muñoz et al. comparison of correlations for estimating product yields from delayed coking

Comparison of Correlations for Estimating Product Yieldsfrom Delayed CokingJ. A. D. Munoz, R. Aguilar, L. C. Castaneda, and J. Ancheyta*

Instituto Mexicano del Petroleo, Eje Central Lazaro Cardenas Norte 152, 07730 Mexico, D.F., Mexico

ABSTRACT: The objective of this paper is to compare the prediction capability of different correlations for calculating delayedcoking yields. The evaluation was developed taking operation data reported in the literature into account for delayed cokingcommercial plants. The effects of pressure, feed type, and temperature on product yields were analyzed. Correlations that includethe effect of operating conditions proved to be more accurate compared to those that consider only feed properties. From thecalculation of yields, it is possible to conclude that an increase of 1 wt % of coke yield is obtained for each 1 wt % increase of thefeed Conradson carbon residue (CCR) or for each 5 psig increase of the coke drum pressure and a reduction of 1 wt % of coke yieldis achieved for each 15 °F increase of the coke drum temperature. The correlation developed by Volk et al. resulted to be the mostaccurate correlation to predict coke yields, while the most popular correlations (Gary-Handwerk and Maples) are the worst.

1. INTRODUCTIONDelayed coking is a type of thermal cracking process used inpetroleum refineries to upgrade and convert petroleum residuum(bottoms from atmospheric and vacuum distillation of crude oil)into liquid and gas product streams, leaving behind a solidconcentrated carbon material, petroleum coke.1−3

The first commercial delayed coker began operation at theWhiting refinery of Standard Oil Co. in 1930. Foster Wheelerand Conoco Phillips are the mayor contributors with regard tothe design, engineering, and construction of delayed coker units.Kellogg has one-third of the world’s delayed coking capacity.Lummus and Flour are the other licensors of the delayed cokingprocess, having relatively lesser market shares.4

In the delayed coking process, the feedstock is introduceddirectly to the bottom of the fractionators, where it is heated,lighter fractions are removed as side streams, and the fractionatorbottoms heated in a furnace with horizontal tubes are used in theprocess to reach thermal cracking temperatures of 485−505 °C.With short residence time in the furnace tubes, coking of the feedmaterial is delayed until it reaches large coking drumsdownstream of the heater. The heated stream enters one of thepairs of coking drums, where the cracking reactions continue. Forcontinuous operation, two coke drums are used, where one is onstream and the other is being cleaned. The physical structuresand chemical properties of the produced coke determine the enduse of the material, such as the fuel and feedstock for use in thealuminum, chemical, petrochemical, or steel industries. Drumoverhead products go to the fractionator, where naphtha and gasoil fractions are recovered; these fractions are unsaturated andunstable and require further hydrogenation. Figure 1 shows theprocess flow diagram of a typical delayed coking unit.5−7

There are four process variables affecting the delayed cok-ing plant. The temperature controls the quality of the cokeproduced, with a high temperature removing more volatilematerials, and the coke yield decreases as the temperatureincreases. An increasing pressure will increase coke formationand slightly increase the gas yield. The recycle ratio is used tocontrol the end point of the coker gas oil, because it has the sameeffect as the pressure. Feedstock variables are the characterization

factor and the Conradson carbon residue (CCR), which affectthe product yield.4,8−10

The delayed coker is integrated with the rest of the refineryprocesses, and its feed originates from the crude oil suppliedto the refinery. A basic scheme of a refinery, including a delayedcoking unit, is shown in Figure 2. A refinery with a coker unitis sometimes called “zero-resid refinery”, which is one of themajor advantages of the coking process. Another advantage is theinherent flexibility that this process has for converting a varietyof feedstocks, which gives the refinery a solution to the problemof a decreasing residual fuel demand and takes advantage of theattractive economics of upgrading it to more valuable lighterproducts.Coking reactions are complex, and deriving a detailed kinetic

model is a complicated task. The main problem with modelinga delayed coker is the ability to adequately characterize the large,multifunctional molecules involved. Xiao et al.11 developed acracking product distribution model. It is assumed that all of thereactions are first-order reactions, the cracked products do nottake part in secondary reactions, there is no consecutive processin the condensation reaction, and the condensation product istoluene-insoluble. A 12 lumped reaction model for productdistribution in thermal conversion of heavy stock was developedby Zhou et al.12 They developed a predictive kinetic model fordelayed coking, investigating group composition, includingresidua. It was concluded that a six-component approach isreasonable to be used as a lumped species for residual stock.Another reaction scheme with 11 lumps was suggested for adelayed coking unit. Results revealed that all kinetic parameterswere invariant with respect to the charge feedstock compositions.Bozzano and Dente13 deal with the extension of a mechanisticapproach to liquid-phase pyrolysis of hydrocarbon mixtures todelayed coking modeling and with the peculiar aspects of thisprocess. Initially, a kinetic scheme of about 1600 equivalent

Received: August 14, 2013Revised: September 26, 2013Published: September 27, 2013

Article

pubs.acs.org/EF

© 2013 American Chemical Society 7179 dx.doi.org/10.1021/ef4014423 | Energy Fuels 2013, 27, 7179−7190

pubs.acs.org/EF

reactions involving 450 equivalent components was prepared.In a second time, the reactions have been reduced to about 700.Using the structure-oriented lumping (SOL) concept, Tianet al.14,15 described the reaction behaviors of the delayed cokingprocess. They proposed 92 types of single-core seed moleculesand 46 types of multicore seed molecules to characterize theresidue. A total of 7004 types of molecular lumps were generatedto characterize the molecular composition of residues. Theseexamples show the complexity of the task, as wasmentioned earlier.

