Production of premium grade petroleum coke

Graphite having a coefficient of thermal expansion of less than 5.times.10.sup.-7 cm/cm/.degree.C. over the range of 0.degree.-50.degree. C. is produced from premium petroleum cokes. The cokes are produced from feedstocks selected and blended on the basis of high resolution nuclear magnetic resonance spectroscopy of the hydrogen atoms in the raw material and multiple linear regression analysis of the various NMR bands as applied to a statistically significant number of feedstocks known to produce premium needle cokes together with a variable relating to thermal reactivity used to derive a predictive equation for the coefficient of thermal expansion.

FIELD OF THE INVENTION 
This invention relates to the production of what is known as a premium coke 
suitable for the production of graphite having a low coefficient of 
thermal expansion (CTE). 
For many years, the bulk of the synthetic graphite produced worldwide has 
used calcined petroleum coke as the principal raw material, and a 
principal use of graphite has been in electrodes for the arc furnace 
melting of steel. In the U.S. during 1970, approximately 20 million tons 
of steel, representing 15% of the total, was produced in electric arc 
furnaces. This increased to 31 million tons in 1980, 20% of the total 
steel produced that year, and it is projected that by 1985 over 30% of the 
total steel production will be in electric arc furnaces. 
This increase in usage of the electric arc furnace has strained the 
capacity of the electrode industry and the supplies of high quality 
petroleum coke. 
The petroleum coke used as raw material for large graphite electrodes is 
premium needle coke, having an acicular crystalline structure and a 
graphite CTE characteristic of less than 5.times.10.sup.-7 
cm/cm/.degree.C. over the range of 0.degree. to 50.degree. C. as 
determined in a standardized test method. It is produced by delayed coking 
of selected petroleum residues, such as catalytic slurry oils, thermal 
tars including residual tars from cracking to produce ethylene and similar 
aromatic materials. The raw coke is calcined at about 1000.degree. to 
1500.degree. C. in a rotary kiln. After calcining, the coke is screened; 
and selected size fractions are combined, wet with a binder, generally 
coal tar pitch, shaped into electrodes, and baked and graphitized. 
Due to the price increases of the past few years, it has become imperative 
that the production of needle coke be put on the most economical base 
possible, which includes the selection of the most advantageous raw 
materials and their blending or pre-coker treatment in order to maximize 
the yield of high quality needle coke at the lowest possible price. 
DESCRIPTION OF THE PRIOR ART 
The art of producing needle coke from petroleum based residues is broadly 
based on the disclosure of U.S. Pat. No. 2,775,549, Shea, Dec. 25, 1956. 
The selection of raw materials by aromaticity is disclosed in U.S. Pat. 
No. 3,896,023, Ozaki et al. and U.S. Pat. No. 4,043,898, Kegler. Brown and 
Ladner in Fuel, Vol. XXXIX, January 1960, p. 87-96, published a study of 
the hydrogen distribution in coal-like materials by high resolution NMR 
spectroscopy. Seshadri, Albaugh and Bacha in Preprints, Div. Petroleum 
Chem., ACS, Vol. 26, No. 2, March 1981, pp. 526-37, published a study of 
the compositional differences between decant oil and pyrolysis tar as 
related to coking characteristics. 
SUMMARY OF THE INVENTION 
The CTE characteristics of delayed petroleum cokes produced from catalytic 
slurry oil feedstocks or a blend of selected aromatic petroleum fractions 
of the type described herein, are predicted from high resolution NMR 
spectroscopy analysis of the feedstock and CTE's of laboratory cokes, 
using multiple linear regression analysis. The CTE's of cokes made with 
other feedstocks may also be predicted by the inclusion of data for other 
parameters. 
Thermal tar, a residue obtained in the thermal cracking of distillate 
fractions in the petroleum refinery, such as virgin or cracked gas oils, 
has been the preferred feedstock for the production of premium coke. 
