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Integrating CDU, FCC and Product Blending Models into Refinery

Planning

Li Wenkaia, Chi-Wai Huia,*, AnXue Lib

aChemical Engineering Department, Hong Kong University of Science and Technology,Clear Water Bay, Hong Kong

bDaqing Refining & Chemical Company, PetroChina Company Limited.

ABSTRACT

The accuracy of using linear models for crude distillation unit (CDU), fluidize-bed catalytic cracker (FCC) and

product blending in refinery planning has been debated for decades. Inaccuracy caused by nonrigorous linear

models may reduce the overall profitability or sacrifice product quality. On the other hand, using rigorous

process models for refinery planning imposes unnecessary complications on the problem because these models

lengthen the solution time and often hide critical issues and parameters for profit improvements. To overcome

these problems, this paper presents a refinery planning model that utilizes simplified empirical nonlinear process

models with considerations for crude characteristics, products’ yields and qualities, etc. The proposed model

can be easily solved with much higher accuracy than a traditional linear model. This paper will present how the

CDU, FCC and product blending models are formulated and applied to refinery planning. Several case studies

are used to illustrate the features of the refinery-planning model proposed.

Keywords: Refinery, Planning, CDU, FCC, Product Blending

1. Introduction

1.1 Two types of CDU and FCC models

Crude Distillation Unit (CDU) and Fluidize-bed Catalytic Cracking (FCC) are the major units in a refinery. To

model them, two types of modelsrigorous and empirical onesare commonly used. Rigorous models

simulate a CDU as a general distillation column, taking into account phase equilibrium, heat and mass balances

along the whole column. Results of a rigorous model include flow rates and compositions of all internal and

* Author to whom correspondence should be addressed. E-mail: kehui@ust.hk

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external streams, and operating conditions such as tray temperatures and pressures. Considerable research has

been carried out with the aim of developing and/or improving rigorous CDU models. For example, Cechetti et

al. (1963) applied simultaneous modeling of the main column and side strippers using the θ method. Their

algorithm may fail to converge when modeling a CDU. Hess et al. (1977) extended this approach and proposed

a “Multi-θ method” to increase the convergent speed and broaden the generality of the algorithm. Russell (1983)

used a rather complicated “inside-out” class of methods to simulate CDU with good speed and wide

specifications variety. Lang et al. (1991) proposed an algorithm that integrated BP (Bubble-Point) and SR (Sum-

Rates) methods and showed that their calculated values and the experimental data were in good agreement. In

addition to these, some commercial software packages, such as Aspen Plus® (Aspentech), PRO/II® (SimSci-

Esscor) and DESIGN IITM (ChemShare), have also been developed and are commonly used. These accurate

simulation models are highly nonlinear due to the complexity of CDU.

Empirical models use empirical correlations to establish material and energy balances for CDU. First proposed

by Packie (1941), these models were further described in great detail by Watkins (1979). They are good for

preliminary designs with sufficient plant data and/or experience from previous designs (Perry et al, 1997).

Because of their simplicity, relatively easy application and adequate accuracy to reflect actual conditions of a

CDU, empirical models are suitable for overall optimization of a refinery.

Besides the CDU, FCC is another important unit that strongly influences the profitability of a refinery. Many

researchers have studied FCC models. Blanding (1953) developed a mathematical model based on a kinetic rate

expression. Jacob et al. (1976) proposed a more rigorous model using the concept of lumping groupings. These

kinetic models can be used to calculate the conversion of FCC from operation parameters such as reaction

temperature, feed composition, catalyst/oil ratio, etc. However, a planning model incorporated with these

models will be rather complicated and slow. Some correlations have been developed to obtain the yield of FCC

from simple feed properties and known conversion. Nelson (1958) and Gary et al. (2001) described different

methods to obtain the yields of FCC products by predetermined charts and figures. These correlations are very

useful for obtaining typical yields for preliminary studies and to determine the trends of product yields when

changes are made in conversion levels (Gary et al., 2001).

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1.2 Current approaches to refinery planning

Mathematical programming has been extensively studied and implemented for long-term plant-wide refinery

planning. Although accurate results of processing units can be obtained by using rigorous models, their

complexity and the length of the solution time prevent them from being used commonly. Using rigorous models

for planning might be an overkill (Barsamian, 2001). The inefficiency of solution often hides critical issues and

parameters13. Some commercial software, such as Aspen PIMSTM (Aspentech), applied nonlinear recursion

algorithm to handle nonlinearities or provided interface to an external rigorous simulator to refinery planning.

This could be a time-consuming procedure due to the long solution time of external simulator. Zhang et al.

(2001) took into account the effect of changes in feed properties and operation conditions, using a linear

constraint with some parameters (e.g., the base yields of CDU fractions and the sizes of swing cuts) not directly

available in most of the refineries. In Zhang’s work, due to the inaccuracies arising from assuming fixed

volume/weight transfer ratios (the volume/weight percentage of a CDU fraction over the overall CDU feed) and

linear models of CDU and FCC, the cutpoints of CDU and conversion of FCC may not be rigorously optimized.

Results obtained in this way cannot guarantee that the properties of the final refinery products meet the required

specifications. Pinto et al. (1998, 2000) proposed a nonlinear planning model that took into account the

influences of feed properties and operation parameters such as severity and temperature on unit operation cost

and unit product yields. The overall accuracy of their planning model is limited due to the application of some

simple linear unit models such as FCC. Furthermore, the coefficients of highly nonlinear property calculation

correlations and the influences of operation condition on unit operation cost are not available in many refineries.

Appropriate tradeoff between the accuracy and the solvability of process unit models remains an essential

challenge in refinery planning and these will be the main concern to be addressed in this paper.

1.2.1 Approaches to modeling CDU in refinery planning

To include product yields and properties of the crude oil distillation in a refinery-planning model, approaches

that are lately reported include Fixed Yield Structure Representations model, Mode or Categorization model

(Brooks et al., 1999) and the Swing Cut model (Zhang et al., 2001). In the fixed yield structure representations

model, distillation behavior is predetermined using the crude assay with an external distillation simulation

program. The simulation program determines cuts at designated temperature, and then passes the resulting yield

and property information to the LP planning model (Trierwiler et al., 2001). Figure 1 illustrates the structure of

this approach (simplified figure from Trierwiler et al., 2001). A noticeable drawback of this approach is that the

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cutpoints of distillates are predetermined therefore cannot guarantee the optimality of the cutpoint settings for

CDU distillates. Some researchers (Trierwiler et al., 2001) applied a method called “Adherent Recursion” to

optimize cutpoints. The results of LP planning model (new cutpoints) were sent back to simulation software to

update the yields and properties. However, the long solution time of the simulation software running iteratively

made it a time-consuming procedure to obtain the final results.

In actual plant operation, CDU operations are often defined into several operating modes, such as gasoline mode

or diesel mode, according to the crude properties, process constraints and marketing strategies, etc. Each mode

has a set of predetermined cutpoints based upon the experience from the previous production settings. Until

now, quite a few of refineries are still using these operating modes for planning their operation due to the

simplicity of this method. In the mode or categorization approach, the LP planning model selects one of the

operation modes or the combinations of these modes to maximize the total profit. The challenge lies in how to

blend these modes effectively. Brooks et al. (1999) applied a visual approach using some figures to obtain

optimal plan by blending operating modes. They first calculated the yields and properties of CDU fractions

using rigorous CDU model. Then, taking into consideration the specifications of the final products, they used a

spreadsheet to blend CDU modes pair-wise in 1% steps. With the help of the spreadsheet, the procedure was

performed visually using some figures. However, applying their approach is rather time-consuming and, only

the yield of certain CDU fraction being maximized, the total profit maximization of a refinery is still not

guaranteed.

Another widely used method is the swing cut modeling. Several swing cuts physically nonexistent are defined in

the LP model. The definition of swing cut is illustrated in Figure 2 (Zhang et al., 2001). In Figure 2, GO (Gross

Overhead) and HN (Heavy Naphtha) are the two distillates of a CDU. In order to give the LP model the

flexibility of adjusting the volume transfer ratios of GO and HN, two adjustable pseudo-cuts, shown as the two

rectangles in Figure 2, are added. The range of a swing cut is defined as a certain ratio on the crude feed

bounded by a lower and upper limit. For example, segment B-D defined the amount of a cut (say 5% of the

overall crude fed) that can go to either GO or HN. The final volume transfer ratio of GO is shown as segment

A-C. Similarly, after the apportionment of the HN swing cut, the final volume transfer ratio of HN can be shown

as segment C-E.

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Hartmann (1999) used swing cut, called “balancing cut” in his paper, to address the problem of setting cutpoints

of a CDU. The cutpoints were changed after the analysis of the marginal values of intermediate streams and

units. Zhang et al. (2001) determined the optimal flow rates of CDU fractions on the basis of fixed swing cuts.

They fixed the size of a swing cut to a certain proportion of the total feed whose value is not available directly

from a refinery.

In general, two issues need to be considered in swing cut modeling: the sizes of swing cuts and the properties of

the cut fractions. The size of a swing cut can either be expressed as certain volume transfer ratio on crude feed

or as certain boiling temperature range. Some researchers estimate the size of a swing cut by experience. Zhang

et al. (2001) used 5% and 7% volume transfer ratio on crude feed as the sizes of naphtha and kerosene swing

cuts respectively. A typical 50 degrees of boiling temperature range, can also be set to swing cuts (Trierwiler, et

al., 2001). Modelers commonly use a rather wide swing cut sizes in their initial LP run, and shorten the swing

cut sizes subsequently. This is a time-consuming procedure and also risks blocking an optimum cutpoint value

out of consideration (Trierwiler et al., 2001). Since the accurate sizes of swing cuts are unknown, some

researchers divide swing cuts into small segments in an attempt to improve modeling accuracy. Each segment is

allowed to be blended with adjacent distillates individually. While this approach may improve accuracy, the size

of the LP model grows significantly. This approach also involves applying complex mixed integer programming

to obtain reasonable results (Trierwiler et al., 2001).

