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A new non-linear calculation method of isomerisation gasoline research octane number based on gas chromatographic data
Fuel 83 (2004) 517–523 www.fuelfirst.com
A new non-linear calculation method of isomerisation gasoline research octane number based on gas chromatographic data N. Nikolaoua,1, C.E. Papadopoulosa,*, I.A. Gagliasb, K.G. Pitarakisc,2 a
T.E.I Kavalas, Department of Petroleum and Natural Gas Technology, School of Technological Applications, Kavala, Greece b Hellenic Petroleum S.A., Aspropyrgos Attikis, Greece c Water Supply and Sewerage Municipal Of Larissa, Terma Tyherou St., 41222-Larissa, Greece Received 3 March 2003; revised 23 September 2003; accepted 25 September 2003; available online 14 October 2003
Abstract A new simple no-linear calculation method of isomerisation gasoline Research Octane Number (RON) is presented. The calculation method effectively utilizes the compositional data from high-resolution capillary GC analysis, and the measured pure and blending RON values of various HCs, which are widely published. The method, which is a simple non-linear method, utilizes calculable weighting factors, which are specific for each gasoline blend, and showed an excellent agreement with the RON values of various refinery isomerates samples as they measured in a standard CFR engine. q 2004 Elsevier Ltd. All rights reserved. Keywords: Non-linear methods; Gasoline; Isomerates; Octane number calculation; Gas chromatography
1. Introduction Gasoline is a multicomponent or complex mixture that may comprise of several hundred hydrocarbons. It actually varies from refinery to refinery and there is not such a thing as a single criterion that can characterise gasoline purity and quality in general. The new environmental and performance considerations have introduced several difficulties in gasoline production and have made a standard general definition of gasoline quality a very difficult task. So, new specifications [1] for the unleaded gasoline were drawn and increased demands on the quality of gasoline as indicated by its octane numbers (Research octane number or RON and Motor octane number or MON), among others, have been placed. The RON accounts for fuel performance under low severity engine operation while the MON for more severe operation that might be incurred at high speed or high load. In practise the octane of a gasoline is reported * Corresponding author. Tel.: þ 30-23210-20562; fax: þ 30-2321056246. E-mail addresses: [email protected] (C.E. Papadopoulos); nicnik@ otenet.gr (N. Nikolaou). 1 Tel: þ30-2510 46223-1. 2 Tel: þ30-2410 687240-1. 0016-2361/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2003.09.011
as an Antiknock Index or Pump Octane which is the average of RON and MON AI ¼
RON þ MON 2
ð1Þ
Both RON and MON are still measured in a standardised single cylinder, variable compression ratio (from 4:1 to 18:1), internal combustion engine (Cooperative Research Fuel-CFR engine), following the standard methods ASTMD2699 [3] and D2700 [4], respectively. Both RON and MON are relative values based on accepted standard fuel mixtures. The engine is operated at a constant speed (RPMs) for both RON and MON and the compression ration is increased until the onset of knocking. Engine speed is set at 600 rpm for RON and 900 rpm for MON. Petroleum industry has always been used empirical performance standards of its gasoline products, such as RON and MON. Only after gas chromatography became widely available, compositional standards and constraints on gasoline’s composition have started to be applied. The direct determination of RON (and MON) with the standard CFR engine is a complicated, relatively expensive operation. CFR engines require constant maintenance and frequent calibrations [5].
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N. Nikolaou et al. / Fuel 83 (2004) 517–523
The fast, easy and accurate determination of octane number is important for refiners today, since optimisation of refining process at a reasonable cost is an ever-increasing requirement. 1.1. Gasoline compositional data by analytical techniques There are various existing analytical techniques to obtain gasoline compositional data from which calculation of octane numbers can be done. The main disadvantage of most them, for an accurate octane prediction is their limiting detail in terms of the compositional data they can provide. A detailed gasoline compositional analysis can always allow a more accurate calculation of octane number. Thus, analytical techniques such as fluorescent indicator adsorption (FIA) [2], Near infra-red (NIR) [6,8]. Nuclear magnetic resonance (NMR) [7,8], e.g. can provide only limited compositional information, corresponding to structural groups such as aromatics, olefins and saturates. This limited compositional (analytical) detail makes these methods not to be the most accurate for an octane number prediction, with an exception those cases where a gasoline mixture mainly consists of some predominant group. To the contrary, GC methods with the use of modern capillary columns can provide an almost excellent HCs separation and detailed compositional data, far beyond the aromatic, olefin etc. groups [9,10].