Therefore, empirical modeling techniques appear to be the bestapproach to calculate product yields and are preferable in refiningpractice.Companies and consultants of petroleum industries have

defined different correlations for determining delayed cokeryields; however, these correlations have shortly been used to takeinto account the yields and product properties that are useful inpreliminary studies for making a decision when a delayed coker isdesired to be incorporated in an existing or new refining scheme.

Figure 1. Simplified process scheme of delayed coking.25

Figure 2. Delayed coking in a scheme of petroleum refining.

Energy & Fuels Article

dx.doi.org/10.1021/ef4014423 | Energy Fuels 2013, 27, 7179−71907180

http://pubs.acs.org/action/showImage?doi=10.1021/ef4014423&iName=master.img-000.jpg&w=450&h=340

http://pubs.acs.org/action/showImage?doi=10.1021/ef4014423&iName=master.img-001.png&w=373&h=186

One method of modeling a delayed coker is an empiricalapproach, which is based on the fact that the coke yield correlatedvery well with the CCR of the feed,16 as seen in Table 1, which

shows typical yields of the delayed coker for different carbonresidue contents in the feedstock.17

The yields of the other products are found to correlate betterwith the coke yield than with the CCR. The feed AmericanPetroleum Institute (API) gravity is only used for the massbalance, because it has been found that CCR is a better predictorthan feed API gravity.The objective of this paper is to compare the prediction

capability of the different correlations reported in the literatureusing data of coking of different vacuum residua.

2. CORRELATIONS2.1. Gary and Hankwert.18 In the book by Gary and Hankwert,18 a

series of correlations are given to obtain the yields of coke, gas (C4−),gasoline (C5−400 °F), and gas oil (400−925 °F) in weight percent andgasoline and gas oil in volume percent. They also reported a typical splitof naphtha and gas oil, including the API gravity. The yield data used todevelop the correlations came from commercial and pilot plants, with acoke drum pressure of 35−45 psig. The feed was a straight-run residualof less than 18° API. The correlations are

= +gas (wt %) 7.8 0.144(CCR, wt %) (1)

= +naphtha (wt %) 11.29 0.343(CCR, wt %) (2)

=coke (wt %) 1.6(CCR, wt %) (3)

= − − −gas oil (wt %) 100 gas naphtha coke (4)

The weight and volume percents are based on the net fresh feed to thecoking unit. To transform naphtha and gas oil yields from weight tovolumetric basis, the following equations are used:

= +naphtha (vol %) 186.5/(131.5 API) (naphtha, wt %)(5)

= +gas oil (vol %) 155.5/(131.5 API) (gas oil, wt %) (6)

where API is the gravity of the feed.To split the coker naphtha into light and heavy, the authors proposed

= °light naphtha 35.1 vol %, 65 API (7)

= °heavy naphtha 64.9 vol %, 50 API (8)

Similarly, to split the coker gas oil, they proposed

= °light gas oil (LCGO) 67.3 vol %, 30 API (9)

= °heavy gas oil (HCGO) 32.7 vol %, 13 API (10)

Gary and Handwerk’s correlations do not include terms to account forthe operating conditions, and the only independent variable is the CCRof the feedstock. The application of this method, in general, leads to veryunpractical and inaccurate results.

2.2. Maples.16 This approach also uses the residual carbon contentof the feed as a single independent variable. Correlations were obtainedfrom an extensive database collected in delayed coking plants at typicaloperating conditions for a wide range of feeds (Figure 3). Feed properties

range between 1.4 and 21.5° API gravity and CCR content between 2.84and 25.5 wt %. The correlations are

= +gas yield (wt %) 0.2745(CCR, wt %) 4.1264 (11)

= − +naphtha yield (wt %) 0.0082(CCR, wt %) 17.025 (12)

= − +gas oil yield (wt %) 1.9418(CCR, wt %) 79.225 (13)

= −coke yield (wt %) 1.6755(CCR, wt %) 0.3765 (14)

The correlations for sulfur distribution and API gravity of the products arebelow.

Sulfur distribution:

=naphtha (wt %) 0.2002(sulfur in feed, wt %) (15)

Table 1. Typical Coke Yields from Delayed Coking17

carbon residue (wt %) API gravity (deg) coke yield (wt %)

1 NRa 05 26 8.510 16 1815 10 27.520 6 35.525 3.5 42

aNR = not reported.

Figure 3. Maples yields of the delayed coking plant.




=gas oil (wt %) 0.74889(sulfur in feed, wt %) (16)

=coke (wt %) 1.395(sulfur in feed, wt %) (17)

API gravity:

= +naphtha 0.3404(API gravity in feed) 53.60 (18)

= +gas oil 0.9131(API gravity in feed) 10.356 (19)

2.3. Castiglioni.19 Castiglioni19 proposed a graphical method todetermine delayed coker yields as a function of two feed properties(API gravity and CCR) and three operation variables (combined feedrate, drum pressure, and drum temperature). The products of the delayedcoker are dry gas, gasoline, gas oil, and coke. The dry gas is divided into thelighter fraction and propane, and the gasoline is divided into butanes andthe C5−400 °F fraction.The method comprises three stages. In the first stage, a reference

pressure (0 psig) and the actual drum pressure are considered to estimatethe coke yield using the feed CCR and the operation temperature. In thesecond stage, a series of correction factors are determined as a function ofcalculated combined feed rate (CFR). In the third stage, a second series ofcorrection factors are obtained as a function of the operationCFR. Finally,gasoline and coke factor corrections are obtained as a function of the yieldof gasoline and coke, respectively.The following correlations based on these graphical methods19 were

recently reported.20

Coke yield at reference and actual pressures

=−

yA a

a( )

coke1

2 (20)

where

= +A b bCCR1 2 (21)

