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.
Keyword: Gas sweetening plant, Stabilizer column, Least square support vector machine, RBF
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