Combine Molecular Modeling with Optimization to Stretch Refinery

Ind. Eng. Chem. Res. 2002, 41, 825-841

825

Combine Molecular Modeling with Optimization to Stretch Refinery Operation Shanying Hu,† G. Towler,‡ and (Frank) X. X. Zhu*,‡ Department of Process Integration, University of Manchester Institute of Science and Technology (UMIST), P.O. Box 88, Manchester M60 1QD, U.K.

A systematic methodology is presented for refinery modeling and optimization on the basis of much more detailed molecular information than is used in conventional lumped methods. The novelty of this work is that it incorporates models based on microscopic understanding into optimization to achieve macroscopic improvements. A molecular matrix based on a homologous series of hydrocarbon compounds is used to characterize refining streams. A transformation method is developed for obtaining molecular compositions from stream bulk properties. Molecular models are then built for several processes. Molecular modeling and optimization are finally integrated into an overall refinery optimization framework. Results show that molecular information can provide much better understanding and new insights into refining operation. Consequently optimization based on molecular modeling can greatly improve total profit while making quality products that satisfy environmental regulations. 1. Introduction Because of strong market competition, the petroleum refining industry is expected to produce high-quality products at low cost. Meanwhile, stricter environmental regulations call for higher requirements for petroleum and petrochemical production. As an additional challenge for refining operations, petroleum refineries will be required not only to produce clean fuels but also to produce increasing amounts of petrochemicals (e.g., lubricants, xylene, ethylene and pentane) with particular properties or special molecular compositions.1 To achieve these goals, more accurate and reliable process modeling methods must be developed to achieve better design, management, optimization, and control of refining processes. The existing modeling technologies are mainly based on lumping and pseudocomponent methods, which cannot capture precise molecular information. To overcome this problem, a molecular modeling method that incorporates fundamental chemistry, thermodynamics, and kinetics must be developed for future improvements in refining practice. The basis of a molecular refining model is the characterization of the petroleum composition. Modern analytic technologies such as gas chromatography (GC), high-performance liquid chromatography (HPLC), and mass spectroscopy (MS) can characterize most petroleum fractions. This makes it possible to obtain the molecular compositions of streams in refining processes. Molecular thermodynamic and kinetic approaches can be used to correlate physical property and reaction kinetic parameters. On the basis of feed compositions and parameters, molecular models can be built for the reaction and separation units to predict product molecular compositions and properties. By incorporating these molecular characterizations and models with overall * Corresponding author. E-mail: [email protected]. † Contact address: Department of Chemical Engineering, Tsinghua University, Beijing 100084, China. ‡ Current address: UOP, 25 East Algonquin Avenue, Des Plaines, IL 60016.

plant optimization, we can monitor, control, and allocate important species. Thus, we can maximize certain desired molecules to form valuable products (e.g., gasoline, diesel and xylene), minimize undesired molecules, and route all species (desired and undesired) to their most appropriate destinations to achieve maximal profit. Furthermore, by monitoring and controlling the sulfur and nitrogen contents and the carcinogenic aromatic compounds in petroleum products, such molecular-level modeling will enable environmental regulations to be achieved without major capital investments. In this way, a microscopic understanding of refining streams and processes can be fully exploited through macroscopic optimization. This combination will help refining to achieve its true potential. The above vision and perspective for refining modeling and optimization is implemented in the new method presented here. In this work, the molecular matrix representation proposed by Peng and Towler2 is used to describe the molecular composition of a petroleum fraction. A transformation method is developed to convert between bulk properties and molecular compositions for refining streams. Molecular models are built for several processes including catalytic reforming, naphtha hydrotreating, reformate stabilizing, and gasoline blending. The incorporation of molecular information into optimization leads to a very large and complex optimization problem. To make the optimization problem solvable while still maintaining solution quality, a level-by-level optimization approach proposed by Zhang and Zhu3 is applied for the overall refinery optimization. 2. Current Practice in Refinery Modeling Chemical modeling is at the core of refinery design, control, and optimization. The common practice for modeling refinery processes is to build lumped models, where individual molecules in a hydrocarbon feedstock are grouped into broad but measurable categories of compound classes or boiling ranges. Reaction networks reflecting a macroscopic understanding of the process chemistry are then constructed for these lumps. Lumped

10.1021/ie0010215 CCC: $22.00 © 2002 American Chemical Society Published on Web 01/22/2002

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Ind. Eng. Chem. Res., Vol. 41, No. 4, 2002

