Ind. Eng. Chem. Res. 1999, 38, 2171-2176
2171
Correlations between Octane Numbers and Catalytic Cracking Naphtha Composition Heli J. Lugo* Centro de Superficies y Cata´ lisis, Facultad de Ingenierı´a, Universidad del Zulia, Apartado 15251, Maracaibo 4003A, Venezuela
Giuseppina Ragone and Jose Zambrano
Ind. Eng. Chem. Res. 1999.38:2171-2176. Downloaded from pubs.acs.org by UNIV OF EDINBURGH on 01/23/19. For personal use only.
Grupo de Ingenierı´a de Proceso, Refinerı´a de Amuay, LAGOVEN, Judibana, Venezuela
This work describes the development of correlations between the research and motor octane numbers (RON and MON, respectively) and the composition of catalytic cracking naphtha. The correlations were developed using a nonideal model. The adjustable parameters in the model were determined by multiple regression using 67 naphtha samples produced at Amuay Fluid Catalytic Cracking Unit. These correlations are useful for predicting catalytic cracking naphtha RON and MON, using its composition determined by gas chromatography. The confidence limits (95%) of these predictions are (0.6 for RON and (0.4 for MON. With the information from the octane-composition relationship, it is possible to establish strategies for catalytic cracking naphtha RON and MON enhancement. Introduction
Table 1. Definition of the Analyzed Component Groups
The naphtha of the process of fluid catalytic cracking corresponds approximately to 35 vol % of the total gasoline produced in a refinery. The research octane number (RON) and the motor octane number (MON) are some of its most important properties. The main purpose of any refinery is to maximize the RON and MON because the higher these values, the greater its commercial value. The octane number, as defined by ASTM methods, is an empirical property. It cannot be submitted to any analysis that would allow octane-optimizing strategies. Therefore, it must be related to measurable properties inherent to gasoline, such as its molecular composition. We must know the relationship between the octane number and the composition of gasoline. After that, we can determine which components enhance or detract from that property and what actions must be taken to achieve the desired goal. Anderson et al.1 have developed a very useful and simple method for predicting the research octane number of different types of naphtha. This method is based on a detailed analysis of gasoline by gas chromatography. The individual components of the samples are in 31 groups defined according to their chemical nature, boiling point, and the retention time of every hydrocarbon (Table 1). The equation proposed to evaluate the octane number from these data corresponds to a lineal combination of the octane number and the composition of each group:
group no.
31
RON )
∑ i)1
(aici)
(1)
where ai is the octane number of the group i and ci is the composition of the group i. Anderson et al.1 have reported an average error of 1.1 RON numbers, for samples with only 16% catalytic cracking naphthas. * To whom correspondence should be addressed. E-mail:
[email protected]. Phone: 58-61-598593. Fax: 58-61-525732.
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 27 28 29 30 31
components components before n-butane n-butane components between n-butane and isopentane isopentane components between isopentane and n-pentane n-pentane components between n-pentane and 2-methylpentane 2- and 3-methylpentane and components between them components between 2-methylpentane and n-hexane n-hexane components between n-hexane and benzene benzene components between benzene and 2-methylhexane 2- and 3-methylhexane and components between them components between 3-methylhexane and n-heptane n-heptane components between n-heptane and toluene toluene components between toluene and 2-methylheptane 2- and 3-methylheptane and components between them components between 3-methylheptane and n-octane n-Octane components between n-octane and ethylbenzene ethylbenzene components between ethylbenzene and p-xylene p-xylene and m-xylene components between m-xylene and o-xylene o-xylene components between o-xylene and n-nonane components between n-nonane and decane from n-decane henceforth
main chemical class olefin paraffin olefin isoparaffin olefin paraffin olefin isoparaffin olefin paraffin olefin aromatic olefin isoparaffin naphthene paraffin olefin aromatic isoparaffin isoparaffin isoparaffin paraffin naphthene aromatic isoparaffin aromatic isoparaffin aromatic paraffin naphthene aromatic/paraffin
Huskey and Ehrmann2 evaluated the applicability of Anderson’s method to catalytic cracking naphthas. They found that the difference between the octane number
10.1021/ie980273r CCC: $18.00 © 1999 American Chemical Society Published on Web 04/10/1999
2172 Ind. Eng. Chem. Res., Vol. 38, No. 5, 1999 Table 3. Results of the Multiple Lineal Regression for the Research Octane Number
Figure 1. Comparison between real and calculated RON. Table 2. Ideal Octane Number of Each Group (ai) group no.
