Article pubs.acs.org/EF
Eight-Lump Reaction Kinetic Model for the Maximizing Isoparaffin Process for Cleaning Gasoline and Enhancing Propylene Yield Hongbo Jiang* and Shuai Huang Research Institute of Petroleum Processing, East China University of Science and Technology, Shanghai 200237, China ABSTRACT: The maximizing isoparaffin process for cleaning gasoline and enhancing propylene yield (MIP-CGP) is the main derived technology of catalytic cracking in China. Based on the reaction mechanism of catalytic cracking and the characteristics of MIP-CGP with two reaction zones, the eight-lump reaction kinetic model was established. The measured data was from the industrial unit. Twenty-two groups of kinetic parameters were estimated, and the model parameters were validated that the predicted values were close to measured values. The analysis of the reaction rate constants and activation energies showed that the model parameters can well reflect the reaction law. The model can predict the main product distribution and provide the referential value for the operation optimization of MIP-CGP technology.
1. INTRODUCTION Gasoline is the major vehicle fuel. With increasingly stringent environmental requirements, the national standard of motor gasoline in China is continuously rising. Since January 2014, motor gasoline has been required for the national IV standard in China. The General Administration of Quality Supervision, Inspection and Quarantine (AQSIQ) has released the motor gasoline mandatory national V standard that will be widely executed at the end of 2016. Reducing the olefin content in gasoline aims to reduce CO and NOx that exhaust from combustion engines, which will alleviate environmental pollution and reduce the damage to engines.1,2 The strictly limited olefin content transforms from no more than 28% in the IV standard to 24% in the V standard. In China, about 80% motor gasoline is generated by catalytic cracking. The content of gasoline olefins generated by the conventional fluid catalytic cracking (FCC) process is usually more than 35%. Therefore, obviously reducing the gasoline olefin content in the FCC process is the key for cleaning-gasoline production in China. Over the years, many researchers in China have developed a series of specific derived FCC technologies for lowering gasoline olefin content, such as the two-stage riser fluid catalytic cracking process3,4 (TSRFCC) developed by China University of Petroleum, the flexible dual-riser fluid catalytic cracking process5,6 (FDFCC) developed by Luoyang Petrochemical Corp., and the maximizing isoparaffin process7,8 (MIP) developed by Research Institute of Petroleum Processing. Due to the successful application of these new technologies, not only the quality of the gasoline has been dramatically improved, but also the level of catalytic cracking technology has been greatly promoted in China. The MIP technology adopts a new reaction system and corresponding process conditions. The sketch of an MIP reactor is shown in Figure 1. It is divided into two reaction zones connected by direct cascade with different diameters and retains the high reaction intensity. The first reaction zone (the diameter is smaller in this section) mainly triggers heavy oil cracking reaction. The preheated feedstock oil and regenerated catalyst mix in the bottom of the riser. With higher reaction intensity than traditional catalytic cracking, most heavy © XXXX American Chemical Society
Figure 1. MIP process diagram of the reactor.
