TY - JOUR
T1 - Proposing a modified mechanism for determination of hydrocarbons dynamic viscosity, using artificial neural network
AU - Ahmadi, Shayan
AU - Motie, Mohadeseh
AU - Soltanmohammadi, Ramin
PY - 2020
Y1 - 2020
N2 - In this study, to have an accurate approximation of dynamic viscosity, radial basis function artificial neural network (RBF-ANN) is employed and developed for normal alkanes. This is done by considering the distinct number of carbons in n-alkanes, certain temperatures, and different pressures. Moreover, in order to train and test the predicting model, a databank of 228 experimental data is gathered from reliable sources in the literature. As a result, training and testing coefficient values are measured 0.99739 and 0.99051; consequently, the robustness and accuracy of RBF-ANN in providing an estimation of n-alkane viscosity is confirmed by graphical analysis and determined indexes.
AB - In this study, to have an accurate approximation of dynamic viscosity, radial basis function artificial neural network (RBF-ANN) is employed and developed for normal alkanes. This is done by considering the distinct number of carbons in n-alkanes, certain temperatures, and different pressures. Moreover, in order to train and test the predicting model, a databank of 228 experimental data is gathered from reliable sources in the literature. As a result, training and testing coefficient values are measured 0.99739 and 0.99051; consequently, the robustness and accuracy of RBF-ANN in providing an estimation of n-alkane viscosity is confirmed by graphical analysis and determined indexes.
KW - dynamic viscosity
KW - normal alkane
KW - predicting model
KW - RBF-ANN
KW - reservoir conditions
UR - http://www.scopus.com/inward/record.url?scp=85087122038&partnerID=8YFLogxK
U2 - 10.1080/10916466.2020.1780256
DO - 10.1080/10916466.2020.1780256
M3 - Article
SN - 1091-6466
VL - 38
SP - 699
EP - 705
JO - Petroleum Science and Technology
JF - Petroleum Science and Technology
IS - 10
ER -