In this paper, an effective method is proposed (see section 3) to determine the sizes of swing cuts. These sizes

are obtained by using the WTRs of CDU fractions, which are calculated using the empirical procedure described

by Watkins (1979) and ASTM boiling ranges for CDU fractions. Once the WTRs/swing cuts are determined, a

planning model is then used to optimize cutpoints of CDU.

The second issue is about the properties of swing cuts and fractions. Most of the research works of refinery

planning assumed that the properties of CDU fractions and the swing cut materials are constant across their

temperature ranges. However, moving a swing cut to its adjacent lighter distillate will bring heavy ends to this

lighter distillate. This will influence the properties such as octane number, pour point of the lighter distillate, and

the sulfur and cloud point that are sensitive to heavy ends. Similarly, moving a swing cut to its adjacent heavier

distillate will bring light ends to this heavier distillate, which will influence the octane number, pour point of the

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heavier distillate, especially properties such as viscosity and flash point that are sensitive to light ends. Besides

being influenced by swing cuts, distillate properties themselves are most often highly nonlinear, and this is the

primary area where swing cut modeling fails to represent distillation behavior accurately (Trierwiler et al., 2001).

To address these problems, regression models based upon crude properties will be used to calculate the octane

numbers, pour points and API gravities of CDU distillates. Case studies are used to illustrate the importance of

the properties calculation.

In brief, the proposed refinery planning model optimizes CDU cutpoints by integrating a set of predefined

operating modes into a modified swing cut method. The predefined CDU modes are used to determine the sizes

of swing cuts (expressed as Weight Transfer Ratio Ranges (WTR) which will be defined in section 3). Beside

CDU cutpoints, the properties of CDU fractions, which are usually ignored in conventional planning models, are

calculated using the basic crude data.

1.2.2 Approaches to model FCC in refinery planning

Pinto et al. (2000) used a linear model of FCC. Due to the nonlinearity of FCC behavior, a linear model of FCC

may give inaccurate yields and properties of FCC distillates. Figure 3 (Decroocq, 1984) shows a typical FCC

gasoline vs. FCC conversion level curve. The nonlinearity of this figure, especially in high conversion area, is

obvious. To accurately model FCC without introducing a too complex FCC model, a regression model based

upon the work from Gary et al. (2001) is applied in the proposed refinery planning model(section 4).

2. Problem description

An example shown in Figure 4 is used to illustrate the proposed modeling techniques and solution methods..

The refinery process contains four main processing units: CDU, FCC, GB (Gasoline Blending) and DB (Diesel

oil Blending). At first, crude oil is separated into five fractions by CDU, namely, gross overhead (GO), heavy

naphtha (HN), light distillate (LD), heavy distillate (HD) and bottom residua (BR). Then CDU bottom residua

enter FCC as a feed to produce C2~C4, FCC gasoline, TGO (Total Gas Oil) and coke. Part of TGO is recycled

to become FCC feed. Note that for simplicity, Vacuum Distillation Unit (VDU) was not included in the system.

CDU gross overhead, CDU heavy naphtha, FCC gasoline and MTBE enter GB to produce two products: 90#

gasoline and 93# gasoline. CDU light distillate and heavy distillate enter DB to produce another two products: -

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10# diesel oil and 0# diesel oil. C2~C4 from FCC and TGO, which is not recycled, are sold as final products.

Coke is assumed to be burned in regenerator thus valueless. The prices (yuan/ton) of raw materials and products

are shown in Table 1. The capacities of CDU and FCC are both 400 tons/day; the operation costs of CDU and

FCC are 20 and 110 yuan/ton, respectively. The market demand for each product is 200 tons/day. The octane

number of MTBE is 101. The blending requirement of gasoline blending is that the octane number of 90# and

93# gasoline products should be equal to or greater than 90 and 93, respectively. The blending requirement of

diesel oil blending is that the pour point of -10# and 0# diesel oil should be equal to or smaller than -10°C and

0°C, respectively. The objective of the problem is to maximize the total profit of the refinery by varying the

cutpoints of the CDU and conversion level of the FCC by taking into account the property changes of the

intermediate and final products.

3. Determination of the CDU Weight Transfer Ratio Ranges (WTR)

3.1. Determination of the volume transfer ratios of CDU fractions

The objective of this section is to describe the procedure of determining the flow rate range of each CDU

fraction. The ability of a refinery to meet all the specifications and conditions of final products is initially set by

the CDU fractions (Brooks et al., 1999). Thus the flow rates of CDU fractions are adjusted in a refinery all the

time to produce different quality and specification of products. However, these flow rates cannot be changed

arbitrarily, they can only be changed in their specific ranges. CDU is used to separate crude oil by distillation

into fractions according to boiling point. It is the first major processing unit in the refinery. Crude oil is a

mixture of some 100,000 liquid chemical compounds, primarily hydrocarbons ranging from methane to

extremely heavy hydrocarbon molecules with up to 80 carbon atoms. A CDU fraction is a mixture that usually

defined in terms of its ASTM (American Society for Testing Materials) boiling range. ASTM boiling range (see

Appendix A.1 for details) defines the general composition of the fraction and is usually one of the key

specifications for most distillates (Watkins, 1979). Different refineries have slightly different definitions of

ASTM boiling ranges for CDU fractions. According to the definitions of Watkins (1979), gross overhead

consists of light-ends through 250-275 °F ASTM end point; heavy naphtha consists of pentane through 400 °F

ASTM end point; light distillate has an ASTM boiling range of approximately 300-600 °F; heavy distillate has

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an ASTM boiling range of approximately 525-675 °F. All distillates heavier than heavy distillate are called

bottom residua. Bottom residua have an ASTM end point over 1300 °F.

Figure 5 shows the TBP curve of a crude oil. True boiling point (TBP) distillation (see Appendix A.1 for details)

is used to analyze the component distribution of a material being tested. This method uses a distillation column

with certain number of stages and reflux so that the temperature on the curve represents the actual (true) boiling

point of the hydrocarbon material present at the corresponding volume percentage (Watkins, 1979). The

volumetric yield (also expressed as volume transfer ratio) of a CDU fraction can be obtained from the crude oil

TBP curve and its boiling point. In Figure 5, points “A”, “B”, “C” and “D” represent the cutpoints of GO, HN,

LD and HD, respectively. Draw a dotted horizontal line through each point; then draw a dotted vertical line

through the intersection of the dotted horizontal line and the crude oil TBP curve. The gap (such as segments E-

F, F-G, G-H and H-I in Figure 5) between two neighbor dotted vertical lines determines the volume transfer

ratio of a CDU fraction. In Figure 5, the volume transfer ratios of GO, HN, LD, HD and BR are 11.5, 4.0,

21.0,11.5 and 52 (=100-48.0), respectively.

3.2. Determination of operation modes

Since a CDU fraction is still a mixture of many hydrocarbons, it has a boiling range. To meet the demand for

different specifications of products from different customers or to maximize the total profit, the refinery has to

adjust the operation conditions to change the properties of CDU fractions; hence the boiling ranges of CDU

fractions vary under different operation conditions. A typical ASTM boiling range of CDU fractions is listed in

Table 2 (Watkins, 1979). The EPs (End Point, the temperature at which a distillate is 100% vaporized) and IBPs

(Initial Boiling Point, the temperature at which a distillate begins to boil) of CDU fractions provided by Watkins

(1979) are adopted in Table 2. The IBPs of HN and BR, which were not included in Watkins (1979), were

estimated here (see Appendix A.2 for details). Note that most of the refineries provide ASTM boiling ranges to

define CDU fractions, from which the boiling ranges can be adopted in Table 2.

Although ASTM boiling ranges can be easily obtained and used conveniently for product identifications, they

cannot be used directly to estimate weight transfer ratios of CDU fractions. Thus ASTM boiling ranges should

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be converted to TBP boiling ranges. The conversion method is described in Appendix A.4. Table 3 lists the

converted TBP boiling ranges from the ASTM boiling ranges of Table 2.

Table 3 provides rough TBP ranges of the CDU fractions. For instance, if GO is the preferable product, the EP

of GO should be increased to its maximum value (290.9 °F); if HN is the preferable product, then a smaller

value (276.5 °F) is assigned to the EP of GO. With this understanding, the TBP boiling ranges of three CDU

operation modes can then be determined (Table 4). These operation modes are maximizing heavy naphtha

(M.N.), maximizing light distillate (M.L.) and maximizing heavy distillate (M.H.). The number of operation

modes defined above is relatively small and thus has a potential to reduce the size of a planning model. Note

that the three operation modes defined here are used for demonstrating the approach in this paper. Other sets of

operation modes, such as the frequently used five operation modes or the eight operation modes defined in

Brooks et al. (1999), are categorized for other CDUs according to their design and operation conditions. In fact,

the approach that we developed is independent of the number of operation modes. One can maximize the yield

of only one product on any given operation (Watkins, 1979). Thus a CDU can be at only one operation mode at

one time. A refinery can determine the operation of the CDU to be either at one of the operation modes or

somewhere among these operation modes.