=a f T( )i (22)

=b f P( )i (23)

An equivalent CFR

= − + −B BCFR 0.2621 1.2806 0.00722 (24)

where

=By

y

at operating pressure

at reference pressureicoke

coke (25)

Yields of gas and gasoline and the total product yield are calculated by

= + +y c y c y ci 1 coke2

2 coke 3 (26)

Factor to adjust the product yields of the delayer coker in the secondstep

= + +F d d d1 CFR CFRi 12

2 3 (27)

Another factor employed in the last step for updating gasoline and cokeyields

= + =F e y e i2 ( gasoline, coke)i i coke 2 (28)

where

=e f (CFR)i (29)

The final product yields, Ytotal and YG calculated in step 2 and Ycoke andYGNE calculated in step 3 can be applied to obtain the yield of gas oil bythe difference between the total yield and the dry gas, gasoline, and cokeyields.20

= − + +Y Y Y Y Y( )VGO total G coke GNE (30)

Castiglioni’s charts do not allow for prediction at pressures above 30 psigor feedstocks with CCR higher than 25%; therefore, for such condi-tions, extrapolation is necessary, which is an important limitation of thisapproach.

2.4. Smith et al.21 The basis of the Smith et al. correlation21 comesfrom Gary and Handwerk.18 They developed equations based on thefeed CCR to estimate the yields of coke, gas, gas oil, and naphtha. Theeffect of pressure (P) was considered in the correlations as seen asfollows:

= + + −Pgas (wt %) 7.4 0.1CCR 0.8(( 15)/20) (31)

= + + −Pnaphtha (wt %) 10.29 0.2CCR 2.5(( 15)/20) (32)

= + −Pcoke (wt %) 1.5CCR 3(( 15)/20) (33)

= − − −gas oil (wt %) 100 gas naphtha coke (34)

2.5. Volk et al.22 Volk et al.22 proposed a set of linear correlations topredict the product yields as function of themicrocarbon residue (MCR,in wt %), temperature (T, in °F), pressure (P, in psia), and liquid spacevelocity (LSV, in min−1). The range of operating conditions usedto develop the correlations is 900−950 °F, 6−40 psig, andMCR from 16to 29 wt %. The correlations are

= − + −

+ +

T Pliquid (wt %) 1.1139MCR 0.0419 0.2897

1103.08LSV 41.59 (35)

= − +

− +

T Pcoke (wt %) 0.9407MCR 0.0609 0.1529

319.759LSV 65.075 (36)

= + +

− −

T Pgas (wt %) 0.1729MCR 0.0191 0.13646

786.319LSV 6.762 (37)

= − + +

− +

T Pnaphtha (wt %) 0.3086MCR 0.0137 0.1571

819.63LSV 16.461 (38)

= − − −

+ +

T Pdiesel (wt %) 0.3339MCR 0.02635 0.0392

70.957LSV 50.452 (39)

= − + −

+ −

T Pgas oil (wt %) 0.4714MCR 0.0546 0.4076

1851.76LSV 25.315 (40)

The authors stated that the correlations could not be used to predictyields from industrial cokers, because of the lower liquid yields obtainedin the microreactor, as compared to those observed in refineries, whichbecomes worse at the lowest feed rate. Also, the correlations include theeffects of LSV, which has a different meaning than that for commercialunits. For these reasons, the following correction was proposed to deriveproduct yields:

* =coke (wt %) 0.91coke (41)

* =gas (wt %) 0.82gas (42)

* = − * + *liquid (wt %) 100 (coke gas ) (43)

* = *gasoline (wt %) 0.75gasoline(liquid /liquid) (44)

* = *diesel (wt %) 0.90diesel(liquid /liquid) (45)

* = * − * + *gas oil (wt %) liquid (gasoline diesel ) (46)

2.6. Ren et al.23 This approach suggests that the product yieldsof delayed coking are dependent upon some feedstock propertiesand operating conditions using a quadratic polynomial with sevenparameters

ρ ρ= + + + + + + + +

+ + + + + +

Y a b c dC eC fT gT hT iT

jP kP lRe mRe nR oR

2R R

2F F

2CT CT

2

CT CT2 2

WO WO2 (47)

where Y is the product yield, ρ is the feedstock density (g/cm3),CR is thefeedstock carbon residue (wt %), TF is the outlet temperature of theheating furnace (°C), TCT is the temperature at the top of the coking



tower (°C), PCT is the pressure at the top of the coking tower (MPa), Reis the recycle ratio, RWO is the water injection/oil weight ratio, and a, b, c,d, e, f, g, h, i, j, k, l,m, n, and o are themodel coefficients (Table 2) for eachproduct. The authors reported good accuracy with average absoluteerror from 1 to 2.3 wt %.

3. RESULTS AND DISCUSSIONThe correlation proposed by Ren et al.23 was excluded from theanalysis because it exhibited quite high deviations to predict theproduct yields with different vacuum residua. For the othercorrelations, the predicted values were compared to real infor-mation recovered from commercial cokers. The comparisonswere performed to examine the effects of the feed properties(CCR content), pressure, and temperature.

