Integrating CDU, FCC and product blending models into refinery planning.pdf

8/14/2019 Integrating CDU, FCC and product blending models into refinery planning.pdf

1/19

Computers and Chemical Engineering 29 (2005) 20102028

Integrating CDU, FCC and product blending models intorefinery planning

Wenkai Li a, Chi-Wai Hui a,, AnXue Li b

a Chemical Engineering Department, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, PR Chinab Daqing Refining & Chemical Company, PetroChina Company Limited, PR China

Received 4 September 2003; received in revised form 23 June 2004; accepted 19 May 2005

Available online 11 July 2005

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

linearmodel. This paper will present howthe CDU, FCCand product blending modelsare formulated and applied to refinery planning. Several

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

2005 Elsevier Ltd. All rights reserved.

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 models rigorous and empirical ones

are commonly used. Rigorous models simulate a CDU as a

general distillation column, taking into account phase equi-

librium, heat and mass balances along the whole column.

Results of a rigorous model include flow rates and com-positions of all internal and external streams, and operating

conditions such as tray temperatures and pressures. Consid-

erable research has been carried out with the aim of devel-

oping and/or improving rigorous CDU models. For example,

Cechetti et al. (1963)applied simultaneous modeling of the

main column and side strippers using the method. Their

Corresponding author. Tel.: +852 2358 7137; fax: +852 2358 0054.

E-mail address:[email protected] (C.-W. Hui).

algorithm may fail to converge when modeling a CDU.Hess

et al. (1977)extended this approach and proposed a Multi-

method to increase the convergent speed and broaden the

generality of the algorithm. Russell (1983) used a rather com-

plicated inside-out class of methods to simulate CDU with

good speed and wide specifications variety. Lang et al. (1991)

proposed an algorithm that integrated bubble-point (BP) and

sum-rates (SR) methods and showed that their calculated val-

ues 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) andDESIGN 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, Green, & Maloney, 1997).Because

0098-1354/$ see front matter 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.compchemeng.2005.05.010


2/19

W. Li et al. / Computers and Chemical Engineering 29 (2005) 20102028 2011

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 rateexpression.Jacob, Gross, Voltz, and Weekman (1976) pro-

posed 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)andGary

and Handwerk (2001)described different methods to obtain

the yields of FCC products by predetermined charts and fig-

ures. These correlations are very useful for obtaining typical

yields for preliminary studies and to determine the trends ofproduct yields when changes are made in conversion levels

(Gary & Handwerk, 2001).

1.2. Current approaches to refinery planning

Mathematical programming has been extensively studied

and implemented for long-term plant-wide refinery plan-

ning. 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 oftenhides critical issues and parameters (Hartmann, 2001).Some

commercial software, such as Aspen PIMSTM (Aspentech),

applied nonlinear recursion algorithm to handle nonlineari-

ties or provided interface to an external rigorous simulator

to refinery planning. This could be a time-consuming pro-

cedure due to the long solution time of external simulator.

Zhang, Zhu, and Towler (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 Zhangs work, due to

the inaccuracies arising from assuming fixed volume/weight

transfer ratios (the volume/weight percentage of a CDU frac-

tion 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 prod-

ucts meet the required specifications. Moro, Zanin, and Pinto

(1998) andPinto, Joly, and Moro (2000) proposed a non-

linear planning model that took into account the influences

of feed properties and operation parameters such as sever-

ity and temperature on unit operation cost and unit product

yields. The overall accuracy of their planning model is lim-

ited due to the application of some simple linear unit models

Fig. 1. The flow diagram of fixed yield structure representations approach.

such as FCC. Furthermore, the coefficients of highly non-

linear 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 essen-

tial 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 representationsmodel, 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 distilla-

tion simulation program. The simulation program determines

cuts at designated temperature, and then passes the result-

ing yield and property information to the LP planning model

(Trierwiler & Tan, 2001). Fig.1 illustratesthe structure of this

approach (simplified figure fromTrierwiler & Tan, 2001).A

noticeable drawback of this approach is that the cutpoints of

distillates are predetermined therefore cannot guarantee the

optimality of the cutpoint settings for CDU distillates. Someresearchers (Trierwiler & Tan, 2001) applied a method called

Adherent Recursion to optimize cutpoints. The results of

LP planning model (new cutpoints) were sent back to simu-

lation 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 plan-

ning 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 combi-

nations of these modes to maximize the total profit. The

challengelies in howto blendthese modeseffectively. 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 spread-


3/19

2012 W. Li et al. / Computers and Chemical Engineering 29 (2005) 20102028

Fig. 2. Swing cuts of distillates.

sheet to blend CDU modes pair-wise in 1% steps. With the

help of the spreadsheet, the procedure was performed visu-ally 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 inFig. 2

(Zhang et al., 2001). In Fig.2, gross overhead(GO) andheavy

naphtha (HN) 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 inFig. 2,are added. The range of a swingcut is defined as a certain ratio on the crude feed bounded by

a lower and upper limit. For example, segments BD 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 segments AC. Similarly, afterthe apportionment

of the HN swing cut, the final volume transfer ratio of HN

can be shown as segments CE.