models are fast and easy to formulate and solve. This helps them remain dominant in process control, design, and optimization. The lumping method has been applied to model almost all refinery reaction processes, including catalytic cracking,4,5 hydrotreating,6,7 hydrocracking,8-11 hydropyrolysis,12 and asphaltene and residue pyrolysis.13 The lumped models prove very useful when the main requirement is the prediction of broad information such as the yield and octane number of gasoline. However, the lumped models cannot provide detailed molecular information such as the contents of paraffins, olefins, benzene, and sulfur, which are very important for current refinery operations. Lumped modeling approaches are also widely applied in separation processes by researchers14-19 and commercial software such as ASPEN, PRO II, and HYSIS; but the lumping method is different from the approaches used in modeling reaction processes. For example, in modeling distillation, petroleum mixtures are characterized in terms of narrow boiling point cuts as pseudocomponents, which is different from chemical lumps used for reaction modeling. The incompatible lumping methods make the modeling of both reaction and separation processes inconsistent. On the other hand, mechanistic molecular models20 can provide rich information in chemistry. Klein and co-workers have carried out research on mechanistic modeling for the reaction of catalytic cracking,21-25 hydroprocessing,26,27 and pyrolysis.28-31 These models were developed on the basis of fundamental reaction steps such as β-scission, hydride shift, methyl shift, bond fission, etc., that involve elementary transitions of active centers, e.g., surface intermediates in catalyzed reactions or free radicals in thermal reactions. Froment and co-workers also developed detailed models that track surface intermediates and fundamental molecular transformations for hydrocracking,32,33 thermal cracking,34,35 and hydrodesulfurization,36 as well as catalytic cracking.37-39 The mechanistic models have the advantage of being rich in fundamental chemistry. However, they face several difficulties in application to optimization problems. This is because a reaction network based on mechanistic molecular models can lead to a combinatorial explosion of molecular species. Furthermore, the large number of species and reactions involved can lead to a prohibitive number of differential algebraic equations. These equations are often difficult to deal with numerically and therefore create difficulties for computation and impose a considerable demand for CPU time. In addition, these mechanistic models require the precise determination of the mixture compositions, which are difficult to measure. In an attempt to overcome the above problems, Mobil developed a structure-oriented lumping (SOL) approach to model catalytic hydroprocessing40,41 and fluidized catalytic cracking (FCC)42 processes. A complex petroleum mixture in SOL is described using vectors of structural increments. Reaction rules (e.g., ring saturation, sulfur removal) derived from fundamental reaction chemistry are then applied to track the changes of the set of structural vectors and thus build the reaction network. One advantage of SOL is that it allows for the development of a mathematical or algorithmic description of process chemistry. However, the details of this approach are not available in the public literature. Recently, Peng and Towler2 presented a framework for refinery molecular modeling. Unlike the average

structural parameter characterization method, Peng and Towler proposed a characterization method that is based on a MTHS (molecular type homologous series) matrix representation. It uses homologous groups and carbon number groups to characterize a petroleum fraction. This method can effectively represent basic molecular information for crude, which can include 108 different molecules. Zhang43 extended Peng and Towler’s research and developed thermodynamic correlations for the main molecules in the MTHS matrix, which makes it possible for physical properties to be obtained from molecular compositions. Although much research has been carried out for molecular modeling, little effort has been dedicated to the the incorporation of molecular modeling into overall refining optimization. To achieve this goal, we developed a method for obtaining molecular information from bulk properties for different refining streams, built molecular models for different refining process units, and developed an optimization framework that can accommodate molecular information to a great extent. 3. Modeling of Refinery Streams 3.1. MTHS Matrix. To model a refinery using molecular information, first, we need to model the refinery streams, which are petroleum mixtures consisting of numerous hydrocarbon molecules that can be divided into several categories: paraffins, olefins, naphthenes, aromatics, and heteroatom compounds. It is impossible to describe each molecule of a stream as too many molecules are involved, and it is also unnecessary because many types of molecules are present in very small quantities. The traditional characterization methods for hydrocarbon mixture are based on pseudocomponents, compound classes,44 and average structure parameters.45,46 These methods cannot provide detailed molecular information. In this work, the MTHS matrix representation2 (Figure 1) is used to represent molecular compositions of a petroleum mixture, where nP, iP, O, N, and A express different homologous series corresponding to normal paraffins, isoparaffins, olefins, naphthenes, and aromatics, respectively. A homologous series has the same base structure but with varying carbon number, for example, through the addition of alkyl chains of increasing length. Different sulfur and nitrogen compounds are also shown in this matrix. As an example, Figure 2 shows a homologous series of one-ring aromatic compounds. The molecular types, such as benzene ring, naphthene ring, aromatic ring, etc., capture the different base structures of each series. The molecules that belong to a homologous series with the same carbon number have similar physical properties, and thus, they are lumped into a single component as an element of the matrix. The molecular type, together with a specified carbon number, then determines the reactivity and physical and chemical properties of a species. The homologous series also form the basis for molecular structure-property correlations. According to Peng and Towler,2 this hydrocarbon characterization method has four essential features: (1) measurability, in that the entire composition of the matrix can be measured; (2) adequacy, in that the matrix contains sufficient detail to determine all product properties of interest; (3) accuracy, in that the matrix contains sufficient detail to model accurately the reaction networks and kinetics

Ind. Eng. Chem. Res., Vol. 41, No. 4, 2002 827

Figure 1. Matrix representation of petroleum mixture.

Figure 2. Molecular type homologous series of one-ring aromatic compounds.

Figure 3. Molecular matrix for a gasoline fraction.

of refinery processes based on the fundamental chemistry and, at the same time, is within a manageable size, so that it is possible to model a reaction network corresponding to the matrix; and (4) consistency, in that the same matrix format can be used to model any refining mixtures, no matter whether they are associated with separation or reaction processes. It should be noted that, although the homologous series is used as the basis for modeling refinery streams, more-or-less detailed modeling can be applied to achieve the level of detail required for different purposes. In general, the matrix of Figure 1 contains several hundred elements in terms of composition flows, which become optimization variables. The matrix could be too large, which might cause problems in data extraction and also in optimization. However, the size of the matrix can be reduced for specific streams. For example, we can represent a gasoline fraction using Figure 3 with a much smaller matrix. 3.2. Predicting Bulk Properties from Molecular Compositions. After obtaining a molecular matrix for

a refinery stream, we know molecule structure parameters, such as molecular type and carbon number, from which we can derive bulk properties for the stream. The calculation procedure is given in Figure 4. First, using the molecular structure-property correlations of Zhang,43 fundamental molecular properties (e.g., molecular weight, density, and boiling point) can be calculated. These molecules are mixed according to compositions and accumulated by volume percent to generate a true boiling point (TBP) distillation curve of the stream. The TBP curve is then converted into an ASTM (American Society for Testing and Materials) D86 distillation curve using existing correlations. Following this step, bulk properties of a stream, including density; molecular weight; viscosity; refractive index; flash point; pour point; cetane number; and contents of paraffins, naphthenes and aromatics, can be estimated. The research octane number (RON), motor octane number (MON), and Reid vapor pressure (RVP) of the stream can be calculated by molecular-composition-based correlations with the given values of the RONs, MONs, and RVPs

