These 10 groups were: paraffins, cycloparaffins, dicycloparaffins, tricycloparaffins, naphthalenes, benzenes, pyrenes/ fluoranthenes, acenaphthalenes/fluorenes ...
Molecular Weight and Its By-Boiling-Point Distribution of Middle Distillates for Hydroprocessing Modeling and Simulation Jinwen Chen, Norma Mclean, Yevgenia Briker, Darcy Hager, and Zbigniew Ring National Centre for Upgrading Technology One Oil Patch Drove, Devon, AB T9G 1A8, Canada Prepared for Presentation at the 2007 AIChE National Spring Meeting, TA001: Advances in Hydroprocessing I, April 22-27, 2007, Houston, TX, USA
Copyright ©, Her Majesty the Queen in Right of Canada, represented by the Minister of Natural Resources
Unpublished AIChE Shall Not Be Responsible For Statements or Opinions Contained in Papers or Printed in its Publications
March 2007
Abstract Molecular weight (MW) and its by-boiling-point distribution are important physical properties of petroleum feedstocks. This paper presents a quick and convenient method to determine the MW and its by-boiling-point distribution using off-the-shelf GC-FIMS (Gas Chromatography-Field Ionization Mass Spectrometry) data. The predicted MW by-boiling-point distributions matched reasonably well with those measured by experiment. 1. Introduction The implementation of ultra-low sulphur diesel (ULSD) specifications requires that refineries produce diesel fuels containing sulphur less than 15 wppm (Chen et al., 2003, 2004; Song, 2003). To achieve this target, refineries have to either use highly efficient hydrotreating catalysts and/or optimize hydrotreating process operations through mathematical modeling and simulation (Ito and van Veen, 2006; Babich and Moulijn, 2003; Song, 2003; Hu et al., 2001). To precisely establish the hydrotreating process model and to accurately evaluate the required model parameters, it is essential to have detailed physical properties and characterizations of the feedstocks and hydrotreated products. Among these are molecular weight (MW) and its by-boiling-point distribution, which is required in process and reactor modeling, reaction kinetics studies, vapor-liquid equilibrium analysis, etc. Currently, there are no universally applicable methods or correlations to estimate the average MW for petroleum samples with wide boiling ranges, even for middle distillate samples, although some studies have been published (Retzekas et al., 2002; Sun and Yang, 2000; Khan and Kumar, 1989; Glavincevski and Gardner, 1986). An efficient method to determine the MW distribution by-boiling-point is simply unavailable in the open literature. Direct measurement is not easy for a sample with a wide boiling range. Established methods are either for samples with low (e.g. Freezing Point Depression, FBP220°C) boiling points to provide an averaged MW. There is no suitable method for samples in the diesel boiling range without pre-separation. Measurement of MW distribution by-boiling-point requires distillation of the sample into a number of fractions, which is time and effort consuming. This paper presents a quick and convenient method to determine the MW and its byboiling-point distribution using off-the-shelf GC-FIMS (Gas Chromatography-Field Ionization Mass Spectrometry) data. This method is simple, requires a very small amount of sample, and does not need any distillation. 2. Experimental To establish and validate the method, a total of 12 different middle distillate samples were chosen to undergo distillation to obtain 8 fractions for each sample. MW was then measured for each of the fractions by using either an in-house developed Freezing Point TM Depression method (using Cryette WR Cryoscope, Model 5009, Precision Systems Inc.) or ASTM D2503, depending on the boiling range of the fraction. At the same time, density and simulated distillation (SimDis) were also measured by using standard ASTM methods. From SimDis data, the average boiling point of each fraction of the sample was calculated, and, therefore, the by-boiling-point distributions of MW and density were obtained for each sample. These measured MW by-boiling-point distributions were then used to validate and calibrate the GC-FIMS predicting method.