feedstock oil is cracked and a large amount of olefin is generated in this zone; the second reaction zone (the diameter is larger in this section) utilizes some process measures to ensure the continual reaction of oil vapor under lower temperature and longer reaction time, such as expanding the riser diameter, injecting shock chilling medium, and forming reversed flow of some used catalyst. The second zone increases the hydrogen transfer and isomerization reactions that can increase isoparaffins, and simultaneously suppresses the secondary cracking reaction.9 Compared with MIP, there are some characteristics of the MIP-CGP (CGP, cleaning gasoline and enhancing propylene yield) process. The temperature is higher and reaction time is shorter in the first reaction zone, for making heavy feedstock oil crack to olefin more quickly and thoroughly; the reaction temperature of the second reaction zone is also a little higher in Received: August 31, 2016 Revised: October 30, 2016 Published: October 31, 2016 A
DOI: 10.1021/acs.energyfuels.6b02208 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
the yield of the target product, choosing reasonable reaction conditions and appropriately controlling the undergoing of secondary reaction are important and necessary. According to the reaction mechanism of the catalytic cracking, a reaction network of the eight-lump kinetic model for the MIP-CGP process was established, as shown in Figure 3. The cracking reaction and other side reactions happen in the
order to provide an advantageous condition for generating more propylene that cracked from gasoline olefin. The MIPCGP process uses a tailored catalyst, whose active center of cracking reaction and hydrogen transfer reaction has been adjusted, whose function in different reaction zones has been strengthened, and whose performances for reducing gasoline olefins and enhancing propylene yield have been further enhanced. At present, the MIP-CGP technology has been widely applied in oil refineries such as those of Jiujiang petrochemical and Yanshan petrochemical corporations. Researching the reaction model of the MIP-CGP system, in order to promote its development and provide guidance on its operation optimization of industrial installation, has become quite important. Traditionally, the procedures of modeling need to spend much time on kinetic experiments in the lab, and its developed kinetic model needs to be modified to fit the industrial units, for example, the adoption of unit factors. In this paper the authors directly used the measured data of an industrial unit to build the model which jumped the step of laboratory experiments and model modification. Therefore, much time was saved and the modeling process was also simplified. A large number of studies10−16 have shown that the lump method is effective to research the kinetic model of complex reaction systems such as catalytic cracking and its derived technologies. In this paper, the eight-lump kinetic model of MIP-CGP has been set up based on the industrial data from an oil refinery in China to provide direct guidance for optimizing the industrial operation.
Figure 3. Reaction network of eight-lump kinetic model for MIP-CGP process.
two reaction zones though the reaction temperatures in two zones are different, and they follow the same parallel− sequential reaction mechanism as Figures 2 and 3. The mixture of fresh feedstock and recycle oil enters the first zone, and material from the outlet of the first zone is the feed material of the second zone. 2.2. Model Expressions. For reasonably simplifying the reaction kinetics model, based on the reaction mechanism of the catalytic cracking, the model has been hypothesized as follows: (1) Reactions between the lumps are irreversible firstorder. (2) All reactions are triggered on the same acidic active center. (3) All reactions have the same catalyst deactivation function, which keeps the same effect on reactions along with the inactivation of the active center. (4) There is no interaction among the lumps of feedstock oil. (5) Two reaction zones have the same reaction network, but have the different reaction temperatures and material concentrations that cause the differences of reaction rate. In addition, the adsorption influence of heavy aromatics on reactions must be considered since its quantity in feedstock oil is abundant. It was assumed that the gas flow state in the riser reactor is isothermal piston flow and the outlet temperature of the reaction zone was assumed as the reaction temperature of that zone, though the whole MIP-CGP reaction system should be considered as endothermic.17,18 The inner diffusion of catalyst particles was neglected because the size of catalyst particles in the riser reactor was quite small, and the external diffusion was neglected because the linear velocity of gas was high enough in the riser reactor. The model basic equation was deduced from the continuity equation, reaction rate equation, and other auxiliary equations:19
2. SIMULATION DETAILS 2.1. Establishment of Reaction Network. Based on the property of feedstock oil and four-component analysis, the feedstock oil was divided into three lumps: saturates (SS), aromatics (SA), and resin−asphaltenes (SR) (the asphaltene is rare in feedstock oil and its reacting characteristics are similar to resin, so the resin and the asphaltene were merged into a lump). Taking the actual situation of the MIP-CGP process and the available data gathered from a refinery into consideration, the products were divided into five lumps: diesel (D), gasoline (G), liquefied gas (LPG), dry gas (DR), and coke (C). Therefore, the model contains eight lumps in total. It is applicable to predict the main product yields of the MIP-CGP process. As we know, the catalytic cracking of heavy oil is a complex parallel and sequential reaction system and it can be simply described as in Figure 2. A significant characteristic of this reaction system is that reaction depth has important influence on distribution of each product yield. With the increase of reaction time, the conversion of feedstock is rising and the yield of final productsgas and cokeis also steady rising. But the yields of diesel and gasoline as intermediate products in the process of catalytic cracking have a peak. In order to improve
ρ da = −Ka φ(tc) f (A) f (N) dX S WH
where three deactivation functions, f(A), f(N), and φ(tc), are put forward because of the catalyst deactivation. a is the composition vector of lumps, K is the matrix of reaction rate constants, X is the dimensionless reactor length (relative distance), SWH is the weight-hourly space velocity (s−1), and ρ is the gas density (kg·m3).
