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Energy & Fuels 1989, 3, 312-315
of the fuels as well as the percent olefins and naphthenics; (3) verification of the differentiation of the total percent saturates into percent straight-chain and branched-chain saturates obtained from proton NMR analysis by using other analytical techniques including 13C high-resolution NMR performed on the saturate fraction isolated during the HPLC analysis.
Acknowledgment. We gratefully acknowledge funding support of this work from the Office of Naval Research. We also thank B. D. Shaver (David Taylor Research and Development Center, Annapolis, MD) for providing the documented data on the fuels and B. K. Bailey (Southwest Research Institute, San Antonio, TX) for providing the NMR spectra.
Pseudocomponent Model for Prediction of Molecular Weight Distribution of Pyrolysis Liquids Generated at Slow and Rapid Heating Rate Reactors M. Rashid Khan* and K. Hemanth Kumart Morgantown Energy Technology Center, US.Department of Energy, Morgantown, West Virginia 26505 Received September 26, 1988. Revised Manuscript Received January 9, 1989
A model for prediction of fuel-related properties of pyrolysis liquids derived from coal, oil shale, and tar sand is presented. The bulk hydrocarbon liquid is distributed by using an existing method developed for petroleum fuels into pseudocomponent molecular weights and mole fractions. Comparison of this distribution with experimental field ionization mass spectrometry (FIMS) molecular weight profiles on several liquids indicates good agreement. The results from analyses such as this will serve as input to conventional process simulators with pseudocomponent capability.
Introduction and Background Morgantown Energy Technology Center (METC) results demonstrated that a relatively highly quality liquid fuel can be produced by pyrolysis of coal under mild condition~.’-~In order to design processes utilizing liquid fuels, it is necessary t o understand physical and chemical properties of the liquids under various processing conditions. For example, for predictions of the thermophysical properties of the pyrolysis liquids by using corresponding-state correlations, values of the critical constants, the acentric factor, and other characterization parameters of the liquids (or the boiling curves) are necessary. This information in turn is utilized for thermophysical property correlations and to quantitate fluid properties such as enthalpies, phase equilibria, viscosities, etc. at various temperatures and pressures. For petroleum fluids, the lighter (than hexane) hydrocarbons are generally well characterized and their physical and chemical properties are known. However, the components above hexane are lumped together as a single pseudocomponent known as the ‘plus fraction.” This “plus fraction” is further characterized by performing a distillation and obtaining a boiling point curve. Pseudocomponents are derived from the boiling point curve by choosing components at present amounts of the plus fraction distilled. Subsequently, specific gravities of the plus fraction or of the individual pseudocomponents are measured. From the boiling point and specific gravity information in available empirical correlation^^^ the mo-
* Author to whom correspondence should be addressed. ‘Present address: Reservoir Simulation Research Corp., Tulsa,
OK.
lecular weights, and the critical properties can be estimated. This information and the composition of the petroleum fluid are used to perform process calculations. However, when the plus fraction is not characterized as described above, different techniques have to be used. For hydrocarbon liquids not derived from crude oils, the techniques described above are not directly applicable. Depending on the information that is available on these liquids, different methods can be developed. The objective of this study is to develop models to predict hydrocarbon liquid properties derived from lowtemperature pyrolysis of coal, oil shale, and tar sand. The liquids generated from these feedstocks are typically more aromatic and they contain more heteroatoms (sulfur, nitrogen, and oxygen) compared to petroleum-derived liquids, making them more susceptable to coke formation and instability. Hence, correlations that are more characteristic of these liquids are needed. In this study, it is assumed that only the bulk liquid average molecular weight and its average specific gravity are known. Additional information, such as field ionization mass spec(1)Khan, M. R.Fuel Sci. Technol. Int. 1987,5, 185-231. (2)Khan, M. R. Proceedings. In International Conference on Coal Science, Coal Science and Technology International; Moulijn, J., Nater, K., Chemin, H., Eds.; Elsevier: Amsterdam, 1987;Vol. 11, pp 647-651. (3)Khan, M. R. Characterization and Mechanisms of Mild Gasification Processes: Low-Temperature Devolatilization Studies. In Proceedings of the Fifth Annual GasificationProjects Contractors Meeting; US.Government Priting Office: Washington, DC, 1985;DOE/METC85/6024,NTIS/DE8508618, pp 67-69. (4) Brul6, M. R.; Lin, C. T.; Lee, L.L.; Starling, K. E. AZChEJ. 1982, 28,616. (5)Watanasiri, S.; Owens, V. H.;Starling, K. E. Ind. Eng. Chem. Process Des. Dev. 1985,24, 294-296.