828


Figure 4. Calculation procedure for translating molecular composition to bulk properties. Table 1. Comparison of Calculated and Measured Bulk Properties property

calculated

density MW SPGR API K

735.4 98.1 0.7557 60.83 11.86

measured 98.7 0.767

property

calculated

refractive index viscosity (100 °F) viscosity (210 °F) flash point RON

1.41 0.628 0.420 -33.8 94.5

of all molecules.16,47-50 The molecular approach for RON and MON enables a proper accounting of nonlinear and composition-dependent blending phenomena. To show the high precision achieved by this conversion method, a gasoline stream for which the molecular matrix is given in Figure 3 is used as an example. By using the above-mentioned method, the bulk properties of the gasoline were predicted (Table 1). Measured values of some properties are also shown in the table. By comparison, we can see that most of calculated values agree well with the measured data. 3.3. Converting Bulk Data into Molecular Information. In the previous section, we explained how to convert molecular information to bulk properties, but what about the reverse case, where molecular information needs to be derived from bulk properties? This is a very common problem as, in most cases, we can obtain bulk properties for crude oils and other refining streams, but without knowledge of molecular information. It would be a very tedious task to measure molecular information for every mixture. The method proposed here is to derive molecular information from measured bulk data and the molecular matrices that have previously been obtained for similar types of streams. For example, we want to derive a molecular matrix for a gasoline stream with given bulk properties. The molecular matrix has the same format as Figure 3. In total, there are 37 composition elements in the matrix that need to be determined. The compositions for molecule N4, A4, and A5 are zero because these molecules do not exist. Before determining the molecular matrix for the gasoline stream under consideration, we construct a database that includes molecular matrices for several different gasoline streams such as FCC gasoline, reforming gasoline, straight-run gasoline, alkylate gasoline, etc. The more matrices stored in the database the better the result. using the database, we can derive the 37 molecular elements using the follow-

measured

property

calculated

measured

94.8

MON RVP parafins naphthalenes aromatics

83.9 5.27 39.79 5.79 31.55

82.7 4.99 39.79 5.79 31.55

ing optimization model (eq 1) based on the least-squares fitting method n

min

wj(Bgj - Bcj )2 ∑ j)1

s.t. Bcj ) fj(Mc),

j ) 1, ..., n

m

c

M )

aiMi ∑ i)1

(1)

m

ai ) 1 ∑ i)1 0 e ai e 1 where Bg is the vector of given measured bulk properties, Bc is the vector of calculated bulk properties, f is the function transfering a molecular matrix to its bulk properties, n is the number of bulk properties, wj is the weight for bulk property j, Mc is the molecular composition matrix to be determined, Mi is the sample molecular matrix i given in the database, m is the number of sample matrices, and ai is the coefficient for sample matrix i to be optimized. From the above model, the new matrix is constructed by the addition of sample matrices, each with an attached coefficient. These coefficients are optimized to minimize the difference between the given bulk properties for the stream under consideration and those to be calculated from the new matrix. For given bulk properties Bg (Table 2) corresponding to a particular gasoline,18 we can derive the molecular composition shown in Figure 5 for the gasoline. For comparison, the calculated bulk properties Bc for the matrix of Figure 5 are provided in Table 2 as well. The errors are acceptable, except for the ASTM 0% point. This is because we set a

Ind. Eng. Chem. Res., Vol. 41, No. 4, 2002 829 Table 2. Given and Calculated Bulk Properties for a Gasoline Example bulk properties

ASTM 0%

ASTM 10%

ASTM 50%

ASTM 90%

ASTM 100%

RON

MON

given calculated

37 23.6

59 59.8

96 99.8

167 165.4

194 194.5

87.1 86.6

78.2 77.5

Figure 5. Derived molecular compositions of the gasoline example.

very small weight for this point as the value at the ASTM 0% point is generally not reliable. We also set a high weight for RON because of its importance. This case shows that we can derive relative precise molecular matrix using bulk properties and sample molecular matrices. 4. Molecular Modeling of Refining Processes By molecular modeling for refinery streams, we can now obtain the necessary feed molecular information for refinery process units. The next step is to develop process models on a molecular basis for use in predicting product molecular matrices and properties. Previously, we at UMIST have developed molecular models for catalytic reforming,2 naphtha hydrotreating,51 reformate stabilizing,51 and gasoline blending units.52 4.1. Catalytic Reforming. The catalytic reforming process is one of the most commonly used refining processes, which can greatly improve gasoline quality. Its flowsheet is shown in Figure 6. Feedstock naphtha has the molecular matrix given in Figure 7. The reactions involved in this unit include paraffin cracking, naphthene and aromatic side-chain cracking, ring opening, ring closure, hydrogenation, and dehydrogenation, which occur in fixed catalyst beds in three reactors. As a result of these reactions, the amount of aromatics increases significantly while the amounts of paraffins and naphthenes decrease. This consequently increases the RON for the product. In catalytic reforming, 51 reactions are involved, which can be represented by the reaction network shown in Figure 8. Reaction kinetics is the key for any molecular modeling of a reactor. In this work, the effective reaction rate constant kj for the reaction j in the network is correlated with temperature T and pressure P. kj is calculated on the basis of the model developed by Ancheyta and Aguilar53

