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Spotlight on Gulf Coast Conference


signifi cant concentrations of higher boiling saturated hydrocarbon compounds. Figure 3 shows a GC-VUV chromatogram of a blended gasoline sample with substantial C10-C12 aromatic content. Spectral fi lters have been applied during post-run data analysis to highlight the relative amounts of aromatics (175- 205nm, 125-160nm) and saturates (125-160nm) present in the sample. This example presents the ideal scenario for DHA in that aromatic peaks can be assigned via peak detection and retention time matching, and integrated with relative accuracy.


The application of DHA becomes signifi cantly more diffi cult when gasoline samples have signifi cant concentrations of both C10-C12 aromatics and saturates. The noticeable difference in chromatographic profi les can be seen in Figure 4. The 125-160nm spectral fi lter shows substantial saturate response between and overlapping with aromatic peaks. Determining the amount of response attributable to aromatics and saturates in this type of sample using a detector capable of only response and retention time output is very diffi cult, even when the aromatic peaks are correctly identifi ed. Dropping vertical integration lines attributes too much response to aromatics. Depending on how poorly resolved the saturate response is, some of it may be skipped over, treated as background, or mislabelled. In either case, there is potential of over-reporting aromatic concentration and under- reporting saturated hydrocarbon content. The impact of this type of error could be especially signifi cant in the case of heavy naphthas, where particularly high concentrations of C10 – C12 saturates elute with similar boiling range aromatics. Increasing the GC runtime or employing tuned pre-columns could help improve the chromatographic separation, but the sheer number of saturated hydrocarbon species in this carbon range means these tactics will not fully resolve the aromatic and saturate overlap. Other techniques utilising complex multidimensional chromatography can separate hydrocarbon classes well but offer limited speciation of individual compounds.


VUV PIONA+ delivers a more accurate, reproducible alternative for characterizing hydrocarbon content in complex gasoline samples. Co-elution and unresolved chromatography is addressed through the use VUV spectral matching and software deconvolution. Figure 5 demonstrates how VUV AnalyzeTM deconvolution1


performs time interval (TID) to determine the contribution of analyte


classes to the total absorbance response within each time slice interval of co-elution events. This analysis methodology was applied to the blended gasoline example with signifi cant aromatic and saturated hydrocarbon chromatographic overlap in the C10 – C12 region of the sample. The software reports both compound types according to their PIONA classifi cation in the bulk analysis that includes carbon number and mass % composition.


References


1. P. Walsh, M. Garbalena, K.A. Schug, Rapid Analysis and Time Interval Deconvolution for Comprehensive Fuel Compound Group Classifi cation and Speciation Using Gas Chromatography−Vacuum Ultraviolet Spectroscopy, Anal. Chem. 2016, 88, 11130−11138


For more detailed information please visit our website at www.vuvanalytics.com, or contact us at info@vuvanalytics.com.


Figure 2: The deconvolution of toluene and 2,3,3-trimethylpentane by VUV PIONA+. VUV AnalyzeTM the identities and/or class and relative concentrations of co-eluting compounds.


fi ts spectral profi les with VUV library spectra to provide


25


Figure 3: GC-VUV chromatogram of blended gasoline sample containing high aromatic content and low saturate content in the C10-C12 range (~C10 aromatics region shown). Spectral fi lters have been applied during post-run data analysis to highlight the relative amounts of aromatics (175-205nm, 125-160nm) and saturates (125-160nm) present in the sample. The chromatogram shows the ideal DHA scenario where aromatics peaks can be reliably integrated without interference from co-eluting saturated hydrocarbons.


Figure 4: GC-VUV chromatogram of blended gasoline sample containing signifi cant co-elution of saturated hydrocarbons with C10-C12 aromatics (~C10 aromatics region shown). Spectral fi lters have been applied during post-run data analysis to highlight the relative amounts of aromatics (175-205nm, 125- 160nm) and saturates (125-160nm) present in the sample. The comparison shows signifi cant saturate response between and overlapping with the aromatic peaks. VUV PIONA+ eliminates the error of over-reporting aromatics and under-reporting saturates by using their spectral responses to provide accurate compound class identifi cation and quantitation.


Author Details Paul Johnson1 John J. Grills III3


Envantage, Inc., Highlands, TX Email: info@vuvanalytics.com • Website: www.vuvanalytics.com


3


, Mike Scussel2 , Phillip Walsh1


, Bob Patzelt2


1 VUV Analytics, Inc., Cedar Park, TX 2 Gage Products Company, Ferndale, MI


Figure 5: Deconvolved GC-VUV analysis of the blended gasoline sample containing high C10-C12 saturated hydrocarbon and aromatic content. VUV AnalyzeTM


performs time interval deconvolution (TID) to resolve the co-elution of saturates and aromatics in the chromatogram. AUGUST / SEPTEMBER • WWW.PETRO-ONLINE.COM


, Tom Grills3 , and Dan Wispinski1


,


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