Increased demand and changes in refinery practice have made it necessary 
to develop other feedstocks for this purpose. Decanted slurry oils from 
the catalytic cracking of gas oils and ethylene pyrolysis tars are now 
used extensively. Unfortunately, a knowledge of the source and processing 
variables is not always adequate to qualify a feedstock for production of 
premium grade coke. It is normal practice to evaluate a feedstock in the 
laboratory by coking in a bench scale or pilot scale coker, followed by 
calcination of the coke, fabrication of small extruded rods from a mixture 
of the coke with coal tar pitch binder and a puffing inhibitor (optional), 
baking and graphitization of the rods, and finally measurement of the 
axial CTE of the graphite rods. This procedure requires a minimum elapsed 
time of one week and is necessarily quite expensive. It would be highly 
desirable from both a time and cost standpoint, to develop a procedure 
that would predict the CTE of a delayed coke from an easily measured 
feedstock property. Many attempts have been made to predict coke CTE from 
feedstock properties. Keglar, supra, teaches that an aromaticity 
characterization index, known as the Bureau of Mines Characterization 
Index (BMCI), has been found to reliably predict product (coke) quality. 
BMCI is calculated from the average volumetric boiling point of the 
feedstock and its specific gravity (or API gravity). While API gravity is 
an easily measured property, the volumetric average boiling point requires 
that a distillation test be conducted. Such distillation tests require 
several hours to conduct, including preparation and cleaning of the 
distillation equipment, and reproducible results are difficult to obtain 
by any but the most experienced operators. Furthermore, correlation of CTE 
with BMCI does not appear to be as good as with the method of the present 
invention. 
Prior to and in conjunction with the experiments which led to the present 
invention, attempts were made to correlate coke CTE with the structural 
parameters of feedstocks as developed by Brown and Ladner, supra. The 
Substitution Index, defined as the degree of substitution of the aromatic 
systems, i.e., the fraction of the aromatic edge atoms occupied by 
substitutes, was found to correlate well with coke CTE over a wide range 
of CTE values (CTE=0.0 to 20.times.10.sup.-7 /.degree.C.), but was not 
sufficiently useful over the narrow range of CTE values represented by 
premium grade cokes (CTE=0.0 to 6.0.times.10.sup.-7 /.degree.C.). 
Calculation of the Substitution Index requires nuclear magnetic resonance 
(NMR) proton analysis and elemental analysis of carbon and hydrogen. NMR 
proton analysis is very rapid, requiring 5 to 10 minutes, while C and H 
analyses (combustion train) require several hours. 
According to the present invention, the CTE of delayed petroleum cokes 
produced from feedstocks known as catalytic slurry oils (S.O.) or ethylene 
tars (E.T.) can be predicted with a high degree of confidence from high 
resolution NMR proton analysis of the feedstock. The equations enabling 
the prediction of coke CTE are generated from NMR analyses of feedstock 
samples and the CTE values of laboratory cokes by the statistical 
technique of multiple linear regression analysis. Expansion of the method 
of this invention to feedstocks other than catalytic slurry oils requires 
the determination of additional feedstock properties. Success has been 
obtained with samples of ethylene tar, using rate of quinoline insoluble 
matter formation in addition to NMR analyses, in multiple linear 
regression analysis.

DETAILED DESCRIPTION OF THE INVENTION 
Definition of Variables. The dependent variable used in the regression 
analysis technique of the invention is defined as the coefficient of 
thermal expansion (CTE), over the range of 0.degree. to 50.degree. C., of 
graphite rods fabricated from laboratory coke, using 2 pph iron oxide as a 
puffing inhibitor. A CTE value of 3.4 is understood to mean thermal 
expansion of 3.4.times.10.sup.-7 per degree C. in the extrusion direction. 
The independent variables are several analyses, properties, and calculated 
structural parameters of the feedstocks from which the laboratory cokes 
were made. The percentages of total hydrogen in five proton NMR bands were 
initially treated as independent variables. AR1 denotes aromatic hydrogen 
atoms of the polycyclic type, primarily "bay protons". AR2 denotes 
aromatic hydrogens of the benzenoid type. AL1, AL2, AL3 denote aliphatic 
hydrogens of the benzylic, methylene, and methyl types, respectively, or 
.alpha.H, .beta.H, and .gamma.H in the conventional NMR terminology. FA is 
the Aromaticity, and SIGMA is the Substitution Index, structural 
parameters calculated from NMR and carbon/hydrogen analyses by methods 
described by Brown and Ladner. SUS is viscosity in Saybolt Universal 
Seconds at 99.degree. C. (210.degree. F.). QI2 is the rate of formation of 
quinoline insoluble material (QI), expressed as percent of QI in the 
feedstock after heat treating at 450.degree. C. for 2 hours. 
