Prediction of Fuel Properties

Abstract

A system is described that includes a known fuels database of data from gas chromatography-mass spectrometry analyses of a library of fuels with known fuel properties for a multiple known fuel samples. Gas chromatography-mass spectrometry equipment can acquire gas chromatography-mass spectrometry data for an unknown fuel sample. A metaspectrum module can accept and transform the gas chromatography-mass spectrometry data collected by the gas chromatography-mass spectrometry equipment for the unknown fuel sample into a single metaspectrum for the unknown fuel sample, wherein the metaspectrum is a quantitative representation of every compound detected in the unknown fuel sample. A correlation module can correlate the metaspectrum for the unknown fuel sample to a plurality of fuel properties of known fuel samples using a regression model to predict fuel properties for the unknown fuel sample. A reporting module can report the fuel properties for the unknown fuel sample to a user.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to provisional patent application entitled, “Software tool to provide compositional information and predicted properties of mobility fuels from chemometric modeling of gas chromatography-mass spectrometry (GC-MS) data,” filed on Jul. 29, 2014, and assigned U.S. Application No. 62 / 030,360, and “Software tool to provide compositional information and predicted properties of mobility fuels from chemometric modeling of gas chromatography-mass spectrometry (GC-MS) data,” filed on Sep. 29, 2014, and assigned U.S. Application No. 62 / 056,666; the entire contents of both which are hereby incorporated by reference.FIELD OF THE INVENTION[0002]The present disclosure relates to a tool for determining compositional information and predicted properties of mobility fuels.BACKGROUND[0003]Research has been ongoing to determine ways to reduce the time, manpower, and amounts of fuel required to measure standard ASTM specificat...

AI Technical Summary

Benefits of technology

This patent describes a method and system for analyzing fuel samples using gas chromatography-mass spectrometry. The system includes a database of data from known fuel samples, a module that collects data from an unknown fuel sample, and a module that predicts fuel properties based on the data from the unknown sample using a regression model. The system can also include a reporting module to provide the predicted fuel properties to a user. The technical effect of this invention is to provide a faster and more accurate way to analyze fuel samples for research and development purposes.

Problems solved by technology

Previous research efforts have typically focused upon analytical techniques that do not provide direct information regarding chemical composition, such as near-infrared (NIR) spectroscopic data.

However, research has shown that adapting NIR property prediction models based on petrochemical fuels to properly accommodate non-petrochemical fuels generally requires a devoted research effort.

Critically, there is an inherent unpredictability in both the type and quantity of non-petrochemical fuels that might exist in worldwide fuel populations for the near future.

Therefore, it was determined that the accommodation of non-petrochemical fuels could not be realistically performed on a case-by-case basis.

Although some strategies have been developed to improve NIR fuel property modeling algorithms in an automated fashion to enhance the performance of the previously developed stand-alone prototype instrument, these were discrete, incremental modeling improvements that are insufficient to address the fundamental challenge presented by uncalibrated fuel types.

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