Affiliation:
1. Department of Organic Chemistry and Petroleum Chemistry, Gubkin Russian State University of Oil and Gas, Leninsky Av. 65, Moscow 119991, Russia
Abstract
This study explores the potential application of NIR spectroscopy coupled with different linear and nonlinear models for rapid evaluation of n-alkanes in crude oil. Samples for calibration were 30 model mixtures of n-eicosane in crude oil samples with a concentration of 1–15%. The prediction models were established based on 21 methods: linear regression, regression trees, support vector machines, Gaussian process regression, ensembles of trees, and neural networks. The spectral range 4500–9000 cm−1 was determined to be the most informative for prediction. The prediction capability of lineal regression methods turned out to be unsatisfactory. Nonlinear models were preferred over linear models; better results were obtained using the regression trees method, including «fine tree» (RMSE = 2.8635) and neural networks (RMSE = 2.0157). The LS-SVM model exhibited satisfactory prediction performance (R2 = 0.96, RMSE = 0.91), as did the Gaussian Process Regression Matern 5.2 GPR (R2 = 0.96, RMSE = 1.03) and Gaussian Process Regression (Rational Quadratic) (R2 = 0.95, RMSE = 1.04). Among the 21 chemometric algorithms, the best and weakest models were the LS-SVM and PLSR models, respectively. The LS-SVM model was the optimal model for the prediction of n-alkanes content in crude oil.
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Reference57 articles.
1. Advances and Future Challenges of Wax Removal in Pipeline Pigging Operations on Crude Oil Transportation Systems;Li;Energy Technol.,2020
2. Crude oil wax: A review on formation, experimentation, prediction, and remediation techniques;Kiyingi;Pet. Sci.,2022
3. Analysis of mechanisms and factors of formation of ASF in cavities of field pipelines and equipment;Lifanov;Trends Dev. Sci. Educ.,2022
4. Control of the initial stages of phase formation in oil dispersed systems;Safieva;Chem. Technol. Fuels Oils,2020
5. Wax deposition and prediction in petroleum pipelines;Alnaimat;J. Pet. Sci. Eng.,2019