The main properties of the 12 middle distillate samples are listed in Table 1. These samples have quite different boiling ranges, densities and hydrocarbon type compositions. Table 1 Main properties of 12 middle distillate samples Density, IBP, FBP, Paraffins, CycloAromatics, g/ml °C °C wt% Paraffins, wt% wt% A-LT-Dist 0.8763 143 359.5 13.07 29.13 57.80 B-HY-Dist 0.8546 246.8 425 20.65 36.14 43.22 B-HY-Naphtha 0.7985 97 277.3 36.31 39.75 23.93 B-LT-Dist 0.8291 120.9 344.5 33.6 39.92 26.48 ColGO 0.9569 138.5 375.1 2.35 4.31 93.34 H-Gas Oil 0.8207 92.5 341.9 38.71 33.57 27.72 K-HY-Dist 0.8694 145 403.1 0.12 69.79 30.09 K-LT-Dist 0.8357 118 334.1 5.58 73.92 20.49 Nkht PC-Stove 0.8237 138.5 297.8 28.95 42.21 28.85 S-Bottom HGO 0.8694 204.7 371.4 13.05 33.29 53.66 S-Liquid T-Stove 0.8537 151.6 299.5 18.62 36.12 45.26 Stove 0.8184 142.2 312.8 24.55 53.92 21.53 Some of them have relatively high aromatics contents, such as GolGo, S-Bottom HGO and ALT-Dist, while others have relatively high paraffin contents, such as K-LT-Dist, Stove, and BHY-Naphtha. Therefore, these samples are a good representation of middle distillates. The distillation of the 12 samples was conducted in a spinning band distillation system (Model 36 100A, B/R Instrument Corporation). Each sample was distillated into 8 fractions. Each fraction was then measured for MW, SimDis and density. All the fractions (total of 96) together with the12 original samples were submitted for GC-FIMS analysis. Typical measured molecular weight by-boiling-point distributions are shown in Figure 1. 360
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(a) ColGo (b) B-HY-Dist Figure 1. Measured molecular weight by-boiling-point distribution The GC-FIMS analysis was performed on a Macromass GCT instrument with an orthogonal acceleration TOF (Time of Flight) MS (mass spectrometer) coupled with an HP 6890 GC (Gas Chromatograph). The GC column was 30-m Factor Four VF-5ht (fused silica in the range of -60oC to 400oC with 0.1-µm film thickness and 0.32mm i.d) from VARIAN. The oven temperature was ramped from room temperature to up to 370oC at the rate of 10oC /min
and held for 15 min. The injection of 1µL sample was made with the program temperature vaporized (PTV) injector used in the splitless mode. The inlet was heated from room o o temperature 380 C at the rate of 100 C/min. Constant flow of 1mL/min was maintained throughout the run. The temperature on the GC-TOF interface was maintained at 300oC. In order to ionize petroleum molecules and produce molecular ions, the FI emitter from CarboTeck was used. It consisted of 10-µm tungsten wire on which the carbon dendrites were generated. The emitter was placed in a high electric field (~10-7-10-8 V/cm) generated by a pair of extraction rods held at a high potential (-12Kv). Under these conditions it was believed that an electron could be removed from the molecule via quantum tunneling effects, generating radical molecular ions with minimal fragmentation. The emitter current was set at 0 mA, and the flash-off current was set at 30 mA with the scan time of 1.2 seconds and inter-scan delay of 0.2 second. Tris (trifluoromethyl)-1, 3, 5-triazine with the molecular weight of 284.9914 DA was used as the internal standard for mass calibration 3. Methodology Development 3.1 Determination of Response Factors The raw data from GC-FIMS analysis are the mass/molecular weight, abundance and GC retention time for each detected compound/ion. The abundance represents the concentration of the compound in the oil sample. The abundance threshold of 4 was chosen to filter out the instrumentation noises. Normally there are about 5000 compounds detected in a typical sample. These compounds are identified with a mass window of 0.03 using a preestablished database containing 90 groups of hydrocarbons with carbon number raging from 1 to 50. To estimate the concentration of each compound, it is necessary to perform a precalibration using a standard sample with known hydrocarbon type distribution to establish the abundance-concentration correlation, which is defined here as response factor. In this study, a standard sample was analyzed using GC-MS to obtain hydrocarbon type distribution. A total of 10 hydrocarbon groups were identified and quantified. These 10 groups were: paraffins, cycloparaffins, dicycloparaffins, tricycloparaffins, naphthalenes, benzenes, pyrenes/ fluoranthenes, acenaphthalenes/fluorenes, benzothiophenes, phenanthrenes/anthracenes. At this stage, we assumed that all the compounds in the same group had the same response factor. Therefore, the response factor of each group was obtained by dividing the weight percentage of that group by the sum of the abundance of the compounds in the same group. For better data representation, relative response factors were defined and used by dividing the response factor of each group by that of paraffins. The other groups that are present in the sample but without response factors from GC-MS data were assumed to have response factors of 1. This simplification is reasonable since a) response factors are normally from 0.5 to 2; and b) the final result was not sensitive to the response factors of other groups since their contents in the sample were very low. Once the response factors were evaluated, they were included in the database for data processing of real samples. The determination flowsheet of response factors is illustrated in Figure 2. 3.2 Data processing of real samples The raw data from GC-FIMS for real samples were also molecular weight, abundance and retention time of each compound (about 5000 in total). Data filtration was performed first to remove instrumentation noises. The compounds were then identified and assigned for response factors. A pre-established relationship between boiling point and GC retention time, obtained from standard paraffins, was used to determine the boiling point of each compound.
