Building and Application of Delayed Coking Structure-Oriented

Article pubs.acs.org/IECR

Building and Application of Delayed Coking Structure-Oriented Lumping Model Lida Tian, Benxian Shen,* and Jichang Liu State Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237 P.R. China ABSTRACT: A structure-oriented lumping (SOL) model to predict the product distribution of delayed coking was built. The effects of feedstock properties and operational conditions on product distribution were analyzed with the proposed model. It was known from model calculation results that for the feedstock residue of this paper, the liquid yield increased 2.5% on average with a temperature increase of 10 °C, that mainly was equal to a 0.15 increase H/C of feedstock; the liquid yield increased 3.5% on average with a recycle ratio decrease of 0.15, that was mainly equal to a 5% carbon residue decrease of feedstock; and liquid yield increased 0.6% on average with a pressure decrease of 0.03 MPa.

1. INTRODUCTION The capacity of delayed coking industry devices is generally large. A slight increase of liquid yield of delayed coking could bring very considerable economic benefits, especially for gasoline and diesel. So, liquid yield increase was always one of the development directions of delayed coking technology. At present, there are four methods available to increase the liquid yield of delayed coking:1,2 (1) improve the feedstock properties; (2) improve the operational conditions; (3) improve the process flows; (4) add assistant agents. Analyzing the variable-effects associated with these methods on the liquid yield increase in the delayed coking process, and then using optimal operation conditions, might bring more liquid products of delayed coking. But it is hard to compare these effects on the same device, for one device usually adopts only one method. In addition, feedstock properties do not change easily once the process is determined. Building a delayed coking products prediction model was a feasible way to achieve these analyses and comparisons. Several product-prediction models of delayed coking have been reported.3−6 In 1992, Quann and Jaffe proposed a structure-oriented lumping (SOL) method to describe the molecular reaction behaviors of complex reaction systems.7−9 This paper reports the building of a SOL model for delayed the coking process. The proposed model was a lumped kinetic model in the molecular level. The effects of feedstock properties and operational conditions on delayed coking liquid yield were analyzed by this model.

for delayed coking, 22 structure vectors were needed. They are shown in Figure 1.

Figure 1. Structure vectors.

The definitions of the 22 structure vectors are as follows: A6, an aromatic ring, benzene when other vectors are 0; A4, four carbon atoms in aromatic ring, it must attach to another A6 or A4; A2, two carbon atoms in aromatic ring, it is used to construct condensed aromatics; N6, a six-member naphthenic ring, cyclohexane when other vectors are 0; N5, a five-member naphthenic ring, cyclopentane when other vectors are 0; N4, four carbon atoms are added to ring; N3, three carbon atoms added to ring; N2, two carbon atoms added to ring; N1, one carbon atom is added to ring; R, total alkyl carbon number, each R means a −CH2−; br, branch number in carbon chain; me, methyl number in aromatic ring or naphthenic ring; IH, saturation except aromatics, each IH means two hydrogen atoms; AA, bridge bond between two rings; NS, sulfur atom between C−C bond (except aromatics); AN, nitrogen atom in aromatic ring; NN, nitrogen atom between C−C bond (except aromatics); RO, oxygen atom between C−H bond; KO, oxygen atom in carbonyl or aldehyde; Ni, a nickel atom; V, a vanadium atom; cc, strength and carbon numbers between cores and cores. So, molecules could be represented by these structure vectors. For example, a molecule like Figure 2 could be represented as Table 1.

2. MODELING 2.1. Elements and Representation of SOL. The most important difference between traditional lumping method and SOL is the way these models divide lumps. The traditional lumping method divides molecules based on invariable properties into lumps, resulting in a small number of lumps; while SOL divides molecules on the base of molecular structure vectors into lumps, the number of lumps was expanded greatly. At the same time, because of the limited number of molecular sections, the complexity of the model did not magnify greatly. These molecular sections were defined as structure vectors. To describe the molecular composition of heavy oils like residue © 2012 American Chemical Society

Received: Revised: Accepted: Published: 3923

November 4, 2011 February 9, 2012 February 10, 2012 February 10, 2012 dx.doi.org/10.1021/ie2025272 | Ind. Eng. Chem. Res. 2012, 51, 3923−3931