RON ai
MON ai
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31
96.4 94.0 98.6 92.3 91.0 62.0 93.8 74.0 94.7 24.8 93.6 109.0 88.3 53.3 83.4 0 82.1 119.7 84.5 26.5 68.9 0 57.1 111.2 117.2 73.3 100.0 34.9 110.0 104.0
85.8 89.1 83.2 90.3 86.3 63.2 81.5 73.8 81.0 26.0 79.9 106.2 76.3 55.4 76.3 0 76.9 109.1 81.7 31.8 68.2 0 58.4 97.9 113.7 73.3 100.0 34.9 99.0 94.0
calculated and the one obtained by the ASTM standard method was up to 2.8 RON numbers. Other authors3-5 have shown that the octane number is not generally a linear mixing property due to the interactions existing between the compounds of different chemical natures (olefins, paraffins, naphthenes, and aromatics). This can generate effects of synergy or inhibition and give the mixture a higher or lower octane number than its individual components. Van Leeuwen et al.6 have applied nonlinear regression techniques to the results of gas chromatographic analysis of gasoline, such as projection pursuit regression and neural networks. These techniques do not have well-established guidelines for use yet. In projection pursuit techniques the number of smooth to include in the final model is not easily determined. In neural network model building, the number of units in the middle (hidden) layer is often open to discussion. Therefore, the most important parameters and others of less importance must be found through trial and error or set without any theoretical basis for selecting the optimal setting. This makes the use of these techniques time-consuming for a nonexperienced user. Twu and Coon7,8 have proposed a generalized interaction method for predicting octane numbers for gasoline blends. This method requires octane numbers of olefins, aromatics, and saturates in the gasoline cuts, which are
index i
coefficient βi
standard deviation
t value
0 1 4 7 13 15 19 27 28 29 30
0.937 0.277 0.195 0.147 0.867 0.295 1.453 0.699 3.249 0.935 -0.596
0.017 0.091 0.049 0.043 0.349 0.066 0.373 0.190 0.501 0.196 0.084
55.056 3.037 3.931 3.429 2.482 4.440 3.900 3.669 6.484 4.757 -7.127
R2 adjusted residual standard deviation Durbin-Watson Factor degrees of freedom
0.999 993 0.261 2.181 56
not available from standard laboratory analysis. Therefore, they proposed a methodology that the octane numbers of these components in a gasoline cut be computed from the available standard laboratory inspection data and the derived binary interaction parameters between components so that the gasoline cut octane number is recovered by blending these components’ octane numbers together in the interaction blending equation. They reported that the overall average absolute deviation percent (AAD%) for 161 blends from 157 gasoline cuts is 0.97% for RON and 1.19% for MON. Recently, Sasano9 has described a procedure similar to that of Anderson,1 using a gas chromatograph to measure the chemical composition of a sample gasoline followed by calculating the RON using an ideal model and a lineal combination of the octane number of the components and their composition. In our previous publication,10 we proposed a nonideal model to correlate the RON and the catalytic cracking naphtha composition. In this paper, we extend the model to correlate also the MON, reporting the calculated values for the ideal RON and MON of each group. The advantages of this model are that it is very simple (linear regression), it considers the nonideal interactions between components, and it predicts octane numbers with a confidence similar to that of the ASTM methods. Hence, the nonideal model proposed to correlate RON and MON with the composition is 31
31
(aici)] + ∑(βici) ∑ i)1 i)1
octane number ) β0[
(2)
31
octane number )
(β0ai + βi)ci ∑ i)1
(3)
where ai is the ideal octane number of group i, ci is the weight fraction of group i, and β0 and βi are the coefficient factors determined by multiple regression. The component groups are defined according to Anderson’s proposal1 (Table 1). The term ∑(aici) represents the ideal octane number that the mixture would have if there were no interactions among the compounds with different chemical characteristics. The terms β0 and ∑(βici) represent the adjustments to the nonidealistic octane number. The coefficient (β0ai + βi) states the effective octane number of every group within the mixture.