(iso-octane) is equal to 100 (the octane number of pure isooctane). Eq. (2) is generally extended and used for a gasoline consisting of k components as: RONG ¼
k X
RONi yi
ð3Þ
i¼1
Wherever Eq. (3) has been employed by others to predict research octane number from GC data, the RONi values used are not those of pure hydrocarbons present in ASTM STP 225 [13], but are ‘blending octane numbers’ that have been usually obtained by comparison with measured data and using multiple linear regression analysis [7]. This has been done so, because in systems other that blends of nheptane and iso-octane the octane numbers of the constituent components (Blending octane numbers from ASTM STP 225), or in other cases the ‘partial octane numbers’ (employing the directly relevant term from thermodynamics), are functions of their concentration and of the overall composition of the gasoline blend. Some of the published methods referred above, for the prediction of octane number from GC data, might be criticised for two main disadvantages:
ð2Þ
† Although GC methods can give detailed compositional data, as it was already said, the grouping of the analysed HCs into similar structural groups such as paraffins, olefins etc. for the octane number prediction, which has been usually the case, automatically removes the main advantages offered by GC methods themselves. This grouping procedure is based on the weak assumption that these structural groups behave similarly during the combustion of different gasolines [7]. † Another disadvantage of the conventional methods available is that the blending or effective octane numbers that have been obtained under some specific conditions are equally applied in Eq. (3) for all types of gasoline blends. For example, measured blending (and pure) octane numbers of a wide range of hydrocarbons has been published in API project 45 [13] and some of them are shown in Table 1. These blending octane numbers were estimated from the measured (CFR) octane number of a blend of 48%v/v iso-octane, 32%v/v n-heptane and 20%v/v of the specific HC. It must be recognised that these blending octane numbers have given some qualitative information in terms of the RON performance, related with specific HCs in these simple blends, however, this has been extended and used under another weak assumption that these blending octane numbers are the same (constants) for any number, type and concentration of HC components in a gasoline.
where y1 and y2 are the volume fractions of n-heptane and iso-octane in the blend respectively, RON (n-heptane) is equal to 0 (the octane number of pure n-heptane) and RON
Recently, and with the continuous advancement of computational power, some new and quite promising nonlinear utilisation methods, based on pattern recognition,
1.2. Octane number calculation methods—new method proposed Even though GC data offer detailed gasoline’s compositional information, a common problem of all analytical methods for an accurate calculation of octane number is the non-linear blending characteristics of the octane contributions from the individual components (or the structural groups) in a gasoline, which is a highly complex mixture. Thus, octane numbers measured directly for pure compounds cannot be simply (linearly) combined, according to the gasoline composition, to calculate the octane number of that gasoline. The non-linearity in the combination of pure components octane numbers is the most significant complication to the accurate calculation of gasoline octane numbers as well as to other fuel properties. Other researchers have done many efforts for the prediction of gasoline’s octane number utilising GC data of reformed gasoline [9,11,12]. Most of the methods developed utilise the basic equation (linear) of octane number for a blend of n-heptane and iso-octane which is: Octane number ¼ RONðn-heptaneÞy1 þ RONðiso-octaneÞy2
N. Nikolaou et al. / Fuel 83 (2004) 517–523
519
Table 1 Octane number data for various HCs present in isomerization gasolines Ordered by retention time
Boiling point (8C)
Type of H/C
Molecular weight
Pure RON
Pure MON
Blending RON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
20.5 9.5 27.9 36 49.7 49.6 58 60.2 63.2 68.7 79.2 71.8 80.5 80.7 86.1 80.8 90 89.8 87.9 91.8 90.7 91.7 93.4 98.5 100.9 103.5
n-C4 2,2-DM-C3 iso-C5 (2-M-C4) n-C5 2,2-DM-C4 Cyclo-C5 2,3-DM-C4 iso-C6 (2-M-C5) 3-M-C5 n-C6 2,2-DM-C5 M-cyclo-C5 2,4-DM-C5 2,2,3-TM-C4 3,3-DM-C5 Cyclo-C6 iso-C7 (2-M-C6) 2,3-DM-C5 1,1-DM-Cyclo-C5 3-M-C6 1,3-DM-cis-cyclo-C5 1,3-DM-trans-cyclo-C5 3-E-C5 n-C7 M-cyclo-C6 E-cyclo-C5
58.123 72.15 72.15 72.15 86.177 70.134 86.177 86.177 86.177 86.177 100.203 84.161 100.203 100.203 100.203 84.161 100.203 100.203 98.188 100.203 98.188 98.188 98.188 100.203 98.188 112.215
93.8 85.5 92.3 61.7 91.8 101.3 103.5 73.4 74.5 24.8 92.8 91.3 83.1 112.1 80.8 83 42.4 91.9 92.3 52 79.2 80.6 65
89.6 80.2 90.3 62.6 93.4 85 94.3 74.5 74.3 26 95.6 80 83.8 104.3 86.6 77.2 46.4 88.5 89.3 55.8 73.1 72.6 69.3
113.0 100.0 100.0 62.0 89.0 141.0 96.0 82.0 86.0 19.0 89.0 107.0 76.0 112.0 84.0 110.0 40.0 88.0 96.0 56.0 98.0 90.0 64
74.8 67.2
71.1 61.2
104 61.2
have been proposed [7] for the prediction of octane number. In the new calculation method proposed by the authors, the following equations for the prediction of isomerisation gasoline research octane number (RONG) are used. Similar equations can be equally applied for MON: RONG ¼
N X
Ki RONi yi
ð4Þ
i¼1
where yi ; are volume fractions of HCs present in the gasoline and obtained by GC, RONi is the measured (published) pure HC research octane number and Ki is a weighting factor that is calculated from: XN yi BRONi RONi Ki ¼ Xi¼1 N BRONi yi RONi
ð5Þ
analysed by the GC, as long as there are data available for their pure and blending octane numbers.
