Improving hydrocarbon classification analysis for lubricant base oils

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The analysis of crude oil and products derived therefrom via the refining process is and has always been of crucial importance in the petrochemical industry. In particular, mass spectrometry remains the prominent technique for the analysis of petrochemicals. Suncor's lubricants division manufactures a wide range of lubricant products, for which crude oil is the main raw material. Indeed, the need for higher performing automotive and industrial oils is growing and the race to develop and produce such material is highly competitive. Fossil oils are extremely complex mixtures of many compounds, varying greatly in composition depending on their geological age and geographical origin. The refining process begins with the analysis of the raw material. Base oils are analyzed by mass spectrometry and the resulting mass spectra used to place the oils in one of five classes as specified by the Americal Petroleum Institute (API). These groups differ in the chemical composition of the oils. On the basis of the analytical result for the base oil, a refining method is chosen. Choosing an inappropriate refining method can have expensive consequences: the resulting product may not meet specifications, and/or the lifetime of the catalysts used for refining can be greatly reduced. The company-specific problem is that the mass spectrometric methods currently in use do not always produce correct predictions in terms of the needed refining method. New instruments and new sample inlet systems produce different profiles from their base oils, which makes using existing classification techniques difficult or impossible to translate from one platform to the next. We propose to apply the theoretical, computational and instrumentation knowledge and expertise present in the group at the University of Ottawa to solve this by developing structure-property relationships to enable the classification of the base oil hydrocarbons in a reliable, platform-independent manner. We will focus on enhancing the information obtained from the mass spectrometric data, which in turn will lead to improved predictive value related to the refining process.