2007年8月
Prediction of vapor-liquid equilibria using reconstruction - learning neural network method
FLUID PHASE EQUILIBRIA
- ,
- 巻
- 257
- 号
- 2
- 開始ページ
- 169
- 終了ページ
- 172
- 記述言語
- 英語
- 掲載種別
- DOI
- 10.1016/j.fluid.2007.01.026
- 出版者・発行元
- ELSEVIER SCIENCE BV
This paper deals with the proposal of a predictive method for Margules parameters using reconstruction-learning neural network (NN). The input layers in the NN method are critical volume, acentric factor, dipole moment, entropy of vaporization and electronegativity of components I and 2. The number of Margules parameters used for evaluating the weight matrix in the NN method is 872, and the obtained correlation coefficient is R-2=0.8537. The Margules parameters not used as learning data were predicted for 17 binary systems. The vapor-liquid equilibria were then predicted for 17 binary systems using these Margules parameters in combination with Riedel vapor pressure constants predicted by the NN method proposed previously. We observed a high degree of similarity between experimental and predicted vapor compositions. (c) 2007 Elsevier B.V. All rights reserved.
- リンク情報
- ID情報
-
- DOI : 10.1016/j.fluid.2007.01.026
- ISSN : 0378-3812
- eISSN : 1879-0224
- Web of Science ID : WOS:000248232600008