kj ) k0j exp

[(

Ej 1 1 R 766 T

P ) )](300

Rj

(2)

where k0j is the effective rate constant at 766 K and 300 psia. Ej and Rj are the activation energy and the pressure effect parameter, respectively. The purpose of modeling catalytic reforming is to predict the molecular composition of the reformate product according to the given feed composition and

reactor operating conditions. The model includes rate equations for all reactions in the reaction network (Figure 8), as well as mass and heat balance equations. The reaction rate equations are differential equations that are solved by using the Runge-Kutta method. For the above feed molecular matrix (Figure 7) at a feed temperature of 750 K, a pressure of 206 psia, residence times of 0.15, 0.25, and 0.4 h for three reactors, the reformate molecular compositions and bulk properties were predicted using the model (Figure 9). The predicted reformate compositions and bulk properties agree well with the experimental results. 4.2. Modeling of Naphtha Hydrotreating. Naphtha hydrotreating occurs in a preprocessing unit before naphtha catalytic reforming, to remove materials that poisons the reforming catalyst. The main equipment in this process is a fixed-bed reactor. The purpose of modeling is to build reaction kinetic model for this reaction process and then obtain the molecular matrix of the product for any given feed molecular matrix. The possible reactions in naphtha hydrotreatment include desulfurization, denitrogenation, deoxygenation, olefin saturation, chloride removal, and metal removal. Because the contents of nitrogen, oxygen, chlorine, and metals are much less than that of sulfur in the naphtha feed, the reactions for denitrogenation, deoxygenation, chloride removal, and metal removal can be neglected. Therefore, the modeling work focuses on reactions for desulfurization. According to the types of sulfur compounds existing in the naphtha, five types of reactions can occur, namely, mercaptan, sulfide, disulfide, cyclic sulfide, and thiophene desulfurizations (eqs 3-7).

mercaptan C-C-C-C-C-SH + H2 f C-C-C-C-C + H2S

(3)

sulfide C-C-C-S-C-C + 2H2 f C3H8 + C2H6 + H2S

(4)

disulfide C-C-C-S-S-C-C + 3H2 f C3H8 + C2H6 + 2H2S (5)

A part of the reaction network is shown in Figure 10, which is thiophene desulfurization. Thiophene desulfurization proceeds along two parallel pathways. The thiophene hydrogenation and hydrogenolysis reactions occur simultaneously to generate cyclic sulfide and cisand trans-2-butene and then proceed further to 1-butene and butane. This reaction network also includes olefin saturation reactions.51,54 Hydrogenolysis and hydrogenation reactions take place on different sites of the catalyst simultaneously. In general, the hydrogenolysis reaction is represented as occurring at the σ site, and hydrogenation at the τ

830


Figure 6. Catalytic reforming flowsheet.

Figure 7. Molecular matrix for reforming feedstock.

site. Two reaction equations were developed for thiophene hydrogenolysis and hydrogenation by Van Parijs55 with the catalyst CoMo/γ-Al2O3 (eqs 8 and 9).

rT,σ )

kT,σKT,σKH2,σPTPH2 [1 + (KH2,σPH2)1/2 + KT,σPT + KH2S,σPH2S/PH2]3 (8)

rA,τ )

kB,τKB,τKH2,τPBPH2 [1 + (KH2,τPH2)1/2 + KA,τPA + KB,τPB]2

(9) Figure 8. Reaction network of catalytic reforming.

where

kT,σ ) 5.22 × 107 exp(-29.9/RT) KT,σ ) 5.60 × 10-4 exp(10.7/RT) KH2,σ ) 0.536 KH2S,σ ) 91.2 kB,τ ) 2.21 × 1011 exp(-38.1/RT) KA,τ ) KB,τ ) 4.07 × 10-4 exp(10.6/RT) KH2,τ ) 8.88 × 10-13 exp(26.4/RT) PA is the partial pressure of butane, PB is the partial pressure of butene, PH2 is the partial pressure of hydrogen, PH2S is the partial pressure of hydrogen sulfide, PT is the total pressure, and the temperature range is 260-350 °C. The kinetics of the hydrodesulfurization reactions was reported by Phillipson in 1971.56 The reaction rate rHDS is shown in eq 10

rHDS )

k′′PSPH21/2PT1/2 1/2

PHC

(

1 + 0.21

)

PH2S PT

(10)

where k′′ is the reaction rate constant, PHC is the partial pressure of hydrocarbon, and PS is the partial pressure of sulfur. In this work, sulfur compounds are lumped into the two classes SI and SII, where SI includes mercaptan only. SII consists of sulfide, disulfide, cyclic sulfide, and thiophene. The reaction kinetics for SII is mainly based