Ind. Eng. Chem. Res., Vol. 38, No. 5, 1999 2173
Figure 2. Residual between real and calculated RON.
Additionally, 20 samples of catalytic cracking naphtha were collected. They were not included in the development of the correlation but were used to verify their confidence. The analyzed naphtha samples have the following typical properties: gravity, API RVP, psi RON/MON end boiling point, °C
Figure 3. Comparison between real and calculated RON (Samples no used to obtain the correlation).
55 7-8 92/80 221
Development of the Correlations. The steps followed by the development of the correlations were the following: (a) Weight percentages were determined for every group. Anderson’s group 25 was not considered because its percentage was equal to zero. (b) ai values were determined by a linear mixing of the octane number of components of every group. The octane numbers of the pure components have been published by API.11 Calculated ai values for each group are shown in Table 2. (c) The ideal RON and MON of the naphtha were calculated by the following equation: 31
ideal octane )
Figure 4. Comparison between real and calculated MON.
Experimental Section Sample Correlation and Analysis. Sixty-seven samples of catalytic cracking naphtha were collected from the Amuay Refinery over a period of 18 months. Their octane numbers, RON and MON, were determined by ASTM D-2699 and D-2700 methods, respectively. The samples were analyzed in a gas chromatograph HP 5890, with a capillary column Supelco Petrocol DH (L ) 100 m, i.d. ) 0.25 mm). The stationary phase was fused silica and methyl silicon. The initial temperature of 308 K was maintained for 14 min. Then the temperature was increased at a rate of 1.1 K/min until it reached 333 K, and was kept there for 19 min. After that, the temperature was increased again at a rate of 2 K/min until the final temperature of 453 K was reached. The HFI detector was kept at 523 K.
(aici) ∑ i)1
(4)
where ai is the octane number of group i and ci is the weight fraction of group i. (d) The coefficient factors of the deviation term and the ideal octane number term were determined by a multiple linear regression method. Because of the large number of variables (30 groups plus the ideal RON or MON), the stepwise regression procedure was used. In this procedure the most significant variables are added to the model one at a time. With this procedure, the less important variables may then be discarded.12,13 (e) Finally, the correlations developed for RON and MON were applied to 20 samples of catalytic naphtha, not previously used. This was done in order to evaluate the degree of confidence of the model in the octane number predictions. Results and Discussion Research Octane Number (RON). The most significant variables for this correlation are (a) ideal RON and (b) groups 1, 4, 7, 13, 15, 19, 27, 28, 29, and 30.
2174 Ind. Eng. Chem. Res., Vol. 38, No. 5, 1999
Figure 5. Residual between real and calculated MON. Table 4. Results of the Multiple Lineal Regression for the Motor Octane Number index i
coefficient βi
standard deviation
t value
0 5 18 19 27 28 29 30 31
0.973 0.228 -0.255 0.935 0.415 1.513 0.846 -0.229 -0.149
0.007 0.063 0.116 0.204 0.117 0.329 0.112 0.052 0.018
135.099 3.597 -2.207 4.579 3.539 4.592 7.539 -4.369 -8.217
R2 adjusted residual standard deviation Durbin-Watson factor degrees of freedom
0.999 996 0.163 1.717 57
Figure 6. Comparison between real and calculated MON (Samples no used to obtain the correlation).
Figure 1 shows the relationship between the octane number calculated by the correlation and the octane number measured experimentally. Table 3 shows the multiple linear regression results. The coefficient value of the adjusted determination is 0.999 993. This value indicates that the obtained correlation satisfactorily explains the changes of the independent variables. Therefore, there may be an excellent relationship between the octane number value calculated by the correlation and the octane number value determined by the ASTM D-2699 method, as shown in Figure 1. All the calculated values of the octane numbers were in the (0.6 range of the octane number values determined by the ASTM D-2699 method. This 0.6 difference is very satisfactory because its repetition order is equal to the ASTM D-2699 method order used to determine RON ((0.5).
