Prediction of asphaltene precipitation using support vector regression tuned with genetic algorithms

Abstract

Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry, developing a quick and accurate model, to predict the asphaltene precipitation under different conditions, seems crucial. In this study, a new model, namely genetic algorithm – support vector regression (GA-SVR) is proposed, which is applied to predict the amount of asphaltene precipitation. GA is used to select the best optimal values of SVR parameters and kernel parameter, simultaneously, to increase the generalization performance of the SVR. The GA-SVR model is trained and tested on the experimental data sets reported in literature. The performance of the GA-SVR model is compared with two scaling equation models, using statistical error measures and graphical analyses. The results show that the prediction performance of the proposed model, is highly reliable and satisfactory.

Keywords

• Asphaltene precipitation
• Prediction
• Support Vector Regression (SVR)
• Genetic Algorithm (GA)
• Parameter optimization