NMR analyses of the feedstocks were made using a JEOL-C60H high resolution 
NMR spectrometer. Carbon and hydrogen were analyzed by combustion of 
feedstock samples in an oxygen atmosphere. Coking was conducted batchwise 
in steel pots at atmospheric pressure under carefully controlled 
conditions. Preparation of CTE rods was by standard methods. Measurement 
of CTE was conducted over the 0.degree. to 50.degree. C. range. 
Data for 17 slurry oil feedstocks and 2 ethylene tars are presented in 
Table I. CTE, NMR analyses (AR1, AR2, AL1, AL2, and AL3), SUS, and NMRCTE 
(to be defined below) are tabulated for all 19 feedstocks, while C/H 
(atomic carbon/hydrogen ratio) and the calculated structural parameters FA 
and SIGMA were determined only for Case Nos. 1-9. QI2 values were 
determined only for Case Nos. 3, 5, 6, 9, 13, 14, 18, and 19. Three data 
bases were used in the regression analysis as described at the bottom of 
Table I. 
Table II presents the simple descriptive statistics and the bivariate 
correlation matrix for CTE and the 5 NMR variables from Data Base I (17 
catalytic slurry oils). In general, bivariate correlations among the five 
NMR variables are quite good, but no significant bivariate correlation 
exist between CTE and any of the NMR variables. However, highly 
significant correlations were obtained by the technique of multiple linear 
regression analysis, as illustrated in Table III. Correlation was poor 
when the E.T. samples were included but excellent when they were removed 
from the data base. Matrix difficulties precluded the calculation of a 
meaningful equation with all five NMR bands as independent variables. When 
any four of the five were used, five highly significant equations 
(Equation Nos. 1 to 5) were generated. The Coefficient of Correlation, R, 
was 0.9060 for all five equations, the statistical Significance Level was 
99.98%, and the Standard Error of Estimate (of computed CTE using the 
regression equation) was 0.3262. The numbers in parentheses under each 
regression equation are the significance levels, in percent, of the 
intercept and each coefficient in that equation. It will be observed that 
in each equation in which both AR2 and AL3 appear, they dominate the 
equation. 
Equation No. 6 represents the best of the ten possible combinations of 
three NMR bands, and Equation No. 7 the best of ten possible combinations 
of two NMR bands. Equation No. 6 was used to calculate the new variable, 
NMRCTE, which is listed for each feedstock in the last column of Table I. 
NMRCTE is the only independent variable appearing in Equation No. 8. The 
Standard Error of Estimate is less than that listed in Equation No. 6 as a 
consequence of combining three variables into one, thus increasing the 
number of degrees of freedom available to the error sum of squares. 
The structural parameters aromaticity (FA) and the Substitution Index 
(SIGMA) of Brown and Ladner have been proposed as useful in evaluation of 
coking feedstocks. Carbon and hydrogen analyses, in addition to NMR 
analyses, were required to calculate these parameters for Case Nos. 1 to 9 
(Data Base II). The superiority of NMRCTE over both FA and SIGMA for 
evaluation purposes is clearly illustrated in Table IV (Equation Nos. 9 to 
12). 
Referring again to Table I (and to FIG. 1), Case Nos. 18 and 19 are 
ethylene tars from two refineries. It will be noted that while one of the 
tars may be considered a premium feedstock (Case No. 19, CTE=3.7), and the 
other is marginal (Case No. 18, CTE=6.0), the computed CTE's using NMR 
analyses alone from a slurry oil data base are significantly lower than 
the observed CTE's of the laboratory cokes (NMRCTE=1.92 and 0.75, 
respectively). It has been observed that the ethylene tars differ from the 
slurry oils in two important respects; (1) the tars are significantly more 
viscous than the slurry oils, and (2) the tars tend to form mesophase 
material (optically active liquid crystals) at a lower temperature, and of 
significantly smaller size and greater number, than is the case with 
slurry oils. It is further anticipated that other properties associated 
with either rheology in the coking operation or propensity for the 
formation of low-temperature, small-domain mesophase may serve as useful 
correction variables in regression equations. Examples of the latter 
category might be solubility of the tar in various solvents or blends of 
solvents such as used in deasphalting processes. 
The coefficients in Equation 12 (Table IV) are different from those in 
Equation 8 (Table III) since the NMR CTE variable generated in Table III 
from 17 slurry oils was used in Table IV with a partial data base (9 of 
the 17 slurry oils). 