2. Experimental The demand for increased octane number in gasoline production from refineries in consequence of the reduction, under the new specifications, of high octane ratings aromatics and olefins led to the investigation of any possible alternative processes for octane number improvements. For example, one of the best processing options for light straight run (LSR) has been determined to be the isomerisation. Isomerisation units rearrange the molecules from straightchain, low-octane hydrocarbons to branched-chain, highoctane hydrocarbons known as isomers. This is done for two reasons:
i¼1
where BRONi is the measured (published) blending research octane number of each HC. The product Ki RONi in Eq. (4), gives a calculated effective or blending octane number, characteristic for the specific gasoline, which also depends, except on the known pure and blending octane numbers, on the number, type and concentration of the HCs present in the specific gasoline. Thus there is not limit in the number of the HCs present and
† They create extra isobutane feed for alkylation. † They improve the octane number of straight run pentanes and hexanes and hence make them into better petrol blending components. So, the resulting isomerate is a superior gasoline blending stock. Twenty (20) pure refinery isomerates samples, with different number and components concentrations were collected and their research octane number (RON) was
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N. Nikolaou et al. / Fuel 83 (2004) 517–523
Fig. 1. A typical gas chromatogram obtained for an isomerization gasoline sample and column/detector characteristics.
carefully measured in a standard CFR engine. The same samples were analysed in a PERKIN ELMER 8700 GC with a Petrocol DH by Supelco, 150 m capillary column, which is known to have an excellent HC’s separation performance. A summary of the GC operating conditions and capillary column characteristics is provided in Fig. 1. Standard analysis calculations of area% (normalization) in a standard integrator incorporated into the chromatograph were used. The GC TALK software available in the 8700 GC used for the transfer of compositional data through an RS232 to a PC, where they were fatherly processed with a Visual Basic program.
3. Results A typical chromatogram from a full range isomerisation gasoline blend is shown in Fig. 1 and a typical composition with the HCs’ peaks allocated also shown in Table 2. The RON and various other data for these HCs are also present in Table 1. A maximum number of 26 different HCs was detected and allocated. In all the samples more than 98%w/w of
the gasoline was quantitatively determined. In Table 3 are shown typical calculated weighting factors and effective research octane numbers for a specific sample. The weighting factors Ki calculated values were close to one. As it would be normally expected, for a pure component ðyi ¼ 1Þ from Eq. (5) we obtain Ki ¼ 1 and subsequently from Eq. (4) RONG is equal to the pure RON.
Table 2 Typical composition (%w/w) of detected HCs 1 2 3 4 5 6 7 8 9 10 11 12 13
n-C4 2,2-DM-C3 iso-C5 (2-M-C4) n-C5 2,2-DM-C4 Cyclo-C5 2,3-DM-C4 iso-C6 (2-M-C5) 3-M-C5 n-C6 2,2-DM-C5 M-cyclo-C5 2,4-DM-C5
0.320 0.031 41.596 12.323 25.818 1.037 2.971 5.398 2.200 2.869 0.054 1.221 0.033
14 15 16 17 18 19 20 21 22 23 24 25 26
2,2,3-TM-C4 3,3-DM-C5 Cyclo-C6 iso-C7 (2-M-C6) 2,3-DM-C5 1,1-DM-cyclo-C5 3-M-C6 1,3-DM-cis-cyclo-C5 1,3-DM-trans-cyclo-C5 3-E-C5 n-C7 M-cyclo-C6 E-cyclo-C5
0.013 0.026 2.677 0.076 0.032 0.020 0.066 0.020 0.024 0.030 0.029 0.493 0.011
N. Nikolaou et al. / Fuel 83 (2004) 517–523
521
Table 3 Example of calculated weighting factor and effective research octane numbers Ordered by retention time
H/C
Volume fraction yi
Weighting factor Ki
Pure RON
Measured blending RON
Calculated blending RON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
n-C4 2,2-DM-C3 iso-C5 (2-M-C4) n-C5 2,2-DM-C4 Cyclo-C5 2,3-DM-C4 iso-C6 (2-M-C5) 3-M-C5 n-C6 2,2-DM-C5 M-cyclo-C5 2,4-DM-C5 2,2,3-TM-C4 3,3-DM-C5 Cyclo-C6 iso-C7 (2-M-C6) 2,3-DM-C5 1,1-DM-cyclo-C5 3-M-C6 1,3-DM-cis-cyclo-C5 1,3-DM-trans-cyclo-C5 3-E-C5 n-C7 M-cyclo-C6 E-cyclo-C5
0.0043 0.0003 0.4507 0.1335 0.2342 0.0116 0.0270 0.0490 0.0200 0.0260 0.0004 0.0113 0.0003 0.0001 0.0002 0.0249 0.0006 0.0003 0.0002 0.0005 0.0002 0.0002 0.0002 0.0002 0.0039 0.0001
0.876 0.902 0.974 1.050 1.088 0.758 1.137 0.944 0.914 1.377 1.100 0.900 1.153 1.056 1.015 0.796 1.118 1.102 1.014 0.979 0.852 0.945 1.071
93.8 85.5 92.3 61.7 91.8 101.3 103.5 73.4 74.5 24.8 92.8 91.3 83.1 112.1 80.8 83 42.4 91.9 92.3 52 79.2 80.6 65 0 74.8 67.2
113.0 100.0 100.0 62.0 89.0 141.0 96.0 82.0 86.0 19.0 89.0 107.0 76.0 112.0 84.0 110.0 40.0 88.0 96.0 56.0 98.0 90.0 64 0 104 61.2
82.1 77.1 89.9 64.8 99.9 76.8 117.7 69.3 68.1 34.1 102.1 82.2 95.8 118.4 82.0 66.1 47.4 101.2 93.6 50.9 67.5 76.1 69.6 0 56.7 77.8
In Fig. 2 are shown the RON values measured in the CFR engine and those calculated from the GC data with the new method proposed. The agreement was very good. Considering the RON uncertainty of the ASTM method to ^ 1 RON, and the fact that the standard deviation of the difference DRON ¼ RON(ASTM)-RON(GC) was sDRON ¼ 0:23RON it is easily justified the consistency of the estimation.