3.3 Determination of Cutpoints

Due to the limitation of stage number and reflux ratio, the TBP boiling ranges of two adjacent CDU fractions

always overlap. To specify the separation temperature being used in conventional distillation columns between

two adjacent fractions, a “cutpoint” is used. It is defined as the mid-point of the TBP overlapping temperatures

(TBP cutpoint = 0.5*(EPL + IBPH), where EPL is the EP of the light fraction and IBPH is the IBP of adjacent

heavy fraction). The definition of TBP cutpoint between two fractions is shown in Figure 6 (Watkins, 1979).

The TBP cutpoint (point D) is the average temperature of the EP of light fraction (point A) and the IBP of heavy

fraction (point B). With the TBP cutpoints among fractions determined, the corresponding volume transfer

ratios of CDU fractions can then be obtained using the procedure described in Section 3.1.

3.4 Determination of VTR (Volume Transfer ratio Range)

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In a refinery, adjusting the cutpoints will change the volume transfer ratios (hence flow rates) and properties of

CDU fractions that affect the overall economics of the refinery. The cutpoints among CDU fractions can be

calculated using the procedure proposed in Section 3.3. These cutpoints are then used to determine VTR. The

maximum volume transfer ratio of a CDU fraction is called the upper limit of the VTR while the minimum

value, the lower limit of the VTR. This procedure is illustrated in Figure 7. For M.N. mode, using the data

given in Table 4, the cutpoints of HN can be calculated (256 °F for GO/HN and 372 °F for HN/LD). Then the

corresponding volume transfer ratio of HN is obtained 10.6% (=24.2%-13.6%) as shown by segment C-D.

Similarly, cutpoints in M.L. mode (263 °F for GO/HN and 299 °F for HN/LD) can be calculated, the

corresponding volume transfer ratio of HN is obtained 3.2% (= 17.4%-14.2%)) as shown by segment A-B. The

cutpoints in the M.H. mode are the same as those in the M.L. mode in this case, thus the volume transfer ratio of

HN in M.H. mode is the same. The upper limit of the VTR of HN is then 10.6% and the lower limit is 3.2%.

Thus the VTR of HN is (3.2%, 10.6%).

In a refinery, flow rates of CDU fractions are often based on weight. It is more convenient to express the ratios

of CDU fractions as weight transfer ratios. The volume transfer ratio in crude oil TBP curve should then be

converted to weight transfer ratio and the VTRs become WTRs. To perform this conversion, the API gravity

( 5.131141.5/d gravity API 15.615.6 −= , where 6.15

6.15d is the specific density at 60 °F) has to be used. This API gravity

is usually included in a crude assay. As an illustration, the crude assay data from Watkins (1979) are used here

to calculate the API gravity of crude oil and CDU fractions (see Appendix A.3 for details). For the example

illustrated in Figure 7, the corresponding WTR is (2.8%, 9.5%).

The VTR/WTR focuses on the transfer ratio range of a fraction while the commonly used swing cut is a pseudo-

cut that exists between two fractions. The sizes of swing cuts can be determined with the knowledge of

VTR/WTR, and vice versa. For the example illustrated in Figure 7, the size of the swing cut (if expressed as

volume ratio on crude feed) between GO and HN is 0.6% (= 14.2% – 13.6%) which is small and the size of the

swing cut between HN and LD is 6.8% (= 24.2% - 17.4%) which is rather large. The accurate sizes of swing

cuts can thus be determined by the procedure proposed in this paper. For easy integration of the CDU model

with the main planning model, VTR/WTR is used in this paper.

3.5. WTR determination procedure

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The procedure described in Sections 3.1 to 3.4 for determining the WTR of CDU fractions is summarized in this

section and illustrated in Figure 8. The manual procedure described by Watkins (1979), the accuracy of which is

tested by rigorous simulation in Appendix B.1, is used for computer calculation. The procedure uses ASTM

boiling ranges of CDU fractions and crude assay data available in most refineries. The detailed procedure

consists of four major steps as follows.

Step 1. The determination of ASTM D86 boiling ranges and operation modes

The ASTM boiling ranges of CDU fractions can be obtained from refineries, CDU designers or from literatures

(e.g., Watkins, 1979, Gary et al., 2001). These ASTM boiling ranges are used as the starting point of the

procedure proposed here. The ASTM boiling ranges used in this paper are listed in Table 2. These ASTM

boiling ranges are converted to TBP boiling ranges using the correlations developed by Watkins (1979) (see

Appendix A.4 for details). Other correlations (Arnold, 1985) for ASTM to TBP boiling range conversion can

also be used according to their accuracies for different crude oils. The converted TBP boiling ranges are listed in

Table 3 and the TBP boiling ranges of the three operation modes are then determined and listed in Table 4.

Step 2. Calculate the cutpoints for operation modes

The cutpoints for operation modes are calculated using the method described in Section 3.3. For example, to

calculate the cutpoint between GO and HN in the M.N. mode, we know from Table 4 that EPL is 276.5 °F and

IBPH is 235.4 °F, therefore the cutpoint of GO/HN is (276.5+235.4)*0.5 = 255.9 °F. The calculated cutpoints are

listed in Table 5.

Step 3. Calculate CDU fractions weight transfer ratios for the operation modes

The crude oil TBP data and CDU fractions API data from crude assay are correlated to form the crude oil TBP

equation and CDU fractions API equations (See Appendix A.3 for details). The calculated cutpoints for

operation modes (Table 5) are then sent to crude oil TBP equation to calculate the volume transfer ratios of

CDU fractions. For example, the cutpoint for GO/HN in the M.N. mode (255.9 °F) is sent to crude oil TBP

equation and then the volume transfer ratio of GO (13.61) in this mode can be obtained. The API gravity of each

fraction is calculated by inserting its volume transfer ratio into its API gravity equation. Using this calculated

API gravity, the volume transfer ratio of a CDU fraction is then converted to weight transfer ratio. The above

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procedure is performed for each operation mode to obtain the weight transfer ratios of CDU fractions in each

mode. The calculated API gravities of CDU fractions are listed in the last column of Table 6. The calculated

weight transfer ratios and volume transfer ratios for each operation mode are listed in the second last and the

third last columns of Table 6, respectively.

Step 4. Determination of WTR

After the weight transfer ratios corresponding to the operation modes obtained in Step 3, the maximum and

minimum weight transfer ratios are selected from the modes for each CDU fraction. The maximum and

minimum volume and weight transfer ratios are listed in the third and fourth columns of Table 6, respectively.

For example, the number 11.73 in the fourth column is the maximum value of the three numbers (11.17, 11.72,

11.73) in the second last column; the number 11.17 in the fourth column is the minimum value of the three

numbers (11.17, 11.72, 11.73) in the second last column. These maximum and minimum weight transfer ratios

are then sent to the main planning model as WTRs to optimize the cutpoints of CDU fractions. It is assumed that

the crude oil is Tia Juana Light and the crude assay data from Watkins (1979) are used in this paper. The

calculated WTRs are also compared with results of rigorous simulation in Appendix B.2.

4. Model for FCC fractions transfer ratios

4.1 Description of the procedure

A procedure for the determination of FCC fractions’ weight transfer ratios (the weight percentage of a FCC

fraction over the overall FCC feed) as a function of FCC conversion is proposed in this section. The major

operating variables affecting the FCC conversion level are the cracking temperature, catalyst/oil ratio, space

velocity, etc. The hand-calculation procedure described by Gary et al. (2001) is implemented in our proposed

procedure. The procedure is illustrated in Figure 9. Firstly, we obtained FCC fractions yield correlations (when

zeolite catalyst is used in FCC) from figures provided by Gary et al. (2001) (see Appendix C for details). Then

the feed properties, API gravity and Watson characterization factor were read. The lower limit and upper limit

of FCC conversion are determined according to FCC operation conditions such as the regenerator coke burning

ability. The conversion range used in this paper is (60%, 85%). This is followed up by a sequence of actions: Set

the conversion level to its lower limit (60%), perform FCC material balance according to the procedure

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described by Gary et al. (2001), and calculate the weight transfer ratios of FCC fractions. Next, there is the need

to increase the conversion by a small value (2%) and calculate the weight transfer ratios corresponding to the

current conversion level until the conversion level reaches its upper limit (85%). Finally, using the data obtained

above, FCC fractions weight transfer ratios and FCC conversion level are correlated. An equation of each FCC

fraction weight transfer ratio vs. FCC conversion level is now obtained and can be used in refinery planning

model to optimize the FCC conversion level. Table 7 lists the correlations for FCC fractions (the feed properties

is assumed to be: Watson characterization factor =11.8, API=19). In Table 7, “WT” represents the weight

transfer ratio of C2~C4 or FCC gasoline, etc. “Conv” represents the conversion level of FCC. For different feed

properties of FCC, one can apply the same procedure described above to obtain the same type of correlations

with different parameters.

4.2 Discussion of the procedure

The discrepancy between the correlations obtained above and the figures provided by Gary et al. (2001) is about

1~3%, which is within the accuracy of those figures. The procedure is much simpler and faster compared with a

rigorous FCC model. The required input (API gravity and characterization factor of the feed) can also be easily

obtained from refineries. The emphasis of the FCC model proposed in this paper was put on its solution speed

and the effectiveness so that it can be integrated into the main planning model directly. Since the input-output

relationship of FCC can be updated by some online learning methods or through the improvement of relevant

technologies, the FCC model can be readily updated with higher accuracy.

Further improvement for this procedure can be made by:

• Updating the figures provided by Gary et al. (2001). It is pointed out (Magee et al., 1993) that as the

improvement of catalysts and unit design, the yield data of FCC fractions will change and hence

corresponding figures should be updated. Besides, the yield of gasoline vs. conversion keeps increasing in

the figure provided in Gary et al. (2001). In reality, the yield of gasoline will decrease as the conversion

increases to a certain value.