Figure 2. Catalytic cracking reaction of heavy oil. B
DOI: 10.1021/acs.energyfuels.6b02208 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 1. Properties of CGP-1J Catalyst bulk density (g·mL−1) −1
sp surf. area (m ·g ) 2
114
−1
pore vol (mL·g )
aerated
sedimentary
0.826
0.887
0.203 particle size distribution (%)
compacted
microactivity index (%)
1.018 metal content (μg·g−1)
65.0
0−20 μm
20−40 μm
40−80 μm
>80 μm
Ni
V
Fe
Na
Cu
Sb
10.31
22.75
41.59
25.35
3535
1501
2307
1375
31
2367
The expressions are as follows.
Table 2. Four Components of Feedstock Oil
catalyst time-varying deactivation function: φ (t ) = e
four components (wt %)
−αt
heavy aromatic adsorption deactivation function: f (A) =
1 1 + kACA /R C/O
basic nitrogen adsorption deactivation function: f (N) =
1 1 + kNC Nt /R C/O
where α is the catalyst time-varying deactivation coefficient, t is the catalyst retention time (s), kA is the heavy aromatic adsorption coefficient, kN is the basic nitrogen adsorption coefficient, CA is the mass fraction of heavy aromatics in feedstock oil (wt %), CN is the mass fraction of basic nitrogen in feedstock oil (wt %), and RC/O is the catalyst/oil ratio. With regard to less than 0.04% of basic nitrogen in feedstock oil and high catalyst/oil ratio, the basic nitrogen adsorption deactivation can be neglected because of its insignificance,20 which means f(N) = 1. 2.3. Method to Optimal Parameters. The eight-lump kinetic model for the MIP-CGP process involves 22 reaction rate constants; in other words, there are 22 pre-exponential factors and 22 reaction activation energies that should be taken into account according to the Arrhenius equation. Besides, with the consideration of heavy aromatic adsorption deactivation and catalyst time-varying deactivation, the model also includes the heavy aromatic adsorption factor kA and catalyst timevarying deactivation factor α. All of these are the key to establish and analyze the eight-lump reaction kinetic model. The model kinetic parameters were obtained by MATLAB as a programming platform. The classical Runge−Kutta method was applied to solve differential equations, and the genetic algorithm was applied to optimize the parameters to fit the objective function. The objective function of optimization is m
obj =
set no.
saturates
aromatics
resins and asphaltenes
1 2 3 4 5 6 7 8 9 10 11 12 13 14
50.89 51.51 50.91 48.47 53.78 51.74 52.14 50.77 51.82 53.78 50.89 50.91 51.74 53.78
36.59 37.85 39.22 41.42 34.50 38.12 37.88 39.08 37.64 34.50 36.59 39.22 38.12 34.50
12.52 10.64 9.87 10.11 11.72 10.14 9.98 10.15 10.54 11.72 12.52 9.87 10.14 11.72
Table 3. Product Distributions (wt %) set no.
diesel
gasoline
LPG
dry gas
coke
slurry oil
1 2 3 4 5 6 7 8 9 10 11 12 13 14
30.86 30.33 30.15 31.00 29.41 30.60 30.43 31.18 30.38 29.78 30.42 30.20 29.60 30.40
43.66 45.37 46.53 46.06 46.83 45.66 45.49 46.06 46.53 46.32 44.71 47.38 46.35 45.88
12.29 11.70 11.33 10.89 11.67 11.43 11.70 11.04 11.78 10.99 12.21 10.91 12.08 11.04
2.35 2.79 2.48 2.49 2.46 2.75 2.57 2.53 2.36 2.63 2.44 2.21 2.54 2.36
6.24 5.64 5.62 5.57 5.55 5.47 5.75 5.61 5.30 5.83 5.79 5.35 5.70 5.48
4.63 4.17 3.90 3.99 4.07 4.09 4.06 3.57 3.65 4.44 4.44 3.95 3.72 4.82
optimization, and data sets 11−14 were used to verify the model parameters.
