Hydrocarbon class analysis

Hydrocarbon Class Analysis. The hydrocarbon class or type separation of the pentane-soluble fraction was performed on a silica gel-alumina column according... 

FIGURE 13.5 ASF plot for hydrocarbon classes as determined by full analysis of the FT product. 

Naphtha feed is often characterized using PINA analysis that simply is the weight % of K-paraffin, Ao-paraffin, naphthene and aromatic compounds. If the typical commercial indexes (specific gravity, PINA analysis and TBP curves or ASTM D86) are used properly, it is possible to empirically derive detailed naphtha composition by referring to the four different hydrocarbon classes and only to a limited number of reference components within each class. In fact, the PINA information indicates the relative abundance of the four different classes directly. The specific gravity and boiling curve allow the specification of the initial and final cuts of the hydrocarbon mixture as well as the relative presence and distribution of the reference pseudo components inside each fraction. 

There are analogies between the characterization of crude distillation residues and the previous feeds. The main difference is that for atmospheric and vacuum distillation residues of crude PINA and H/C can be estimated only through the kind of analysis (such as NMR) not normally available in refineries. Moreover, their final boiling point is not defined and the internal distribution of the different hydrocarbon classes of alkanes, cyc/o-alkanes and aromatics has to be deduced in a different way. Inside each macro-class, the relative amount of the components can be derived from the following statistical distribution ... 

Mass Spectrometry. Electron impact (El) mass spectrometry was done at NRL on the effluent from a 6 ft. OV-101 packed GC column programmed from 70 to 210°C. Field ionization mass spectrometry (FIMS) was performed by SRI International on contract to NRL. In this latter analysis, the fuel sample was frozen on a solids inlet probe prior to insertion into the mass spectrometer. The spectra accumulated for each mass during a temperature program were normally totaled for data presentation (6). Molecules boiling below 140°C are lost or depleted with this technique but such compounds comprise a very small fraction of JP-5 or DFM. Since the ionization efficiency for hydrocarbon classes is currently under study, the FIMS data are utilized primarily in a qualitative sense. 

FIMS Fingerprint. Field ionization mass spectrometry of a mixture affords a spectrum of the molecular ions since fragmentation is minimal. Thus a distribution of molecular sizes and hydrocarbon classes can be obtained from a single analysis. This is illustrated in Figure 1 which compares the FIMS fingerprints for JP-5 from Shale-I and Shale-II refining. Distinct differences can be noted. The preponderance of alkanes (C... 

Fractionation into Hydrocarbon Classes. All extracts were chromatographed on Davison grade 923 silica gel, as reported earlier (6,7). Two fractions, containing saturated and unsaturated hydrocarbons, respectively, were collected in separate 25-mL Kontes concentrator tubes. These fractions were concentrated to 1 mL on a modified Kontes tube heater. After adding 2 mL of hexane, the extract was reconcentrated to 1 mL and transferred to GC sample vials. After adding 4 fig of hexa-methylbenzene (GC internal standard) in hexane, the vials were sealed for GC analysis. 

Analysis of specific components or classes of components in refinery gases can be accomplished with single-column analyses. However, combinations of columns and valving are required for more complete analyses. The various aspects of hydrocarbon gas analysis have been discussed by Thompson (61). Applicable columns for these applications can also be found in column supplier catalogs and the reviews by Mindrup (62) and Leibrand (63). 

The carrier gas flow rates, A, B, and C times must be adjusted to produce acceptable analytical performance with the hydrocarix>n test mixture in 7.6. These conditions are then recorded and must be used for sample analysis. The system is considered to meet the test method specifications if the hydrocarbon test mixture analysis absolute errors, as calculated in Sections 11 and 12, are equal to or less than the following 0.3 % per carbon number per hydrocarbon type (for example, Cs paraffins), and 0.3 % per hydrocarbon class (for example, all paraffins). 

We will give two examples of analysis of these components, bearing in mind that the distinction between condensable and noncondensable hydrocarbons rarely holds in actual refining streams, most of them producing both classes of hydrocarbons simultaneously. 

Classes II and III include all tests in which the specified gas and/or the specified operating conditions cannot be met. Class II and Class III basically differ only in method of analysis of data and computation of results. The Class II test may use perfect gas laws in the calculation, while Class III must use the more complex real gas equations. An example of a Class II test might be a suction throttled air compressor. An example of a Class III test might be a CO2 loop test of a hydrocarbon compressor. Table 10-4 shows code allowable departure from specified design parameters for Class II and Class III tests. 

