Petroleomics: Composition/Structure Relationship with Properties/Performance of Petroleum and Its Fractions[J]. Journal of Chinese Mass Spectrometry Society, 2010, 31(增刊): 19-19.
Citation: Petroleomics: Composition/Structure Relationship with Properties/Performance of Petroleum and Its Fractions[J]. Journal of Chinese Mass Spectrometry Society, 2010, 31(增刊): 19-19.

Petroleomics: Composition/Structure Relationship with Properties/Performance of Petroleum and Its Fractions

  • Petroleum, coals and shale oils are the most complex mixtures in nature, containing thousands of components. They require sophisticated analytical technologies for correlation and prediction of the properties/performance of the products as well as the processability of the feeds. After decades of endeavor and development, the analyses of heavy fractions of these hydrocarbon resources remain to be challenges. Among a variety of analytical techniques, mass spectrometry provides unique capabilities for looking into detailed composition down to molecular-level.
    In mass spectrometry, various ionization techniques have been developed for a wide range of molecular information. Its coupling with chromatography greatly enhances the separation power and specificity of components in complex mixtures. Petroleome, which consists of ~40,000 chemical constituents, are now achievable using ultra-high resolution Fourier-transform ion cyclotron resonance (FT-ICR) spectroscopy with advanced data analysis.
    Another challenge is to interpret overwhelming analytical data for its physical and chemical significance. Chemometrics have been used as a data mining tool to identify key species that would mostly affect the overall properties of feed and products. Recent development in molecular modeling provides us with abilities for prediction of effectiveness of refining processes from the molecular composition data.
    This presentation will give an overview of the analytical, particularly mass spectrometric, and modeling approaches to obtain relevant molecular data or information for further understanding of the product quality and the prediction/assessment of petroleum process effectiveness.
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