Industrial & Engineering Chemistry Research

Article

Different from other structure vectors, “R” and “cc” needs two digits to represent. And every odd number digit of “cc” means the −CH2− numbers of the bond, every even number digit of “cc” means the strength of bond. 2.2. Building a Molecular Composition Matrix by SOL. A first premise to build a kinetic model with the SOL is to describe the molecular composition with structure vectors; however, it is not possible to analyze the molecular composition in full detail (cis−trans isomers, etc.). So it is necessary to simulate molecular compositions of residues. Without considering the carbon number of the side chain, this paper assumed that residues were made of 92 kinds of single-core seed molecules and 46 kinds of multicore seed molecules; 7004 kinds of molecular lumps were formed after adding 0−50 −CH2− side chains to each seed molecule, respectively, and omitting some nonexistent molecules. This paper assumed that

Figure 2. A multicore molecule for illustration.

Table 1. Representation of a Multicore Molecule for Illustration A6

A4

A2

N6

N5

N4

N3

N2

N1

R

br

110101 me 0

120201 IH 0

0 AA 0

001010 NS 0

0 AN 0

002010 NN 0

0 RO 0

0 KO 0

0 Ni 0

020302020204 V 0

0 cc 2220322121

Figure 3. Single-core seed molecules.

Figure 4. Sulfur multicore seed molecules. 3924

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any residue was constituted by these molecular lumps. The reason that different residues had different properties was that the relative contents of these molecular lumps were different. The single-core and one kind of multicore seed molecules were shown in Figure 3 and Figure 4, respectively. Thus, if the relative contents of molecular lumps were known, the molecular composition matrix of the feedstock could be built after representing each molecular lump with the above structure vectors. 2.3. Reaction Rules. Reaction rules are used to describe molecular behaviors; these rules decide how the feedstock molecular matrix will turn to product molecular matrix. Reaction rules include two parts: the reactant selection rules and the product generate rules. The reactant selection rule decides whether a reaction will occur on each molecular lump. In the program of the model, the reactant selection rules were compiled as a select case. When searching in the molecular composition matrix of feedstock, if a row (means a molecular lump) met the requirements of a certain reactant selection rule, then this row could be changed (means this kind of reaction would occur for this kind of molecular lump). The product generate rule decides what products will be generated after this kind of reaction. In the program if a row is to be changed, the product generate rules shows the way to be changed. To describe the delayed coking reaction behaviors of residue molecules, this paper formulated 92 kinds of reaction rules. Some typical reaction rules were as follows. (1) Side chain breaking (single-core molecules).Reactant selection rules: [(A6 > 0) ∨ (N6 > 0) ∨ (N5 > 0)] ∧ (R > me + KO) ∧ (A6 < 10) ∧ (N6 < 10). Product 1: R1 = R − me − KO, br1 = (br − 1) × (br > 0); the rest were 0. Product 2: R2 = me + KO, br2 = 0; the rest were the invariant.

(A62 < 10 ∧ N62 < 10) ∧ (N62 + N42 > 0) ∧ (IH2 = −1). Product: N6 = N62, N4 = N42 + 1, R = R1 + R2 − 4, IH = −1, br = 0, me = me2 + (R1 = 5), the rest were their sum.

