Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy

Mahsa Mohammadi; Mohammadreza Khanmohammadi Khorrami; Ali Vatani; Hossein Ghasemzadeh; Hamid Vatanparast; Alireza Bahramian; Afshin Fallah

doi:10.1016/j.saa.2020.118945

Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy

Spectrochim Acta A Mol Biomol Spectrosc. 2021 Jan 15:245:118945. doi: 10.1016/j.saa.2020.118945. Epub 2020 Sep 12.

Authors

Mahsa Mohammadi¹, Mohammadreza Khanmohammadi Khorrami², Ali Vatani³, Hossein Ghasemzadeh⁴, Hamid Vatanparast⁵, Alireza Bahramian⁶, Afshin Fallah⁷

Affiliations

¹ Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran. Electronic address: m.mohammadi70.ac@gmail.com.
² Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran. Electronic address: m.khanmohammadi@SCI.ikiu.ac.ir.
³ Institute of Liquefied Natural Gas (I-LNG), School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
⁴ Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
⁵ Petroleum Engineering Research Division, Research Institute of Petroleum Industry (RIPI), Tehran, Iran.
⁶ Institute of Petroleum Engineering, College of Engineering, University of Tehran, Tehran, Iran.
⁷ Department of Statistics, Imam Khomeini International University, Qazvin, Iran.

PMID: 32977106
DOI: 10.1016/j.saa.2020.118945

Abstract

In the current research, an analytical method was proposed for rapid quantitative determination of saturates, aromatics, resins and asphaltenes (SARA) fractions of crude oil samples. Rapid assessments of SARA analysis of crude oil samples are of substantial value in the oil industry. The conventional SARA analysis procedures were determined with the standards established by the American Society for Testing and Materials (ASTM). However, the standard test methods are time consuming, environmental nonfriendly, expensive, and require large amounts of the crude oil samples to be analyzed. Thus, it be would useful to approve some supportive approaches for rapid evaluation of the crude oils. The attenuated total reflection Fourier-transform infrared spectroscopy ATR-FTIR coupled with chemometric methods could be used as analytical method for crude oil analysis. A hybrid of genetic algorithm (GA) and support vector machine regression (SVM-R) model was applied to predict SARA analysis of crude oil samples from different Iranian oil field using ATR-FTIR spectroscopy. The result of GA-SVM-R model were compared with genetic algorithm-partial least square regression (GA-PLS-R) model. Correlation coefficient (R²) and root mean square error (RMSE) for calibration and prediction of samples were also calculated, in order to evaluate the calibration models for each component of SARA analysis in crude oil samples. The performance of GA-SVM-R is found to be reliably superior, so that it can be successfully applied as an alternative approach for the quantitative determination of the SARA analysis of crude oil samples.

Keywords: ATR-FTIR spectroscopy; Chemometric; Crude oil; Multivariate calibration; SARA analysis.