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Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy ( IF 4.4 ) Pub Date : 2020-09-12 , DOI: 10.1016/j.saa.2020.118945
Mahsa Mohammadi , Mohammadreza Khanmohammadi Khorrami , Ali Vatani , Hossein Ghasemzadeh , Hamid Vatanparast , Alireza Bahramian , Afshin Fallah

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.



中文翻译:

基于遗传算法的支持向量机回归,通过ATR-FTIR光谱预测原油样品中的SARA分析

在当前的研究中,提出了一种分析方法,用于快速定量测定原油样品中的饱和物,芳烃,树脂和沥青质(SARA)馏分。快速评估原油样品的SARA分析在石油工业中具有重要价值。常规的SARA分析程序是根据美国测试与材料协会(ASTM)建立的标准确定的。但是,标准测试方法耗时,不环保,价格昂贵,并且需要分析大量原油样品。因此,批准一些支持方法以快速评估原油将是有用的。衰减全反射傅里叶变换红外光谱ATR-FTIR结合化学计量学方法可作为原油分析的分析方法。遗传算法(GA)和支持向量机回归(SVM-R)的混合模型用于通过ATR-FTIR光谱预测来自伊朗不同油田的原油样品的SARA分析。将GA-SVM-R模型的结果与遗传算法-偏最小二乘回归(GA-PLS-R)模型进行了比较。相关系数(R2)以及用于样品校准和预测的均方根误差(RMSE)也被计算出来,以便评估原油样品中SARA分析的每个成分的校准模型。发现GA-SVM-R的性能可靠可靠,因此可以成功地用作定量测定原油样品的SARA分析的替代方法。

更新日期:2020-09-22
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