3. RESULTS AND DISCUSSION 3.1. Parameter Analysis. In the established model, the recycle process of residual slurry oil in the industrial unit must be taken into account. Although the quantity of slurry oil is not very high, it has a certain influence on catalytic reaction since its composition is quite different from that of fresh feedstock, and it will decrease the accuracy of the model prediction if we do not consider its effect. However, the obtained industrial data did not include the composition of slurry oil and its composition must be decided during the future model prediction. The iterative estimation method was used to decide it. First we assume the composition of slurry oil, then using MATLAB software we calculate the reaction result after mingling it with fresh feedstock oil at a certain proportion.
n 2
∑ ∑ (xj ,cal − xj ,real) i=1 j=1
xj,cal is the model calculation value of the j lump, and xj,real is the measured industrial value of the j lump. There are m groups of data and n lumps. Catalyst is very important in the MIP-CGP process. The adopted catalyst is CGP-1J in the industrial unit described in this paper. The properties of CGP-1J catalyst are shown in Table 1. Four components of feedstock oil are shown in Table 2, correspondingly, product distributions are shown in Table 3, and the operating conditions of the MIP-CGP riser reactor are shown in Table 4. Data sets 1−10 were used for parameter C
DOI: 10.1021/acs.energyfuels.6b02208 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 4. Operating Conditions of MIP-CGP Processa reaction temperature (K)
3.1.1. Reaction Rate Constants. The reaction rate constants directly represent the speed of reaction. This can be seen from Table 5 as follows: (1) The diesel is mainly produced by the aromatics, resins, and asphaltenes. The reaction rate for different lumps generating gasoline is negatively correlated with its aromatic number, because the aromatic ring is hard to open under the reaction conditions of catalytic cracking. The trend of feedstock oil generating main products is fast in this model and is the main process. (2) The contribution degree for generating the LPG is large by resin−asphaltenes and aromatics, which is due to the existence of aromatic rings. These aromatic hydrocarbons easily trigger the dealkylation reaction. The short side chain alkyl directly enters the lump of LPG after removal from aromatic rings. (3) The reaction rates of generating the dry gas are small. As the product of the thermal cracking reaction, dry gas usually forms quickly and largely under the ultrahigh temperature (>520 °C). Under the reaction conditions of this study, although there are bits of the thermal cracking reaction, the rate is far less than that of the catalytic cracking reaction. (4) The reaction rates for forming the coke by aromatics and resin−asphaltenes are very high. It is thus clear that the rate of coke formation is associated with aromatic content. Also, the trend of gasoline generating coke is the least. It is obvious that the larger the relative molecular mass is, the easier the reaction of coke formation is triggered, which accords with the coke formation rule of hydrocarbons. 3.1.2. Reaction Activation Energy. Reaction activation energy reflects the reactive obstacle, and it also expresses the degree of sensitivity of the reaction to temperature. The following can be seen from Table 5: (1) The activation energies that need to be provided for reaction of feedstock oil to the diesel and gasoline are small, which shows that feedstock oil is prone to cracking into diesel and gasoline. (2) Saturates and aromatics easily crack into diesel. Resins and asphaltenes contain more polycyclic aromatic hydrocarbon which has highly molecular rigidity; thus it is not easy to crack. Aromatics, resins, and asphaltenes are difficult to generate gasoline. Therefore, the higher the content of aromatic hydrocarbon is, and the worse the cracking performance is. (3) The activation energies of the cracking reaction for diesel or gasoline to gas (contains LPG and dry gas) are generally larger than that for feedstock oil to gas. It is evidently explained that the deeper the cracking degree is, the higher the reaction temperature is needed to overcome the bigger barrier. (4) Comparing activation energies of all coke formation reactions shows that bigger molecules have lower energy barriers, and reaction temperature has a bigger influence on the reaction rates of small molecules. The above analysis shows that the estimated kinetic parameters comply with the rules of the hydrocarbon catalytic cracking reaction and reflect the characteristics of the MIPCGP process. 3.2. Model Verification. The data for model verification was collected from the same industrial unit using the same catalyst, and the calculated values of the model and the industrial values are listed in Table 7. The error between the calculated values and the industrial values are very low. All absolute errors are below 1.7%, and the relative errors are within 9%. In the four sets of data listed, the average relative
residence time (s)
set no.