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 CDUfractions

on the basis of fixed swing cuts. Theyfixed 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 boil-

ing 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

andkeroseneswing cuts respectively. A typical 50 of boiling

temperature range, can also be set to swing cuts (Trierwiler

& Tan, 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 con-

sideration (Trierwiler & Tan, 2001).Since the accurate sizes

of swing cuts are unknown, some researchers divide swing

cuts into small segments in an attempt to improve model-ing 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 signifi-

cantly. This approach also involves applying complex mixed

integer programming to obtain reasonable results (Trierwiler

& Tan, 2001).

In this paper, an effective method is proposed (see Sec-

tion3) to determine the sizes of swing cuts. These sizes are

obtained by using the WTRs of CDUfractions, which are cal-

culated using the empirical procedure described byWatkins

(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 distil-

late 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 heavier distillate, especially properties suchas 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 & Tan, 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 definedin Section 3). Beside CDUcutpoints,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. Fig. 3

(Decroocq, 1984)shows a typical FCC gasoline versus FCC

conversion level curve. The nonlinearity of this figure, espe-


4/19


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

cially in highconversion area,is obvious.To accurately model

FCCwithout introducing a toocomplex FCCmodel, a regres-

sion model based upon the work fromGary and Handwerk

(2001)is applied in the proposed refinery-planning model(Section4).

2. Problem description

An example shown inFig. 4is used to illustrate the pro-

posed modeling techniques and solution methods. The refin-

ery process contains four main processing units: CDU, FCC,

gasoline blending (GB) and diesel oil blending (DB). 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 C2C4,

FCC gasoline, total gas oil (TGO) and coke. Part of TGO isrecycled to become FCC feed. Note that for simplicity, vac-

uum 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: 10# diesel oil and 0#

diesel oil. C2C4 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/t) of raw materi-

als and products are shown in Table 1. The capacities of CDU

and FCC are both 400 t/day; the operation costs of CDU and

FCC are 20 and 110 yuan/t, respectively. The market demand

for each product is 200 t/day. The octane number of MTBEis 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 blend-

ing requirement of diesel oil blending is that the pour point

of10# and 0# diesel oil should be equal to or smaller than

10 and 0 C, respectively. The objective of the problem is

Fig. 4. Basic configuration of a refinery.

Table 1

Price data (yuan/t)

Raw material Products

MTBE Crude oil FCC C2C4 90# Gasoline 93# Gasoline 10# Deisel oil 0#Deisel oil FCC TGO

3500 1400 2500 3215 3387 3000 2500 1500


5/19


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 ratioranges (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 condi-

tions 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 changedin 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 (seeAppendix A.1for 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 definitionsof ASTM boiling ranges for CDU fractions. According to

the definitions ofWatkins (1979), gross overhead consists

of light-ends through 250275 F ASTM end point; heavy

naphtha consists of pentane through 400F ASTMend point;

light distillate has an ASTM boiling range of approximately

300600 F; heavy distillate has an ASTM boiling range of

approximately 525675 F. All distillates heavier than heavy

distillate are called bottom residua. Bottom residua have an

ASTM end point over 1300 F.

Fig. 5 shows the TBP curve of a crude oil. True boil-

ing 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 tempera-

ture on the curve represents the actual (true) boiling point of

the hydrocarbon material present at the corresponding vol-

ume 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.

InFig. 5, points A, B, C and D represent the cut-

points of GO, HN, LD and HD, respectively. Draw a dotted

horizontal line through each point; then draw a dotted verti-

cal line through the intersection of the dotted horizontal line

and the crude oil TBP curve. The gap (such as segments EF,

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

FG, GH and HI inFig. 5)between two neighbor dotted

vertical lines determines the volume transfer ratio of a CDU

fraction. In Fig. 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 hydro-

carbons, 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 frac-

tions; hence the boiling ranges of CDU fractions vary under

differentoperation conditions. A typical ASTM boiling range

of CDU fractions is listed inTable 2(Watkins, 1979). The

end points (EPs, the temperature at which a distillate is 100%

vaporized) and initial boiling points (IBPs, the temperature

at which a distillate begins to boil) of CDU fractions pro-

vided byWatkins (1979)are adopted inTable 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

Table 2

ASTM boiling ranges of CDU fractions (F)

CDU fractions Boiling range

GO

EP 260275

HN

IBP 270

EP 325400

LD

IBP 300375

EP 550600

HD

IBP 525575

EP 675

BR

IBP 635652


6/19


Table 3

TBP boiling ranges of CDU fractions (F)

CDU fractions Boiling range

GO

EP 276.5290.9

HN

IBP 235.4EP 340.6418.4

LD

IBP 257.3325.1

EP 577.9631.1

HD

IBP 488.6545.0

EP 711.3

BR

IBP 611.8630.6

most of the refineries provide ASTM boiling ranges to define

CDU fractions, from which the boiling ranges can be adoptedinTable 2.

Although ASTM boiling ranges can be easily obtained

and usedconvenientlyfor product identifications, theycannot

be used directly to estimate weight transfer ratios of CDU

fractions. Thus, ASTM boiling ranges should 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 ofTable 2.