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• It is assumed in this paper that the feed properties of FCC feed remain constant. However, the physical

properties of the feed will change as the recycle stock or the CDU operation conditions change. This should

be considered in future works.

5. Product Blending

Blending is a very important and complicated issue in refinery planning. As a demonstration, two commonly

used blending models are described in this section. However, the whole modeling concept is not limited to these

two blending models which can be replaced by other state-of-the-art models.

5.1 Blending rule

Some quality indicators, such as octane number and freezing point, are used to prove whether or not the gasoline

meets the quality specifications. In the case of diesel oil, pour point, cetane number and viscosity, among others,

are used as key quality indicators. In this paper, octane number (ON) and pour point (PP) are used as the quality

index of gasoline and diesel oil, respectively.

• Gasoline blending

In gasoline blending, the octane number of a blended product can be simply calculated using the following

linear equations:

ppii f*Of*O =∑

ff pi =∑

where iO is the octane number of intermediate stream i; if is the flow rate of the intermediate stream i; pO is

the octane number of product p; pf is the sum of if .

• Diesel oil blending

For diesel blending, diesel properties such as pour point cannot be calculated using a simple linear equation.

Some correlations have been proposed for diesel oil blending. Reid and Allen (1951) used linear combination of

pour point blending indexes of intermediate streams to predict product pour points. Hu and Burn (1970)

proposed a nonlinear one-parameter pour point equation. Semwal et al. (1995) proposed an improved nonlinear

correlation which is used in this paper:

)(T*)(V1

iA

i∑=

=n

i

BBbT

where bT is the pour point of product, Vi and Ti are volume fraction and pour point (in the Rankine degree, °R)

of intermediate stream i, respectively. Four sets of parameters “A” and “B” are given in different pour point

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ranges. The wide pour point range (from –21 to 51 °C) is used in this paper. The corresponding values of A and

B are 1.105 and 12.987, respectively.

5.2 Calculation of the properties of CDU fractions

The octane numbers or pour points of CDU fractions will change as the cutpoints of CDU change. In our

planning model, the octane numbers or pour points of CDU fractions are correlated to their mid-point weight

transfer ratios. The relationship between mid-point volume transfer ratio (Mid_VTR) and octane number/pour

point are given by Watkins (1979). In this paper, mid-point weight transfer ratio (Mid_WTR) is used instead of

Mid_VTR for consistence. The equations for relating mid-point weight transfer ratios and octane numbers/pour

points from the crude assay data provided by Watkins (1979) are given in Table 8. In Table 8, the outputs of

row “GO” and “HN” are octane numbers (ON) while the outputs of row “LD” and “HD” are pour points (PP).

6. Main flow diagram for solving the refinery planning model

The main flow diagram for solving the refinery planning model is illustrated in Figure 10. The whole procedure

consists of the following steps:

• Call CDU WTR determination model to calculate the maximum and minimum weight transfer ratios of

CDU fractions.

• Call FCC yield model to obtain equations of FCC fraction weight transfer ratio vs. FCC conversion. These

equations are used in the refinery model.

• Read initial data, which include the data of unit capacities, unit operation costs, initial octane numbers and

pour points and CDU WTRs.

• Integrate the CDU and FCC models with the main NLP planning model and solve the main model.

Compare to the rigorous CDU and FCC models, the solution time of the two CDU and FCC models proposed

here was reduced significantly. In most of the cases tested in this study, the CPU time needed to solve the main

planning model is 0.1 to 0.2 second.

7. Case studies

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Several case studies demonstrate the effectiveness of the CDU and FCC models proposed in this paper. The

refinery planning model is formulated in GAMS (Brooke et al., 1992) on a 933MHz Pentium III PC. The code

MINOS5 in GAMS 2.25 is used for NLP. The planning model is described in Appendix D.

The configuration of the cases studied here is illustrated in Figure 4. The price data for these cases are listed in

Table 1. The unit capacities, operation costs, market demands for products and blending requirements for

blending units are described in Section 2. The influences of different CDU cutpoint setting methods on total

profit will be studied in Section 7.1 while the influences of different FCC conversion level determination

methods on total profit and FCC fractions weight transfer ratios will be studied in Section 7.2. Finally Section

7.3 studies the influences of different methods of determining CDU fractions’ properties on total profit and the

weight transfer ratios of CDU fractions.

7.1 CDU cutpoints determination

Up to now, many refineries still use one of the operation modes, such as M.N., M.L. or M.H., as their CDU

operation condition, whose cutpoint setting of CDU fractions is not optimal. Different cutpoint setting methods

are compared here (The FCC conversion level and CDU fractions’ properties in these cases can be varied by the

planning model). Firstly, a refinery may fix the weight transfer ratios of CDU fractions at either M.N., M.L. or

M.H. mode. (The fixed weight transfer ratios of these modes are listed in Table 9). The corresponding solution

results are listed in the second to fourth rows of Table 10. The optimal solution (in which the weight transfer

ratios of CDU fractions are optimized) obtained by solving the planning model proposed in this paper, is listed

in the first row. It can be seen that the optimal cutpoint setting is located somewhere among the operation

modes. If the cutpoint’s setting is fixed for M.N., the profit will decrease by 24.5%. After the determination of

CDU WTRs from the proposed procedure and the data obtained from refineries, the optimal cutpoint can be

calculated by solving the planning model. A refinery should not set the CDU cutpoints according to any of the

operation modes arbitrarily. The optimal cutpoint should be obtained by solving the planning model with the

consideration of factors such as current market prices of raw materials and products, unit operation costs, and

processing constraints, etc. The results of this case study are mainly consistent with those of Brooks et al. (1999)

with total profit maximized and simpler procedure.

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7.2 FCC conversion optimization

The FCC severity (expressed as conversion level) is a very important parameter in a refinery. Similar to the

setting of the cutpoints, the optimal FCC conversion should also be decided according to the current situation.

Two cases are studied in this section (The CDU fractions’ weight transfer ratios and properties can be varied

and optimized). The FCC conversion is fixed at 65.0% in the first case while the FCC conversion level is

calculated and optimized in the planning model in the second case. The results are listed in Table 11. It can be

seen from the table that the total profit of the fixed conversion level case decreased by 2.1% compared to the

optimal conversion (73.4%) case. A refinery should then adjust the FCC operation conditions, such as reaction

temperature or catalyst/oil ratio, to change the conversion to the optimal value.

7.3 Physical properties calculation

Most of the research works of refinery planning still assumed that the properties of CDU fractions are constant.

However, following the change of the cutpoints, the properties of fractions will also change. From Appendix

B.3, it can be seen that the octane numbers of CDU fractions change several units in different situations. Table

12 lists the comparison of different calculation methods of properties (The CDU weight transfer ratios and FCC

conversion level can be varied by the planning model). In the first row, the octane numbers and pour points are

calculated whenever the cutpoint settings are changed while the octane numbers and pour points are estimated

and fixed in the second and the third rows. After the planning model is solved (the first row), the final properties

can be obtained. For example, it is found that the final octane number of CDU gross overhead is 60.9. This

value can be used in gasoline blending to calculate the yield of final gasoline products. If the properties are

estimated and fixed to certain values, for example, the octane numbers of GO and HN are fixed to 50.0 and 65.0

respectively and the pour points of LD and HD to –40.0 and 5.0 degree C respectively, then the total profit may

be underestimated (the second row) or overestimated (the third row) and the corresponding CDU cutpoint

setting and product yields will also be influenced. The reason is that the product quality in the second row was

underestimated (The real octane number of 90# gasoline is 92.7) while the product quality in the third row was

overestimated (The real octane number of 90# gasoline is 88.3). The refinery may lose the potential of earning

more money (the second row) or risk the products refused by customers (the third row). Thus it is important to

calculate the properties of CDU fractions using some correlations.

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8 Conclusions

In this paper, the optimal planning strategies of refineries are studied and a procedure for the CDU WTRs

determination is proposed. A yield model is used for the determination of equations of FCC fractions’ weight

transfer ratios vs. FCC conversion level. With the CDU and FCC models integrated into the planning model, the

CDU cutpoints and FCC conversion level can be optimized accurately. The properties of CDU fractions are

calculated in the model to reflect the influence of CDU cutpoints’ changes that guarantee the quality of the final

products. Finally, several case studies are described and solved using the proposed planning model to illustrate

the significance of the CDU and FCC models and the calculation of CDU fractions’ properties.

Acknowledgments

The authors would like to acknowledge financial support from the Research Grant Council of Hong Kong

(Grant No. HKUST6014/99P & DAG00/01.EG05), the National Science Foundation of China (Grant No.

79931000) and the Major State Basic Research Development Program (G2000026308).

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References

Arnold, V. E. (1985) Microcomputer program converts TBP, ASTM, EFV distillation curves, Oil & Gas Journal. 83(6) Feb 11, 55-62

ASPEN PLUS, version 11.1; Aspen Technology, Inc. : Cambridge, MA, 2001

Barsamian, A. (2001) Fundamentals of Supply Chain Management for Refining, IBC Asia Oil &Gas SCM Conference Proceedings, April

26, Singapore

Blanding, F. H. (1953) Reaction rates in Catalytic Cracking of Petroleum, Industrial & Engineering Chemistry, 45(6), 1186~1197

Brooke, A., Kendrick, D. & Meeraus, A. (1992) GAMS — A User’s Guide (Release 2.25); San Francisco, CA: The Scientific Press.