T1
T2
t1
t2
P (kPa)
catalyst/ oil ratio
recycle ratio
1 2 3 4 5 6 7 8 9 10 11 12 13 14
793.72 789.66 783.94 782.31 783.19 786.73 787.06 787.68 784.30 780.49 791.32 782.71 787.58 779.44
766.32 765.93 760.12 765.45 764.93 761.28 759.70 762.89 768.37 763.47 764.15 756.22 765.31 762.50
0.94 0.96 0.93 0.96 0.96 0.95 0.94 0.94 0.94 0.98 0.94 0.94 0.95 0.96
4.73 4.72 4.66 4.80 4.78 4.71 4.73 4.75 4.66 4.92 4.73 4.70 4.72 4.80
215.98 214.25 210.17 213.13 214.64 218.56 209.80 214.72 210.48 214.48 208.13 212.48 215.91 212.96
3.58 3.61 3.62 3.68 3.71 3.47 3.80 3.87 3.74 3.76 3.77 3.54 3.60 3.63
0.11 0.08 0.10 0.10 0.11 0.07 0.09 0.08 0.10 0.10 0.12 0.09 0.10 0.10
T1 means the reaction temperature of first reaction zone (outlet), and T2 means the reaction temperature of second reaction zone (outlet). t1 is the residence time of catalyst in the first reaction zone, and t2 is the residence time of catalyst in the second reaction zone. P is the reaction pressure.
a
Its new composition is obtained via the calculation of catalytic cracking reaction in the riser, and the final composition of slurry oil can be determined by iteration. The simplified block diagram for determination of slurry oil composition is shown in Figure 4.
Figure 4. Block diagram for determination of slurry oil composition.
Table 5 lists the optimal parameters of the pre-exponential factor (k0,i), the reaction activation energy (E0,i), and the calculated reaction rate constants (k1,i, k2,i) at 788.15 and 763.15 K. Table 6 lists the average relative error and the maximum relative error between the calculated values and measured values of data sets 1−10. As the main products of the MIPCGP process, the average relative errors of diesel and gasoline are 0.92 and 1.00%, respectively. Though the maximum relative error of dry gas is 15.75%, considering its small amount, it is still acceptable. D
DOI: 10.1021/acs.energyfuels.6b02208 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels Table 5. Optimal Kinetic Parameters of Eight-Lump Kinetic Model for MIP-CGP Processa
a
reaction no.
reaction pathway
k0,i (m3·kg−1·h−1)
E0,i (kJ·mol−1)
k1,iT1 (m3·kg−1·h−1)
k2,iT2 (m3·kg−1·h−1)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
SS → D SS → G SS → LPG SS → DR SS → C SA → D SA → G SA → LPG SA → DR SA → C SR → D SR → G SR → LPG SR → DR SR → C D→G D → LPG D → DR D→C G → LPG G → DR G→C
× × × × × × × × × × × × × × × × × × × × × ×
63.59 42.92 82.05 99.62 105.27 63.90 72.57 94.66 106.97 99.83 89.05 84.32 114.04 93.18 98.76 61.46 125.33 112.89 111.84 117.47 106.27 127.64
68.84 181.36 62.21 0.01 3.22 154.68 112.55 9.82 17.08 30.42 98.72 0.15 17.47 4.28 14.01 15.36 0.02 0 4.26 0.25 1.46 0.03
50.09 146.33 41.28 0.01 1.90 112.38 78.30 6.12 10.00 18.47 63.25 0.10 9.88 2.68 8.55 11.30 0.01 0 2.43 0.14 0.86 0.01
1.13 1.27 1.71 4.01 3.05 2.66 7.26 1.85 2.10 1.26 7.88 5.76 6.32 6.41 4.92 1.82 5.03 9.89 1.10 1.50 1.61 7.79
6
10 105 107 104 107 106 106 107 108 108 107 104 108 106 107 104 106 104 108 107 107 106
T1 = 788.15 K, T2 = 763.15 K, kA = 2.07, and α = 0.13.