0.759 1.158
By employing Chebyshev’s probability theorem [14], which holds for any distribution of observations: Pð0 2 ksDRON , DRON , 0 þ ksDRON Þ $ 1 2
1 k2
ð6Þ
and for an uncertainty ksDRON ¼ ^1 RON; we can safely conclude that at minimum 95% of our observations are falling within the ^ 1 RON interval, which is a very good
Fig. 2. Measured RON (ASTM) versus calculated RON(GC) from GC data for 20 isomerates. (Uncertainty of RON(ASTM) taken as ^1RON).
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N. Nikolaou et al. / Fuel 83 (2004) 517–523
agreement. In fact, all our calculated RON(GC) values were well within ^ 0.5 RON of the RON(ASTM) measured values. From Fig. 2, it can be easily seen that RON(ASTM) from the CFR engine is, in most cases, higher than the RON(GC). This is another fact that provide further support to this method since this can be explained by the small loss of information due to GC analysis, in which slightly less than 100% w/w of the gasoline has been quantitatively determined in all the samples, with the tail end loss mainly consisting of HCs with high octane number ratings. It is also worth noticing in Table 3, that the calculated blending RONs by this method follow a negative correlation with the measured blending RONs published in API project 45, when both they are compared with the pure RON values. However, this is the effect of the weighting factor that depends not only on the specific gasoline’s composition but also on the blending RONs.
4. Discussion The method and the results obtained in this project can be compared with those obtained by multiple regression analysis [9] and those regarding catalytic reformed gasoline [10,12]. The main advantages of the method presented, is its simplicity and the fact that it can be easily integrated in an on line octane number GC analyser for blending quality control. This analytical non-linear calculation method it can be also classified as a pattern recognition method, since it employs past data for RON and Blending RON values in order to calculate the RON of a new unknown sample. In the end, it was realised that a reliable computational method for the prediction of RON from GC data would generally need the following information: 1. Number and type of HC components in the gasoline as well as detail compositional data. Rich qualitative and quantitative information can be available by modern GC analysers with capillary columns. The increased number and the type of HCs present in a refinery isomerate also increase the number of interaction and non-linear terms, partitioning the contribution of each single component to the overall octane number. 2. Octane numbers of pure components provide information about the combustion characteristics of each component. This could be mathematically combined in some way with blending octane numbers, and so all this quantitative and qualitative information is more effectively utilised because the octane number predictor takes into account interaction characteristics between the components.
5. Conclusions Octane number (RON or MON) is a fuel performance quality metric under various but specified engine’s operation conditions. Blending is one of the final operations in refining, in which two or more different components or structural groups are mixed together to obtain the desired range of properties in the final product. In practise octane numbers do not blend linearly. To accommodate this, complex blending calculations employing blending octane numbers as opposed to the values for pure hydrocarbons are routinely employed. There is no universal blending program used industry wide. In fact, for a given oil company, blending calculations that are refinery specific are not uncommon. As an improvement over octane numbers of pure compounds, there are tabulations of blending octane numbers for both RON and MON. These numbers are measured by blending 20 vol.% of the specific hydrocarbon in 80 vol.% of a 60/40 iso-octane/n-heptane mixture. Although still not indicative of the actual blending octane number for a specific gasoline composition, the blending octane numbers are more representative and could be utilised more effectively by providing qualitative information when combined in mathematical models with pure octane numbers. Further research is necessary to test the applicability of the method to other gasoline types, as well as further research on other non-linear utilisation methods of GC data. Such a research effort has already been initiated in the Department of Petroleum and Natural gas Technology. Various non-linear methods are tested that employ pattern recognition techniques, along with the utilisation of past RON data, such as those used in this project.
References [1] Directive 98/70/EC of the European Parliament and of the Council of 13 October 1998 relating to the quality of petrol and diesel fuels and amending Council Directive 93/12/EEC (28/12/1998). Official Journal L 350, p. 0058–0068. [2] ASTM D 1319-Hydrocarbon types in liquid petroleum products by fluorescent indicator adsorption. [3] ASTM D 2699-92. IP237/69 Standard test method for knock characteristics of motor fuels by the research method, Annual book of ASTM standards v.05.04; 1994. [4] ASTM D 2700-92. IP236/83. Standard test method for knock characteristics of motor and aviation fuels by the motor method, Annual book of ASTM standards v.05.04; 1994. [5] Durand JP, Boscher Y, Dorbon M. On-line chromatographic analyzer for determining the composition and octane number of reforming process effluents. J Chromatogr 1990;509:47–51. [6] Rohrback BG. Computer-assisted rating of gasoline octane. Trends Anal Chem 1991;10(9).