Table 3provides 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). Theseoperation modes are maximizing

heavy naphtha (MN), maximizing light distillate (ML) and

maximizing heavy distillate (MH). The number of operation

modes defined above is relatively small and thus has a poten-

tial to reduce the size of a planning model. Note that the three

Table 4

TBP boiling ranges of CDU fractions in the three operation modes

CDU fractions MN (F) ML (F) MH (F)

GO

EP 276.5 290.9 290.9

HN

IBP 235.4 235.4 235.4

EP 418.4 340.6 340.6

LD

IBP 325.1 257.3 257.3

EP 631.1 631.1 577.9

HD

IBP 545.0 545.0 488.6

EP 711.3 711.3 711.3

BR

IBP 611.8 611.8 630.6

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 opera-

tion modes defined in Brookset al. (1999), are categorized for

other CDUs according to their design and operation condi-

tions. In fact, the approach that we developed is independent

of the number of operation modes. One can maximize theyield 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 adja-

cent fractions, a cutpoint is used. It is defined as the

mid-point of the TBP overlapping temperatures (TBP cut-

point = 0.5(EPL+ IBPH), where EPL is the EP of the light

fraction and IBPHis the IBP of adjacent heavy fraction). The

definition of TBP cutpoint between two fractions is shown

inFig. 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 cut-

points among fractions determined, the corresponding vol-

ume transfer ratios of CDU fractions can then be obtained

using the procedure described in Section3.1.

3.4. Determination of volume transfer ratio range (VTR)

In a refinery, adjusting the cutpoints will change the vol-

ume 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 Section3.3. 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

Fig. 6. Definition of cutpoint between two fractions.


7/19


Fig. 7. Definition of VTR.

the minimum value, the lower limit of the VTR. This proce-

dure is illustrated in Fig.7.ForMNmode,usingthedatagiven

inTable 4, the cutpoints of HN can be calculated (256 F forGO/HN and 372F for HN/LD). Then the corresponding vol-

ume transfer ratio of HN is obtained 10.6% (=24.2 13.6%)

as shown by segments CD. Similarly, cutpoints in ML mode

(263 FforGO/HNand299 F for HN/LD) canbe calculated,

the corresponding volume transfer ratio of HN is obtained

3.2% (=17.4 14.2%) as shown by segments AB. The cut-

points in the MHmode are the same as those in the MLmode

in this case, thus the volume transfer ratio of HN in MH 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%

and 10.6%).

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

CDU fractions as weight transfer ratios. The volume trans-

fer 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 (API gravity =

141.5/d15.615.6 131.5, where d15.615.6 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 (seeAppendix A.3for details).

For the example illustrated in Fig. 7, the corresponding WTR

is (2.8% and 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 Fig. 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.

Fig. 8. Procedure for WTRs determination of CDU fractions.

3.5. WTR determination procedure

The procedure described in Sections 3.13.4 for determin-

ing the WTR of CDU fractions is summarized in this section

and illustrated inFig. 8.The manual procedure described by

Watkins (1979),the accuracy of which is tested by rigorous

simulationin 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. Thedetailed

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., Gary & Handwerk, 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 TBPboiling 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 3and the TBP boiling ranges of the three operation

modes are then determined and listed inTable 4.

Step 2. Calculate the cutpoints for operation modes

Thecutpoints for operation modes are calculated using the

method describedin Section 3.3. For example,to calculatethe


8/19


Table 5

Calculated cutpoints (F)

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

MN 255.9 371.8 588.0 661.6

ML 263.1 298.9 588.0 661.6

MH 263.1 298.9 533.2 670.9

cutpointbetween GOand HNin the MNmode, weknow from

Table 4that EPL is 276.5F and IBPHis 235.4

F, therefore

the cutpoint of GO/HN is (276.5 + 235.4)0.5 = 255.9F. The

calculated cutpoints are listed inTable 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 equa-

tion 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 MN 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 trans-

fer 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 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

ofTable 6.The calculated weight transfer ratios and volume

transfer ratiosfor each operationmode arelisted in thesecondlast and the third last columns ofTable 6,respectively.

Table 6

Calculated transfer ratios and WTRs

VTR (vol.%) WTR (wt.%) vol.% wt.% API

GO

H 14.23 11.73 MN 13.61 11.17 67.2

L 13.61 11.17 ML 14.23 11.72 66.1

MH 14.23 11.73 66.1

HN

H 10.60 9.46 MN 10.60 9.46 51.0

L 3.17 2.79 ML 3.17 2.79 53.5

MH 3.17 2.79 53.5LD

H 27.79 26.21 MN 20.98 20.04 39.0

L 20.98 20.04 ML 27.79 26.21 41.1

MH 22.52 21.03 42.9

HD

H 13.04 12.89 MN 6.91 6.88 32.0

L 6.91 6.87 ML 6.91 6.87 32.0

MH 13.04 12.89 33.2

BR

H 47.90 52.45 MN 47.90 52.45 17.3

L 47.04 51.56 ML 47.90 52.40 17.3

MH 47.04 51.56 17.1

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 fourthcolumns ofTable 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 operat-

ing variables affecting the FCC conversion level are the

cracking temperature, catalyst/oil ratio, space velocity, etc.

The hand-calculation procedure described by Gary andHandwerk (2001) is implemented in our proposed procedure.