on that for thiophene as thiophene’s concentration is much higher than the concentrations of the other components. SI is easier to remove than SII. After reaction, a delumping strategy is needed according to the initial lumping ratio and the stoichiometric ratio of each reaction class to estimate the distribution of each product and the hydrogen consumption.51 The model for a naphtha hydrotreating reactor is similar to that for catalytic reforming. It includes a plugflow reactor. Along the axis direction, the reactor is modeled so that it is divided into several reacting zones. Each zone is considered as a continuous stirred-tank reactor. The temperature increase in the naphtha hydrotreating reactor during reaction is very small, and thus, a constant temperature is assumed for the whole reactor. The computation procedure is shown in Figure 11. In the case study, a feedstock molecular matrix is given for the naphtha hydrotreating unit (Figure 12). Operating conditions are 300 °C and 25.7 atm, with a liquid hourly space velocity of 5 h-1 and a feed molar flow rate of 376.2 mol/s. As a result of applying the model for the unit, the product composition shown in Figure 13 is obtained. The product molar flow rate is 369.4 mol/s. The total sulfur content is reduced from its initial value of 2540 ppm (by weight) in the naphtha feed to 0.41 ppm (by weight) in the product, which satisfies the product specification of 0.5 ppm (by weight). In the breakdown, the mercaptan content is 0.38 ppm, while the thiophene content is 0.03 ppm. It is observed that, when the temperature increases, the thiophene concentration drops, whereas the mercaptan tends to increase. The complete olefin amount is removed during reaction, and the hydrogen consumption is estimated to be 7.21 mol/s. 4.3. Modeling of Gasoline Stabilizer. Gasoline stabilization mainly involves distillation. As mentioned in section 2, the modeling of distillation is currently


Figure 9. Comparison of modeled and experimental compositions for catalytic reforming.

Figure 10. Thiophene hydrodesulfurization reaction pathway.

different from the modeling of reactions as the former is based on pseudocomponents whereas the latter is

based on chemical lumps. In this work, we attempt to model gasoline stabilization on the same basis as reaction processes using molecular information. The gasoline stabilizer is a unit following the catalytic reformer in sequence to separate the reforming product into stabilizing gasoline and light hydrocarbons. The flowsheet is shown in Figure 14 and mainly consists of a gas/oil splitter and a reformate stabilizer column. The outlet stream from the catalytic reforming reactor goes to the gas/oil splitter for recovering hydrogen. The liquid stream enters the reformate stabilizer column to be separated into light hydrocarbons at the top and stabilizing gasoline at the bottom. The molar composition of the feed (reforming product) to the splitter is given in Figure 15. A total of 28 components are in this molecular matrix. The vaporliquid equilibrium constant k for each component is correlated with the temperature T and the pressure P (psia) as in eq 11. The correlations are valid for temperatures of 255-525 K and pressures of 14.7-120 psia.51,57

( )

()

1 1 + aT2 + aT3(ln T) + aT4T + 2 T T 1 1 aT5T 2 + aT6 + aP1(ln P) + aP2 2 + aP3 + P P

ln k ) aT1

( )

2

Figure 11. Modeling procedure for hydrotreating rector.

()

3

aP4(ln P) + aP5(ln P) + aP6(P) (11)

832


Figure 12. Feed matrix of naphtha hydrotreating.

Figure 13. Predicted product molecular compositions for naphtha hydrotreater.

Figure 14. Gasoline stabilizer flowsheet.

where aTi and aPi (i ) 1, ..., 6) are the temperature and pressure correlation coefficients, respectively. The gas/ oil splitter model is a simple single-flash calculation. At 35 °C and 13 bara, the molar compositions for the vapor and liquid streams of the splitter are calculated accordingly. The stabilizer is a distillation column with 32 stages. The liquid stream from the splitter goes to the column at stage 24. The feed temperature and pressure are 180 °C and 12.5 bara, respectively. The distillation column is solved by the shortcut method, which uses empirical correlations and an average relative volatility to perform distillation modeling computation. The Fenske method58 is used to compute minimum number of trays required, and the minimum reflux ratio is determined by the

Figure 15. Feed molecular matrix of gasoline stabilizer unit.

Ind. Eng. Chem. Res., Vol. 41, No. 4, 2002 833 Table 3. Bulk Properties of the StabilizIng Gasoline property

value

property

value

RVP (psia) RON MON density (kg/m3) specific gravity API

4.40 101.1 84.6 786.2 0.7865 48.40

molecular weight Watson K viscosity (100 °F) viscosity (210 °F) refractive index

99.4 11.6 0.666 0.422 1.422

ties considered are the RON, benzene content, and aromatic content. The optimal blending ratio is obtained by an optimization for maximizing profit under RON, benzene, and aromatics constraints. Figure 16. Modeled molar compositions of the stabilizing gasoline.

Underwood method.59 The Gilliland method59 is used to calculate the number of theoretical trays required, the actual reflux ratio, and the condenser and reboiler duties for a given set of ratios between the actual and minimum reflux. Finally, the Kirkbride method60 is used to determine the optimum feed location. Normal butane is chosen as a light key component with a recovery ratio of 0.64, and isopentane is chosen as a heavy key component with a recovery ratio of 0.98. As a result, the molar composition of the bottom product (stabilizing gasoline) is obtained, as shown inFigure 16. The recovery ratio of stabilizing gasoline is 93.66%. Meanwhile, the properties of the gasoline are calculated as in Table 3. It can be seen that the calculated compositions of the gasoline product agree very well with the results obtained by a rigorous simulation using PRO II. Optimization for the feed temperature and pressure of the column was also carried out. The results show that an increase in temperature is helpful for increasing gasoline product flow rate but that the RVP also becomes higher. Thus, it is sensible to optimize the feed temperature under a suitable RVP constraint. It is also found that the pressure has little influence on the result. 4.4. Modeling of Gasoline Blending. The gasoline blending process has 19 possible gasoline feeds and produces four kinds of gasolines (90#, 93#, 95#, and 97#). One of the feeds is reforming stabilizing gasoline, which has molecular information obtained previously. The other feeds come from the atmospheric distillation column, FCC, hydrocracking, delayed coking, etc., for which we have not yet built molecular models. However, we can calculate bulk properties for these 18 gasoline streams from nonmolecular models. Thus, the method of conversion from bulk data to molecular information developed in section 3.3 can be used to obtain molecular compositions for these streams. After all feed molecular information is obtained, the blending model in eq 12 can be used to determine the molecular compositions of the blended products for a given blending ratio bij for feed i and product j.

