Document Type: Original Article
National Iranian Gas Company (NIGC), South pars Gas Complex (SPGC), Asaluyeh, Iran
School of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Avenue, Tehran 15914, Iran
In present study, Least Square Support Vector Machine (LSSVM) and Radial Basis Function (RBF) are employed to develop models
to predict Reid vapor pressure of a sour condensate which is the output variable of stabilizer column of Assaluyeh industrial natural
gas sweetening plant. A set of 4 input/output plant data each consisting of 660 data has been used to train, optimize, and test the
models. Model development that consists of training, optimization and test was performed using randomly selected 80%, 10%, and
10% of available data respectively. Test results from the LSSVM developed model showed to be in better agreement with operating
plant data. Squared correlation coefficients for developed models are 0.83 and 0.91 for RBF and LSSVM based results, respectively.
According to the results of the present case study, LSSVM could be regarded as a reliable accurate approach for modeling of a natural
gas processing plant.