The properties of the crude oil and vacuum residue, operat-ing conditions, and yields of delayed coking units used for thecorrelations are shown in Table 3. To evaluate the effect of the feedproperties on product yields, two data sets at different operatingconditions were used: (1) columns A−C at 30 psig and 900 °F and(2) columns D−I at 15 psig and 925 °F. The effect of the pressurewas examined with values of columns J−K, and the effect of thetemperature was examined with values of columns L−M.

3.1. Effect of the CCR Content. In a conventional refineryscheme, the feed to delayed coker is typically vacuum residue,whose properties are a function of the type of crude fed torefinery. The main variable that affects the product yields of thedelayed coker is the CCR content of the feedstock, which is whyall correlations include it.

Table 2. Coefficients of the Ren et al. Correlation23

coefficient gas naphtha diesel gas oil coke

a −1604.84 8204.07 296.17 −17013.22 10217.82b 2975.15 −1684.88 4195.53 −4142.02 −1343.78c −1535.6 902.68 −2156.8 2071.01 718.71d −2.0776 0.6467 1.6932 −9.376 9.1137e 0.0745 −0.0333 −0.0531 0.2823 −0.2704f −2.0453 −23.9931 −13.3962 75.9003 −36.4657g 0.0022 0.0242 0.0134 −0.0763 0.0366h 3.2603 −7.0614 4.9307 1.6519 −2.7814i −0.00395 0.00849 −0.00581 −0.00216 0.00342j −113.08 143.73 −125.82 −9.68 104.85k 323.99 −430.81 392.27 −5.02 −280.43l −0.904 8.7034 −10.4991 −6.8568 9.5564m 3.1095 −0.6101 14.2094 −9.3768 −7.332n −0.3115 −0.9956 0.4796 1.4769 −0.6494o 0.0087 0.0826 −0.0387 −0.1182 0.0656

Table 3. Properties of Crude Oil and Vacuum Residues, Operating Conditions and Yields of Delayed Coking Commercial Plantsa

effect of the feedstock propertieseffect of thepressure

effect of thetemperature

effect A B C D E F G H I J K L M

Crude Oil PropertiesCCR (wt %) 10.48 6.8 4.38 11.83 11.24 11.00 10.82 8.73 7.50 5.4 5.4 8.24 8.24API gravity (deg) 21.56 27.4 32.1 21.24 22.36 22.81 23.15 26.99 29.50 31.6 31.6 27.00 27.00characterization factor (K) 11.53 11.88 11.75 11.70 11.75 11.77 11.79 11.84 11.90 NRb NR NR NR

Vacuum Residue PropertiesCCR (wt %) 31.00 29.00 22.44 31.00 30.04 29.05 29.00 26.13 22.44 15.60 15.60 19.00 19.00API gravity (deg) 2.3 3.7 7.9 0.1 0.713 0.778 1.4 2.27 5.79 10.3 10.3 5.7 5.7sulfur (wt %) 6.08 5.3 2.34 5.15 5.01 4.97 4.85 4.61 4.50 2.62 2.62 2.16 2.016viscosity at 210 °C (cSt) 205965 27968 4081 3744000 137000 12100 85.640 36525 12117viscosity at 275 °C (cSt) 46000 5340 4490 2605 1753 935 120 120 703 703viscosity at 305 °C (cSt) 2227 189 309 309

Operating Conditionspressure (psig) 30 30 30 15 15 15 15 15 15 15 40 30 30temperature (°F) 900 900 900 925 925 925 925 925 925 930 930 900 930feed rate (min−1) 0.01 0.01 0.01 0.008 0.008 0.008 0.008 0.008 0.008 0.0073 0.0073 0.014 0.014recycle ratio 1.10 1.10 1.10 1.05 1.05 1.05 1.05 1.05 1.05 1.10 1.10 1.10 1.10

Yieldsgas (wt %) 9.05 9.00 8.59 9.2 9.02 8.99 9 8.96 8.6 8.20 12.77 5.00 6.50naphtha (wt %) 13.50 12.50 12.30 9.21 9.04 8.90 8.81 8.90 9.06 17.20 21.20 14.00 15.00gas oil (wt %) 39.80 35.51 30.00 39.80 36.57 35.50 35.51 32.00 30.00 56.83 43.33 59.00 58.50coke (wt %) 37.65 42.99 49.11 41.79 45.37 46.61 46.68 50.14 52.34 17.77 22.70 22.00 20.00aData A−C, Cold Lake, Arabian Heavy, and Alaska North Slope crude oil processing in refineries in the U.S.A., respectively; data D−I, Maya−Isthmus crude oils with different mix processing in refineries in Mexico; data K−M, data taken from ref 21; data J and K, paraffinic crude oil; and dataL and M, naphthenic crude oil. bNR = no reported.



3.1.1. Data Set 1. Figure 4 illustrates a parity plot of the resultsobtained with the correlations for the data set 1 at constantconditions with a temperature of 900 °F and a pressure of 30 psig.The predictability of correlations can be divided in three groups:(1) Gary−Handwerk18 and Maples16 correlations showing lessaccurate predictions with a global average absolute error higherthan 33%, which is partially due to the use of these correlationsfor predicting coking yields for feeds with properties out of therange of applications and the dependency of yields only uponfeed CCR, (2) Smith et al.21 and Castiglioni19 correlations witha global average absolute error of 28−29%, which include theeffects of the pressure and temperature, respectively, and (3)Volk et al.22 correlation providing an average relative absoluteerror of 5.89%, with the important error reduction in this casebeing because of the incorporation of the liquid space velocity asa variable and further adjustment of yields with the relationshipderived from industrial data of commercial plants. In general, allcorrelations tend to overestimate the yields of gas, naphtha, andcoke and consequently underestimate the yield of gas oil, mainlybecause the latter is obtained as the difference (balance) of thetotal yield and the remaining fraction yields.Surprisingly, the most used and well-known correlations