The procedure is illustrated in Fig. 9. Firstly, we obtained

FCC fractions yieldcorrelations (whenzeolite catalyst is used

in FCC) from figures provided by Gary andHandwerk (2001)

(seeAppendix Cfor details). Then the feed properties, API

gravity and Watson characterization factor were read. The

lower limit andupper limit of FCCconversion aredetermined

according to FCC operation conditions such as the regener-

ator coke burning ability. The conversion range used in this

paper is (60% and 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

described byGary and Handwerk (2001), and calculate the

weighttransfer ratiosof FCCfractions. Next,there is theneed

to increase the conversion by a small value (2%) and calcu-

late 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, FCCfrac-

tions weight transfer ratios and FCC conversion level are

correlated. An equation of each FCC fraction weight transfer

ratio versus FCC conversion level is now obtained and can

be used in refinery-planning model to optimize the FCC con-

version level.Table 7lists the correlations for FCC fractions

(the feed properties is assumed to be: Watson characteriza-


9/19


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

tion factor = 11.8, API = 19). In Table 7, WT represents

the weight transfer ratio of C2C4 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 byGary and Handwerk (2001) is

about 13%, 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 thefeed) canalso be easily obtained

from refineries. The emphasis of the FCC model proposed

in this paper was put on its solution speed and the effec-

tiveness so that it can be integrated into the main planningmodel directly. Since the inputoutput relationship of FCC

can be updated by some online learning methods or through

Table 7

Correlations for FCC fractions

a0 a1 a2 z

C2C4 0.20624 0.00323 3.6E05 72.92857

Gasoline 0.44699 0.004367 5.7E05 72.92857

TGO 0.2922 0.00842 3.59E06 72.92857

Coke 0.05455 0.000816 1.73E05 72.92857

WT = a0+ a1(convz) + a2(convz)2.

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 and Handwerk

(2001). It is pointed out (Magee, Maurice, & Mitchell,

1993) that as the improvement of catalysts and unit design,the yield data of FCC fractions will change and hence cor-

responding figures should be updated. Besides, the yield

of gasoline versus conversion keeps increasing in the fig-

ure provided inGary and Handwerk (2001). In reality, the

yield of gasoline will decrease as the conversion increases

to a certain value.

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 opera-

tion 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 blend-

ing models, which can be replaced by other state-of-the-art

models.

5.1. Blending rule

Some quality indicators, such as octane number and freez-ing 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 canbe simplycalculated using the following linear

equations:

Oifi = Opfp

fi = fp

whereOiis the octane number of intermediate streami,fithe flow rate of the intermediate stream i, Op the octane

number of productp, andfpis the sum offi.

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


10/19


Table 8

Correlations of the properties of CDU fractions

a0 a1 a2 z

GO 58.8138 2.2372 0.0699 6.4876

HN 49.9794 1.8023 0.0641 10.6140

LD 395.9257a 4.7582 0.0454 26.3285

HD 509.9056a 3.0821 0.0261 54.9158

ON (or PP) = a0+ a1(Mid WTR-z) + a2(Mid WTR z)2.

a Pour points of LD and HD are converted to the Rankine degree.

point blending indexes of intermediate streams to pre-

dict product pour points.Hu and Burns (1970)proposed a

nonlinear one-parameter pour point equation. Semwal and

Varshney (1995)proposed an improved nonlinear correla-

tion, which is used in this paper:

TBb =

ni=1

(Vi)A(Ti)

B

whereTb is the pour point of product, Vi andTi are vol-ume 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 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 plan-

ning model, the octane numbers or pour points of CDU

fractions are correlated to their mid-point weight transferratios. 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 byWatkins (1979)are given inTable 8.InTable 8,

the outputs of row GO and HN are octane numbers (ON)

while theoutputs of row LD andHD arepour points(PP).

6. Main flow diagram for solving therefinery-planning model

The main flow diagram for solving the refinery-planning

model is illustrated inFig. 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 frac-

tion weight transfer ratio versus FCC conversion. These

equations are used in the refinery model.

Fig. 10. Flow diagram for solving the refinery-planning model.

Read initial data, which include the data of unit capacities,

unit operation costs, initial octanenumbers andpour 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 solu-

tion time of the two CDU and FCC models proposed here

was reduced significantly. In most of the cases tested in thisstudy, the CPU time needed to solve the main planning model

is 0.10.2 s.

7. Case studies

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, Kendrick,

& Meeraus, 1992) on a 933 MHz Pentium III PC. The code

MINOS5 in GAMS 2.25 is used for NLP. Theplanning model

is described inAppendix D.

The configuration of the cases studied here is illustrated in

Fig. 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 Section2.The influences of different CDU cutpoint set-

ting methods on total profit will be studied in Section 7.1

while the influences of different FCC conversion level deter-

mination methods on total profit and FCC fractions weight

transfer ratios will be studied in Section7.2.Finally Section

7.3studies the influences of different methods of determin-

ing CDU fractions properties on total profit and the weight

transfer ratios of CDU fractions.


11/19


12/19


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 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 wasunderestimated (The real octane number of 90# gasoline is

92.7) while the product quality in the third row was overesti-

mated (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.

8. Conclusions

In this paper, the optimal planning strategies of refineries

are studied and a procedure for the CDU WTRs determina-tion is proposed. A yield model is used for the determination

of equations of FCC fractions weight transfer ratios versus

FCC conversion level. With the CDU and FCC models inte-

grated into the planning model, the CDU cutpoints and FCC

conversion level can be optimized accurately. The proper-

ties 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 sup-

port 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).

