TY - JOUR
ID - 5584
TI - Estimation of Binary Infinite Dilute Diffusion Coefficient Using Artificial Neural Network
JO - Journal of Chemical and Petroleum Engineering
JA - JCHPE
LA - en
SN - 2423-673X
AU - Mohadesi, Majid
AU - Moradi, Gholamreza
AU - Mousavi, Hosnie-Sadat
AD - Chemical Engineering Department, Faculty of Engineering, Razi University,
Kermanshah, Iran
Y1 - 2014
PY - 2014
VL - 48
IS - 1
SP - 27
EP - 45
KW - Artificial Neural Network
KW - Binary mixture
KW - Infinite dilute diffusion coefficient
KW - Supercritical Fluid
DO - 10.22059/jchpe.2014.5584
N2 - In this study, the use of the three-layer feed forward neural network has been investigated for estimating of infinite dilute diffusion coefficient ( D12 ) of supercritical fluid (SCF), liquid and gas binary systems. Infinite dilute diffusion coefficient was spotted as a function of critical temperature, critical pressure, critical volume, normal boiling point, molecular volume in normal boiling point, molecule diameter, Lennard-Jonesâ€™s (LJ) energy parameter, temperature and pressure. For each set of SCF, liquid and gas systems a three-layer network has been applied with training algorithm of Levenberg-Marquard (LM). The obtained results of models have shown good accuracy of artificial neural network (ANN) for estimating infinite dilute diffusion coefficient of SCF, liquid and gas binary systems with mean relative error (MRE) of 2.88 % for 231 systems containing 4078 data points (mean relative error for ANN model in SCF, liquid and gas binary systems are 3.00, 2.99 and 1.21 %, respectively)
UR - https://jchpe.ut.ac.ir/article_5584.html
L1 - https://jchpe.ut.ac.ir/article_5584_fd15a49035b5a2f9d471071d75a05576.pdf
ER -