Brooks, R.W. et al. (1999) Choosing cutpoints to optimize product yields, Hydrocarbon Processing, November, 53-60

Cechetti, R.C. et al. (1963) Hydrocarbon Processing, 42(9), 159

Decroocq, D., (1984) Catalytic Cracking of Heavy Petroleum Fractions, Editions Technip.

Gary, J. H., & Handwerk, G. E. (2001) Petroleum Refining Technology and Economics, 4th Ed., Marcel Dekker

Hartmann, J.C.M. (1999) Interpreting LP outputs, Hydrocarbon Processing, February, 64-68

Hartmann, J.C.M. (2001) Determine the optimum crude intake level—a case history, Hydrocarbon Processing, June, 77-84

Hess, F.E. et al. (1977) Hydrocarbon Processing, 56(6), 181

Hu, J., & Burns, A.M. (1970) New Method Predicts Cloud, Four Flash Points of Distillate Blends, Hydrocarbon Processing, 49(11), 213-16

Jacob, J.S., Gross, B., Voltz, S.E. & Weekman, V.W. (1976) A lumping and reaction scheme for catalytic cracking, AIChE J., 22(4),

701~713

Lang, P., et al. (1991) Modelling of a crude distillation column, Computers and Chemical Engineering, 15(2), 133-139

Magee, J.S., Maurice, M. & Mitchell, J. (1993) Fluid catalytic cracking: science and technology, Amsterdam : Elsevier

Nelson, W.L. (1958) Petroleum Refinery Engineering, 4th Ed., McGraw-Hill Book Co., Inc.

Packie, J.W. (1941) Distillation equipment in the oil-refining industry, AIChE Transactions, 37, 51-78

Perry, R. H., Green, D. W., & Maloney, J. O. (1997). Perry’s chemical engineers’ handbook (7th ed.). New York: McGraw-Hill.

Pinto J.M., Joly M. & Moro L.F.L (2000) Planning and scheduling models for refinery operations, Computers and Chemical Engineering, 24

(9-10), 2259-2276.

Reid, E.B., & Allen, H.L. (1951) Estimating Pour Points of Petroleum Dist. Blends, Petroleum Refiner, 30 (5), 93-95

Russell, R.A. (1983) A flexible and reliable method solves single-tower and crude-distillation-column problems, Chem. Engng., Oct 17, 53-

59

Semwal, P.B. & Varshney, R.G. (1995) Predictions of pour, cloud and cold filter plugging point for future diesel fuels with application to

diesel blending models, Fuel 74(3), 437-444

Trierwiler, D. & Tan, R.L. (2001) Advances in crude oil LP modeling, Hydrocarbon Asia, November/December, 11(8), 52-58

Watkins, R.N. (1979) Petroleum Refinery Distillation, 2nd Ed., Gulf Publishing Co., Houston

Zhang, J., Zhu, X.X. & Towler, G.P. (2001) A Level-by-Level Debottlenecking Approach in Refinery Operation, Industrial and

Engineering Chemical Research 40(6), 1528-1540

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Appendix A

A.1 Definition of ASTM and TBP curves

True boiling point (TBP) is run in columns with 15 or more theoretical plates, which provides a very accurate

component distribution for the material being tested. However, due to the large sample size and time

requirement, TBP tests are generally only run on crude oil streams. The ASTM D86 test, which is the

standardized method established by the American Society for Testing Materials, is a batch laboratory distillation

involving approximately one equilibrium stage and no reflux. ASTM D86 test is mainly applied for products

and petroleum fractions such as CDU fractions. Typical TBP and ASTM curves of a CDU fraction are shown in

Figure A.1. Points A and B in Figure A.1 are the initial boiling points (IBP) of TBP and ASTM curves of a

CDU distillate, respectively. IBP is the temperature at which a distillate begins to boil. Points C and D in Figure

A.1 are the end points (EP) of TBP and ASTM curves of a CDU distillate. EP is the temperature at which a

distillate is 100% vaporized. Even though the ASTM tests are the simplest and most common distillations

performed on petroleum fractions, they do not provide the type of information given in TBP distillations

necessary for prediction of operating conditions or equipment design. Thus the ASTM data of petroleum

fractions need to be converted to TBP data using some correlations.

A.2 Estimation of the IBPs of HN and BR

The IBPs of HN are estimated in this paper with the consideration of the ASTM (5-95) Gap between GO and

HN. The ASTM (5-95) Gap defines the relative degree of separation between adjacent fractions. It is determined

by subtracting the 95 volume percent ASTM temperature of a fraction from the 5 volume percent ASTM

temperature of the adjacent heavy fraction (Watkins, 1979). The ASTM (5-95) Gap between GO and HN

recommended by Watkins (1979) is +20 to +30 °F. The IBPs of BR are estimated using a trial-and-error method

with the consideration of the TBP overlap between HD and BR. TBP overlap is determined by subtracting the

TBP EP of a fraction from the TBP IBP of the adjacent heavy fraction (TBP overlap = EPL- IBPH). A TBP

overlap of 80 to 100 °F between HD and BR is recommended by Watkins (1979).

A.3 Correlations of crude oil TBP curve and API gravity

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The crude oil TBP data and CDU fractions API data from crude assay provided by Watkins (1979) are

correlated using LSM to form the crude oil TBP equation (Equation (A.1)) and CDU fractions API equations

(Equations (A.2) and (A.3)). The volume transfer ratios of CDU fractions can be calculated by inserting

cutpoints into Equation (A.1). Equation (A.2) is obtained after correlating the API gravity data of CDU fractions

(except BR) from crude assay data. The API gravity of BR is correlated into Equation (A.3). The API gravity of

each fraction can be calculated by inserting the volume transfer ratio of each fraction into Equation (A.2) or

(A.3). Note that in Equation (A.2), the API gravity is correlated to the mid-point volume transfer ratios. The

definition of a mid-point volume transfer ratio is shown in Figure A.2. The mid-point volume transfer ratio of a

CDU fraction is half of its volume transfer ratio plus the sum of the volume transfer ratios of fractions that are

lighter than it. For example, in Figure A.2, D is the mid-point of segment A-B, E is the mid-point of segment B-

C, then the mid-point volume transfer ratio of GO is shown by segment A-D and the mid-point volume transfer

ratio of HN is shown by segment A-E (= “A-B” + “B-E”).

∑=

=6

0

iz)-(TBP_CP*aVOL i

i

(A.1)

where: VOL, percent volume transfer ratios; TBP_CP, TBP cutpoint temperature;

a0, 31.25; a1, 0.09775; a2, 3.22E-06; a3, -7.646E-08; a4, 1.1817E-10; a5, -2.28E-14;

a6: -1.366E-16; z: 444.25

∑=

=8

0

iz)-(Mid_Vol*aAPI i

i(A.2)

where: API, API gravity of the CDU fraction (except BR);

Mid_Vol, Mid-volume transfer ratio of the CDU fraction;

a0, 35.4666; a1, -0.476; a2, -0.0034; a3, -0.0005855; a4, 0.0000291;

a5, 1.02E-06; a6: -3.7E-08; a7: -5.4E-10; a8: 1.6E-11; z: 41.97

∑=

=2

0

iz)-(Vol*aBR_API i

i(A.3)

where: BR_API, Bottom residua API gravity; Vol, percent volume transfer ratio of BR;

a0, 15.552; a1, 0.2932; a2, -0.00199; z, 41.6875

A.4 Conversion of ASTM boiling ranges to TBP boiling ranges

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The ASTM boiling ranges are converted to TBP boiling ranges using the correlation proposed by Watkins

(1979). The figure for the relationships between ASTM and TBP initial and final boiling points provided by

Watkins (1979) are correlated in this paper to form Equations (A.4) and (A.5). The ASTM IBPs are converted to

TBP IBPs using Equation (A.4) while the ASTM EPs are converted to TBP EPs using Equation (A.5).

∑=

=4

0

iz)-(ASTM_IBP*aTBP_IBP i

i(A.4)

where: TBP_IBP, calculated TBP IBP; ASTM_IBP, ASTM IBP;

a0, 522.458; a1, 1.1274; a2, -8.27E-05; a3, -8.19E-07; a4, 3.336E-09; z: 555.0

∑=

=4

0

iz)-(ASTM_EP*aTBP_EP i

i(A.5)

where: TBP_EP, calculated TBP EP; ASTM_EP, ASTM EP;

a0, 547.783; a1, 1.06536; a2, -8.53E-06; a3, -8.5E-08; a4, 1.41E-09; z: 521.769

Appendix B: Comparison with Rigorous CDU Simulation Results

Part of the manual method described by Watkins (1979) is transformed for computer calculation and applied for

WTRs determination in this paper (described in section 3.5). In this appendix, the accuracies of the Watkins’

method, the WTRs determination procedure and the fractions’ property calculation are tested by rigorous CDU

simulation using Aspen Plus® version 11.1 (Aspentech, 2001). The configuration of the CDU is the same as

example 2.5 in Watkins (1979). The CDU has 29 stages in which the condenser is the first stage. Crude oil was

fed at stage 26. There exist three sidestrippers which draw oils from the main column at stages 7, 15 and 21,

respectively. Each sidestripper has 4 stages. The flow rates of the stripping steams of the main column and

sidestrippers 1# to 3# are 12,000 Ib/hr, 4,292 Ib/hr, 7,250 Ib/hr and 4,167 Ib/hr, respectively. No pumparound

exists in this example. The condenser and the bottom stage pressures are 27.8 and 38.5 psi, respectively. The

furnace overflash is 2.0 volume percent of crude feed.