Appendix A

A.1. Definition of ASTM and TBP curves

True boiling point (TBP) isrun incolumns with 15or 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 gener-

ally only run on crude oilstreams.The ASTM D86test,which

is the standardizedmethod established by the American Soci-

ety 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

Fig. A1. TBP and ASTM curves for a CDU distillate.

curves of a CDU fraction are shown in Fig. A1. Points A

and B inFig. A1are 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 inFig. A1are the end points (EP) of TBP and ASTM

curves of a CDU distillate. EP is the temperature at whicha 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 infor-

mation 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 con-

sideration of the ASTM (5-95) Gapbetween GO andHN. TheASTM (5-95) Gap defines the relative degree of separation

between adjacent fractions.It is determinedby subtracting the

95 vol.% ASTM temperature of a fraction from the 5 vol.%

ASTM temperature of the adjacent heavy fraction (Watkins,

1979).The ASTM (5-95) Gap between GO and HN recom-

mended byWatkins (1979)is +20 to +30 F. The IBPs of BR

are estimated using a trial-and-error method with the con-

sideration 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 over-

lap=EPL IBPH). A TBP overlap of 80100F between HD

and BR is recommended byWatkins (1979).

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

gravity

The crude oil TBP data and CDU fractions API data from

crude assay provided byWatkins (1979)are correlated using

LSM to form the crude oil TBP equation (Eq. (A.1)) and

CDU fractions API equations (Eqs. (A.2) and (A.3)). The

volume transfer ratios of CDU fractions can be calculated by

inserting cutpoints into Eq.(A.1).Eq.(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 corre-


13/19


Fig. A2. Definition of mid-point volume transfer ratio.

lated into Eq.(A.3). The API gravity of each fraction can be

calculated by inserting the volume transfer ratio of each frac-

tion into Eq.(A.2)or (A.3). Note that in Eq.(A.2), the APIgravity is correlated to the mid-point volume transfer ratios.

The definition of a mid-point volume transfer ratio is shown

inFig. A2.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, inFig. A2,D is the mid-point of segments AB,

E is the mid-point of segments BC, then the mid-point vol-

ume transfer ratio of GO is shown by segments AD and the

mid-point volume transfer ratio of HN is shown by segments

AE (=AB + BE).

VOL=

6i=0

ai(TBP CP z)i (A.1)

where VOL is the percent volume transfer ratios, TBP CP

the TBP cutpoint temperature; a0: 31.25, a1: 0.09775,

a2: 3.22E06, a3: 7.646E08, a4: 1.1817E10, a5:

2.28E14,a6: 1.366E16,z: 444.25

API=

8i=0

ai(Mid Vol z)i (A.2)

where API is the API gravity of the CDU fraction(except BR), Mid Vol the Mid-volume transfer ratio of the

CDU fraction; a0: 35.4666, a1: 0.476, a2: 0.0034, a3:

0.0005855,a4: 0.0000291,a5: 1.02E06,a6: 3.7E08,

a7: 5.4E10,a8: 1.6E11;z: 41.97

BR API=

2i=0

ai(Vol z)i (A.3)

where BR API is the Bottom residua API gravity, Vol the

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

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 byWatkins (1979)are correlated in this paper to form Eqs. (A.4) and (A.5).

The ASTM IBPs are converted to TBP IBPs using Eq.

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

Eq.(A.5):

TBP IBP=

4i=0

ai(ASTM IBP z)i (A.4)

where TBP IBP is the calculated TBP IBP, ASTM IBP the

ASTM IBP; a0: 522.458, a1: 1.1274, a2: 8.27E05, a3:

8.19E07,a4: 3.336E09,z: 555.0

TBP EP=

4i=0

ai(ASTM EP z)i (A.5)

where TBP EP is the calculated TBP EP, ASTM EP the

ASTM EP; a0: 547.783, a1: 1.06536, a2: 8.53E06, a3:

8.5E08,a4: 1.41E09,z: 521.769.

Appendix B. Comparison with rigorous CDU

simulation results

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

determination in this paper (described in Section3.5).In this

appendix, the accuracies of the Watkins method, the WTRs

determination procedure and the fractions property calcu-

lation 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 four stages. The flow

rates of thestripping steamsof the main columnand sidestrip-

pers 13# are 12,000, 4292, 7250 and 4167 Ib/h, respectively.

No pumparound exists in this example. The condenser and

the bottom stage pressures are 27.8 and 38.5 psi, respec-

tively. The furnace overflash is 2.0 volume percent of crude

feed.

The crude feed has a flow rate of 100,000 bbl/day, a tem-

perature 200 F and pressure 60 psi. The crude oil is Tia Juana

Light and the crude assay data (including the TBPcurve, light

ends composition and the API gravity curve) fromWatkins

(1979)are used. The simulation is carried out with pseudo-

components spaced at 8 F in the range 100800 Fand10 F

in the range 8001640 F.


14/19


Table B1

Actual EP settings (F) used in the CDU simulation

GO HN LD HD

Watkins example 2.5 275 380 560 740

MN 260 400 600 740

ML 275 330 600 740

MH 275 330 550 740

B.1. Comparison of the Watkins CDU calculation

results

The end point settings of CDU fractions used in the Aspen

PlussimulationarelistedinthefirstrowofTable B1. Notethat

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 B2 shows the Aspen Plus simulation

results and the results calculated by Watkins. In Table B2, it

can be seen that the difference of the mass balance between

the two methods is rather small. For heat balance, as one ofthe figures showing the accuracy, the calculated heat duty of

the condenser by Watkins is 205.147 MMBTU/h while by

Aspen Plus is 203.914 MMBTU/h, where the difference is

0.6%.