(Gary−Handwerk, Maples, and Smith et al.) presented higherrors, 20−40% for gas, 24−70% for naphtha, 27−57% for gas oil,and 20−36% for coke yields. It should be remembered that Garyand Handwerk and Maples approaches only use the feed CCRcontent, while Smith et al. incorporates the effect of the pressure,which slightly improves its accuracy.On the other hand, Volk et al. and Castiglioni correlations,

in addition to the feed CCR content and the effect of thepressure, include the effects of the temperature and liquid spacevelocity. These two additions improve the accuracy of these twoapproaches to a great extent. Previous reports have indicated thatCastiglioni correlations predict reasonably well the yields of thecoking process.24 Volk et al. correlation showed the lowest errors,

0.6−8.9% for gas, 1.61−21.79% for naphtha, 3.88−5.5% for gasoil, and 0.22−6.68% for coke yields.For feedstock properties and evaluated operating condi-

tions, the correlation of Gary−Handwerk is outside the pressurerange up to 5 psig (35 psig) and the Castiglioni correlation is60 °F lower in temperature (840 °F); however, average globalerrors derived from these correlations are greater than 28%,4 times larger than the error obtained by Volk et al. (6%).In the commercial delayed coker, an increase in the coke yield

from 30 to 39.8 wt % was observed when the CCR increasedfrom 22.44 to 31 wt %. For this range, a ratio of 1 wt % increasein the coke yield is observed for each 1 wt % of CCR increase inthe feed.In summary, the correlation with the best performance

for calculating yields of the delayed coker was that developedby Volk et al., followed by Castiglioni, Smith et al., Maples, andGary−Handwerk.

3.1.2. Data Set 2. The results of the predictions for thedata set 2 at a constant temperature and pressure of 925 °F and15 psig, respectively, are shown in Figure 5.The accuracy of prediction of product yields obtained from

the delayed coker with the different correlations is similar to thatencountered for data set 1, with the Volk et al. correlation beingthe best correlation.Global error for the different correlations showed, in general,

an average 10% increase compared to those obtained for dataset 1. The increment in error may be attributed to the differentoperating conditions reported for data set 2.The data set 2 comprises five different feedstocks in

commercial delayed coking plants in Mexican refineries usingvacuum residue from Maya and Isthmus crudes in differentvolume ratios (from 100% Maya to 30% Maya/70% Isthmusmixtures). The vacuum residue from Maya crude typically hasa low API gravity, which is out of range for the correlationsof Maples and Castiglioni. For the Gary−Handwerk correlation,

Figure 4. Yields of the delayed coker as a function of the CCR content in the feedstock, for data set 1.




the pressure is out of range at 20 psig and for the Castiglionicorrelation, the temperature is below 85 °F. The average globalerror for Gary−Handwerk, Maples, and Castiglioni correlationswas 61, 50, and 46%, respectively, 2 or 3 times greater than theerror obtained by Volk et al. (17%).The effect of feed was examined with only three points for

data set 1 and five points for data set 2. It is then anticipated thatmore information is needed for a better ranking of correlations.Despite this, it can be established that those approaches that

include pressure and temperature effects tend to reproducecoking product yields with lower error.

3.2. Effect of the Pressure. Figure 6 illustrates the resultsobtained with the different correlations for the effect of thepressure in the coking drum at a constant temperature of 930 °Fand 15.6 wt % of the CCR content in the feed.Only Volk et al., Smith et al., and Castiglioni correlations

were examined because they consider the effect of the pressure.The global error in the Castiglioni correlation showed an average

Figure 6. Yields of the delayed coker as function of the pressure in the drum coker.

Figure 5. Yields of the delayed coker as a function of the CCR content in the feedstock, for data set 2.





value of 20.47%, while Smith et al. and Volk et al. correlationsreduced the error to 17.27 and 7.08%, respectively.Volk et al. correlation presented errors of 0.05−13.85% for gas,

4.18−5.51% for naphtha, 2.68−4.42% for gas oil, and 7.82−18.15% for coke yields. The high error observed for the gas yield

is due to a negative effect of the pressure in vapor−liquidequilibrium in the coker drum; i.e., an increase of the pressureincreases the gas yield, leading to higher error in correlations.The evaluated pressure range of 15−40 psig is outside the

upper limit for the Castiglioni and Smith et al. correlations,

Figure 7. Yields of the delayed coker as function of the temperature in the drum coker.

Figure 8. Comparison of commercial and calculated yields.




http://pubs.acs.org/action/showImage?doi=10.1021/ef4014423&iName=master.img-007.jpg&w=419&h=309

accounting for 30 and 35 psig, respectively. The average globalerror for these correlations is between 2 and 3 times higher thanthe value of the Volk et al. correlation.In commercial delayed coker, increasing the pressure from

15 to 40 psig causes an increase in the coke yield from 17.17 to22.7 wt %. That is, an increase in the coke yield for each 1 wt % isobtained for each 5 psig increment of operating pressure in thecoking drum.The ranking of correlations for the effect of the pressure based

on better performance follows the order: Volk et al. > Smith et al.> Castiglioni.