A more detailed study was made of seven slurry oils, seven ethylene tars, 
and two mixtures, with data shown in Table V. In this table, data from six 
of the slurry oils and two of the ethylene tars for which the QI2 figures 
were available were carried over from Table I with the original numbers in 
parentheses. 
Five additional slurry oils were included to achieve a more representative 
data base, and a seventh slurry oil was included since it had been used in 
mixtures with an E.T. 
Table VI presents four regression equations in which coke CTE is correlated 
with NMR analysis alone and in combination with ET, SUS, and QI2. The NMR 
analyses were combined into single variables as shown in Table VII to 
enable the computer program to assign a more realistic distribution of 
degrees of freedom in the analysis of variance. Statistical significance 
levels associated with the intercept and the coefficients of each equation 
are shown in parentheses. 
In the study below, the correction factors ET, SUS, and QI2 were evaluated. 
ET was helpful, but not as good as QI2, and SUS was not helpful. 
Multiple linear regression analysis produced excellent correlation of lab 
coke CTE with two feedstock characteristics for a group of coker 
feedstocks comprising catalytic slurry oils, ethylene tars, and blends of 
the two. The feedstock characteristics used as independent variables in 
the preferred regression equation were proton NMR analysis and quinoline 
insoluble content after a two-hour heat treatment at 450.degree. C. 
FIG. 2 shows the correlation of observed coke CTE with computed coke CTE 
for the samples in Table V, using the equation generated from NMR data for 
17 slurry oils only (Equation 8 from Table III). It may be seen that 
prediction of results was excellent for slurry oils but poor for ethylene 
tars and slurry oil-ethylene tar blends. 
FIG. 3 shows the observed vs. computed coke CTE using both NMR and QI2 for 
the samples in Table V (Equation 4 of Table VI). It may be seen that the 
correlation is excellent. 
It has been demonstrated in the foregoing that QI2 is a very useful 
correction variable, when used with NMR, in a mixed data base consisting 
of SO's, ET's and blends of the two. In order to determine whether 
correction variables were required in a data base consisting of ET's only, 
a further set of regression analyses was run on a subset of Table V, viz. 
case nos. 10-16. The resulting Equations 5, 6, and 7 are shown in Table 
VIII, and the compositions of the NMR variables of Table VIII are given in 
Table IX. Equation 5 of Table VIII illustrated that NMR analyses alone 
result in a good predictive equation, as was true when the data base was 
confined to SO's. However, it was found that the use of QI2 as a 
correction variable, with NMR, resulted in significant improvement in the 
quality of the correlation, while the use of SUS was not helpful. 
The above data indicate that NMR data alone is usually sufficient to 
predict the CTE of a coked product of a single feedstock type, such as 
slurry oil or ethylene tar when analyzed by multiple linear regression 
analysis. However, NMR data alone is insufficient to predict CTE values 
accurately for data bases containing multiple feedstocks or mixtures, and 
the use of another factor is needed. Evaluation of viscosity and 
reactivity at elevated temperatures as shown by SUS and QI2 in the above 
shows that SUS viscosity is not very useful on either slurry oils or 
ethylene tars but that QI2 is highly useful as an independent variable in 
linear multiple regression analysis. Although QI2 as determined herein is 
the amount of quinoline insolubles formed in two hours at 450.degree. C., 
some other measure of thermal reactivity could also be used, including 
variations in the time and temperature of the test and the method used to 
determine reactivity. Other solvents than quinoline may be useful and 
other measurements such as viscosity increase, calorimetric, or 
thermogravimetric analyses may also be useful. 
TABLE I 
__________________________________________________________________________ 
DATA BASES FOR CORRELATION STUDY 
Coking Feedstock Calculated 
Case 
Coke 
NMR Analysis Feedstock Parameters 
No. 