CPj )

∑i (bijCFi)

(12)

∑i

bij ) 1.0

where CPj is the composition matrix of blending product j, CFi is the composition matrix of blending feed i, and bij is the weight percent of feed i blended into product j. Then, product properties can be predicted according to the molecular compositions. In this work, the proper-

5. Molecular Optimization of Processes Using the molecular models, we can conduct an optimization for refinery processes. In this paper, the combined unit of the catalytic reformer and gasoline stabilizer is used as an example. In the case study, we consider a given feed molecular matrix for the first reforming reactor (Figure 6). It should be noted that the feed composition and flow rate are fixed for the standalone process optimization. However, they can be optimized in the overall refinery model, which will be discussed later. In the context of the reforming process optimization, the temperature and pressure of the catalytic reformer are selected as optimization variables because they control the reaction kinetics, which can cause variations in the RON and benzene and aromatic contents of the reforming stabilizing gasoline. This will have a significant impact on the gasoline blending. The process optimization can be described as in Figure 17. The naphtha feed is converted into three products, namely, hydrogen, light hydrocarbon, and gasoline after the reforming reactors and stabilizer. We focus on variations in the benzene and aromatic contents and RON during the reforming reaction and seek the best operating temperature and pressure for the reforming reactor with different objectives. The optimization model is expressed as in eq 13. nc

max

(pi × ci) ∑ i)1

s.t.

dci ) gi(k1,...,knr,c1,...,cnc) dt

T,P

ci(0) ) c0i i ) 1, ..., nc kj ) fj(T,P) j ) 1, ..., nr

(13)

RON ) RON(ci) TL e T e TU PL e P e PU RONL e RON e RONU where ci is the mole fraction of component i in the reforming product, c0i is the feed mole fraction of component i, gi is the reaction rate equation for component i, kj is the kinetic constant for reaction j

834


Figure 17. Molecular optimization for catalytic reforming process. Table 4. Optimization Results for Catalytic Reforming Unit profit initial value max RON min aromatics max profit

T (K)

P (psia)

RON

Ba Ab Gc (wt %) (wt %) (wt %)

221.46 765 206 99.64 201.84 790 204.2 103.46 223.45 761.1 206.4 99.00

5.66 5.68 5.61

50.61 57.75 49.39

80.22 62.73 82.33

224.87 759.9 190.1

5.59

49.47

83.41

99.00

a

B (wt %) is the weight percentage of total benzene in the stabilizing gasoline. b A (wt %) is the weight percentage of total aromatics in the stabilizing gasoline. c G (wt %) is the weight percentage of the stabilizing gasoline in all three products.

correlated with temperature T and pressure P as in eq 2, nc is the number of reforming components, nr is the number of reforming reactions, pi is the price of component i, t is the time, superscript L indicates the lower bound, and superscript U indicates the upper bound. Although the objective is to maximize profit, other objective functions can be used. If necessary, other specifications, such as MON, RVP, benzene, etc., can also be added to the model. In this case, three different optimizing objectives, namely, maximizing RON, minimizing aromatics, and maximizing profit, are investigated under the same constraint RON g 99.0 and within the temperature range 750-790 K and the pressure range 190-200 psia. The prices for hydrogen, light hydrocarbon, and stabilizing gasoline are 600, 185, and 230 $/ton, respectively. The results are given in Table 4. The iteration steps for the case of maximizing the profit are given in Figure 18, which also shows variations of several key variables during optimization. From the optimization results, it is observed that raising the temperature can make the RON higher for the gasoline, as well as increasing the benzene and aromatics contents. However, the recovery ratio of the stabilizing gasoline decreases significantly, which generally has a negative effect on the profit. Better operation would be to produce as much stabilizing gasoline as possible while maintaining a sensible RON value. For the catalytic reforming unit, by proper molecular modeling of the reacting components, the reaction network, and the kinetics, the optimization can control and optimize the reaction pathway through the appropriate choice of reaction temperature, pressure, and/or catalyst so as to obtain the best performance. Ideally, the process optimization should be considered in the context of overall refinery optimization to determine operating conditions. It is possible that integrated optimization might determine operating conditions very different from those determined on the basis of stand-

alone optimization. This issue will be discussed in the following sections. 6. Overall Refinery Optimization Strategy To further explore the benefits of molecular modeling, the straightforward idea is to integrate all molecular models to form an overall model. However, this would lead to an optimization problem with a prohibitively large size. To solve such a complex problem, we adopt the decomposition optimization strategy proposed by Zhang and Zhu.3 The decomposition has the following format

objective maximize profit ) product sales - feed costs operating costs subject to for process i, productsi ) f(feedsi, operating conditionsi) where feedsi are determined at the site level and operating conditionsi are determined at the process level. The objective is to maximize the total plant profit subject to constraints of all process models. The optimization method decomposes the overall plant optimization into two levels: a site level (master model) and a process level (submodels). The master model deter mines common issues among processes, such as selection of feeds and allocation of utilities. With these common issues determined, submodels then optimize the individual processes. The results from submodel optimization are then fed back to the master model for further optimization. This procedure terminates when convergence criteria are met (Figure 19). In this way, individual process optimizations are effectively coordinated by the central master model. The optimization objective for a refinery at the site level is the total profit

max profit )