The method by transforming the Watkins manual proce-

dure 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 s while the Aspen

Plus CDU simulation model needs around 30190 s to obtain

the results. Another drawback of Aspen Plus CDU simulation

model is its instability. We found that Aspen Plus CDU simu-

lation modelsometimes gives us significantly differentresultseven 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 incorpo-

rating Aspen Plus CDU simulation model into a commercial

software such as Aspen PIMSTM (Aspentech). We conclude

that the method described in Section3.5has 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 calcu-

late the WTRs by setting the end points of CDU fractions at

Table B3

Weight transfer ratios of CDU fractions by Aspen Plus simulation

Methods GO HN LD HD BR

MN

ASPEN 9.87 12.66 16.97 5.30 55.20

This paper 11.17 9.46 20.04 6.88 52.45

MLASPEN 12.04 2.45 25.47 4.58 55.46

This paper 11.72 2.79 26.21 6.87 52.40

MH

ASPEN 12.05 2.47 21.43 10.22 53.83

This paper 11.73 2.79 21.03 12.89 51.56

Table B4

WTRs of CDU fractions

This paper ASPEN

GO

H 11.73 12.05

L 11.17 9.87

HNH 9.46 12.66

L 2.79 2.45

LD

H 26.21 25.47

L 20.04 16.97

HD

H 12.89 10.22

L 6.87 4.58

BR

H 52.45 55.46

L 51.56 53.83

three operation modes (rows 24 inTable B1).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 inTable B3.The calculated WTRs of CDU fractions

are listed in Table B4. In Tables B3 and B4, it can beseen that

thedifference of the results between the two methods is rather

small. The differences may originate from different correla-

tions used for property calculation and CDU mass balance

and other unknown parameters such as the Murphree effi-

ciencies of stages. In the Watkins manual calculation, some

correlations were read from graphs, which may bring inac-

curacies. For example, a curve for converting ASTM initial

Table B2

Results of CDU mass balance

Methods GO HN LD HD BR

Mass flow (Ib/h)

ASPEN 142386 113245 203083 123952 682363

Watkins 138802 116175 208728 124958 675879

Mass ratio (%)

ASPEN 11.26 8.95 16.05 9.80 53.94

Watkins 10.98 9.19 16.51 9.88 53.45

Difference (%) 2.48 2.63 2.82 0.85 0.91


15/19


Table B5

ONs of CDU fractions

WTRs ON

This paper ASPEN This paper ASPEN

GO

H 11.73 12.05 60.2 60.0

L 11.17 9.87 60.9 62.7HN

H 9.46 12.66 42.2 42.4

L 2.79 2.45 45.9 43.6

boiling points to TBP initial boiling points (see Appendix A.4

for details) wasused. With updated correlations, the accuracy

of the calculation can be readily improved.

B.3. Comparison of property calculation

As the change of the weight transfer ratios of CDU frac-

tions, the properties of CDU fractions will also change.Table B5lists 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 cal-

culation, 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 and Handwerk

(2001)are correlated using LSM for computer calculation

(Tables C1 and C2). As the results in Table C1show, the

equationz =3

i=1

2j=1aij(x x-)

i1(y y-

)j1 should be

used to calculate the weight or volume transfer ratios of FCC

fractions or the API gravity of FCC fractions. In column z,

Table C2

Coefficients of C3 correlations

a0 a1 a2 x- Maximum

absolute bias

C3 2.759957 0.0558333 0.000574 70 0.06

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 xand y show the two

input variables. In x, Conv is the FCC conversion level

(in percentage); iny,Kis 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 C1 are

aggregated into one FCC fraction C2C4 (Fig. 4) in our

planning model, TGO is the aggregate of HGO and LGO.

Table C2 shows the correlated result of C3. Theequation used

for Table C2 is Vol = a0+ a1(conv x-) + a2(conv x-)2,

where Vol is the volume transfer ration of C3 and Conv

is the conversion level of FCC.

Appendix D. Definitions and mathematical

formulations of the main planning model

D.1. Definitions of indices and parameters

(a) Indices

u different units in the refinery, represents CDU and

FCC

p different types of products, represents 90#, 93#

gasoline,10#, 0# diesel oil, FCC C2C4 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

C2C4, gasoline, HO and coke, respectively

n coefficients of correlations

(b) Sets

U units in a refinery

P types of products

Table C1

Coefficients of FCC fractions correlations

z x y a11 a12 a21 a22 a31 a32 x y Maximum

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 3.00E05 70 23 0.19

C4= VOL% Conv API 8.515 0.0757 0.14736 0.00117 1.80E05 1.40E05 70 23 0.07

i-C4 VOL% Conv API 5.956 0.1091 0.0998 0.001716 1.17E-05 1.10E05 70 23 0.06

n-C4 VOL% Conv API 2.2747 0.064 0.03557 0.00077 5.30E05 1.00E05 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 1.50E05 1.70E06 70.091 23 0.01

TGO API Conv API 8.0929 0.078 0.0146 0.000595 0.00019 7.00E06 70.091 23 0.04

Gasoline API Conv API 6.2337 0.00125 0.001804 0.00044 0.000119 2.623E05 70.091 23 0.01