3.3. Effect of the Temperature. Figure 7 shows the resultswith the correlations for the effect of the operating temperaturein the coke drum, maintaining a constant pressure of 30 psig andCCR content in the feed of 19 wt %.Volk et al. and Castiglioni correlations showed similar global

errors of 10.56 and 17.41%, respectively.The gas, naphtha, and gas oil yields were better estimated with

the Volk et al. correlation, while the estimation of the coke yieldwas better predicted using the Castiglioni correlation.The evaluated temperature range of 900−930 °F is outside

the upper limit for the correlation of Castiglioni at 840 °F. Theaverage global error for the Castiglioni correlation was almosttwice the value obtained with the Volk et al. correlation.In commercial delayed coker, increasing the temperature from

900 to 930 °F reduces the coke yield from 22 to 20 wt %. Thus,the coke yield increases 1 wt % for each 15 °F increment in theoperating temperature of the coking drum.

3.4. Comparison of Correlations. The former correlationsfor calculating product yields of delayed coking were published atthe earliest in the last quarter of the past century by Gary−Handwerk,18Maples,16 and Castiglioni,19 while Smith et al.21 andVolk et al.22 correlations were published at the beginning of thiscentury. The first two correlations only take into account theCCR content in the feedstock; Castiglioni also includes tem-perature and pressure in ranges of 800−840 °F and 0−30 psig,respectively. Smith et al.21 adds the pressure correction con-sidering a range of 15−35 psig, Volk et al.22 appends more thanall of the previous variables, evolving a broader range of the CCRcontent, API gravity, temperature, and pressure. Under theseconditions, the following comparison was made.Figure 8 shows the comparison between calculated and

commercial product yields for all of the data reported in Table 4using all correlations.A large dispersion is observed for all cases. However, the yields

predicted with the Volk et al. correlation are closer to the 45° linepractically along the interval, as compared to the othercorrelations, which indicates better accuracy of fit.Gary−Handwerk and Maples approaches showed higher devi-

ations virtually in the entire range, while Smith et al. correlationresults are among those of Volk et al., Gary−Handwerk, andMaples.The Castiglioni correlation gave similar values as Smith et al.

and Volk et al. correlations in the low yield (gas and naphtha)region, while in the high yield (oil and coke) region, in somecases, the estimated values showed larger deviations than Gary−Handwerk and Maples correlations.The average absolute error including all considered effects is in

the following increasing order: Volk et al. (10.14%) < Smith et al.(27.57%) < Castiglioni (28.03%) < Maples (41.63%) < Gary−Handwerk (49%).Figure 9 shows the residual of the data points for all of the

correlations. As seen, most of the residual data are negatives,Table4.Percentageof

AbsoluteRelativeError

forYield

Fraction

sforAllCorrelation

s

gas

naphtha

gasoil

coke

global

min

max

average

min

max

average

min

max

average

min

max

average

min

max

average

Feed

Effect,Set1

Gary−

Handw

erk

28.42

35.51

32.33

54.37

69.90

62.22

30.61

56.94

46.71

19.68

30.67

24.99

19.68

69.90

41.56

Volketal.

0.60

8.90

5.28

1.61

21.79

10.94

3.88

5.50

4.85

0.22

6.68

2.51

0.22

21.79

5.89

Maples

19.75

39.62

31.22

24.23

36.92

31.81

27.41

49.46

41.19

24.07

35.77

29.80

19.75

49.46

33.51

Smith

etal.

19.25

22.65

21.01

35.39

43.72

38.38

24.27

42.14

35.79

19.70

28.84

23.67

19.25

43.72

29.71

Castiglioni

8.27

19.34

15.13

45.93

60.00

52.38

1.20

46.45

22.39

2.00

52.67

24.50

1.20

60.00

28.60

Feed

Effect,Set2

Gary−

Handw

erk

28.27

34.43

31.90

109.57

141.06

132.32

34.89

61.20

52.69

19.68

31.43

28.00

19.68

141.06

61.23

Volketal.

0.51

7.78

5.12

39.05

60.54

49.45

3.10

6.35

4.34

5.99

13.94

9.22

0.51

60.54

17.03

Maples

19.61

37.35

31.52

82.09

90.55

86.94

31.89

54.46

47.58

24.07

36.60

32.95

19.61

90.55

49.75

Smith

etal.

11.75

15.34

13.74

63.11

82.63

76.72

19.91

37.76

32.50

12.20

23.22

20.00

11.75

82.63

35.74

Castiglioni

2.97

33.67

21.72

66.56

143.80

111.63

2.35

37.72

13.70

29.16

52.01

35.57

2.35

143.80

45.65

Pressure

Effect

Volketal.

0.05

13.85

6.95

4.18

5.51

4.84

2.68

4.42

3.55

7.82

18.15

12.98

0.05

18.15

7.08

Smith

etal.

9.27

22.00

15.64

22.00

22.03

22.02

4.58

6.98

5.78

19.60

31.68

25.64

4.58

31.68

17.27

Castiglioni

18.29

42.05

30.17

4.07

15.09

9.58

8.39

34.09

21.24

14.46

27.31

20.89

4.07

42.05

20.47

Tem

perature

Effect

Volketal.

7.01

11.49

9.25

8.28

12.40

10.34

2.96

5.29

4.12

16.85

20.22

18.53

2.96

20.22

10.56

Castiglioni

21.54

66.00

43.77

10.67

20.00

15.33

3.25

9.15

6.20

3.18

5.50

4.34

3.18

66.00

17.41



Figure 9. Residual values.