N. Nikolaou et al. / Fuel 83 (2004) 517–523 [7] Meusinger R, Moros R. Application of genetic algorithms and neural networks in the analysis of multi-component mixtures using NMR spectroscopy. 1995. www2.chemie.uni-erlangen.de/external/cic/ tagungen/workshop95/meusinge/index.html. [8] http://www.process-nmr.com/chemo.htm (2003)-NMR Chemometric modeling. [9] Protic-Lovasic G, Jambrec N, Deur-Siftar D, et al. Determination of catalytic reformed gasoline octane number by high resolution gas chromatography. Fuel 1990;69(4):525– 8. [10] Albright LF, Eckert RE. New equations help rapidly determine alkylate octane numbers. Oil Gas J 1999;97(3):51–4.
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[11] Durand JP, Boscher Y, Dorbon M. On line chromatographic analyser for determining the composition and octane number of reforming process effluents. J Chromatogr 1990;509(1):47 –51. [12] Lugo HJ, Ragone G, Zambrano J. Correlations between octane numbers and catalytic cracking naptha composition. Indust Engng Chem Res 1999;38(5):2171 –6. [13] ASTM STP 225 -API research project 45, Knocking characteristics of pure hydrocarbons; 1994. [14] Walpole RE, Myers RH. Probability and statistics for engineers and scientists, 5th ed. ; 1993. 002-424201-2.
A new non-linear calculation method of isomerisation gasoline research octane number based on gas chromatographic data N. Nikolaoua,1, C.E. Papadopoulosa,*, I.A. Gagliasb, K.G. Pitarakisc,2 a
T.E.I Kavalas, Department of Petroleum and Natural Gas Technology, School of Technological Applications, Kavala, Greece b Hellenic Petroleum S.A., Aspropyrgos Attikis, Greece c Water Supply and Sewerage Municipal Of Larissa, Terma Tyherou St., 41222-Larissa, Greece Received 3 March 2003; revised 23 September 2003; accepted 25 September 2003; available online 14 October 2003
Abstract A new simple no-linear calculation method of isomerisation gasoline Research Octane Number (RON) is presented. The calculation method effectively utilizes the compositional data from high-resolution capillary GC analysis, and the measured pure and blending RON values of various HCs, which are widely published. The method, which is a simple non-linear method, utilizes calculable weighting factors, which are specific for each gasoline blend, and showed an excellent agreement with the RON values of various refinery isomerates samples as they measured in a standard CFR engine. q 2004 Elsevier Ltd. All rights reserved. Keywords: Non-linear methods; Gasoline; Isomerates; Octane number calculation; Gas chromatography
1. Introduction Gasoline is a multicomponent or complex mixture that may comprise of several hundred hydrocarbons. It actually varies from refinery to refinery and there is not such a thing as a single criterion that can characterise gasoline purity and quality in general. The new environmental and performance considerations have introduced several difficulties in gasoline production and have made a standard general definition of gasoline quality a very difficult task. So, new specifications [1] for the unleaded gasoline were drawn and increased demands on the quality of gasoline as indicated by its octane numbers (Research octane number or RON and Motor octane number or MON), among others, have been placed. The RON accounts for fuel performance under low severity engine operation while the MON for more severe operation that might be incurred at high speed or high load. In practise the octane of a gasoline is reported * Corresponding author. Tel.: þ 30-23210-20562; fax: þ 30-2321056246. E-mail addresses: [email protected] (C.E. Papadopoulos); nicnik@ otenet.gr (N. Nikolaou). 1 Tel: þ30-2510 46223-1. 2 Tel: þ30-2410 687240-1. 0016-2361/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2003.09.011
as an Antiknock Index or Pump Octane which is the average of RON and MON AI ¼
RON þ MON 2
ð1Þ
Both RON and MON are still measured in a standardised single cylinder, variable compression ratio (from 4:1 to 18:1), internal combustion engine (Cooperative Research Fuel-CFR engine), following the standard methods ASTMD2699 [3] and D2700 [4], respectively. Both RON and MON are relative values based on accepted standard fuel mixtures. The engine is operated at a constant speed (RPMs) for both RON and MON and the compression ration is increased until the onset of knocking. Engine speed is set at 600 rpm for RON and 900 rpm for MON. Petroleum industry has always been used empirical performance standards of its gasoline products, such as RON and MON. Only after gas chromatography became widely available, compositional standards and constraints on gasoline’s composition have started to be applied. The direct determination of RON (and MON) with the standard CFR engine is a complicated, relatively expensive operation. CFR engines require constant maintenance and frequent calibrations [5].
518
N. Nikolaou et al. / Fuel 83 (2004) 517–523
The fast, easy and accurate determination of octane number is important for refiners today, since optimisation of refining process at a reasonable cost is an ever-increasing requirement. 1.1. Gasoline compositional data by analytical techniques There are various existing analytical techniques to obtain gasoline compositional data from which calculation of octane numbers can be done. The main disadvantage of most them, for an accurate octane prediction is their limiting detail in terms of the compositional data they can provide. A detailed gasoline compositional analysis can always allow a more accurate calculation of octane number. Thus, analytical techniques such as fluorescent indicator adsorption (FIA) [2], Near infra-red (NIR) [6,8]. Nuclear magnetic resonance (NMR) [7,8], e.g. can provide only limited compositional information, corresponding to structural groups such as aromatics, olefins and saturates. This limited compositional (analytical) detail makes these methods not to be the most accurate for an octane number prediction, with an exception those cases where a gasoline mixture mainly consists of some predominant group. To the contrary, GC methods with the use of modern capillary columns can provide an almost excellent HCs separation and detailed compositional data, far beyond the aromatic, olefin etc. groups [9,10].