∑k ∑n ∑j Cj,kFj,n,k - ∑k ∑n ∑i Ci,kFi,n,k ∑r ∑k CrQr,k (15)

where the first and second terms indicate the product sales and raw material purchases, while the third term represents the cost of utilities. The following limits are taken into account to include process capacity limits, market demands, product


Figure 18. Maximizing profit for catalytic reforming unit.

specifications, emission control, etc. U L total feed flow limit F i,n,k e Fi,n,k e F i,n,k U ∑n xl,i,n,k e xl,i,k

(17)

∑n ∑i Fi,n,k e F Uk

(18)

L e component limit xl,i,k

throughput limit F Lk e

(16)

L U e pi,k e pi,k property limit pi,k

(19)

L U e qj,k e qj,k qj,k

(20)

resource limit QLn e Qn e QU n

(21)

benzene limit BL e B e BU

(22)

L

U

aromatics limit A e A e A

(23)

where the superscripts L and U indicate lower and upper bounds, respectively. In addition, we need to address processes in the sitelevel optimization. A process k consists of a set of inlet flows i ∈ CFk associated with a set of physical properties

pi,n,k ∈ PIk and a set of outlet flows j ∈ CPk with corresponding physical properties qj,n,k ∈ POk. The mass balance for each product flow j and its physical properties are given by

(Fj,n,k, qj,k) ) f(Fi,n,k, pi,k, OPk)

(24)

The formulation of f(Fi,n,k, pi,k, OPk) depends on the mechanics behind the individual processes. Normally, it represents a set of equations describing the kinetics, thermodynamics, and hydromechanics, which are usually highly nonlinear. To evaluate the performance of each process accurately, the consumption of resources should be taken into account through

Qn,k ) g(Fi,n,k, pi,k, OPk)

(25)

In this decomposition scheme, these equations cannot be directly used in the master model. Otherwise, the overall model would be too large and too complex. Instead, these equations are used in the process-level optimization. To avoid complexity of the overall sitelevel model while considering the performances of the

836


Figure 19. Overall optimization procedure.

individual processes, we use linear yield and resource correlations (eqs 26 and 27) without involving too many details of the process operation.

Fj,n,k )

∑i Rj,i,n,kFi,n,k

(26)

Qn,k )

∑i βi,n,kFi,n,k

(27)

These linear yield correlations are derived from process submodels by using a finite difference approximation. This simplification might cause significant errors. Thus, the linear yield correlations must be updated continuously by process simulation to reduce the errors. Overall, we can obtain the site model consisting of eqs 15-23, 26, and 27, which also includes equations for process connections, splitters, and mixers. In the process-level optimization, some processes are modeled in molecular level. As an example, naphtha catalytic reforming is modeled using eq 13 together with equations for physical properties and mass and heat balances. This overall model is solved using the RungeKutta method. Three reactors are divided into 15 reaction zones in sequence. Each zone is considered as a continuous stirred-tank reactor. The outlet of the previous zone becomes the inlet of the next zone. The model is solved sequentially from zone 1 to zone 15. The objective is to maximize the profit for the process. We integrate the site-level and process-level models in the following way. The site-level optimization determines the optimal feed conditions in terms of bulk properties and flow rates for the processes, which are fixed in the process-level optimization. These bulk properties are converted to molecular information, which is used as the basis for optimization, at the process level, to determine the optimal operating conditions (temperature and pressure). The optimized products from this optimization are also modeled on the basis of molecular information, which is converted to

bulk properties again (Figure 22). These products are then mixed with other products from other processes. For nonmolecular models, the site model also provides information (bulk properties and flow rates) for the relevant processes. In this case, streams are represented by the bulk properties, which are used directly in optimization based on nonmolecular models for the processes. The molecular and nonmolecular models are interfaced using the transformation methods in sections 3.2 and 3.3. The convergence between the site-level and the process-level optimizations is explained in Figure 19. Ideally, we would like to build molecular models for all processes and then conduct optimization based purely on molecular information. However, this requires too much time and might also be unnecessary. To make use of molecular models developed for major processes and take advantage of them in optimization, we integrate the molecular models with the rest of the nonmolecular models. 7. Integration of Process Molecular Models with Refinery Optimization We use two examples to show in more detail how molecular models are integrated with the site model. 7.1. Integrating Reforming Molecular Model. The reforming molecular model is integrated into the overall refinery optimization by replacing the former nonmolecular model as shown in Figure 20. First, the site-level optimization determines the bulk properties and flow rate for the 12 naphtha feeds, which are mixed together as the feed to the reforming unit. Then, process optimization is carried out individually for all processes including the reforming unit. In the optimization of the reforming unit, the molecular matrix for each of 12 streams is obtained from their bulk properties by the conversion method. Thus, the optimization determines the optimal operating conditions for the unit and provides the molecular matrix for the


Figure 20. Integrating molecular reforming model into the overall refinery optimization. Table 5. Optimization Results for Integrating Reforming Molecular Model with Overall Plant base casea max RON min aromatics max Profit total profit T (K) P (psia) RON B (wt %) A (wt %) G (wt %)

131.76b 765 206 99.64 N/A N/A 80.22

125.89 790 195 103.35 5.623 57.57 62.61

130.63 760.3 211.3 99.01 5.599 49.44 82.75

133.04 764.9 190.0 99.01 5.422 49.29 79.77

a Nonmolecular-based. b Profit calculated from optimization and not a real profit because the products in this case cannot satisfy the limits on the benzene and aromatic contents.