obtain credible calculation results for different feedstocks and operation conditions. 3.3. Influence of Reaction Conditions. With the established model for the MIP-CGP process, the calculated product distribution and yield under different operating conditions of the riser can provide an important reference for the optimization of industrial operation conditions. The feedstock composition and operating conditions are as same as those for data set 5, which can be checked from Tables 2 and 4 except for the operation variables which will be investigated during the prediction calculation. 3.3.1. Influence of Temperature. The riser reactor of the MIP-CGP process is designed to have two reaction zones with different temperatures. The temperature in the second zone is often lower than that in the first zone by a shock chilling medium that filled the transition region between the two reaction zones. Based on the physical truth of the industrial unit, we assume 22 °C is reduced by this medium while analyzing the influence of reaction temperature on the product distribution with the model. Figure 5 shows the impact of changing output temperature of the first zone on product yields under these operating conditions. It can be seen from Figure 5 that both the diesel and gasoline yields first have a slow growth, which is due to the catalytic activity of continuous rising with the increase of temperature. But the rise of temperature also accelerates the gasoline cracking to gas and coke. When the rate of gasoline decomposing into the gas is greater than that of gasoline generation, the net gasoline production begins to decrease. The diesel yield has a similar trend. Therefore, it is really necessary to select an optimal reaction temperature for increasing the gasoline and diesel output. Besides, the yield of gas and coke is continuously rising when the temperature is increasing, which also should be considered to make the selection. As shown in Figure 5, in the reaction temperature range from 765 to 795 K, the product yield of diesel is high and more than
Table 6. Relative Error between Calculated Values and Industrial Data relative error (%)
diesel
gasoline
LPG
dry gas
coke
average maximum
0.92 2.57
1.00 1.99
2.59 6.73
6.85 15.75
4.34 8.87
Table 7. Calculated Values from Eight-Lump Kinetic Model and Industrial Data of MIP-CGP Process set no. 11
12
13
14
component
calculated (wt %)
industrial (wt %)
absolute error (%)
relative error (%)
diesel gasoline LPG dry gas coke diesel gasoline LPG dry gas coke diesel gasoline LPG dry gas coke diesel gasoline LPG dry gas coke
30.68 44.92 11.74 2.62 5.88 30.51 45.74 10.85 2.42 5.25 30.19 46.26 11.67 2.67 5.78 30.02 46.16 11.60 2.37 5.25
30.42 44.71 12.21 2.44 5.79 30.20 47.38 10.91 2.21 5.35 29.60 46.35 12.08 2.54 5.70 30.40 45.88 11.04 2.36 5.48
0.26 0.21 −0.47 0.18 0.09 0.31 −1.64 −0.06 0.21 −0.1 0.59 −0.09 −0.41 0.13 0.08 −0.38 0.28 0.56 0.01 −0.23
0.85 0.47 −4.00 6.87 1.53 1.02 −3.59 −0.55 8.68 −1.90 1.95 −0.19 −3.51 4.87 1.38 −1.27 0.61 4.83 0.42 −4.38
errors of diesel, gasoline, LPG, dry gas, and coke are 1.27, 1.21, 3.22, 5.21, and 2.30%, respectively. In fact, the prediction errors of other conditions not listed here are also within a reasonable scope. Overall, the model has good prediction ability, and can E
DOI: 10.1021/acs.energyfuels.6b02208 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels
The essence of increasing the catalyst/oil ratio aims at increasing the catalyst circulation volume, so the surface area of catalyst touched with feedstock oil increases in the reaction zone, meanwhile reducing the amount of carbon deposition on the surface of the catalyst unit and reducing the rate of catalyst deactivation, which increase the conversion rate of the feedstock oil and promote the cracking reaction. When the catalyst enters the second reaction zone, the hydrogen transfer and isomerization reaction are also promoted because of the enhanced activity. 3.3.3. Simultaneous Influences of Temperature and Catalyst/Oil Ratio. In this section, the influences of temperature and catalyst/oil ratio are considered simultaneously. The temperature of the first reaction zone ranges from 770 to 790 K, and the catalyst/oil ratio ranges from 4.5 to 5.5 in this optimization. The impacts of temperature and catalyst/oil ratio are shown in Figure 7. When the temperature of the first Figure 5. Impact of changing output temperature of the first zone on product yields.