CTE 
AR1 
AR2 
AL1 
AL2 
AL3 
C/H 
SUS 
QI2 
FA SIGMA 
NMRCTE 
__________________________________________________________________________ 
1 4.2 
2.0 
16.6 
17.6 
43.3 
20.5 
0.761 
53 -- 0.465 
0.396 
4.3078 
2 5.6 
2.8 
17.3 
27.1 
37.4 
15.4 
0.836 
87 -- 0.522 
0.445 
5.6252 
3 4.6 
2.8 
21.5 
26.2 
33.1 
16.4 
0.869 
59 5.7 
0.564 
0.394 
4.2366 
4 4.9 
3.5 
20.2 
22.7 
37.9 
15.7 
0.827 
60 -- 0.539 
0.371 
4.8381 
5 3.8 
2.7 
23.3 
25.7 
32.8 
15.5 
0.866 
61 9.7 
0.539 
0.372 
4.0054 
6 3.4 
2.4 
23.1 
25.5 
32.2 
16.8 
0.870 
65 5.8 
0.572 
0.379 
3.6668 
7 4.3 
4.7 
27.1 
32.2 
24.8 
11.2 
0.977 
47 -- 0.651 
0.361 
4.3870 
8 4.7 
4.9 
30.5 
38.1 
18.4 
8.1 
1.022 
130 
-- 0.684 
0.366 
4.3777 
9 3.1 
6.5 
33.8 
30.1 
19.9 
9.7 
1.022 
47 2.4 
0.708 
0.293 
3.2002 
10 5.0 
3.4 
19.0 
31.6 
31.6 
14.4 
-- 52 -- -- -- 5.5106 
11 5.0 
3.0 
21.8 
31.2 
29.0 
15.0 
-- 59 -- -- -- 4.5681 
12 5.0 
4.9 
26.0 
29.8 
28.3 
11.0 
-- 50 -- -- -- 4.7511 
13 5.3 
1.9 
16.2 
18.6 
44.8 
18.5 
-- 72 10.0 
-- -- 4.9656 
14 4.8 
0.9 
12.4 
15.6 
48.0 
23.1 
-- 39 4.0 
-- -- 4.5886 
15 4.3 
3.0 
25.8 
29.0 
31.0 
11.2 
-- 57 -- -- -- 4.5791 
16 4.3 
2.6 
17.7 
23.1 
38.3 
18.3 
-- 47 -- -- -- 4.6882 
17 3.9 
7.3 
31.4 
32.6 
19.0 
9.7 
-- 50 -- -- -- 3.9044 
18 6.0 
1.5 
40.7 
35.4 
16.2 
6.2 
-- 377 
76.0 
-- -- 1.9181 
19 3.7 
1.3 
50.5 
41.0 
6.1 
1.1 
-- 238 
66.5 
-- -- 0.7461 
__________________________________________________________________________ 
Data Base I -- 17 Catalytic Slurry Oils (Case Nos. 1-17) From 8 
Refineries. 
Data Base II -- 9 Catalytic Slurry Oils (Case Nos. 1-9) From 4 Refineries 
For Which Both NMR And C/H Data Were Available For Calculation Of FA And 
SIGMA. 
TABLE II 
__________________________________________________________________________ 
BIVARIATE CORRELATION MATRIX FROM DATA BASE I 
6 VARIABLES ARE IN CORRELATION MATRIX. 
17 IS NUMBER OF OBSERVATIONS. 
Standard 
Std. Error 
Coeff. Of 
CORRELATION MATRIX 
Variable 
Mean Variance 
Deviation 
Of Mean 
Variation 
CTE AR1 AR2 AL1 AL2 AL3 
__________________________________________________________________________ 
CTE 4.4824 
0.44529 
0.66730 
0.16184 
14.89% 
1.0000 
-0.3618 
-0.5496 
-0.1073 
0.4089 
0.2225 
AR1 3.4882 
2.7824 
1.6680 
0.40456 
47.82% 
-0.3618 
1.0000 
0.8913 
0.7326 
-0.8838 
-0.8661 
AR2 22.571 
35.108 
5.9252 
1.4371 
26.25% 
-0.5496 
0.8913 
1.0000 
0.7883 
-0.9499 
-0.9184 
AL1 26.865 
35.545 
5.9620 
1.4460 
22.19% 
-0.1073 
0.7326 
0.7883 
1.0000 
-0.9247 
-0.9096 
AL2 32.341 
76.698 
8.7577 
2.1241 
27.08 
0.4089 
-0.8838 
-0.9499 
-0.9247 
1.0000 
0.9340 
AL3 14.735 
17.059 
4.1302 
1.0017 
28.03% 
0.2225 
-0.8661 
-0.9184 
-0.9096 
0.9340 
1.0000 
__________________________________________________________________________ 
TABLE III 
__________________________________________________________________________ 
MULTIPLE LINEAR REGRESSION ANALYSIS, CTE OF LABORATORY COKE 
AS A FUNCTION OF NMR ANALYSES OF SEVENTEEN CATALYTIC SLURRY 
OIL FEEDSTOCKS (DATA BASE I) 
Regression Equation Coefficients Correlation Criteria 
Equation 
(Significance Level Of Coefficient, %) Coeff. Of 
Signif. 