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Pseudocomponent Model for Pyrolysis Liquids troscopy (FIMS) data, is to be used to test the methodology presented here. Details of the pyrolysis and FIMS procedures have been described elsewhere.1-2*6
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.-.c UI
A
>r
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Methodology The I' distribution function producesa molar distribution (mole fraction versus molecular weight). The only input data to the model are the average molecular weight and average specific gravity of the total liquid. Comparisons with FIMS data presented in this report are simply to support the validity of the computed molar distribution. Hence, the FIMS data are not used to "fit" any curve. The additional information that is input to the model is the lowest and highest molecular weights possible. For this purpose, these values can be taken from the FIMS data. If not known,the low end can be set to 16 and the high end to 2-3 times the average molecular weight. The first step in the characterization process is to determine the molecular weight distribution and the composition (mole fraction) of the bulk hydrocarbon liquid. The procedure used for this step is based on a statistical distribution function, namely the r distribution. This function was originally suggested by Whitsonl for crude oils and condensates and was later extended to coal-derived liquids and residual oils by Brul6, Kumar, and Watanasiri.s The choice of the r distribution function is based on the observation that it has the capability of describing the composition (mole fraction) versus molecular weight distribution of condensates, low to high boiling distillates, and resid through the use of a,a characteristic parameter.8 This distribution function is by no means exact, but it provides a powerful tool to perform further characterization of the bulk hydrocarbon liquid. The relation for generating the molecular weight versus composition distribution is given by the following function based on the r distribution:'l8
Mass (MIZ)
0.12
B
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Predicted
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Molecular Weight
Figure 1. Comparison of measured and calculated FIMS spectra for the pyrolysis liquids generated from Pittsburgh No. 8 coal in an entrained-flow reactor at 650 "C (MW = 360, a! = 6.00).
zi = mole fraction of the ith pseudocomponent. zn+ = mole fraction of the complex "plus" fraction, which, in the case of hydrocarbon liquids used in this study, is the total liquid mole fraction and is set to unity. Mi = molecular weight of the ith pseudocomponent normally incremented by one or more carbon 9 = the lowest number groups. j3 = (Mn+- $ / C Y . yi = (Mimolecular weight expected to occur in the hydrocarbon liquid. The value of 1) can be obtained from FIMS data. But for all the liquids for which molar distributions were calculated, the lowest molecular weight was assumed to be 44. The highest molecular weight was set at 900. Mn+= average molecular weight of the total hydrocarbon liquid. CY = adjustable parameter characteristic of the hydrocarbon liquid. The value of CY is adjusted continuously in the code so that the sum of all the 2:s determined Equation (1)equals Zn+within a specified tolerance (e.g., 0.001). r(x)= r function (equal to x ! when x is an integer). The summation in eq 1is ceased when the difference between two successive sums is less than 0.0oOO01. At convergence, this gives the mole fraction of the ith pseudocomponent. The process is carried out until the sum of the pseudocomponent mole fractions equals the parent fluid mole fraction (unity in the cases studied in this work). If not, the value of CY is updated and the process is repeated until convergence. Once the composition versus molecular weight distribution is obtained, empirical correlations for specific gravity as functions of molecular weight are used to determine the individual pseu-
Figure 2. Comparison of measured and calculated FIMS spectra for the pyrolysis liquids generated from Illinois No. 6 coal in a fixed-bed reactor at 500 OC (MW = 262, a! = 3.55).