Table 5. Statistical Analysis

intercept slope residual (+) residual (−) maximum absolute error (%)

GasGary−Handwerk 9.91 0.5514 1 8 138.87Volk et al. 1.53 0.9126 3 10 43.82Maples 7.47 0.6697 1 8 85.60Smith et al. 4.59 0.7686 2 9 80.29Castiglioni 3.20 0.8665 4 9 143.80

NaphthaGary−Handwerk 17.40 0.3263 2 7 138.81Volk et al. 1.89 0.8942 9 4 48.33Maples 12.40 0.4980 2 7 88.62Smith et al. 8.53 0.6278 4 7 80.90Castiglioni 9.48 0.5870 6 7 138.19

Gas OilGary−Handwerk 14.45 0.2926 4 5 141.06Volk et al. 3.41 0.8978 7 6 54.99Maples 11.51 0.4125 4 5 90.55Smith et al. 6.61 0.6836 5 6 82.63Castiglioni 7.44 0.7749 7 6 141.26

CokeGary−Handwerk 18.16 0.4755 2 7 109.57Volk et al. 5.14 0.8323 7 6 60.54Maples 16.54 0.5566 2 7 85.88Smith et al. 11.05 0.7133 2 9 63.11Castiglioni −0.47 0.978 9 4 66.56

GlobalGary−Handwerk 13.87 0.4452 9 27 141.1Volk et al. 2.26 0.9095 26 26 60.5Maples 10.86 0.5654 9 27 90.5Smith et al. 6.95 0.7221 13 31 82.6Castiglioni 4.56 0.8174 26 26 143.8




which mean that the models have the tendency to underpredictcommercial values. Available data yields by fraction or globalwere applied to determine the intercept and the slope of thelinear equation between commercial and predicted yields foreach correlation. The intercept, slope, number of positive andnegative residuals, and maximum absolute error are reported inTable 5. The intercept and slope of the Volk et al. correlation inthe five cases (gas, naphtha, gas oil, coke, and global) are nearestto 0 and 1, respectively, indicating a better agreement withthe real data compared to the other correlations. The negativeand positive residual data in the global analysis are balanced,representing a random distribution. However, in the fractioncases, the gas fraction accounts for more negative residuals andnaphtha fraction accounts for more positive residuals, indicatinga tendency to underpredict and overpredict values, respectively.The Castiglioni correlation behavior is similar to that of the

Volk et al. correlation. The gas fraction predicts greater values,and the coke fraction predicts smaller values; however, the posi-tive and negative residuals are globally balanced. The intercept(between −0.47 and 9.48) and slope (between 0.5740 and0.978) values are very different to 0 and 1, respectively, indicatingpredictions with the greatest errors.Smith et al., Maples, and Gary−Handwerk correlations have

the tendency to underpredict values for all of the fractions.Currently, delayed coking plants have increased severity in

operating conditions to obtain higher yields of products, such asnaphtha and gas oil, and only the Volk et al. correlation showsa wide range of validity in the pressure and temperature, whichleads to more accurate calculations of product yields from thedelayed coker.The characterization factor value indicates that crude oils

are naphthenic−paraffinic-type. The trend is seen that the moreparaffinic crude is (higher characterization factor value), thehigher the coke yield is obtained because of the cracking reactionof paraffinic hydrocarbons.

4. CONCLUSIONThere are few correlations reported in the literature to calculateproduct yields of the delayed coker. Some correlations are basedon the content of CCR in the feedstock, while others includepressure and temperature effects.Correlations that include the effect of operating conditions

proved to bemore accurate compared to those that consider onlyfeed properties. The Volk et al. correlation exhibited the highestaccuracy for the estimation of the delayed coker yields, probablybecause this correlation uses the CCR content in the feed,pressure, temperature, and liquid space velocity as variables and acorrection to improve the prediction of commercial yields.Castiglioni and Smith et al. correlations show less accuracy

than the Volk et al. correlation, because these correlations useCCR, P, and T for Castiglioni and CCR and P for Smith et al.Gary−Handwerk and Maples correlations account for thehighest deviation because they are based only on the contentof CCR in the feedstock.Correlations that only include feed properties underestimate

the commercial values of gas, gasoline, and coke yields. The gasoil yield is neither overestimated nor underestimated.Gas oil yield prediction showed the highest error inmost of the

correlations, because it is obtained as the difference (balance).From the evaluation of different effects, the following ratios

arise for the considered range of operating conditions: (1) Anincrease of 1 wt % of coke yield for each increase of 1 wt % ofCCR in the feed. (2) An increase of 1 wt % of coke yield for each

increase of 5 psig pressure in the coke drum. (3) A reduction of1 wt % of the yield of coke for each increase of 15 °F in thetemperature of the coke drum.

■ AUTHOR INFORMATION

Corresponding Author*Fax: +52-55-9175-8429. E-mail: [email protected].

NotesThe authors declare no competing financial interest.

■ NOMENCLATURE

A = factor in the delayed coker model in eq 20ai = parameter in eq 20, where i = 1 and 2; parameter in eq 22,where i = 0, 1, and 2a, b, c, d, e, f, g, h, i, j, k, l, m, n, and o = model coefficients(Table 2) for each productAPI = gravity of the feed (deg)B = factor in the delayed coker model in eq 24bi = parameter in eq 21, where i = 1 and 2; parameter in eq 23,where i = 0, 1, and 2CR = feedstock carbon residue (wt %)CCR = Conradson carbon residueCFR = equivalent combined feed rateci = parameter in eq 26, where i = 1, 2, and 3di = parameter in eq 27, where i = 1, 2, and 3DC = delayed cokerei = parameter in eq 28, where i = 1 and 2F1i = factor for product “i” of the delayer coker model in eq 27F2i = factor for product “i” of the delayer coker model in eq 28LSV = liquid space velocity (min−1)MCR = microcarbon residue (wt %)P = pressurePCT = pressure at the top of the coking tower (MPa)Re = recycle ratioRWO = water injection/oil weight ratioT = temperatureTCT = temperature at the top of the coking tower (°C)TF = outlet temperature of the heating furnace (°C)Ttotal = total yieldY = product yieldyi = yield of product “i”ycoke = coke yieldYcoke = yield of cokeYG = yield of dry gasygasoline = gasoline yieldYGNE = yield of gasolineYVGO = yield of gas oilρ = feedstock density (g/cm3)