Here, “∧” means “and”, “∨” means “or”, “rem” means “remainder operator”, and “f ix” means “integer operator”. Take item 2 for example to explain the meaning of the computer languages. In item 2, [f ix(rem(cc,100/10)) = 3] means “If the round number of the remainder of structure vector ‘cc’ divided by 100 divided by 10 equal to 3”. R1 = rem(R,100) + rem(cc,10) means “the structure vector “R” of product one equal to the remainder of structure vector “R” of reactant divided by 100 added the remainder of structure vector ‘cc’ of reactant divided by 10”. R2 = f ix(R/100) means “the round number of structure vector “R” of reactant divided by 100”. X2 = f ix(X/10): X means other structure vectors. 2.4. Calculation of Kinetic Constants. Reaction networks of the initial molecular matrix could be constructed by judging the molecular matrix row by row with reaction rules and recording it into computers as the form of reactant product pairs. This paper assumed that all the reactions were first-order irreversible reactions.10 So simultaneous differential equations consisting of first-order linear ordinary differential equations could be generated from reaction networks conveniently. If the rate constant of each reaction was known, the numerical solution of the differential equations could be calculated by the Runge−Kutta method, and then the product molecular matrix at any time could be obtained. Figure 5 shows the simulation process. Based on the transition state theory, equation k(T) = (kBT/ h) exp((TΔS − ΔE)/(RT)) was used to calculate the reaction rate constants.11 Here, kB was the Boltzmann constant; h was the Planck constant; T was temperature; R was ideal gas constant; ΔS was the entropy change of reaction, and ΔE was the activation energy barrier of reaction. The same category of reactions undergoes similar reaction pathways so the kinetics of each category could be considered as, basically, the same, just tiny differences existed because the molecular structure of the reactants and products were different. These tiny differences could be described by structure vectors. ΔS and ΔE of simple reactions could be calculated by Materials Studio software directly. ΔS and ΔE of complex reactions could be described as the functions of structure vectors. The parameters of these functions could be regressed by ΔS and ΔE values of simple reactions. However, such calculations did not work every time. So, a revision by equation

(2) Cracking (multicore molecules, first joint).Reactant selection rules: [f ix(rem(cc,100)/10) = 3] ∨ [f ix(rem (cc,100)/10) = 2]. Product 1: R1 = rem(R,100) + rem(cc,10), me1 = rem(me,10) + 1, cc = 0, the rest: X1 = rem(X,10). Product 2: R2 = f ix(R/100), IH2 = f ix(IH/10) + 1, cc2 = f ix(cc/ 100), the rest: X2 = f ix(X/10). (3) Dehydrogenation condensation (single-core molecules). Reactant selection rules: (A6i < 10 ∧ N6i < 10) ∧ (A6i > 0) ∧ (A6i > 0) ∧ (A6i + A4i + A2i ≥ N6i + N4i). Product 1: IH1 = 1, the rest were 0, product 1 × 2. Product 2: N1 = N11 + N12 + 2, R = R1 + R2 − 2, me = me1 + me2 − 2; the rest were their sum. (4) Diels−Alder reactions.Reactant selection rules 1: (A61 + N61 + N51 = 0) ∧ (IH1 = −1). Reactant selection rules 2:

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Figure 5. Simulation process for molecular reaction behaviors.

Figure 6. Method of the residue cut.

k′ = k × (1 + δ) to the reaction rate constants is needed. Here, k′ was the rate constant after being revised while k was the rate constant before revision; δ was defined as rate revise index. Every category of reactions had the same rate revise index. This paper adjusted these indexes with 16 groups of results from delayed coking experiments of different feedstock under different operational conditions.

composition by SA is as Figure 7. The brief steps of it and the simulation diagram of SA are shown in the Appendix.

3. SIMULATION PROCESS 3.1. Feedstock Fractionation Cut. To simulate the molecular composition of residues, the relative contents of every molecular lump need to be calculated. The method cutting residues in this paper is shown in Figure 6. After the cutting shown in Figure 6, feedstock residue could be divided into dozens of parts. There are several molecular lumps in every part. The values of relative contents could be adjusted according to certain rules. If the properties calculated from the relative contents are close enough to the measured values, these values are considered as optimized values of relative contents. 3.2. Simulation of Feedstock Molecular Composition by Simulated Annealing Algorithm. The simulated annealing algorithm (SA) is a kind of heuristic random search algorithm. It is suitable for dealing with random number problems especially. SA is used as the optimizing algorithm in this paper.12 The flowchart to simulate the feedstock molecular

Figure 7. Flowchart for simulation of feedstock molecular composition.