The crude feed has a flow rate of 100,000 bbl/day, a temperature 200 °F and pressure 60 psi. The crude oil is Tia

Juana Light and the crude assay data (including the TBP curve, light ends composition and the API gravity

curve) from Watkins (1979) are used. The simulation is carried out with pseudocomponents spaced at 8 °F in the

range 100-800 °F and 10 °F in the range 800-1640 °F.

B.1 Comparison of the Watkins’ CDU calculation results

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The end point settings of CDU fractions used in the Aspen Plus simulation are listed in the first row of Table

B.1. Note that the EP of HD is changed to 740 °F because the EPs of heavy fractions (HD and BR) calculated by

Aspen Plus are higher due to different property calculation methods used by Aspen Plus and Watkins. Table B.2

shows the Aspen Plus simulation results and the results calculated by Watkins. In Table B.2, it can be seen that

the difference of the mass balance between the two methods is rather small. For heat balance, as one of the

figures showing the accuracy, the calculated heat duty of the condenser by Watkins is 205.147 MMBTU/hr

while by Aspen Plus is 203.914 MMBTU/hr, where the difference is 0.6%.

The method by transforming the Watkins manual procedure to computer calculation is much faster than the

Aspen Plus simulation. The CDU mass balance by the method described in section 3.5 can be finished in 1

second while the Aspen Plus CDU simulation model needs around 30 to 190 seconds to obtain the results.

Another drawback of Aspen Plus CDU simulation model is its instability. We found that Aspen Plus CDU

simulation model sometimes gives us significantly different results even though we only reinitialize the

calculation without any changes or we change the value of a parameter a bit. It thus brings oscillations into main

planning model when incorporating Aspen Plus CDU simulation model into a commercial software such as

Aspen PIMSTM (Aspentech). We conclude that the method described in section 3.5 has higher accuracy than the

traditional linear CDU models and better solution speed than a rigorous simulation model.

B.2 Comparison of WTRs

The Aspen Plus CDU simulation model is used to calculate the WTRs by setting the end points of CDU

fractions at three operation modes (rows 2 to 4 in Table B.1). The IBPs of CDU fractions are ignored for easy

convergence. The weight transfer ratios of CDU fractions calculated by Aspen Plus CDU simulation model and

method used in this paper are listed in Table B.3. The calculated WTRs of CDU fractions are listed in Table B.4.

In Table B.3 and B.4, it can be seen that the difference of the results between the two methods is rather small.

The differences may originate from different correlations used for property calculation and CDU mass balance

and other unknown parameters such as the Murphree efficiencies of stages. In the Watkins’ manual calculation,

some correlations were read from graphs which may bring inaccuracies. For example, a curve for converting

ASTM initial boiling points to TBP initial boiling points (see Appendix A.4 for details) was used. With updated

correlations, the accuracy of the calculation can be readily improved.

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B.3 Comparison of property calculation

As the change of the weight transfer ratios of CDU fractions, the properties of CDU fractions will also change.

Table B.5 lists the calculated octane numbers of GO and HN corresponding to the maximal and minimal weight

transfer ratios. Note that due to the nonlinearity of the property calculation, the maximal and minimal values of

CDU fractions’ properties may not happen when the weight transfer ratios take their maximal or minimal values.

It can be seen that the octane numbers of CDU fractions change several units in different situations. Assuming

fixed octane numbers of CDU fractions may not guarantee the quality of final products and may obtain sub-

optimal planning results. Similar results can be obtained for pour points calculation of CDU fractions.

Appendix C: Correlations of FCC fractions weight transfer ratios

Relevant figures provided by Gary et al. (2001) are correlated using LSM for computer calculation (Tables B.1

and B.2). As the results in Table C.1 show, the equation ∑∑= =

−− −−=3

1

2

1

11 )(*)(*i j

jiij yyxxaz should be used to

calculate the weight or volume transfer ratios of FCC fractions or the API gravity of FCC fractions. In column

“z”, “WT%” means the output is weight transfer ratio; “VOL%” means the output is volume transfer ratio and

“API” means the output is API gravity. Columns x and y show the two input variables. In x, “Conv” is the FCC

conversion level (in percentage); in y, K is the Watson characterization factor of FCC feed and API is the API

gravity of FCC feed. Note that Fuel Gas, C3=, C4=, i-C4, and n-C4 in Table C.1 are aggregated into one FCC

fraction “C2~C4” (Figure 4) in our planning model, TGO is the aggregate of HGO and LGO. Table C.2 shows

the correlated result of C3. The equation used for Table C.2 is )^2x-(conv*a2)x-(conv*a1a0Vol ++= ,

where “Vol” is the volume transfer ration of C3, “Conv” is the conversion level of FCC.

Appendix D: Definitions and mathematical formulations of the main planning model

C.1. Definitions of Indices and Parameters

(a) Indices

u = different units in the refinery, represents CDU and FCC

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p = different types of products, represents 90#, 93# gasoline, -10#, 0# diesel oil, FCC C2~C4 and FCC heavy oil,

respectively

s,ss = different fractions from CDU, represents GO, HN, LD, HD and BR, respectively

t = different fractions from FCC, represents FCC C2~4, gasoline, HO and coke, respectively

n = coefficients of correlations

(b) Sets

U = units in a refinery

P = types of products

S = number of CDU fractions

T = number of FCC fractions

VSSs,ss = combinations when sequence order of ss less than that of s. The order of s increases from 1 to 5 as s

changes from s1 to s5

(c) Parameters

a_denss,n = coefficients for specific gravities of LD and HD

a_fccrtot,n = coefficients for FCC fractions weight transfer ratios

a_props,n = coefficients for octane numbers of GO and HN, pour points of LD and HD

CAPACITYu = the capacity of units

C_prodp = the price of product p

C_raw = the price of crude oil

C_MTBE = the price of MTBE

C_untu = operation cost of unit u

ON_MTBE, ON_U21 = octane numbers of MTBE and FCC gasoline, respectively

DMmaxp = maximum demand for product p

d) Variables

CDUrtios = weight transfer ratio of CDU fraction s

Conv = the conversion level of FCC

Denss = specific gravity of CDU fraction s. Only LD and HD are included.

Mid_wts = mid-point weight transfer ratio of CDU fraction s, BR not included

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MTBEP01 = quantity of MTBE that attends the blending of 90# gasoline

MTBEP02 = quantity of MTBE that attends the blending of 93# gasoline

Fcdu_frts = flow rate of CDU fraction s

Ffcc_frtt = flow rate of FCC fraction t

FCCrtiot = weight transfer ratio of FCC fraction t

Frecycle = the recycle ratio of FCC

Prop_CDUs = Property of CDU fraction s. It represents octane number for GO and HN, represents pour point

(°R) for LD and HD. BR not included

profit = Total profit of the refinery

qprod p = production rate of product p

UNITu = load of unit u

U11P01 = quantity of GO that attends the blending of 90# gasoline

U11P02 = quantity of GO that attends the blending of 93# gasoline

U12P01 = quantity of HN that attends the blending of 90# gasoline

U12P02 = quantity of HN that attends the blending of 93# gasoline

U13P03 = quantity of LD that attends the blending of -10# diesel oil

U13P04 = quantity of LD that attends the blending of 0# diesel oil

U14P03 = quantity of HD that attends the blending of -10# diesel oil

U14P04 = quantity of HD that attends the blending of 0# diesel oil

U21P01 = quantity of FCC gasoline that attends the blending of 90# gasoline

U21P02 = quantity of FCC gasoline that attends the blending of 93# gasoline

VPU13P03,VPU14P03 = volume flow rates of LD and HD that attend the blending of -10# diesel oil

VPU13P04,VPU14P04 = volume flow rates of LD and HD that attend the blending of 0# diesel oil

C.2 Mathematical Formulations

C.2.1 Objective function

Total profit = money earned by selling products – crude oil cost – MTBE cost - unit operation costs.

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)obj(C_unt*UNIT C_MTBE*MTBEP02)(MTBEP01

C_raw*UNIT

C_prod*qprodProfit Maximize

Uuuu

1u

pp

∑

∑

∈

=

∈

−

+−−

=

u

Pp

C.2.2 Constraints

Material Balance of Units

(i): The load of each unit should be less than its capacity.

Uu∈∀< ,CAPACITY UNIT uu (p1)

Material Balance of CDU Fractions

(ii): The flow rates of gross overhead or heavy naphtha from CDU equal the sum of gross overhead or heavy

naphtha that attends the blending of 90# and 93# gasoline.

= 0=U11P02-U11P01-Fcdu_frt s1s (p2_1)

= 0=U12P02-U12P01-Fcdu_frt s2s (p2_2)

(iii): The flow rates of light distillate or heavy distillate from CDU equal the sum of light distillate or heavy

distillate that attends the blending of -10# and 0# diesel oil.

= 0=U13P04-U13P03-Fcdu_frt s3s (p2_3)

0U14P04-U14P03-Fcdu_frt s4s == (p2_4)

(iv): The weight transfer ratio of each CDU fraction should be greater than its lower limit and less than its upper

limit.

1173.0CDUrtio 0.1117 s1s ≤≤ = (p3_1)

0.0946 CDUrtio 0.0279 s2s ≤≤ = (p3_2)

0.2621 CDUrtio 0.2004 s3s ≤≤ = (p3_3)

0.1289 CDUrtio 0.0687 s4s ≤≤ = (p3_4)

0.5245 CDUrtio 0.5156 s5s ≤≤ = (p3_5)

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Note that the numbers 0.1117, 0.1173 etc. are CDU WTR limits calculated in CDU WTR determination model.

When the ASTM boiling ranges of CDU fractions or crude assay change, the WTR determination model should

be calculated again and the above numbers should be updated.

(v): The sum of the weight transfer ratios of CDU fractions should be 1.