■ REFERENCES(1) Elliott J. D.; Wedlak, D. A. Residue upgrading with SYDEC delayedcoking: Benefits and economics. Proceedings of the 1st Asia Bottom of theBarrel Technology Conference; Kuala Lumpur, Malaysia, May 22−23,2007.(2) Haniford, R. R. Coke is good, but less is better. Proceedings of theNPRA Annual Meeting; San Antonio, TX, March 23−25, 2003; PaperAM-03-09.(3) Castaneda, L. C.; Munoz, J. A. D.; Ancheyta, J. Combined processschemes for upgrading of heavy petroleum. Fuel 2012, 100, 110−127.(4) Ashish, N. S.; Aniruddha, B. P.; Shriniwas, D. S.; Jyeshtharaj, B. J.Petroleum residue upgrading via delayed coking: A review. Can. J. Chem.Eng. 2007, 85, 1−24.



mailto:[email protected]

(5) Ellis, P. J.; Paul, C. A. Tutorial delayed coking fundamental.Proceedings of the AIChE Spring National Meeting; New Orleans, LA,March 8−12, 1998; Paper 29a.(6) Fahim, M. A.; Al-Sahhat, T. A.; Elkilani, A. S. Fundamentals ofPetroleum Refining, 1st ed.; Elsevier: Amsterdam, Netherlands, 2010.(7) Rana, M. S.; Samano, V.; Ancheyta, J.; Diaz, J. A. I. A review ofrecent advances on process technologies for upgrading of heavy oils andresidue. Fuel 2007, 86, 1216−1231.(8) Bensal, B. B.; Gentry, A. R.; Moretta, J. C. Improve your cokingprocess. Hydrocarbon Process. 1994, 73, 63−66.(9) DeBiase, R.; Elliott, J.; Hartnett, T. Delayed-coking process update.In Petroleum-Derived Carbons; Bacha, J. D., Newman, J. W., White, J. L.,Eds.; American Chemical Society (ACS): Washington, D.C., 1986; ACSSymposium Series, Vol. 303, Chapter 11, pp 155−171.(10) Shen, H.; Ding, Z.; Li, R. Thermal conversion an efficient way forheavy residue processing. Proceedings of the 15th World PetroleumCongress; Beijing, China, Oct 12−17, 1997.(11) Xiao, J.; Wang, L.; Chen, Q.; Wang, D. Modeling for productsdistribution in thermal conversion of heavy oil. Pet. Sci. Technol. 2002, 20(5−6), 605−612.(12) Zhou, X.; Chen, S.; Li, C. A Predictive kinetic model for delayedcoking. Pet. Sci. Technol. 2007, 25 (12), 1539−1548.(13) Bozzano, G.; Dentea, M. A mechanistic approach to delayedcoking modeling. In European Symposium on Computer Aided ProcessEngineering; Puigjaner, L., Espuna, A., Eds.; Elsevier Science B.V.:Amsterdam, Netherlands, 2005.(14) Tian, L.; Shen, B.; Liu, J. A delayed coking model built using thestructure-oriented lumping method. Energy Fuels 2012, 26, 1715−1724.(15) Tian, L.; Shen, B.; Liu, J. Building and application of delayedcoking structure-oriented lumping model. Ind. Eng. Chem. Res. 2012, 51,3923−3931.(16) Maples, R. E. Petroleum Refinery Process Economics, 2nd ed.; PennWell Publishing: Tulsa, OK, 1993.(17) Speight, J. G. The Desulfurization of Heavy Oils and Residue, 2nded.; Marcel Dekker: New York, 2000.(18) Gary, J. H.; Handwerk, G. E. Petroleum Refining: Technology andEconomics, 4th ed.; Marcel Dekker: New York, 2001.(19) Castiglioni, B. P. How to predict coker yields. HydrocarbonProcess. 1983, 77−79.(20) Aguilar, R. A.; Ancheyta, J.; Trejo, F. Simulation and planning of apetroleum refinery based on carbon rejection processes. Fuel 2012, 100,80−90.(21) Smith, A.; Frow, M.; Quddus, J.; Howell, D.; Reed, T.; Landrum,C.; Clifton, B. Refinery Modeling, Advanced Chemical Engineering Design;University of Oklahoma: Norman, OK, 2006.(22) Volk, M.; Wisecarver, K.; Sheppard, C. Fundamentals of DelayedCoking Joint Industry Project; Department of Chemical Engineering,University of Tulsa: Tulsa, OK, 2002.(23) Ren, J.; Meng, X.; Xu, C.; Song, Z.; Jiang, Q.; Liu, Z. Analysis andcalculations model of energy consumption and product yields of delayedcoking units. Pet. Sci. 2012, 9, 100−105.(24) Depew, C. A.; Hashemi, M. H.; David, J. Evaluation of alternativecontrol strategies for delayed coker. Proc. Am. Control Conf. 1988, 1,240−246.(25) http://en.wikipedia.org/wiki/File:Delayed_Coker.png.



http://en.wikipedia.org/wiki/File:Delayed_Coker.png