(6) John, G. A.; Buttrill, S.E., Jr.; Anbar, M. Field Ionization and Field Desorption Mass Spectroscopy Applied to Coal Research. In Organic Chemistry of Coal; Larsen, J., Ed.;ACS Symposium Series 71; American Chemical Society: Washington, DC, 1978; p 223. (7)Whitson, C. H. Characterizing Hydrocarbon Plus Fractions. Presented at the European Offshore Petroleum Conference and Exhibition, London, England, Oct 21-24,1980, Paper EUR 183. (8)BrulB, M.R.; Kumar, K. H.; Watanasiri, S. Oil Gas J . 1985,83(6), 87-93. (9) Riazi, M. R.; Daubert, T. E. Ind. Eng. Chem. Res. 1987, 26, 755-759.
docomponent specific gravities. These pseudocomponent specific gravities are checked and updated based on a (volume) balance with the input average specific gravity of the bulk hydrocarbon liquid? Thus,the input average molecular weight is used to obtain the pseudocomponent composition versus molecular weight distribution and the input average specificgravity is used to adjust the computed specific gravities of the pseudocomponenta. The input average molecular weight of the total liquid produced the molar distribution (composition versus molecular weight) with the use of the r distribution function.
100
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Mass ( M R )
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390
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730
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Molecular weight
Khan and Kumar
314 Energy & Fuels, Vol. 3, No. 3, 1989 1.20 I
I
-m L
;
I
ap
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1
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s
Mass (WZ) 0.06
B Predicted o.18
-s
1
Mass (M/Z)
0
Predicted
0
180
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360
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900 0.00
Molecular Weight
180
0
Figure 3. Comparison of measured and calculated FIMS spectra for the pyrolysis liquids generated from Illinois No. 6 coal in a fluid-bed reactor at 500 OC (MW = 361,a = 6.05).
"-7
I
360
540
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Molecular weight
Figure 5. Comparison of measured and calculated FIMS spectra for the pyrolysis liquids generated from Colorado oil shale in a fixed-bed reactor at 500 "C (MW = 396,a = 7.95). 1.20
0.16
100
Predicted
=I 0.12
8 -p
0.08
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ie
'
Y
0
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0.00 180
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1 1
B Predicted
0,124
LL
0
300
Mass (M/Z) B
s
200
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900
0.00 0
Molecular Weight
Figure 4. Comparison of measured and calculated FIMS spectra
for the pyrolysis liquids generated from Asphalt Ridge tar sand in a fixed-bed reactor at 500 "C (MW = 366, = 6.35). (Y
With the molecular weights from the above step a correlation of SG.'s = f(MWj) (SG = specific gravity) is used to calculate SGi's! Then the weighted s u m of these SG;s are compared with the input average specific gravity of the total liquid. If they are not equal within a specified tolerance, the SGls are adjusted up or down to get a match. The boiling points of the pseudocomponents are then estimated by using a correlation that is a function of molecular weight as reported in ref 4. However, for the liquids derived from lowtemperature pyrolysis of coal, oil shale, and tar sands, the correlation reported in ref 4 showed a large deviation from experimental data, primarily because the former correlation was based
180
360
540
720
900
Molecular Weight
Figure 6. Comparison of measured and calculated FIMS spectra
for the pyrolysis liquids generated from Sunbury oil shale in a fixed-bed reactor at 500 O C (MW = 345, a = 5.35).
on model compoundsand liquids typical of coal liquefaction which are, in general, of a much higher molecular weight than the molecular weights measured for liquids studied in this work. The development of a new correlation for a pseudocomponent boiling point as a function of molecular weight and specific gravity will be presented in a future communication. Knowing the mole fractions, specific gravities, and b o i i points of the pseudocomponents, one can use empirical correlationsthat are functions of molecular weight, boiling points, and specific gravity to calculate the pseudocritical properties (pseudocritical
Energy & Fuels 1989,3, 315-320 temperature, pseudocritical pressure, and pseudocritical volume) and the pseudoacentric factor.