∑∈

=Ss

s 1CDUrtio (p4)

(vi): Calculate the flow rate of CDU fractions.

Ss∈∀= = ,CDUrtio*UNIT Fcdu_frt su1us (p5)

(vii): Calculate the mid-point weight transfer ratios of CDU fractions.

Ssss ∈∀≠+= ∑∈

,5,)CDUrtio*0.5 CDUrtio(*100 Mid_wtsss,VSSss

ssss (p6)

(viii): Calculate the octane numbers of GO and HN; the pour points of LD and HD.

Ssss ∈∀≠+

+=

==

===

,5,)a_prop-(Mid_wt*a_prop

)a_prop-(Mid_wt*a_propa_prop Prop_CDU2

n3ns,sn2ns,

n3ns,sn1ns,n0ns,s(p7)

When s equals s1 and s2, Prop_CDUs represents the octane number of GO and HN. When s equals s3 and s4,

Prop_CDUs represents the pour point of LD and HD. n0ns,a_prop = , n1ns,a_prop = , n2ns,a_prop = and

n3ns,a_prop = represent a0, a1, a2 and z in row s of Table 8. For example, when s equals s1 (GO), the values

listed in the first row of Table 8 should be assigned to n0ns1,sa_prop == to n3ns1,sa_prop == , respectively.

Material Balance of FCC Fractions

(i): Calculate the weight transfer ratios of FCC fractions.

Tt∈∀+

+=

==

===

,)a_fccrto-(Conv*)a_fccrto

)a_fccrto-(Conv*a_fccrtoa_fccrto FCCrtio2

n3nt,n2nt,

n3nt,n1nt,n0nt,t(p8)

n0nt,a_fccrto = , n1nt,a_fccrto = , n2nt,a_fccrto = and n3nt,a_fccrto = represent a0, a1, a2 and z respectively in row t of

Table 7. Rows “t1” to “t4” represent the first to fourth rows of Table 7.

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(ii): Calculate the flow rates of FCC fractions.

Ttu ∈∀= = ,FCCrtio*UNIT Ffcc_frt t2ut (p9)

(iii): Calculate the FCC feed flow rate.

FrecycleFcdu_frt UNIT s5s2u += ==u (p10)

(iv): Calculate the flow rate of FCC recycle.

0.5*Fcdu_frt Frecycle s5s=≤ (p11_1)

t3tFfcc_frt Frecycle =≤ (p11_2)

“0.5” is the upper limit of the recycle ratio used in this paper.

(v): The FCC conversion level should be greater than its lower limit and less than its upper limit.

60 85 ≥≥ Conv

“85” and “60” are respectively the upper limit and lower limit of FCC conversion level used in this paper.

(vi): The flow rate of FCC gasoline equals the sum of FCC gasoline that attends 90# and 93# gasoline blending.

0U21P02-U21P01-Ffcc_frt t2t == (p12)

Gasoline Blending

(i): Read the octane numbers of MTBE and FCC gasoline.

ON_U21= 95, ON_MTBE= 101

Due to lack of data on FCC gasoline, the octane number of FCC gasoline is assumed to be fixed at 95.0 in the

three cases in Table 12. This octane number can be correlated with the feed of FCC using some correlations

with data available. “101” is the octane number of MTBE.

(ii): The linear combination of the octane numbers of gross overhead, heavy naphtha, FCC gasoline and MTBE

that attend 90# gasoline blending should be equal to or greater than 90.

0qprod*90-U21P01*ON_U21 U12P01*Prop_CDU MTBEP01*ON_MTBEU11P01*Prop_CDU

p01p

s2ss1s

≥+++

=

== (p13)

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(iii): The linear combination of the octane numbers of gross overhead, heavy naphtha, FCC gasoline and MTBE

that attend 93# gasoline blending should be equal to or greater than 93.

0qprod*93-U21P02*ON_U21 U12P02*Prop_CDU MTBEP02*ON_MTBEU11P02*Prop_CDU

p02p

s2ss1s

≥+++

=

== (p14)

Diesel Oil Blending

The nonlinear correlation proposed by Semwal et al. (1995) is used in this paper.

(i): Calculate the specific gravities of LD and HD.

43,)a_dens-(Mid_wt*a_dens

)a_dens-(Mid_wt*a_densa_dens Dens2

n3ns,sn2ns,

n3ns,sn1ns,n0ns,s

ssss =∪=+

+=

==

===(p15)

The above correlations are obtained by correlating the specific gravity data from crude assay provided by

Watkins (1979). The values of parameters used are listed in Table D.1.

(ii): Calculate the volume flow rates of LD and HD from their weight flow rates.

U13P03 = VPU13P03* Denss=s3 (p16_1)

U14P03 = VPU14P03* Denss=s4 (p16_2)

U13P04 = VPU13P04* Denss=s3 (p16_3)

U14P04 = VPU14P04* Denss=s4 (p16_4)

(iii): The pour point of –10# diesel oil should be less than –10. The correlation proposed by Semwal et al. (1995)

was rearranged to avoid possible overflow of variables.

(Prop_CDUs=s3/473.69)12.987*VPU13P031.105+ ((Prop_CDUs=s4/473.69)12.987*VPU14P031.105

< (VPU13P03+VPU14P03)1.105 (p17)

In the above equation, “473.69” (in degrees Rankine, °R) is the maximum pour point of –10# diesel oil.

(iii): The pour point of 0# diesel oil should be less than 0.

(Prop_CDUs=s3/491.69)12.987*VPU13P041.105+( Prop_CDUs=s4/491.69)12.987*VPU14P041.105

<(VPU13P04+VPU14P04)1.105 (p18)

In the above equation, “491.69” (in degrees Rankine, °R) is the maximum pour point of 0# diesel oil.

Product Quantity

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(i) The production rate of each product equals the sum of the streams that attend its blending.

U21P01U12P01MTBEP01 U11P01qprod p01p +++== (P19)

+++== U21P02U12P02MTBEP02 U11P02p02pqprod (P20)

U14P03 U13P03qprod p03p +== (P21)

U14P04 U13P04qprod p04p +== (P22)

Frecycle-Ffcc_frtqprod t3tp05p == = (P23)

t1tp06p Ffcc_frtqprod == = (P24)

(ii) The production rate of each product sent to customers should be less than its market demand.

pp DMmax qprod ≤ (P25)

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LP planning model

Crude Assay and CDU simulation software

Distillate Yields and Properties at pre-determined cutpoint temperature

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Figure 2. Swing cuts of distillates

-50

150

350

550

750

0 10 20 30Vol%

Tem

pera

ture

,deg

rees

F

HN

Crude Oil TBP Curve

GO

Lower limit Upper limitActual volume transfer ratio

Swing-cut of GO

A B C D E

Swing-cut of HN

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Figure 3. The yield of FCC gasoline vs. FCC conversion

FCC Gasoline Yield

30

50

70

40 60 80 100Conversion

Vol

%

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C

D

U

Gross Overhead (GO) G

B

D

B

Light Distillate (LD)

90 # Gasoline

93 # Gasoline

-10 # Diesel Oil

MTBE

Crude Oil

FCC

C2~C4

Heavy Naphtha (HN)

Heavy Distillate (HD)

Bottom Residua (BR)

0 # Diesel Oil

FCC Gasoline

TGO

CokeRecycle Oil

Heavy Oil

Figure 4. Basic configuration of a refinery

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Figure 5. Determination of the volume transfer ratios of CDU fractions

-50

150

350

550

750

0 10 20 30 40 50 60 70

Volume Percent Vaporized

Tem

pera

ture

,deg

rees

F

HN

Crude Oil TBP Curve

AB

E G IF H

GO LD HD

C

D

11.5 15.5 36.5 48.0

BR

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Figure 6. Definition of cutpoint between two fractions

300

350

400

450

500

550

600

0 10 20 30 40 50 60 70 80 90 100Volume Percent Vaporized

Tem

pera

ture

,deg

rees

F

TBP Cutpoint

Crude Oil TBP Curve

Heavy Fraction TBP Curve

Light Fraction TBP Curve

IBP of Heavy Fraction

EP of Light Fraction

A

B

CD

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Figure 7. Definition of VTR

-50

150

350

550

750

0 5 10 15 20 25 30Vol%

Tem

pera

ture

,deg

rees

F

Crude Oil TBP Curve

C D

A B

13.6 14.2 17.4 24.2

256

263

299

372

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Convert ASTM boiling ranges to TBP boiling ranges; determine operation modes

Provide the ASTM boiling ranges of the CDU fractions

Obtain TBP curve of the crude oil

Calculate cutpoints for the operation modes

Calculate volume transfer ratios of CDU fractions for the operation modes

Calculate weight transfer ratios of CDU fractions for the operation modes

API gravity data of CDU fractions

Figure 8: Procedure for WTRs determination of CDU fractions

Determine the WTRs of CDU fractions

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Increase conversion by 2% until it reaches its upper limit

Obtain correlations for FCC fractions from figures

Set the conversion level to its lower limit

Perform FCC material balance

Calculate weight transfer ratios of FCC fractions

Read FCC feed properties

Correlations of weight transfer ratios vs. conversion level

Figure 9. Procedure for correlations of FCC fractions weight transfer ratios

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Call the main planning model

END

Call FCC yield model

Call CDU WTR determination model

Figure 10. Flow diagram for solving the refinery planning model

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Figure A.1 TBP and ASTM curves for a CDU distillate

300

350

400

450

500

550

600

0 10 20 30 40 50 60 70 80 90 100

Volume Percent Vaporized

Tem

pera

ture

,deg

rees

FTBPASTM

B

A

C

D

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Figure A.2 Definition of mid-point volume transfer ratio