The complexity of oil fractions is not so much the number of different classes of compounds, but the total number of components that can be present. Even more challenging is the fact that, unlike the situation with other complex samples, in which only a few specific compounds have to be separated from the matrix, in oil fractions the components of the matrix itself are the analytes. Figure 14.1 presents an estimation (by extrapolation) of the total number of possible hydrocarbon isomers with up to twenty carbon atoms present in oil fractions. Although probably not all of these isomers are always present, these numbers are nevertheless somewhat overwhelming. This makes a complete compositional analysis using a single column separation of unsaturated fractions with boiling points above 100 °C utterly impossible. 

For Class D hazards, the company has defined the evaluation case event to be 8 x 106 Btu (8.4 x 106 kj) energy release as a hemispherical ground level explosion, unless a comprehensive analysis defines a lesser event as the evaluation case. For a VCE evaluation, this is further defined as the release and vaporization of 10,000 lb. (4,500 kg) of ordinary hydrocarbons, or the release and vaporization of 6,600 lb. (3,000 kg) of fast-burning materials [fundamental burning velocity >24 in/sec (>60 cm/sec)] within 5 minutes, when the largest connection to a tank or vessel is broken. [On a TNT basis, this is equivalent to a surface burst of approximately 2 tons (1,800 kg) TNT, calculated on the basis of 4% efficiency for ordinary hydrocarbons or 6.6% efficiency for fast-burning materials.]... 

The methods of analysis for the chlorinated hydrocarbons may be divided into five classes—determination of total organic chlorine, determination of hydrolyzable or labile chlorine, colorimetric methods, physical methods, and bioassays. The last mentioned is beyond the scope of this manuscript and is not considered. 

The third, and largely unexpected, case appeared as a problem in the analysis of petroleum hydrocarbons in seawater [24]. In this case, petroleum hydrocarbons, picked up presumably in the surface layers or surface film, were carried down by the sampling bottles and were measured as par t of the pollutant load of the deeper waters. While the possibility of absorption and subsequent release is obviously most acute with hydrophobic compounds and plastic samplers, it does raise a question as to whether any form of sampler which is open on its passage through the water column can be used for the collection of surface-active materials. The effects of such transfer of material maybe unimportant in the analysis of total organic carbon, but could be a major factor in the analysis of single compounds or classes of compounds. 

Separation procedures for purely organic species do not possess the same degree of selectivity as systems involving metals because of a general lack of suitable complexing and masking reactions. Nevertheless, classes of compounds such as hydrocarbons, acids, fats, waxes, etc., can often be isolated prior to analysis by other techniques. 

FIGURE 6.2 Representation of multivariate data by icons, faces, and music for human cluster analysis and classification in a demo example with mass spectra. Mass spectra have first been transformed by modulo-14 summation (see Section 7.4.4) and from the resulting 14 variables, 8 variables with maximum variance have been selected and scaled to integer values between 1 and 5. A, typical pattern for aromatic hydrocarbons B, typical pattern for alkanes C, typical pattern for alkenes 1 and 2, unknowns (2-methyl-heptane and meta-xylene). The 5x8 data matrix has been used to draw faces (by function faces in the R-library Tea-chingDemos ), segment icons (by R-function stars ), and to create small melodies (Varmuza 1986). Both unknowns can be easily assigned to the correct class by all three representations. 

In contrast to the other large cats, the urine of the cheetah, A. jubatus, is practically odorless to the human nose. An analysis of the organic material from cheetah urine showed that diglycerides, triglycerides, and free sterols are possibly present in the urine and that it contains some of the C2-C8 fatty acids [95], while aldehydes and ketones that are prominent in tiger and leopard urine [96] are absent from cheetah urine. A recent study [97] of the chemical composition of the urine of cheetah in their natural habitat and in captivity has shown that volatile hydrocarbons, aldehydes, saturated and unsaturated cyclic and acyclic ketones, carboxylic acids and short-chain ethers are compound classes represented in minute quantities by more than one member in the urine of this animal. Traces of 2-acetylfuran, acetaldehyde diethyl acetal, ethyl acetate, dimethyl sulfone, formanilide, and larger quantities of urea and elemental sulfur were also present in the urine of this animal. Sulfur was found in all the urine samples collected from male cheetah in captivity in South Africa and from wild cheetah in Namibia. Only one organosulfur compound, dimethyl disulfide, is present in the urine at such a low concentration that it is not detectable by humans [97]. 

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