The objective function of SA is constructed by the form of the quadratic sum of the relative errors between the calculation and text bulk properties. The properties are listed in the bottom of Figure 6. The acceptance criteria of SA is Metropolis chain criteria and the tolerance value is 1× 10−4. 3926



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3.3. Simulation of Delayed Coking Process with SOL Model. Figure 8 shows a brief reaction process of the delayed coking.

final molecular composition was calculated from the initial molecular composition according to Figure 5. After each batch process, gas content at that moment was judged. It was then ascertained whether to keep feeding or discharging products according to Figure 9 until the final product molecular matrix was obtained. Molecular carbon number, boiling point, carbon content, and carbon residue value were the main factors used to divide products. The molecules whose carbon numbers were less than 4 were defined as gas molecules. From the other molecules, the ones whose boiling point is less than 205 °C are defined as gasoline molecules; the ones with a boiling point in the range of 205−365 °C and having carbon numbers less than 24 are defined as diesel molecules; the ones with a carbon content more than 90% (m%), a boiling point more than 450 °C, or carbon residue values more than 5% are defined as coke molecules; finally, the remainder of the molecules are defined as wax oil molecules. After establishment of the generated product molecules, the product distribution of delayed coking could be obtained by the ultimate product molecular matrix.

Figure 8. Simplified diagram of the delayed coking process.

To simulate a complex delayed coking reaction process with an ordered SOL model, reasonable simplifications and assumptions were necessary. Delayed coking is a continuous process for inlet and outlet, but it is not too suitable for the SOL model. So a main assumption is made to replace the real continuous process with multibatch successive processes with fleeting time. For convenience, another assumption was made to consider the molecular composition of every flow distributed equably. This paper simulated the delayed coking reaction process with the method shown in Figure 9.

4. EXPERIMENTAL SECTION 4.1. Feedstock. Four kinds of residues were used in this paper. Table 2 gives the properties of them. 4.2. Delayed Coking Experimental Device. The experimental data for this paper were gathered on the DVSJHJL-1130 delayed coking device like that shown in Figure 10.

Figure 9. Delayed coking reaction process simulation.

Here, “n” means maximum pressure of gas allowed by the reactor; it is calculated by the ideal gas equation of state and is limited by the construction of the reactor and operating conditions. The model assumed that all the reactions occurred in the “reaction” part in Figure 9. For each batch process, the

Figure 10. Delayed coking Pilot Plant for experiments.

Table 2. Properties of Residues Selected for Delayed Coking Experiments area ratio density at 20 °C (kg/m3) CCR (wt %) group composition (wt %)

elemental composition (wt %)

average molecular weight (kg/m3)

saturates aromatics resins asphaltenes C H O N S Ni (μg/g) V (μg/g)

residue 1

residue 2

residue 3

residue 4

Saudi: Diaz: Soros: Oman 2.6:2:0.2:5.2 961.9 13.87 22.90 44.18 30.1 2.82 86.23 11.54 1.33 0.42 0.47 79.62 5.07 995

Iran: Soros: Saudi: Diaz 0.5:1:6.5:2 955.2 16.74 31.22 26.89 40.82 1.07 84.33 12.13 1.96 0.17 1.41 26.07 21.33 922

Basra: Kuwait: Mundo 3.6:2.4:4 1004.3 21.15 10.57 50.52 34.35 4.56 87.36 10.81 0.81 0.61 0.41 9.10 7.88 1197

Soros: Kuwait: Djeno 1.5:5.3:3.2 1001.8 19.60 21.25 40.78 33.93 4.04 87.17 10.97 0.83 0.77 0.26 109.32 61.18 1175

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4.3. Operation Parameters. The operation parameters of experiments in this paper are listed in Table 3.

In any case, the predicted results were close enough to the experimental results. It could be proved that the predicted results of the proposed model were reliable. 5.2. Effects of operation parameters on product distribution. Prediction of SOL model of delayed coking product distribution had been proved to be reliable. So the effects of feedstock and operational conditions on product distribution of delayed coking could be calculated by the proposed model. That avoided a huge waste of time and money. 5.2.1. Effects of Feedstock Composition. Table 4 shows the effect of feedstock properties on product distribution of delayed coking. Author: Here, saturates meant single-core molecular lumps without aromatic and heteroatom structure vectors; polycyclic aromatics meant the single-core molecular lumps with aromatic rings more than naphthenic rings; CCR was defined as the SOL method.13 The method to change the value of these properties takes the molecular lump relative contents of feedstock residue as a standard and leaves the molecular lump contents which did not involve the aim property and changes the molecular lump contents which involved the target property equally. However, the change of the molecular lump relative contents from the target property change would bring changes of other properties. This paper neglected this influence. Seen from overhead, the carbon residue and the contents of nickel and vanadium had obvious effects on the product