HGO VOL% Conv K 5.47656 0.08523 0.26131 0.0108 5.95E05 0.0002 77.5 12.075 0.21


16/19


S number of CDU fractions

T number of FCC fractions

VSSs,ss combinations when sequence order of ss less than

that ofs. The order ofs increases from 1 to 5 as s

changes froms1 to s5

(c) Parametersa 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 productp

C raw the price of crude oil

C MTBE the price of MTBE

C untu operation cost of unitu

ON MTBE, ON U21 octane numbers of MTBE and FCC

gasoline, respectively

DMmaxp maximum demand for productp

(d) Variables

CDUrtios weight transfer ratio of CDU fractions

Conv the conversion level of FCC

Denss specific gravity of CDU fraction s. Only LDand HD

are included

Mid wts mid-point weight transfer ratio of CDU fractions,

BR not included

MTBEP01 quantity of MTBE that attends the blending of

90# gasoline

MTBEP02 quantity of MTBE that attends the blending of

93# gasolineFcdu frts flow rate of CDU fractions

Ffcc frtt flow rate of FCC fractiont

FCCrtiot weight transfer ratio of FCC fractiont

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

qprodp production rate of productp

UNITu load of unitu

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 of10#

diesel oil

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

oil

U14P03 quantity of HD that attends the blending of10#

diesel oil

U14P04 quantity of HD that attends theblendingof 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 HDthat attend the blending of10# diesel oil

VPU13P04, VPU14P04 volume flow rates of LD and HD

that attend the blending of 0# diesel oil

D.2. Mathematical formulations

D.2.1. Objective function

Total profit = money earned by selling products

crude oil cost

MTBE cost unit operation costs.

maximize profit=pP

qprodpC prodp UNITu=u1C raw

(MTBEP01 + MTBEP02)C MTBE

uU

UNITuC untu (obj)

D.2.2. Constraints

Material balance of units

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

UNITu < CAPACITYu, 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.

Fcdu frts=s1 U11P01 U11P02= 0 (p2 1)

Fcdu frts=s2 U12P01 U12P02= 0 (p2 2)

(iii) The flow rates of light distillate or heavy distillate from

CDU equal the sum of light distillate or heavy distil-

late that attends the blending of10# and 0# dieseloil.

Fcdu frts=s3 U13P03 U13P04= 0 (p2 3)

Fcdu frts=s4 U14P03 U14P04= 0 (p2 4)

(iv) The weight transfer ratio of each CDU fraction should

be greater than its lower limit and less than its upper

limit.

0.1117 CDUrtios=s1 0.1173 (p3 1)

0.0279 CDUrtios=s2 0.0946 (p3 2)


17/19


0.2004 CDUrtios=s3 0.2621 (p3 3)

0.0687 CDUrtios=s4 0.1289 (p3 4)

0.5156 CDUrtios=s5 0.5245 (p3 5)

Note that the numbers 0.1117, 0.1173, etc., are CDU

WTR limits calculated in CDU WTR determinationmodel. When the ASTM boiling ranges of CDU frac-

tions or crude assay change, the WTR determination

model should be calculated again and the above num-

bers should be updated.

(v) The sum of the weight transfer ratios of CDU fractions

should be 1:

s S

CDUrtios =1 (p4)

(vi) Calculate the flow rate of CDU fractions:

Fcdu frts =UNITu=u1CDUrtios, s S (p5)

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

fractions:

Mid wts =100(

ssVSSs,ss

CDUrtioss+ 0.5CDUrtios),

s=s5, s S (p6)

(viii) Calculate the octane numbers of GO and HN; the pour

points of LD and HD:

Prop CDUs=a prop

s,n=n0+ a prop

s,n=n1

(Mid wts a props,n=n3)

+ a props,n=n2(Mid wts a props,n=n3)2,

s=s5, s S (p7)

When s equals s1 and s2, Prop CDUs represents the

octanenumber of GO andHN. When s equalss3and s4,

Prop CDUs represents the pour point of LD and HD.

a props,n=n0,a props,n=n1,a props,n=n2 anda props,n=n3represent a0, a1, a2andz in row s ofTable 8. For exam-

ple, when s equals s1 (GO), the values listed in the first

row ofTable 8should be assigned to a props=s1,n=n0to

a props=s1,n=n3, respectively.

Material balance of FCC fractions

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

FCCrtiot= a fccrtot,n=n0+ a fccrtot,n=n1

(Conv a fccrtot,n=n3)+a fccrtot,n=n2)

(Conv a fccrtot,n=n3)2, t T (p8)

a fccrtot,n=n0, a fccrtot,n=n1, a fccrtot,n=n2 and

a fccrtot,n=n3 represent a0, a1, a2 and z respec-

tively in row tofTable 7. Rows t1 to t4 represent

the first to fourth rows ofTable 7.

(ii) Calculate the flow rates of FCC fractions:

Ffcc frtt=UNITu=u2FCCrtiot, t T (p9)

(iii) Calculate the FCC feed flow rate:

UNITu=u2 =Fcdu frts=s5+ Frecycle (p10)

(iv) Calculate the flow rate of FCC recycle:

Frecycle Fcdu frts=s50.5 (p11 1)

Frecycle Ffcc frtt=t3 (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:

85 Conv 60

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:

Ffcc frtt=t2 U21P01 U21P02= 0 (p12)

Gasoline blending

(i) Read the octane numbers of MTBE and FCC gaso-

line.ON U21 = 95, ON MTBE = 101Due 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 ofFCC 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

attend90# gasoline blending shouldbe equal to or greater

than 90.