Std. Error 
No. Intercept 
AR1 AR2 AL1 AL2 AL3 NMRCTE.sup.(1) 
Corr., R 
Level, 
Of 
__________________________________________________________________________ 
Estimate 
1 -14.0503 
+0.37212 
+0.02013 
+0.28225 
+0.28440 0.9060 
99.98 
0.3262 
(97.37) 
(99.14) 
(23.01) 
(99.96) 
( ) 
2 14.3896 
+0.08772 
-0.26427 
-0.00215 -0.28440 0.9060 
99.98 
0.3262 
(99.99) 
(54.68) 
(100.00) 
(4.83) (99.59) 
3 14.1747 
+0.08987 
-0.26212 +0.00215 
-0.28225 0.9060 
99.98 
0.3262 
(100.00) 
(55.73) 
(99.98) (4.83) 
(99.96) 
4 -12.0377 
+0.35199 +0.26212 
+0.26427 
-0.02013 0.9060 
99.98 
0.3262 
(99.73) 
(97.63) (99.98) 
(100.00) 
(23.01) 
5 23.1613 -0.35199 
-0.08987 
-0.08772 
-0.37212 0.9060 
99.98 
0.3262 
(94.04) (97.63) 
(55.73) 
(54.68) 
(99.14) 
6 14.2615 
+0.088779 
-0.263805 -0.280589 0.9059 
100.00 
0.3144 
(100.00) 
(57.63) 
(100.00) (99.99) 
7 14.3859 -0.248494 -0.291470 0.9007 
100.00 
0.3099 
(100.00) (100.00) (100.00) 
8 0.0000 +1.0000 
0.9059 
100.00 
0.2918 
(100.00) (100.00) 
__________________________________________________________________________ 
.sup.(1) NMRCTE = 14.2615 + 0.088779 AR1 - 0.263805 AR2 - 0.291470 AL3, 
from Equation No. 6 
TABLE IV 
__________________________________________________________________________ 
MULTIPLE LINEAR REGRESSION ANALYSIS, CTE OF LABORATORY COKE 
AS A FUNCTION OF STRUCTURAL AMETERS OF NINE CATALYTIC 
SLURRY OIL FEEDSTOCKS (Data Base II), AND AS A FUNCTION OF 
NMR ANALYSES ONLY 
Regression Equation Coefficients 
Correlation Criteria 
Equation 
(Significance Level Of Coefficient, %) 
Coeff. Of 
Significance 
Std. Error 
No. Intercept 
FA SIGMA 
NMRCTE 
Corr., R 
Level, % 
Of Estimate 
__________________________________________________________________________ 
9 6.2590 
-3.38113 0.3531 
64.87 0.7754 
(98.37) 
(64.87) 
10 -1.1560 14.5110 0.7470 
97.93 0.5510 
(45.01) (97.93) 
11 -5.7528 
+4.02953 
+20.5047 0.7999 
95.33 0.5372 
(76.82) 
(71.29) 
(97.37) 
12 -0.3852 +1.08854 
0.9626 
100.00 0.2247 
(53.11) (100.00) 
__________________________________________________________________________ 
TABLE V 
______________________________________ 
DATA BASE FOR CORRELATION STUDY 
Feedstock Characteristics 
Case Coke NMR Analysis 
No. CTE ET SUS QI2 AR1 AR2 AL1 AL2 AL3 
______________________________________ 
1 (3) 
4.6 0.0 59 5.7 2.8 21.5 26.2 33.1 16.4 
2 (5) 
3.8 0.0 61 9.7 3.5 21.8 25.2 34.1 15.4 
3 (6) 
3.4 0.0 65 5.8 3.0 21.6 26.2 33.0 16.2 
4 (9) 
3.1 0.0 47 2.4 6.5 33.8 30.1 19.9 9.7 
5 (13) 
5.3 0.0 72 0.0 1.9 16.2 18.6 44.8 18.5 
6 (14) 
4.8 0.0 39 4.0 0.7 11.0 16.5 49.4 22.4 
7 3.6 0.0 62 26.4 6.0 16.0 32.0 23.0 13.0 
8 3.6 0.25 86 24.4 1.8 27.7 32.6 23.8 14.1 
9 4.4 0.50 92 54.7 2.6 32.1 34.7 19.8 10.8 
10 (18) 
6.0 1.0 377 76.0 1.5 40.7 35.4 16.2 6.2 
11 (19) 
3.7 1.0 238 66.5 2.6 48.0 39.4 8.2 1.8 
12 4.3 1.0 106 65.9 0.0 38.3 37.6 17.6 6.5 
13 4.8 1.0 186 82.2 0.0 37.8 42.6 11.5 8.1 
14 5.0 1.0 124 78.6 4.1 35.1 39.2 15.8 5.8 
15 5.3 1.0 134 77.7 4.0 43.0 36.4 12.6 4.0 
16 5.8 1.0 136 72.7 4.4 48.0 34.4 11.9 1.3 
______________________________________ 
TABLE VI 
______________________________________ 
CORRELATION OF COKE CTE WITH FEEDSTOCK PROP- 
ERTIES MULTIPLE LINEAR REGRESSION ANALYSIS 
Correlation 
Criteria 
Std. 