Table 3. Operation Parameters basic residue feeding velocity (g/h) stripping time temperature (°C) pressure (MPa) coke tower volume (L) water velocity (g/h) feeding time (h) recycle ratio

4 900 2h 480, 490, 500 0.15, 0.18 4 20 3 0, 0.15, 0.3

5. RESULTS AND DISCUSSION 5.1. Validation of Model Reliability. The main aim of the proposed model was to predict product distribution of delayed coking. The principal factors to influence the product distribution were feedstock properties, reaction temperature, reaction pressure, and recycle ratio. This paper adopted the change of one variable at a time method with all other variables being constant to fixed values in order to check the prediction reliability of the model compared to the experimental data. The comparisons of model predicted results and experimental results are shown in Figure 11.

Figure 11. A comparison of predicted results and experimental results. 3928



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Table 4. The Effects of Feedstock Properties on Product Distribution of Delayed Coking temperature (°C)

pressure (MPa)

recycle ratio

liquid yield (%)

gas yields (%)

coke yields (%)

saturates (%)

feedstock properties

17.18 21.25 26.44

500

0.15

0.15

67.82 68.13 68.62

6.81 7.47 8.11

25.37 24.40 23.27

polycyclic aromatics (%)

17.50 22.19 29.04

500

0.15

0.15

69.61 68.13 66.51

7.64 7.47 7.28

22.75 24.40 26.21

1.47 1.51 1.58

500

0.15

0.15

66.87 68.13 70.52

7.93 7.47 7.18

25.20 24.40 22.30

136.57 170.50 221.24

500

0.15

0.15

70.85 68.13 63.70

6.93 7.47 8.28

22.22 24.40 28.02

17.72 19.60 22.51

500

0.15

0.15

70.78 68.13 64.23

7.23 7.47 7.88

21.99 24.40 27.89

0.25 0.28 0.30

500

0.15

0.15

69.25 68.13 66.52

7.19 7.47 7.54

23.56 24.40 25.94

500

0.15

0.15

71.07 69.45 68.13

7.09 7.34 7.47

21.84 23.21 24.40

H/C ratio

Ni, V contents (μg/g)

CCR (%)

aromatic carbon ratio

density (g/mol)

value

981 993 1002

distribution of delayed coking. A common point of these effects was that the feedstock was lighter, the aromatic carbon number was less, and the liquid yield was higher. That the addition of high aromatics oil into feedstock residues would increase the liquid yield of delayed coking was a common recognition.2,14 In fact, as seen from the calculation and analysis by the proposed model, naphthenic aromatics in high aromatics oil were the decisive factors to increase the liquid yield of the delayed coking. The benzene ring is an electron deficient group. With the electron-withdrawing influence of large a π-bond in the benzene rings, naphthenic aromatics more easily suffer ringopening and cracking reactions than naphthenic hydrocarbons. This higher thermal cracking activity made the liquid yield increase. Nonetheless polycyclic aromatics in high aromatics oil had the opposite effect on the liquid yield. That was why liquid yield decreased if too many high aromatics oil were added into the feedstock residues. The conclusion from the proposed model was that it would be helpful to increase the liquid yield of delayed coking as long as the additional components were easier to crack than feedstock residues. 5.2.2. Effects of Operational Conditions. Table 5 shows the effects of operational conditions on product distribution of delayed coking. Temperature increasing, system pressure, and recycle ratio decreasing were all effective ways to increase the liquid yield of delayed coking. System pressure decreasing had the least effect on liquid yield. It only increased nearly a 0.6% average with the pressure decreased to 0.03 MPa. Moreover, it was hard to control the pressure of the delayed coking system well. The liquid yield increased nearly 2.5% on average with a temperature increase of 10 °C. However, the increase of temperature was limited by factors such as the running period

