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Deconvolution of IR signals from oil characterization and prediction of properties

Michalopoulos Alessandro

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Summary

In order to interpret a Fourier Transform Infrared (FTIR) signal in its entirety, thousands of peaks would have to be identified requiring a significant amount of samples making the process not just expensive but actually impossible. The aim of this research is to instead focus on specific spectral bands in order to significantly reduce the number of required samples for interpretation.A new algorithm is proposed to deconvolve the infrared spectrum of complex hydrocarbon mixtures in the 3000-2750 cm-1, 1400-1330 cm-1 and 1000-700 cm-1 regions. The algorithm is developed based on the analysis of FTIR spectra of 33 oil fractions.The experimentally derived spectra are deconvolved by fitting 5, 2 and 7 Lorentzian distributions, corresponding to aliphatic C-H stretching vibrations, aliphatic C-H scissoring/symmetric deformation vibrations and aromatic C-H out-of-plane bending vibrations for each of the above regions respectively.The distribution of chemical structures in the oils were extracted, and correlations were studied between them and the measured saturates and aromatics percentage concentrations of the samples through the use of a linear regression model.The validity of the results was interpreted by the Root Mean Square Error of Prediction (RMSEP) and through their comparison with the errors calculated from a previous student thesis. The reliability of the selected peaks is backed up by the small calculated NLP errors and the fitting of the first and second derivatives between the composite curve and the FTIR signal.The algorithm facilitates the spectra modeling and the accurate estimation of the fitted methyl and methylene peak areas, which can be used for calculating specific compositional parameters of oil samples instead of the usually employed peak heights. Such modeling is extremely important for heavy petroleum fractions, where detailed compositional information is difficult to be obtained.The results of the research showed that the connection between the peaks and the concentration values becomes clear enough through the usage of this procedure which, with the appropriate corrections, yields low RMSEP errors.

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