Table 3 also shows t values for every coefficient. These values indicate if the variables are or are not significant when compared to the value of t tabulated for N - k 1 degrees of freedom at a certain level of confidence. In this case, for a 95% confidence level, a variable is considered significant if t > 1.68. All the variables included in this correlation meet this value. Figure 2 presents the obtained residues distributed around zero (arithmetic mean). They do not show a definite pattern but an aleatoric one, and their limits always remain within (0.6. This is confirmed by Durbin-Watson’s factor value of the residues that is 2.181 (Table 3). This means that there is no autocorrelation between them. The residues show that they meet the normal distribution. Figure 3 compares the measured octane number and the calculated one for the 20 samples not used in the development of the correlation. It can be seen that the calculated RON closely follows the octane number determined by the ASTM D-2699 method and residues remain within the (0.6 range, which is satisfactory. Motor Octane Number (MON). For this correlation the most significant variables are (a) ideal MON and (b) groups 5, 18, 19, 27, 28, 29, 30, and 31. Figure 4 shows the relationship between the octane number calculated by the correlation and the experimentally measured octane number. Table 4 gives the multiple linear regression results. The value of the coefficient of the adjusted determination is 0.999 996. This indicates that the obtained correlation satisfactorily explains changes in the independent variables. An excellent relationship may therefore be expected between the octane number value calculated by the correlation and the one determined by the ASTM D-2700 method, as shown in Figure 4. All the calculated values were in the (0.4 range of the octane number value determined by the ASTM D-2700 method. This (0.4 difference is a satisfactory value because it is in the same repetition order of the ASTM D-2700 method to determine MON ((0.3). The t values for every coefficient are observed in Table 4. For a 95% confidence level, a variable is considered significant if t > 1.68. All the variables included in the correlation meet this value. Figure 5 shows the obtained residues, distributed around zero (arithmetic mean). They do not show a definite pattern but an aleatoric one, and their limits always remain within (0.4. This is confirmed by Durbin-Watson’s factor value of the residues that is 1.717 (see Table 4), meaning that there is no autocor-
Ind. Eng. Chem. Res., Vol. 38, No. 5, 1999 2175
Figure 7. Comparison between ideal and effective RON of each group.
Figure 8. Comparison between ideal and effective MON of each group.
relation between them. The residues also show that they meet the normal distribution. Figure 6 compares the measured octane number and the calculated one for the 20 samples not used in the development of the correlation. It can be seen that the calculated MON closely follows the octane number determined by the ASTM D-2699 method and residues remain within the (0.4 range, which is satisfactory. RON and MON Optimization. Figure 7 presents the relationship between the ideal RON and the effective RON for every group of components. The effective RON shows the real contribution that every group gives to the octane number of the mixture. As expected, most of the values of the ideal and effective octane numbers are similar. Even though the octane number does not mix linearly, the main contributor for the octane number of the mixture is the one given by every component according to its nature. However, there are three groups that make a higher contribution to the octane number of the mixture. These groups are 13, 19, and 28: olefins, isoparaffins, and alkylbenzenes (Table 1), which coincide with the literature.5,14-16 Groups 10, 16, and 22 (normal paraffins) have the lowest contribution for the RON of the mixture. On the basis of the foregoing, to increase the RON, the olefin,