-50

150

350

550

750

0 5 10 15 20 25 30

Volume Percent Vaporized

Tem

pera

ture

,deg

rees

F

HN

Crude Oil TBP Curve

A B CD E

GO

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Raw material Products

MTBE Crude oil FCC C2~C4

90# Gasoline

93# Gasoline

-10# Deisel Oil

0#Deisel oil FCC TGO

3500 1400 2500 3215 3387 3000 2500 1500

Table 1. Price data (yuan/ton)

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CDU fractions Boiling rangeGO EP 260 - 275

IBP 270 HN EP 325 - 400 IBP 300 - 375 LD EP 550 - 600 IBP 525 - 575 HD EP 675

BR IBP 635 - 652

Table 2. ASTM boiling ranges of CDU fractions, °F

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CDU fractions Boiling range GO EP 276.5 – 290.9

IBP 235.4 HN EP 340.6- 418.4 IBP 257.3 – 325.1 LDEP 577.9 – 631.1 IBP 488.6 – 545.0 HD EP 711.3

BR IBP 611.8 – 630.6

Table 3. TBP boiling ranges of CDU fractions, °F

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CDU Fractions M.N., °F M.L., °F M.H., °FGO EP 276.5 290.9 290.9

IBP 235.4 235.4 235.4 HN EP 418.4 340.6 340.6 IBP 325.1 257.3 257.3 LD EP 631.1 631.1 577.9 IBP 545.0 545.0 488.6 HD EP 711.3 711.3 711.3

BR IBP 611.8 611.8 630.6

Table 4. TBP boiling ranges of CDU fractions in the three operation modes

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GO/HN HN/LD LD/HD HD/BR

M. N. 255.9 371.8 588.0 661.6

M. L. 263.1 298.9 588.0 661.6

M. H. 263.1 298.9 533.2 670.9

Table 5. Calculated cutpoints, °F

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VTR, Vol% WTR, Wt% Vol% Wt% API H 14.23 11.73 M. N. 13.61 11.17 67.2

M. L. 14.23 11.72 66.1 GO L 13.61 11.17 M. H. 14.23 11.73 66.1

H 10.60 9.46 M. N. 10.60 9.46 51.0 M. L. 3.17 2.79 53.5 HN L 3.17 2.79 M. H. 3.17 2.79 53.5

H 27.79 26.21 M. N. 20.98 20.04 39.0 M. L. 27.79 26.21 41.1 LD L 20.98 20.04 M. H. 22.52 21.03 42.9

H 13.04 12.89 M. N. 6.91 6.88 32.0 M. L. 6.91 6.87 32.0 HD L 6.91 6.87 M. H. 13.04 12.89 33.2

H 47.90 52.45 M. N. 47.90 52.45 17.3 M. L. 47.90 52.40 17.3 BR L 47.04 51.56

M. H. 47.04 51.56 17.1

Table 6. Calculated transfer ratios and WTRs

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a0 a1 a2 z C2~C4 0.20624 0.00323 3.6E-05 72.92857

Gasoline 0.44699 0.004367 -5.7E-05 72.92857 TGO 0.2922 -0.00842 3.59E-06 72.92857 Coke 0.05455 0.000816 1.73E-05 72.92857

WT = a0 + a1*(conv - z) + a2*(conv - z)2

Table 7. Correlations for FCC fractions

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a0 a1 a2 z

GO 58.8138 -2.2372 0.0699 6.4876

HN 49.9794 -1.8023 0.0641 10.6140

LD 395.9257* 4.7582 0.0454 26.3285

HD 509.9056* 3.0821 -0.0261 54.9158

*: Pour points of LD and HD are converted to the Rankine degree

ON (or PP) =a0 + a1*(Mid_WTR-z) + a2*(Mid_WTR-z)^2)

Table 8. Correlations of the properties of CDU fractions

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GO HN LD HD BR M. N. 11.17 9.46 20.04 6.88 52.45 M. L. 11.72 2.79 26.21 6.87 52.40 M. H. 11.73 2.79 21.03 12.89 51.56

Table 9. Fixed CDU fractions weight transfer ratios in the three modes, %

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CDU Fractions Weight Transfer Ratios, % Modes Total Profit (Yuan)GO HN LD HD BR

Optimal 551738 11.17 2.79 26.21 8.27 51.56 M. N. 416808 11.17 9.46 20.04 6.88 52.45 M. L. 548350 11.72 2.79 26.21 6.87 52.40 M. H. 549487 11.73 2.79 21.03 12.89 51.56

Table 10. Comparison of CDU cutpoint setting methods

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FCC Conversion Total Profit

(Yuan) Recycle Ratio FCC Fractions Weight Transfer Ratios, % C2~C4 Gasoline Heavy Oil Coke

Fixed (65.0%) 540279 0.5 18.3 40.9 35.9 4.9 Optimal (73.4%) 551738 0.404 20.8 44.9 28.8 5.5

Table 11. Comparison of different FCC conversions

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Table 12. CDU fractions properties calculation

Octane Numbers (ON) or Pour Points (PP) Weight Transfer Ratios (%)Total Profit

(Yuan)GO

(ON)

HN

(ON)

LD

(PP)

HD

(PP)

GO HN LD HD BR

ON & PP Changeable 551738 60.9 46.7 -51.2 -9.7 11.17 2.79 26.21 8.27 51.56

ON & PP Estimated and Fixed 544421 50.0 65.0 -40.0 5.0 11.17 2.79 26.21 8.27 51.56

ON & PP Estimated and Fixed 581482 82.0 65.0 -40.0 5.0 11.73 2.79 26.21 7.71 51.56

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GO HN LD HD Watkins Example 2.5 275 380 560 740

M.N. 260 400 600 740 M.L. 275 330 600 740

M.H. 275 330 550 740

Table B.1 Actual EP Settings (°F) used in the CDU Simulation

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Methods GO HN LD HD BR

ASPEN 142,386 113,245 203,083 123,952 682,363 Mass Flow(Ib/hr) Watkins 138,802 116,175 208,728 124,958 675,879

ASPEN 11.26 8.95 16.05 9.80 53.94

Watkins 10.98 9.19 16.51 9.88 53.45 Mass Ratio(%)

Difference,% 2.48 2.63 2.82 0.85 0.91

Table B.2 Results of CDU mass balance

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Methods GO HN LD HD BR

ASPEN 9.87 12.66 16.97 5.30 55.20 M.N.

This Paper 11.17 9.46 20.04 6.88 52.45 ASPEN 12.04 2.45 25.47 4.58 55.46

M.L. This Paper 11.72 2.79 26.21 6.87 52.40

ASPEN 12.05 2.47 21.43 10.22 53.83 M.H.

This Paper 11.73 2.79 21.03 12.89 51.56

Table B.3 Weight Transfer Ratios of CDU Fractions by Aspen Plus simulation

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This Paper ASPEN

H 11.73 12.05 GO L 11.17 9.87

H 9.46 12.66 HN

L 2.79 2.45

H 26.21 25.47 LD

L 20.04 16.97

H 12.89 10.22 HD

L 6.87 4.58

H 52.45 55.46 BR

L 51.56 53.83

Table B.4 WTRs of CDU Fractions

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WTRs ON This Paper ASPEN This Paper ASPEN

H 11.73 12.05 60.2 60.0 GOL 11.17 9.87 60.9 62.7 H 9.46 12.66 42.2 42.4 HNL 2.79 2.45 45.9 43.6

Table B.5 ONs of CDU Fractions

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Max.

Absolute

Bias

Coke WT% Conv K 4.58 -2.366 0.0644 -0.02562 0.000887 -0.00306 70 12.075 0.42

Fuel Gas WT% Conv K 4.714 -1.392 0.05092 -0.01424 0.001166 -0.0042 70 12.075 0.48

C3= VOL% Conv API 5.793 0.2659 0.104 0.0077 0.001775 3.00E-05 70 23 0.19

C4= VOL% Conv API 8.515 -0.0757 0.14736 -0.00117 1.80E-05 1.40E-05 70 23 0.07

i-C4 VOL% Conv API 5.956 0.1091 0.0998 0.001716 1.17E-05 -1.10E-05 70 23 0.06

n-C4 VOL% Conv API 2.2747 -0.064 0.03557 -0.00077 5.30E-05 -1.00E-05 70 23 0.10

Gasoline VOL% Conv K 56.3968 6.7027 0.63864 0.28925 -0.00486 0.016826 70 12.075 2.30

HGO API Conv API 8.7429 -0.04592 0.023367 0.00013 -1.50E-05 1.70E-06 70.091 23 0.01

TGO API Conv API 8.0929 -0.078 0.0146 -0.000595 0.00019 -7.00E-06 70.091 23 0.04

Gasoline API Conv API 6.2337 -0.00125 0.001804 -0.00044 0.000119 -2.623E-05 70.091 23 0.01

HGO VOL% Conv K 5.47656 0.08523 -0.26131 -0.0108 -5.95E-05 0.0002 77.5 12.075 0.21

Table C.1 Coefficients of FCC fractions correlations

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a0 a1 a2 x Max. Absolute Bias C3 2.759957 0.0558333 0.000574 70 0.06

Table C.2 Coefficients of C3 correlations

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Fractions n0ns,a_dens = n1ns,a_dens = n2ns,a_dens = n3ns,a_dens =

s3 (LD) 0.8078 0.0033 -1.824E-5 26.3285 s4 (HD) 0.8818 0.0022 -1.407E-5 54.9158

Table D.1 Coefficients for specific gravities of LD and HD

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