No. Regression Equation R Error 
______________________________________ 
1 CTE = 10.4735 + NMR1 0.6587 0.6853 
(96.22%) (99.45%) 
2 CTE = 32.7549 + NMR2 + 5.1930 ET 
0.8636 0.4766 
(99.99%) (99.97%) (99.99%) 
3 CTE = -2.7247 + NMR3 + 0.7207 0.6653 
0.00442 SUS 
(81.78%) (98.31%) (95.93%) 
4 CTE = 11.5087 + NMR4 + 0.05268 QI2 
0.9038 0.4044 
(100.00%) (100.00%) (100.00%) 
______________________________________ 
TABLE VII 
______________________________________ 
COMPOSITION OF NMR VARIABLES USED IN 
REGRESSION EQUATIONS OF TABLE II 
Coefficients of Individual NMR Bands 
Variable AR2 AL1 AL2 AL3 
______________________________________ 
NMR1 +0.1400 +0.1712 +0.2713 
-0.1160 
NMR2 -0.2485 -0.4769 -0.3605 
+0.0530 
NMR3 +0.3031 +0.1034 +0.1966 
-0.2033 
NMR4 -0.0142 -0.2420 -0.0722 
+0.0553 
______________________________________ 
TABLE VIII 
______________________________________ 
CORRELATION OF COKE CTE WITH FEEDSTOCK PROP- 
ERTIES MULTIPLE LINEAR REGRESSION ANALYSIS 
Correlation 
Criteria 
Std. 
No. Regression Equation R Error 
______________________________________ 
5 CTE = 48.6381 + NMR5 0.9330 0.2933 
6 CTE = 48.5276 + NMR6 + SUS 
0.9334 0.3566 
7 CTE = -52.9251 + NMR7 + QI2 
0.9947 0.2035 
______________________________________ 
TABLE IX 
______________________________________ 
COMPOSITION OF NMR VARIABLES USED IN 
REGRESSION EQUATIONS OF TABLE II 
Coefficients of Individual NMR Bands 
Variable AR2 AL1 AL2 AL3 
______________________________________ 
NMR5 -0.2775 -0.7065 -0.5057 
+0.2910 
NMR6 -0.2816 -0.7001 -0.5014 
+0.2759 
NMR7 +0.4690 +0.3649 +0.7149 
-0.1373 
______________________________________ 
FIG. 1 illustrates the excellent correlation of observed CTE with computed 
CTE for the 17 slurry oils, and poor correlation for the 2 ethylene tars, 
when NMR analyses only are used in the regression equation. 
Variations in analytical and coking equipment and procedures may result in 
slightly different data, giving rise to slightly different regression 
equations. It is expected, however, that reproducible data will result in 
reliable regression equations when subjected to the multiple linear 
regression analysis technique described herein, even if (when) those 
equations differ somewhat from the examples cited in the claims. 
While CTE as used herein is defined as the CTE using 2 pph iron oxide as a 
puffing inhibitor, other puffing inhibitors including Cr.sub.2 O.sub.3 and 
CaF.sub.2 may be used, and in low sulfur cokes the use of a puffing 
inhibitor may be unnecessary.