Prop CDUs=s1U11P01+ ON MTBE MTBEP01

+ Prop CDUs=s2U12P01+ ON U21 U21P01

90qprodp=p01 0 (p13)

The linear combination of the octane numbers of grossoverhead, heavy naphtha, FCC gasoline and MTBE that

attend93# gasoline blending shouldbe equal to or greater

than 93:

Prop CDUs=s1U11P02+ ON MTBE MTBEP02

+ Prop CDUs=s2U12P02 + ON U21U21P02

93qprodp=p02 0 (p14)

Diesel oil blending

The nonlinear correlation proposed by Semwal and

Varshney (1995)is used in this paper.


18/19


Table D1

Coefficients for specific gravities of LD and HD

Fractions a denss,n=n0 a denss,n=n1 a denss,n=n2 a denss,n=n3

s3 (LD) 0.8078 0.0033 1.824E5 26.3285

s4 (HD) 0.8818 0.0022 1.407E5 54.9158

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

Denss =a denss,n=n0+ a denss,n=n1

(Mid wts a denss,n=n3)

+ a denss,n=n2(Mid wts a denss,n=n3)2,

s= s3 s= s4 (p15)

The above correlations are obtained by correlating the

specific gravity data from crude assay provided by

Watkins (1979). Thevalues of parameters used are listed

inTable D1.(ii) Calculate the volumeflow rates of LDand HDfromtheir

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 andVarshney (1995)was rearranged to avoid possible over-

flow of variables.

Prop CDUs=s3

473.69

12.987VPU13P031.105

+

Prop CDUs=s4

473.69

12.987VPU14P031.105

(VPU13P03 + VPU14P03)1.105 (p17)

In the above equation, 473.69 (in degrees Rankine,

R) is the maximum pour point of10# diesel oil.(iv) The pour point of 0# diesel oil should be less than 0:

Prop CDUs=s3

491.69

12.987VPU13P041.105

+

Prop CDUs=s4

491.69

12.987VPU14P041.105

(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

(i) The production rate of each product equals the sum of

the streams that attend its blending:

qprodp=p01 =U11P01 + MTBEP01 + U12P01

+U21P01 (p19)

qprodp=p02 =U11P02 + MTBEP02 + U12P02

+U21P02 (p20)

qprodp=p03 =U13P03 + U14P03 (p21)

qprodp=p04 =U13P04 + U14P04 (p22)

qprodp=p05 =Ffcc frtt=t3 Frecycle (p23)

qprodp=p06 =Ffcc frtt=t1 (p24)

(ii) The production rate of each product sent to customers

should be less than its market demand:

qprodp DMmaxp (p25)

References

Arnold, V. E. (1985). Microcomputer program converts TBP, ASTM, EFV

distillation curves. Oil & Gas Journal, 83(6), 5562.

Aspen Technology. (2001).ASPEN PLUS, version 11.1. Cambridge, MA:

Aspen Technology Inc.

Barsamian, A. (2001). Fundamentals of Supply Chain Management for

Refining. In IBC Asia Oil & Gas SCM Conference Proceedings .Blanding, F. H. (1953). Reaction rates in Catalytic Cracking of Petroleum.

Industrial & Engineering Chemistry, 45(6), 11861197.

Brooke, A., Kendrick, D., & Meeraus, A. (1992). GAMS A Users

Guide (Release 2.25). San Francisco, CA: The Scientific Press.

Brooks, R. W., et al. (1999). Choosing cutpoints to optimize product

yields. Hydrocarbon Processing, 78(11), 5360.

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 Pro-

cessing, 78(2), 6468.

Hartmann, J. C. M. (2001). Determine the optimum crude intake levelA

case history. Hydrocarbon Processing, 80(6), 7784.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), 213216.

Jacob, J. S., Gross, B., Voltz, S. E., & Weekman, V. W. (1976). A lumping

and reaction scheme for catalytic cracking. AIChE Journal, 22(4),

701713.

Lang, P., et al. (1991). Modelling of a crude distillation column. Com-

puters and Chemical Engineering, 15(2), 133139.

Magee, J. S., Maurice, M., & Mitchell, J. (1993). Fluid Catalytic Crack-

ing: 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, 5178.


19/19


Perry, R. H., Green, D. W., & Maloney, J. O. (1997). Perrys 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(910), 22592276.

Reid, E. B., & Allen, H. L. (1951). Estimating pour points of petroleum

dist. blends. Petroleum Refiner, 30(5), 9395.

Russell, R. A. (1983). A flexible and reliable method solves single-

tower and crude-distillation-column problems. Chemical Engineering,

5359.

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), 437444.

Trierwiler, D., & Tan, R. L. (2001). Advances in crude oil LP modelling.

Hydrocarbon Asia, 8 , 5258.

Watkins, R. N. (1979). Petroleum Refinery Distillation(2nd ed.). Houston:

Gulf Publishing Co.

Zhang, J., Zhu, X. X., & Towler, G. P. (2001). A level-by-level debot-

tlenecking approach in refinery operation. Industrial and Engineering

Chemical Research, 40(6), 15281540.

Integrating CDU, FCC and product blending models into refinery planning.pdf

Documents

Transcript of Integrating CDU, FCC and product blending models into refinery planning.pdf