(iso-octane) is equal to 100 (the octane number of pure isooctane). Eq. (2) is generally extended and used for a gasoline consisting of k components as: RONG ¼
k X
RONi yi
ð3Þ
i¼1
Wherever Eq. (3) has been employed by others to predict research octane number from GC data, the RONi values used are not those of pure hydrocarbons present in ASTM STP 225 [13], but are ‘blending octane numbers’ that have been usually obtained by comparison with measured data and using multiple linear regression analysis [7]. This has been done so, because in systems other that blends of nheptane and iso-octane the octane numbers of the constituent components (Blending octane numbers from ASTM STP 225), or in other cases the ‘partial octane numbers’ (employing the directly relevant term from thermodynamics), are functions of their concentration and of the overall composition of the gasoline blend. Some of the published methods referred above, for the prediction of octane number from GC data, might be criticised for two main disadvantages:
ð2Þ
† Although GC methods can give detailed compositional data, as it was already said, the grouping of the analysed HCs into similar structural groups such as paraffins, olefins etc. for the octane number prediction, which has been usually the case, automatically removes the main advantages offered by GC methods themselves. This grouping procedure is based on the weak assumption that these structural groups behave similarly during the combustion of different gasolines [7]. † Another disadvantage of the conventional methods available is that the blending or effective octane numbers that have been obtained under some specific conditions are equally applied in Eq. (3) for all types of gasoline blends. For example, measured blending (and pure) octane numbers of a wide range of hydrocarbons has been published in API project 45 [13] and some of them are shown in Table 1. These blending octane numbers were estimated from the measured (CFR) octane number of a blend of 48%v/v iso-octane, 32%v/v n-heptane and 20%v/v of the specific HC. It must be recognised that these blending octane numbers have given some qualitative information in terms of the RON performance, related with specific HCs in these simple blends, however, this has been extended and used under another weak assumption that these blending octane numbers are the same (constants) for any number, type and concentration of HC components in a gasoline.
where y1 and y2 are the volume fractions of n-heptane and iso-octane in the blend respectively, RON (n-heptane) is equal to 0 (the octane number of pure n-heptane) and RON
Recently, and with the continuous advancement of computational power, some new and quite promising nonlinear utilisation methods, based on pattern recognition,
1.2. Octane number calculation methods—new method proposed Even though GC data offer detailed gasoline’s compositional information, a common problem of all analytical methods for an accurate calculation of octane number is the non-linear blending characteristics of the octane contributions from the individual components (or the structural groups) in a gasoline, which is a highly complex mixture. Thus, octane numbers measured directly for pure compounds cannot be simply (linearly) combined, according to the gasoline composition, to calculate the octane number of that gasoline. The non-linearity in the combination of pure components octane numbers is the most significant complication to the accurate calculation of gasoline octane numbers as well as to other fuel properties. Other researchers have done many efforts for the prediction of gasoline’s octane number utilising GC data of reformed gasoline [9,11,12]. Most of the methods developed utilise the basic equation (linear) of octane number for a blend of n-heptane and iso-octane which is: Octane number ¼ RONðn-heptaneÞy1 þ RONðiso-octaneÞy2
N. Nikolaou et al. / Fuel 83 (2004) 517–523
519
Table 1 Octane number data for various HCs present in isomerization gasolines Ordered by retention time
Boiling point (8C)
Type of H/C
Molecular weight
Pure RON
Pure MON
Blending RON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
20.5 9.5 27.9 36 49.7 49.6 58 60.2 63.2 68.7 79.2 71.8 80.5 80.7 86.1 80.8 90 89.8 87.9 91.8 90.7 91.7 93.4 98.5 100.9 103.5
n-C4 2,2-DM-C3 iso-C5 (2-M-C4) n-C5 2,2-DM-C4 Cyclo-C5 2,3-DM-C4 iso-C6 (2-M-C5) 3-M-C5 n-C6 2,2-DM-C5 M-cyclo-C5 2,4-DM-C5 2,2,3-TM-C4 3,3-DM-C5 Cyclo-C6 iso-C7 (2-M-C6) 2,3-DM-C5 1,1-DM-Cyclo-C5 3-M-C6 1,3-DM-cis-cyclo-C5 1,3-DM-trans-cyclo-C5 3-E-C5 n-C7 M-cyclo-C6 E-cyclo-C5
58.123 72.15 72.15 72.15 86.177 70.134 86.177 86.177 86.177 86.177 100.203 84.161 100.203 100.203 100.203 84.161 100.203 100.203 98.188 100.203 98.188 98.188 98.188 100.203 98.188 112.215
93.8 85.5 92.3 61.7 91.8 101.3 103.5 73.4 74.5 24.8 92.8 91.3 83.1 112.1 80.8 83 42.4 91.9 92.3 52 79.2 80.6 65
89.6 80.2 90.3 62.6 93.4 85 94.3 74.5 74.3 26 95.6 80 83.8 104.3 86.6 77.2 46.4 88.5 89.3 55.8 73.1 72.6 69.3
113.0 100.0 100.0 62.0 89.0 141.0 96.0 82.0 86.0 19.0 89.0 107.0 76.0 112.0 84.0 110.0 40.0 88.0 96.0 56.0 98.0 90.0 64
74.8 67.2
71.1 61.2
104 61.2
have been proposed [7] for the prediction of octane number. In the new calculation method proposed by the authors, the following equations for the prediction of isomerisation gasoline research octane number (RONG) are used. Similar equations can be equally applied for MON: RONG ¼
N X
Ki RONi yi
ð4Þ
i¼1
where yi ; are volume fractions of HCs present in the gasoline and obtained by GC, RONi is the measured (published) pure HC research octane number and Ki is a weighting factor that is calculated from: XN yi BRONi RONi Ki ¼ Xi¼1 N BRONi yi RONi
ð5Þ
analysed by the GC, as long as there are data available for their pure and blending octane numbers.