29.5%; in the range from 770 to 790 K, the yield of gasoline is high and more than 46%. Considering other factors such as reducing LPG, dry gas, and coke and increasing diesel and gasoline, the temperature of the first reaction zone ranging from 770 to 790 K is the best selection in this optimization. 3.3.2. Influence of Catalyst/Oil Ratio. Figure 6 shows the impact of the changing catalyst/oil ratio on the product yield
Figure 7. Impacts of changing temperature and catalyst/oil ratio on product yields.
reaction zone is 770 K (the temperature of the second reaction zone is 748 K accordingly) and the catalyst/oil ratio is 5.5, the gasoline yield is highest with the value of 48.88%.
4. CONCLUSIONS An eight-lump reaction kinetic model was set up based on the reaction mechanism, which is suitable for the MIP-CGP process. This model divides feedstock oil into three lumps by using four-component analysis, and accurately reflects the differences of reactive properties for different lumps of feedstock oil. The progress of calculating reaction kinetic parameters with industrial data is more concise and accurate. Twenty-two groups of reaction kinetic parameters were estimated which can well reflect the characters of the MIPCGP process. The model can accurately predict the main product distribution with different compositions of feedstock oil under different operating conditions. The relative errors of the main product yield are quite small, which can meet the requirements of industrial application. Under the different reaction temperatures and catalyst/oil ratios, the predicted results of different product distributions are basically in
Figure 6. Impact of changing catalyst/oil ratio on product yields.
under these operating conditions. Within the scope of the catalyst/oil ratio shown in Figure 6, the gasoline yield is continuously rising with its increase, which means that the high ratio is beneficial to gasoline generation. As for diesel, it first increases by increasing the ratio; when the ratio reaches 3.5, the diesel yield begins to decrease. The yield of gas and coke increases slowly with the rising of the catalyst/oil ratio. The diesel yield can reach 28% when the catalyst/oil ratio ranges from 2.5 to 5.5, and the gasoline yield is over 47% when the catalyst/oil ratio is larger than 4.5. Taking all factors into consideration, the best catalyst/oil ratio ranges from 4.5 to 5.5 in this optimization. F
DOI: 10.1021/acs.energyfuels.6b02208 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels
(19) Xiong, K.; Lu, C. X.; et al. Quantitative Correlations of Cracking Performance with Physiochemical Properties of FCC Catalysts by a Novel Lump Kinetic Modeling Method. Fuel 2015, 161, 113−119. (20) Xu, O. G.; Su, H. Y.; et al. 7-lump Kinetic Model for Residual Oil Catalytic Cracking. J. Zhejiang Univ., Sci., A 2006, 7 (11), 1932− 1941.
conformity with the rule of the catalytic cracking reaction and the reactive characteristics of the MIP-CGP process, providing direct guidance for the industrial operation optimization.
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AUTHOR INFORMATION
Corresponding Author
*Tel.: 86-21-64252816. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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REFERENCES
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DOI: 10.1021/acs.energyfuels.6b02208 Energy Fuels XXXX, XXX, XXX−XXX