Table 5. the Effects of Operational Conditions on Product Distribution of Delayed Coking temperature (°C)

pressure (MPa)

recycle ratio

liquid yield (%)

gas yields (%)

coke yields (%)

480

0.15

490

0.15

500

0.15

480

0.18

500

0.18

0 0.15 0.3 0 0.15 0.3 0 0.15 0.3 0 0.15 0.3 0 0.15 0.3

66.63 64.78 60.27 69.76 65.50 61.97 72.19 68.13 64.02 65.45 62.12 59.72 70.64 68.95 63.90

5.01 5.44 7.42 5.25 7.80 10.34 5.88 7.47 10.66 5.07 6.19 7.39 6.97 8.42 11.05

28.36 29.78 32.31 24.99 26.70 27.69 21.93 24.40 25.32 29.48 31.69 32.89 22.39 22.63 25.05

of the heating furnace, coke quantity, and the difficulty of decoking. The room for adjustment was not very big. The liquid yield increased nearly 3.5% on average with a decrease in the recycle ratio of 0.15. This is a widely used method in industrial devices; however, wax oil is the most common component of the increased liquid when this method is used and the quality of light oil becomes worse. On the whole, the effects of feedstock properties on the liquid yield of delayed coking were more obvious than the effects on the liquid yield of operational conditions. Solely for the liquid yield and the feedstock residue tested in this work, the effect of a 5% CCR decrease was found to be almost equal 3929



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Chart A1

to a recycle ratio decrease of 0.15, and the effect of a 0.15 in H/ C ratio increase was nearly equal to the effect of a temperature increase by 10 °C. 5.2.3. Effects of Product Oil Recycle. Another helpful way to increase the liquid yield of the delayed coking was product oil recycle. Wax oil is a kind of product oil. Wax oil recycle is what we call “recycle ratio improving”. Here, product oil means product gasoline and diesel. To analyze the effects of product oil recycle on the liquid yield of delayed coking, this paper mixed 10% (wt %) of product gasoline and diesel with feedstock residue, respectively, to form optimized feedstocks 1 and 2. Table 6 shows the calculation results of the optimized feedstocks.

Table 6. The Effects of Product Oil Recycle on Product Distribution of Delayed Coking feedstock optimized 1 optimized 2

temperature (°C)

pressure (MPa)

recycle ratio

liquid yield (%)

gas yields (%)

coke yields (%)

500

0.15

0.15

70.63 70.13

7.29 6.98

22.08 22.89

In fact, liquid yield increased 1.85% after 10% of product gasoline recycled. The equivalent data for product diesel recycle was 1.2%. Product gasoline recycle was better than product diesel recycle. 3930



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Therefore, a high liquid yield scheme and a low liquid yield scheme were proposed by this paper. The operational conditions of high liquid yield scheme were the following: temperature was 500 °C; pressure was 0.15 MPa; recycle ratio was 0; 10% of product gasoline recycle. The operational conditions of low liquid yield scheme were as follows: temperature was 480 °C; pressure was 0.18 MPa; recycle ratio was 0.3; and without product oil recycle. Experiments and model calculations were performed on these two schemes. Table 7 shows the results.

■

calcd expt

high low high low

gas yields

gasoline yields

diesel yields

wax oil yields

coke yields

liquid yield

5.51 7.39 5.27 7.74

22.37 19.56 22.03 20.86

26.28 28.45 25.21 27.11

24.33 11.71 26.02 11.55

21.51 32.89 21.47 32.74

72.98 59.72 73.26 59.52

AUTHOR INFORMATION

Corresponding Author

Table 7. Experimental and Calculated Results (%) of Two Schemes scheme

Step 3: Compare with test bulk properties and then calculate the objective function Step 4: Judge and generate new values of relative contents according the acceptance criteria Step 5: Repeat searching process until objective function value gets less than given tolerance Step 6: Calculate feedstock composition according to the search results (relative contents for each distillation fraction) and the mass ratio of each distillation fraction

*E-mail: [email protected] . Notes

The authors declare no competing financial interest.

