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Abstract
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In the gas engineering the accurate calculation for pipeline and gas reservoirs
requires great accuracy in estimation of gas properties. The gas density
is one of major properties which are dependent to pressure, temperature and
composition of gas. In this work, the Least squares support vector machine
(LSSVM) algorithm was utilized as novel predictive tool to predict natural gas
density as function of temperature, pressure and molecular weight of gas. A
total number of 1240 experimental densities were gathered from the literature
for training and validation of LSSVM algorithm. The statistical indexes, Root
mean square error (RMSE), coefficient of determination (R2) and average absolute
relative deviation (AARD) were determined for total dataset as 0.033466, 1
and 0.0025686 respectively. The graphical comparisons and calculated indexes
showed that LSSVM can be considered as a powerful and accurate tool for prediction
of gas density.
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