature dependency of adsorption data. The results were compared with experimental data (over temperature range 273-313 K and 0-2 MPa pressure) and it was found that the predictions of the artificial neural network model fit the experimental data very accurately.
Amooey,A. A. (2012). Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network. Iranian Journal of Chemical Engineering (IJChE), 9(4), 49-53.
MLA
Amooey,A. A. . "Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network", Iranian Journal of Chemical Engineering (IJChE), 9, 4, 2012, 49-53.
HARVARD
Amooey A. A. (2012). 'Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network', Iranian Journal of Chemical Engineering (IJChE), 9(4), pp. 49-53.
CHICAGO
A. A. Amooey, "Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network," Iranian Journal of Chemical Engineering (IJChE), 9 4 (2012): 49-53,
VANCOUVER
Amooey A. A. Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network. IJChE, 2012; 9(4): 49-53.
