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Characterisation of heavy oils using near-infrared spectroscopy: optimisation of pre-processing methods and variable
Jérémy Laxalde1, Cyril Ruckebusch, Olivier Devos
1LASIR, CNRS-Université Lille1 Sciences et technologies, bât. C5, 59655 Villeneuve d'Ascq Cedex, France.
Analytica Chimica Acta
|October 4, 2011
Summary
Chemometric models using near-infrared (NIR) spectra accurately quantify saturates, aromatics, resins, and asphaltens (SARA) in heavy petroleum. Genetic algorithms optimized model development, improving prediction accuracy for these critical petroleum components.
Area of Science:
- Petroleum Chemistry
- Analytical Chemistry
- Chemometrics
Background:
- Accurate quantification of Saturates, Aromatics, Resins, and Asphaltens (SARA) is crucial for characterizing heavy petroleum products.
- Traditional methods for SARA analysis can be time-consuming and labor-intensive.
- Near-Infrared (NIR) spectroscopy offers a rapid and non-destructive analytical technique.
Purpose of the Study:
- To develop and optimize chemometric predictive models for the quantitative determination of SARA fractions in heavy petroleum using NIR spectra.
- To evaluate the effectiveness of genetic algorithms (GA) for co-optimizing pre-processing methods and variable selection in model development.
- To compare the predictive performance of different chemometric models and assess their statistical significance.
Main Methods:
- Development of chemometric models based on Near-Infrared (NIR) spectra.
- Optimization of models through pre-processing techniques and variable selection.
- Application of a genetic algorithm (GA) for simultaneous optimization of pre-processing and variable selection.
- Statistical comparison of model predictions using a randomization t-test.
Main Results:
- Accurate quantitative determination of SARA fractions with low root mean square errors of prediction (RMSEP): 1.51% for saturates, 1.59% for aromatics, 0.77% for resins, and 1.26% for asphaltens.
- Genetic algorithm approach demonstrated effectiveness in co-optimizing pre-processing and variable selection for enhanced model performance.
- The developed models showed good agreement with the known chemical composition of SARA fractions, validating their usefulness for global interpretation.
Conclusions:
- Chemometric models utilizing NIR spectra, particularly those optimized with genetic algorithms, provide a robust and accurate method for SARA fraction determination in heavy petroleum.
- The GA-driven optimization strategy enhances predictive accuracy and model interpretability.
- This approach offers a valuable tool for the rapid and reliable analysis of heavy petroleum products.
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