2. Experimental The demand for increased octane number in gasoline production from refineries in consequence of the reduction, under the new specifications, of high octane ratings aromatics and olefins led to the investigation of any possible alternative processes for octane number improvements. For example, one of the best processing options for light straight run (LSR) has been determined to be the isomerisation. Isomerisation units rearrange the molecules from straightchain, low-octane hydrocarbons to branched-chain, highoctane hydrocarbons known as isomers. This is done for two reasons:
i¼1
where BRONi is the measured (published) blending research octane number of each HC. The product Ki RONi in Eq. (4), gives a calculated effective or blending octane number, characteristic for the specific gasoline, which also depends, except on the known pure and blending octane numbers, on the number, type and concentration of the HCs present in the specific gasoline. Thus there is not limit in the number of the HCs present and
† They create extra isobutane feed for alkylation. † They improve the octane number of straight run pentanes and hexanes and hence make them into better petrol blending components. So, the resulting isomerate is a superior gasoline blending stock. Twenty (20) pure refinery isomerates samples, with different number and components concentrations were collected and their research octane number (RON) was
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Fig. 1. A typical gas chromatogram obtained for an isomerization gasoline sample and column/detector characteristics.
carefully measured in a standard CFR engine. The same samples were analysed in a PERKIN ELMER 8700 GC with a Petrocol DH by Supelco, 150 m capillary column, which is known to have an excellent HC’s separation performance. A summary of the GC operating conditions and capillary column characteristics is provided in Fig. 1. Standard analysis calculations of area% (normalization) in a standard integrator incorporated into the chromatograph were used. The GC TALK software available in the 8700 GC used for the transfer of compositional data through an RS232 to a PC, where they were fatherly processed with a Visual Basic program.
3. Results A typical chromatogram from a full range isomerisation gasoline blend is shown in Fig. 1 and a typical composition with the HCs’ peaks allocated also shown in Table 2. The RON and various other data for these HCs are also present in Table 1. A maximum number of 26 different HCs was detected and allocated. In all the samples more than 98%w/w of
the gasoline was quantitatively determined. In Table 3 are shown typical calculated weighting factors and effective research octane numbers for a specific sample. The weighting factors Ki calculated values were close to one. As it would be normally expected, for a pure component ðyi ¼ 1Þ from Eq. (5) we obtain Ki ¼ 1 and subsequently from Eq. (4) RONG is equal to the pure RON.
Table 2 Typical composition (%w/w) of detected HCs 1 2 3 4 5 6 7 8 9 10 11 12 13
n-C4 2,2-DM-C3 iso-C5 (2-M-C4) n-C5 2,2-DM-C4 Cyclo-C5 2,3-DM-C4 iso-C6 (2-M-C5) 3-M-C5 n-C6 2,2-DM-C5 M-cyclo-C5 2,4-DM-C5
0.320 0.031 41.596 12.323 25.818 1.037 2.971 5.398 2.200 2.869 0.054 1.221 0.033
14 15 16 17 18 19 20 21 22 23 24 25 26
2,2,3-TM-C4 3,3-DM-C5 Cyclo-C6 iso-C7 (2-M-C6) 2,3-DM-C5 1,1-DM-cyclo-C5 3-M-C6 1,3-DM-cis-cyclo-C5 1,3-DM-trans-cyclo-C5 3-E-C5 n-C7 M-cyclo-C6 E-cyclo-C5
0.013 0.026 2.677 0.076 0.032 0.020 0.066 0.020 0.024 0.030 0.029 0.493 0.011
N. Nikolaou et al. / Fuel 83 (2004) 517–523
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Table 3 Example of calculated weighting factor and effective research octane numbers Ordered by retention time
H/C
Volume fraction yi
Weighting factor Ki
Pure RON
Measured blending RON
Calculated blending RON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
n-C4 2,2-DM-C3 iso-C5 (2-M-C4) n-C5 2,2-DM-C4 Cyclo-C5 2,3-DM-C4 iso-C6 (2-M-C5) 3-M-C5 n-C6 2,2-DM-C5 M-cyclo-C5 2,4-DM-C5 2,2,3-TM-C4 3,3-DM-C5 Cyclo-C6 iso-C7 (2-M-C6) 2,3-DM-C5 1,1-DM-cyclo-C5 3-M-C6 1,3-DM-cis-cyclo-C5 1,3-DM-trans-cyclo-C5 3-E-C5 n-C7 M-cyclo-C6 E-cyclo-C5
0.0043 0.0003 0.4507 0.1335 0.2342 0.0116 0.0270 0.0490 0.0200 0.0260 0.0004 0.0113 0.0003 0.0001 0.0002 0.0249 0.0006 0.0003 0.0002 0.0005 0.0002 0.0002 0.0002 0.0002 0.0039 0.0001
0.876 0.902 0.974 1.050 1.088 0.758 1.137 0.944 0.914 1.377 1.100 0.900 1.153 1.056 1.015 0.796 1.118 1.102 1.014 0.979 0.852 0.945 1.071
93.8 85.5 92.3 61.7 91.8 101.3 103.5 73.4 74.5 24.8 92.8 91.3 83.1 112.1 80.8 83 42.4 91.9 92.3 52 79.2 80.6 65 0 74.8 67.2
113.0 100.0 100.0 62.0 89.0 141.0 96.0 82.0 86.0 19.0 89.0 107.0 76.0 112.0 84.0 110.0 40.0 88.0 96.0 56.0 98.0 90.0 64 0 104 61.2
82.1 77.1 89.9 64.8 99.9 76.8 117.7 69.3 68.1 34.1 102.1 82.2 95.8 118.4 82.0 66.1 47.4 101.2 93.6 50.9 67.5 76.1 69.6 0 56.7 77.8
