شماره ركورد كنفرانس :
5048
عنوان مقاله :
An Intelligent Approach to Estimate Pressure-Volume-Temperature Properties in the System of Methane- Tetrafluoromethane: Densities and Compressibility Factors
Author/Authors :
M.R ،Nikkholgh Department of Chemical Engineering - Faculty of Engineering - Arak University - Arak, Iran , A.R ،Moghadassi Department of Chemical Engineering - Faculty of Engineering - Arak University - Arak, Iran , F ،Parvizian Department of Chemical Engineering - Faculty of Engineering - Arak University - Arak, Iran
كليدواژه :
Artificial Neural Network , Gas Mixture Density , Compressibility factor , CH4 , CF4
عنوان كنفرانس :
ششمين كنگره بين المللي مهندسي شيمي
چكيده لاتين :
In this study, the ability of Artificial Neural Network or ANN based on back-propagation approach for predicting
the densities and compressibility factor of gaseous binary mixtures of CH4-CF4 has been investigated. Some
experimental data (1507 data points) of gas densities for pure CH4, pure CF4, and three mixtures (0.25, 0.50, and 0.75
mole fraction of methane) are used to find optimal network, for which a density range from 0.75 to 12.5 mole/lit were
covered. Finally, a network included 10-5-1 neurons in its layer is selected. By using this number of neurons,
admissible absolute average deviations (about 0.112593% and 0.121046% for training and testing steps, respectively)
are provided. Then, a comparison of compressibility factors for a mixture containing 50% CH4 shows an acceptable
deviation, about 0.023604%. These results show that there is an excellent agreement between experimental data and
ANN predictions.
