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Peak deconvolution of the infrared spectrum of petroleum fractions and its application in the prediction of their properties

Kabourakis Nomikos

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Summary

The characterization of petroleum fractions is considered to be a complex and time-consuming process, at the same time it is necessary for the development of a fluid model. Oil characterization is the representation of oil property distributions with a set of pseudo-constituents. It is usually based on gas chromatography (GC) or distillation analysis, supplemented by saturates, aromatics, resin and asphaltene (SARA) analysis.In recent decades, infrared spectroscopy has been frequently used in more and more fields because of its advantages over other methods of analysis. One of the most important advantages is the ability to record spectra, without the requirement of solid or liquid sample pretreatment. In addition, the low cost, high speed and accuracy of IR measurements due to being a non-destructive measurement technique for many chemical compounds contribute to the effectiveness of the method in many research fields. The spectrum peaks resulting from the infrared spectroscopy analysis of the petroleum sample could be described by Lorentzian curves through the deconvolution method, since Lorentzian curves are effective for liquids in many operational situations. The resulting deconvolved spectrum is the sum of the individual Lorentzian curves used to reconstruct the original spectrum.The aim of this work is through the minimization method in combination with other methods or its variants, to successfully reconstruct the spectra of the petroleum samples in order to study future possible existing correlations between the characteristic curves resulting and the properties of petroleum fractions.

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