In Fig. 2 are shown the RON values measured in the CFR engine and those calculated from the GC data with the new method proposed. The agreement was very good. Considering the RON uncertainty of the ASTM method to ^ 1 RON, and the fact that the standard deviation of the difference DRON ¼ RON(ASTM)-RON(GC) was sDRON ¼ 0:23RON it is easily justified the consistency of the estimation.
0.759 1.158
By employing Chebyshev’s probability theorem [14], which holds for any distribution of observations: Pð0 2 ksDRON , DRON , 0 þ ksDRON Þ $ 1 2
1 k2
ð6Þ
and for an uncertainty ksDRON ¼ ^1 RON; we can safely conclude that at minimum 95% of our observations are falling within the ^ 1 RON interval, which is a very good
Fig. 2. Measured RON (ASTM) versus calculated RON(GC) from GC data for 20 isomerates. (Uncertainty of RON(ASTM) taken as ^1RON).
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N. Nikolaou et al. / Fuel 83 (2004) 517–523
agreement. In fact, all our calculated RON(GC) values were well within ^ 0.5 RON of the RON(ASTM) measured values. From Fig. 2, it can be easily seen that RON(ASTM) from the CFR engine is, in most cases, higher than the RON(GC). This is another fact that provide further support to this method since this can be explained by the small loss of information due to GC analysis, in which slightly less than 100% w/w of the gasoline has been quantitatively determined in all the samples, with the tail end loss mainly consisting of HCs with high octane number ratings. It is also worth noticing in Table 3, that the calculated blending RONs by this method follow a negative correlation with the measured blending RONs published in API project 45, when both they are compared with the pure RON values. However, this is the effect of the weighting factor that depends not only on the specific gasoline’s composition but also on the blending RONs.
4. Discussion The method and the results obtained in this project can be compared with those obtained by multiple regression analysis [9] and those regarding catalytic reformed gasoline [10,12]. The main advantages of the method presented, is its simplicity and the fact that it can be easily integrated in an on line octane number GC analyser for blending quality control. This analytical non-linear calculation method it can be also classified as a pattern recognition method, since it employs past data for RON and Blending RON values in order to calculate the RON of a new unknown sample. In the end, it was realised that a reliable computational method for the prediction of RON from GC data would generally need the following information: 1. Number and type of HC components in the gasoline as well as detail compositional data. Rich qualitative and quantitative information can be available by modern GC analysers with capillary columns. The increased number and the type of HCs present in a refinery isomerate also increase the number of interaction and non-linear terms, partitioning the contribution of each single component to the overall octane number. 2. Octane numbers of pure components provide information about the combustion characteristics of each component. This could be mathematically combined in some way with blending octane numbers, and so all this quantitative and qualitative information is more effectively utilised because the octane number predictor takes into account interaction characteristics between the components.
5. Conclusions Octane number (RON or MON) is a fuel performance quality metric under various but specified engine’s operation conditions. Blending is one of the final operations in refining, in which two or more different components or structural groups are mixed together to obtain the desired range of properties in the final product. In practise octane numbers do not blend linearly. To accommodate this, complex blending calculations employing blending octane numbers as opposed to the values for pure hydrocarbons are routinely employed. There is no universal blending program used industry wide. In fact, for a given oil company, blending calculations that are refinery specific are not uncommon. As an improvement over octane numbers of pure compounds, there are tabulations of blending octane numbers for both RON and MON. These numbers are measured by blending 20 vol.% of the specific hydrocarbon in 80 vol.% of a 60/40 iso-octane/n-heptane mixture. Although still not indicative of the actual blending octane number for a specific gasoline composition, the blending octane numbers are more representative and could be utilised more effectively by providing qualitative information when combined in mathematical models with pure octane numbers. Further research is necessary to test the applicability of the method to other gasoline types, as well as further research on other non-linear utilisation methods of GC data. Such a research effort has already been initiated in the Department of Petroleum and Natural gas Technology. Various non-linear methods are tested that employ pattern recognition techniques, along with the utilisation of past RON data, such as those used in this project.
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