From the abundance value, the mass concentration of each compound was calculated using the corresponding factor. The entire boiling range was divided into a certain number of (normally 8 to 25) equal interval slices. The averaged molecular weight and the boiling point of
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Figure 2. Response factor determination flowsheet the compounds in each slice were calculated using weighted averaging and sent to Microsoft ® EXCEL for data plotting and curve fitting. The calculation procedure is shown in Figure 3. Figures 4 (a), (b), (c) and (d) show the predicted MW by-boiling-point distributions of selected middle distillate samples. For comparison, the corresponding experimentally measured ones are also plotted in these figures. Clearly the predictions obtained from GCFIMS analysis are quite close to those measured. Note that the predictions and the experimental measurements covered different boiling ranges of the sample. The experimental measurements could not reach the lower boiling end but could reach the higher boiling end while the predictions had the exact opposite trend. Further studies are underway to improve the GC-FIMS analysis and prediction to reach the higher boiling end. Since the 12 middle distillate samples used in this study have quite different boiling ranges, densities and hydrocarbon type distributions, together they are a good representation of middle distillates. Response factors based on these 12 samples should be universally applicable and should provide better predictions. Therefore, a procedure is being developed to optimize the response factors by fitting the predicted molecular weight with the experimentally measured ones for all the individual fractions obtained from the distillation of the 12 samples. The results will be reported in the future.
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Figure 3. MW prediction flowsheet 4. Summary A methodology based on GC-FIMS analysis has been developed to predict the molecular weight and its by-boiling-point distribution for petroleum middle distillates. 12 middle distillates samples, which have different boiling ranges and hydrocarbon type distributions, were used to validate the method. It has proved to be simple and convenient to use. The predicted MW by-boiling-point distributions were close to those obtained by experimentally measured ones. This method needs only a small sample for GC-FIMS analysis and does not need the time- and effort-consuming distillation otherwise required. This method overcomes the shortcomings of the established ASTM and in-house developed methods that can only be used for samples with either low boiling range or high boiling range.
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5. References Babich, I. V. and Moulijn, J. A., “Science and Technology of Novel Processes for Deep Desulphurization of Oil Refinery Streams: a Review”, Fuel, 82(6), 607-631, 2003. Chen, J., Te, M., Yang, H., and Ring, R. “Hydrodesulphurization of Dibenzothiophenic Compounds in a Light Cycle Oil”, Petrol. Sci. Tech., 21(5&6), 911-935, 2003. Chen, J., Yang, H., and Ring, Z., “HDS Kinetics Study of Dibenzothiophenic Compounds in LCO” Catal. Today, 98, 227-233, 2004. Glavincevski, B., and Gardner, L., “Expression for the Average Molecular Weight of Diesel Fuels, SAE Special Publications, 55-64, 1986. Hu, M., Ring, Z., Briker, J., Te, M., “An Integrated Approach in Meeting the Challenges of Low Sulphur Diesel – Analytical Support, Process Research and Computer Simulation”, NPRA Conference Proceedings, paper CC-01-161, Dallas, TX, USA, 2001. Ito, E. and J. A. R. van Veen, “On Novel Processes for Removing Silphur from Refinery Streams”, Catal. Today, 116, 446-460, 2006. Khan, R., and Kumar, H., “Pseudocomponent Model for Predicting of Molecular Weight Distribution of Pyrolysis Liquids Generated at Slow and Rapid Heating Rate Reactors”, Energy. And Fuels, 3(3), 312-315, 1989. Retzekas, E., Voutsas, E., Magoulas, K., and Tassios, D., “Prediction of Physical Properties of Hydrocarbons, Petroleum and Coal Liquid Fractions”, Ind. Engng. Chem. Res., 41(6), 1695-1702, 2002. Song, C., “An Overview of New Approaches to Deep Desulfurization for Ultra-Clean Gasoline, Diesel Fuel and Jet Fuel”, Catal. Today, 86, 211-263, 2003. Sun, Y., and Yang, C., “New Method for Calculating the Molecular Weight of Petroleum Factions”, J. Univ. Petrol. China, 24(3), 5-7, 2000.