isoparaffinic, and aromatic compounds present in groups 13, 19, and 28 must be maximized. Figure 8 shows the relationship between the ideal and effective MON for every group of components. The effective MON shows the real contribution of every group for the MON of the mixture. As expected, most of the values of the ideal and effective octane numbers are similar. However, there are two groups of components that make a high contribution to the octane number of the mixture. These groups are 19 and 28: isoparaffins and alkylbenzenes, which are also attested to in the literature.5,14,15 Hence, to increase the MON, the production of the isoparaffins and alkylbenzene compounds present in groups 19 and 28 should be favored. Normal paraffin production should be decreased because they negatively affect both the MON and RON, as shown by groups 10, 16, and 22. Olefins present a high sensitivity (difference between RON and MON). These are therefore undesirable unless they are isomerized in downstream processes. Conclusions RON and MON can correlate with the composition of catalytic naphtha determined by gas chromatography,
2176 Ind. Eng. Chem. Res., Vol. 38, No. 5, 1999
according to the following nonideal model: 31
∑ i)1
octane number ) β0[
31
(aici)] +
(βici) ∑ i)1
where ai is the ideal octane number of group i, ci is the weight fraction of group i, and β0 and βi are the coefficient factors determined by multiple regression. The correlations obtained allow a prediction of the octane number with a confidence of (0.6 units for RON and (0.4 units for MON. These limits are very satisfactory because they are in the same repeatability order as those of the ASTM D-2699 and D-2700 methods. The components that make the largest contribution to the RON are the olefins, isoparaffins, and alkylbenzenes present in groups 13 (ethylpentenes, methylhexenes, hexadienes, and cyclohexenes), 19 (dimethylhexanes and trimethylcyclopentanes), and 28 (o-xylene). The components that make the largest contribution to the MON are the isoparaffins and alkylbenzenes present in groups 19 and 28. The olefins are undesirable because of their high sensitivity (RON - MON). Literature Cited (1) Anderson, P. C.; Sharkey, J. M.; Walsh, R. P. Calculation of Research Octane Number of Motor Gasolines from Chromatographic Data and a New Approach to Motor Gasoline Quality Control. J. Inst. Pet. 1972, 59, 83. (2) Huskey, D.; Ehrmann, U. Determinacio´n de Octanaje RON mediante Cromatografı´a de Gases en Columnas Capilares. Informe Te´ cnico Intevep; Abril, INTEVEP: Caracas, 1988. (3) Rusin, M. H.; Chung, H. S.; Marshall, J. F. A Transformation Method for Calculating the Research and Motor Octane Numbers of Gasoline Blends. Ind. Eng. Chem. Fundam. 1981, 20, 195.
(4) Habib, E. T. Effect of Catalyst, Feedstock and Operating Conditions on the Composition and Octane Number of FCC Gasoline. Presentation before the ACS Symposium Division of Petroleum Chemistry, Miami, FL, Sep 10-15, 1989. (5) Cotterman, R. L.; Plunkee, K. W. Effects of Gasoline Composition on Octane Number. Presentation before The ACS Symposium Division of Petroleum Chemistry, Miami, FL, Sep 1015, 1989. (6) Van Leeuwen, J. A.; Jonker, R. J.; Gill, R. Octane number prediction based on gas chromatographic analysis with nonlinear regression techniques. Neth. Chem. Intell. Lab. Syst. 1994, 25, 325. (7) Twu, C. H.; Coon, J. E. Predict octane numbers using a generalized interaction method. Hydrocarbon Process. Int. Ed. 1996, 71, 51. (8) Twu, C. H.; Coon, J. E. Estimate octane numbers using an enhanced method. Hydrocarbon Process. Int. Ed. 1997, 76, 65. (9) Sasano, Y. Measuring research octane number of gasoline by gas chromatograph. Japan Kokai Tokyo Koho 1997, JP 09138613. (10) Ragone, G.; Zambrano, J.; Lugo, H. J. Correlation between RON and catalytic cracking naphtha composition. Rev. Te´ c. Ing. Univ. Zulia 1994, 17, 75. (11) American Petroleum Institute. Knocking Characteristics of Pure Hydrocarbons; API Research Project 45: American Society for Testing Materials, 1958. (12) Makridakis, S.; Weelwright S. Forecasting: Methods and Applications; John Wiley & Sons: New York, 1983. (13) Wittink, D. The Application of Regression Analysis; Allyn and Bacon Inc.: New York, 1988. (14) Marcilly, C.; Bourgogne, M. FCC Gasoline: What Is Behind Octane? Presentation before the ACS Symposium Division of Petroleum Chemistry, Miami, FL, Sep 10-15, 1989. (15) Brevoord, E.; Yung, K. Y.; Pouwels, A. C. Octane Enhancement Performance Review. Akzo Catal. Symp. Fluid Catal. Crack. 1991. (16) NPRA (National Petroleum Refiners Association). Q&A Sessions; NPRA: 1986; pp 40-44.
Received for review April 29, 1998 Revised manuscript received November 18, 1998 Accepted November 30, 1998 IE980273R