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The liquid yield difference of the two schemes was about 13%, and this conclusion was proven by experimental results. But an important point must be noted that the increase of liquid yield is mainly due to an increase of wax oil, while the diesel−gasoline ratio of the delayed coking products is decreased. So, the question, “how to establish a suitable production scheme?” still needs to be answered.

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ABBREVIATIONS SOL = structural oriented lumping SA = simulated annealing DAO = deasphalted oil DOA = deoiled asphalt CCR = conradson carbon residue expt = experimental results calcd = calculation results REFERENCES

(1) Bin, Q. Questions and Answers of Delayed Coking Device; Sinopec Press: Beijing, 2007. (2) Chaolin, L.; Benxian, S. Delayed Coking; Sinopec Press: Beijing, 2007. (3) Giulia, B.; Mario, D. A Mechanistic Approach to Delayed Coking Modeling. Comput.-Aided Chem. Eng. 2005, 20, 529−534. (4) Xiaolong, Z. A Predictive Kinetic Model for Delayed Coking. Pet. Sci. Technol. 2007, 25 (12), 1539−1548. (5) Xiao C.; Modeling a Delayed Coking Process with GRNN and Double-Chain Based DNA Genetic Algorithm. Int. J. Chem. React. Eng. 2010, 8, article A75. (6) Chaolin, L.; Benxian, S. Prediction for Product Distribution of Delayed-Coking Process by Stepwise Regression Model. J. East Chin. Univ. Sci. Technol. (Nat. Sci. Ed.) 2009, 35 (2), 1−5. (7) Quann, R. J.; Jaffe, S. B. Structure-Oriented Lumping: Describing the Chemistry of Complex Hydrocarbon Mixtures. Ind. Eng. Chem. Res. 1992, 31, 2483−2497. (8) Quann, R. J.; Jaffe, S. B. Building Useful Models of Complex Reaction Systems in Petroleum Refining. Chem. Eng. Sci. 1996, 51 (10), 1615−1635. (9) Quann, R. J. Modeling the Chemistry of Complex Petroleum Mixtures. Environ. Health Perspect. 1998, 106 (suppl. 6), 1441−1448. (10) Renjun, Z. The Oil Industry Cracking Principle and Technology; Chemical Industry Press: Beijing, 1982. (11) Lida, T.; Jiming, W.; Benxian, S. Building a Kinetic Model for Steam Cracking by the Method of Structure-Oriented Lumping. Energy Fuels 2010, 24, 4380−4386. (12) Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. Optimization by simulated annealing. Science 1983, 220, 671−680. (13) Jaffe, S. B. Extension of Structure Oriented Lumping to Vacuum Residue. Ind. Eng. Chem. Res. 2005, 44, 9840. (14) Benxian, S.; Oil Refining Technology; Sinopec Press: Beijing, 2009.

6. CONCLUSION This paper built a molecular kinetic model to predict product distribution of delayed coking with structure oriented lumping method. It was proven by the experiments that the prediction results were credible. The effects of feedstock properties and operational conditions on the product distribution of delayed coking were analyzed by the proposed model. It was found that making feedstock lighter, increasing temperature, decreasing pressure and recycle ratio or product oil recycle could increase the liquid yield of delayed coking. For the feedstock residue in this paper, liquid yield increased 2.5, 0.6, and 3.5% on average with a temperature increase of 10 °C, the pressure decreased 0.03 MPa and the recycle ratio decreased 0.15, respectively. The effects of feedstock properties were more noticeable than that of operational conditions. The effects of a CCR decrease of 5%, and a H/C ratio increase of 0.15 were mainly equal to the effects of a recycle ratio decrease of 0.15 and a temperature increase of 10 °C, respectively. Product gasoline recycle was better than product diesel recycle. After the optimization of the scheme proposed in this paper, the liquid yield of delay coking could increase approximately 13% at most. However, wax oil was the main component in the incremental liquid, and the diesel−gasoline ratio of products decreased.

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APPENDIX The simulation diagram of SA is shown in Chart A1. Brief steps are as follows: Step1: Determination of initial values of molecular lumps relative contents in each distillation fraction (based on databases of basic properties and BP or MW distribution) Step 2: Calculate the bulk properties with the relative content value 3931


Building and Application of Delayed Coking Structure-Oriented

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