Automated Gasoline Characterization: Fast and Precise
AC PIONA Analyzer Eliminates Operator Variation
by Dr. R. E. Snelling, Phillips Research Center, Bartlesville, OK (U.S.A.)
The U.S. 1990 Clean Air Act requires every refinery or terminal
producing gasoline, either by blending or refining, to estimate
the environmental quality parameters of its gasoline. These
parameters include distillation, olefins, aromatics, sulfur,
benzene, RVP, and oxygen content and are considered the baseline.
The purpose of the baseline is to keep refiners from shifting
undesirable components removed in the reformulation process into
conventional gasoline. The quality of all gasoline produced in
the future will be compared to the baseline to determine whether
the gasoline meets Federal standards. Knowledge of gasoline
composition is important for monitoring processes and verifying
regulatory compliance.
Manual FIA: Potential Sources for Error
One method used to
determine hydrocarbon types is manual fluorescent indicator
adsorption, or FIA (ASTM D-1319). This method separates
hydrocarbon samples using a special glass adsorption column
packed with activated silica gel. Alcohol promotes the migration
of the sample down the column. The FIA method identifies
saturates, nonaromatic olefins, and aromatics up to 315°C.
A number of potential sources of error exist with FIA. When
the different hydrocarbon types separate, the zone boundaries
are often not sharp, and the hydrocarbon types interfere, which
leads to errors in interpreting the data. Human error is a factor,
because the determination of aromatics, olefins, and saturates is
visual. Improper packing of the silica gel or incomplete elution of
hydrocarbons by the alcohols also leads to erroneous results. Samples
containing light hydrocarbons require depentanization, which loses C6
paraffins and leads to inaccuracies (Kosai et al, 1990).
FIA Versus Automated PIONA Method
In our study, we
compared the results of FIA with an automated method
for performing FIA gasoline characterization using an AC Analytical
Controls PIONA analyzer. The AC PIONA analyzer is a multidimensional
gas-chromatographic system that combines four separation columns and
two compound-specific traps. The system consists of a modified
HP 5890
gas chromatograph interfaced to an
HP ChemStation. Dedicated
AC software controls all the
events within the system and performs all data calculations and reporting.
We used 18 standards obtained from the U.S. Air Quality Improvement
Research Program (AQIRP) and containing varying amounts of C5.
Comparing Correlation Coefficients
Results calculated from AC PIONA analyses are more precise,
accurate and free of most operator variation. The figures
show the true composition of the AQIRP standards plotted
against the results found by FIA and AC PIONA analyses for
aromatics and olefins.
In comparing the AC PIONA results versus the true
composition, the correlation coefficients (R²)
calculated for saturates and aromatics are greater
than 0.99. The correlation coefficient for olefins
indicates more scatter, but still yields a 92%
probability of a linear relationship. The correlation
coefficients calculated using the FIA method are lower;
the R² for saturates is 0.96, for aromatics 0.97, and
for olefins 0.74.
It is important to show how closely the AC PIONA results match
the ASTM procedure. In comparing the AC PIONA results versus the
FIA results, the correlation coefficients show good agreement.
The correlation coefficient for saturates is 0.98, for aromatics
0.96, and for olefins 0.83.
Complete Picture With One Injection
The AC PIONA analyzer offers a viable method for performing FIA
analysis of gasoline-range materials. Data from AC PIONA analyses
are accurate and free of most operator variation. The automated
operation of the AC PIONA makes it applicable for screening large
numbers of gasoline samples - an advantage when compared to the
labor-intensive manual FIA method.
The AC PIONA provides fast, precise hydrocarbon-typing for each
carbon number up to C11. In one injection,
an analyst gets a
complete picture of a complicated mixture. The results of
replicate analyses agree within 1% absolute for quantitating the
structural types in a sample. The instrument proves useful for
monitoring relative changes in processes and streams and offers
manpower savings and improved accuracy.
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