Results and Discussion The methods described in the previous sections were used to characterize several hydrocarbon liquids derived from mild pyrolysis of coal, oil shale, and tar sand. The input data used for the characterization are the average molecular weight and the average specific gravity of the hydrocarbon liquid. In Figures 1-6 the computed molecular weight distributions are compared with FIMS data for several hydrocarbon liquids generated a t slow and rapid heating rate reactors, as described elsewhere.2 In general, there is good agreement between the FIMS molecular weight distribution and the calculated molecular weight distribution. The values of the characteristic parameter a ranges between 3.55 and 7.95 for the six liquids whose bulk molecular weights range between 266 and 396. Also, in Figures 2 and 3, the computed curves include a recombination of the pseudocomponents. A recombination to reduce the number of pseudocomponents is desired in process calculations since it helps to reduce the compu-
315
tational time of a process simulation. A computer code for obtaining the characterization data based on an input average molecular weight and an average specific gravity for the hydrocarbon liquid is also developed. The characterization scheme described in this work predicts the mole fractions, molecular weights, and specific gravities of the pseudocomponents for the bulk hydrocarbon liquid. These data along with a boiling point correlation will serve as input to process simulators with pseudocomponent capability.
Summary and Conclusions A model for the characterization and prediction of hydrocarbon liquid properties derived from mild pyrolysis of coal, oil shale, and tar sands has been developed. This technique will be useful for performing process design calculations. A method for distributing a complex bulk liquid into pseudocomponent compositions (mole fractions) and molecular weights based on the statistical r distribution function is presented. Comparisons of the calculated distributions versus experimental FIMS data for several hydrocarbon liquids show good agreement.
Application of Aryl Disulfides for the Mitigation of Sulfur Deposition in Sour Gas Wells Peter D. Clark,* Kevin L. Lesage, and Pratibha Sarkar Department of Chemistry, The University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N IN4 Received December 14, 1988. Revised Manuscript Received February 15, 1989
The ability of aryl disulfides to dissolve sulfur by chemical incorporation has been investigated to determine the potential for using mixtures of these compounds to mitigate sulfur plugging in wellstrings producing sour gas. A variety of disulfides were studied and all were found to dissolve sulfur by incorporation as polysulfides. Electron-donating substituents on the aryl groups caused a moderate reduction in the amount of sulfur taken up, and electron-withdrawinggroups caused large reductions. Rates of sulfur uptake by mixtures of aryl disulfides produced commercially were similar to those observed for other sulfur solvents currently used in sour gas facilities. Sulfur uptake by aryl disulfides was shown to be reversible, suggesting that a solvent based on these compounds could be recycled in field use.
Introduction Sour gas is a mixture of methane and other hydrocarbons containing hydrogen sulfide (H2S) and carbon dioxide in various quantities. Although the amount of H2S is often low (ppm levels), sour gas reserves in Alberta and many other parts of the world contain much greater quantities. In some reservoirs up to 90% H2S is found (e.g. the Bearberry reservoir in Alberta). Elemental sulfur is often associated with high H2S content reserves either in physical solution or in chemical equilibrium with hydrogen polysulfides. The amount of sulfur dissolved in the gases depends on their geological history,but in many cases, the gases are saturated with sulfur.' (1) Hyne, J. B. Sour Gas Technology in the 1990's. Proceedings of the American Institute of Chemical Engineers; AIChE: New York, 1987.
The solubility of sulfur in sour gas is dependent on the temperature, pressure, and mole fraction of H2S.2 Thus, gases saturated or nearly saturated with sulfur may deposit elemental sulfur as they flow down the temperaturepressure gradient existing between the reservoir and the wellhead. In many cases, sulfur is deposited in the wellstring such that gas flow is either severely restricted or completely stopped. The factors controlling sulfur deposition have been investigated thoroughly by Hyne and co-workers,2and this work has enabled the development of protocols to limit the problem. However, since sulfur deposition cannot be totally eliminated by production procedures, the application of solvents to remove sulfur (2) Hyne, J. B. Super-sour Gas: A New Sulfur Source for Exploitation. Preprints; Sulphur 87 International Conference, Houston, TX, 1987; p 25.
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