Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data.

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.