product, which is converted to bulk properties for further optimization at the site level. This procedure is repeated until the criteria for convergence between the site and process levels are met. It should be noted that the site-level optimization does not involve molecular information but rather leaves the molecular information to be dealt with in the processlevel optimization. In this way, the size of the site-level model can be maintained at a manageable level while process optimization can be fully exploited at the molecular level. Table 5 provides a comparison between the overall plant optimization results obtained using the nonmolecular reforming model and those obtained using the molecular reforming model. It can be observed that the results from the nonmolecular reforming model cannot provide molecular information on benzene and aromatics. It is also shown that the process optimization based on the profit objective can effectively improve the total profit, which gives better results than obtained by using other objective functions. 7.2. Integrating Gasoline Blending Model. In new legislation, the contents of benzene and aromatic compounds in gasoline are restricted. These two compounds mainly come from reformate. With the molecular reforming model and the molecular gasoline blending model, we can monitor and control the contents of benzene and aromatics in gasoline products. The two models are integrated as shown in Figure 21. A total of 19 possible gasoline streams feed into the blending unit. The one from the reforming unit contains molecular information, whereas the other 18 gasoline streams contain nonmolecular information. These 18 streams are transformed into molecular compositions according to their bulk properties. Then, they are mixed with the reformate gasoline and sent to the gasoline blending unit to produce four kinds of gasoline products. The molecular information for the gasoline products is further converted into bulk properties at the site level for further optimization. The overall optimization is

Figure 21. Integrating both molecular reforming and blending into the overall optimization. Table 6. Optimization Results for Integrating Both Molecular Models with Overall Plant

total refinery profit gasoline RON 90 gasoline RON 93 gasoline RON 95 gasoline RON 97

flow rate benzene aromatics flow rate benzene aromatics flow rate benzene aromatics flow rate benzene aromatics

base casea

case study 1b

case study 2c

127.85 1487.72 N/A N/A 0.0 N/A N/A 450.0 N/A N/A 350.0 N/A N/A

126.95 1500.0 2.23 23.84 361.9 4.0 39.56 450.0 3.63 36.34 0.0 0.0 0.0

131.92 1500.0 2.49 25.82 430.0 3.04 30.15 450.0 3.88 36.63 0.0 0.0 0.0

a Nonmolecular-based; no constraints on benzene and aromatics contents. b Constraints on benzene and aromatics contents; no consideration of optimization of reforming. c Constraints on benzene and aromatics contents; optimization of reforming.

carried out under constraints of gasoline RON and benzene and aromatic contents. The results are shown in Table 6. The results for the base case are obtained using nonmolecular models, which cannot provide the necessary molecular information for benzene and aromatics. This might cause the benzene and aromatic contents exceed the legislated level. Here, we assume that the upper limits for benzene and aromatics are 4 and 40%, respectively, for the sake of comparison. The upper limits for the gasoline product flow rates are 1500, 550, 450, and 350 corresponding to gasoline RONs of 90, 93, 95, and 97, respectively. Case study 1 is based on the molecular models for both reforming and blending units and uses the same operating conditions as the base case (i.e., no optimization of operating conditions for the reforming process). However, the site-level optimization is constrained by the benzene and aromatic contents. We can observe that, in this case, the benzene content of gasoline RON 93 reaches the upper limit and the aromatics content almost reaches its upper limit. These two limits restrict the production of gasoline RON 93 and cause a total profit decrease. Also, because of the constraints for benzene and aromatics, gasoline RON 97 cannot be produced. This also indicates that gasoline RON 97 in the base case must exceed the limits on the benzene and aromatic contents. Case study 2 was carried out with optimization for the reforming conditions guided by the site-level optimization. Thus, by optimizing the feed compositions and operating conditions for the reforming unit, the reforming process provides the best reformate

838


Figure 22. Molecular information flows in the optimization.

Figure 23. Future refinery.

gasoline to the blending unit, which results in higher gasoline yields under the same limits on the benzene and aromatic contents, thus improving the total profit. A comparison between the base case and case 2 indicates that optimization based on molecular modeling of reforming can improve the total refining profit and satisfy environmental legislation. Obviously, incorporating molecular models for other major processes into the

overall optimization can make even more significant improvements. 7.3. Overall Refinery Molecular Modeling and Optimization. The molecular information flows for catalytic reforming, naphtha hydrotreating, reformate stabilizing, and gasoline blending into refinery optimization can be described in Figure 22.61 From site-level optimization, the bulk properties of 12 naphtha streams


from different processes (e.g., crude distillation, FCC, hydrocracking, delayed coking) are converted into molecular information by naphtha transformation. With the molecular matrix for the naphtha feed, the molecular model for the naphtha hydrotreatment gives the molecular matrix for the treated naphtha, which goes to catalytic reforming and stabilizer. Further, the molecular models for the catalytic reformer and reformate stabilizer are used to calculate the molecular compositions for the relevant products. Molecular modeling is applied to predict the composition of the reforming stabilizing gasoline. In gasoline blending, 18 gasoline streams coming from different processes are converted into molecular compositions from known bulk properties by gasoline transformation; then, they are mixed with reforming gasoline to blend four gasoline products. These gasoline streams have detailed molecular information, which are transformed into bulk properties. All of the products return to the site level for further optimization under molecular and bulk property constraints. For the catalytic reforming and hydrotreating processes, optimization is executed at the molecular level under the guidance of the site level optimization to obtain the best operating conditions. It should be noted that, in the present work, nonmolecular models are used for other processes, and the interfaces between molecular and nonmolecular models are built using the conversion method, which transforms information between molecular and bulk properties. The present authors have been doing research to model other key processes at the molecular level. Soon, it will be possible to model a refining plant on a molecular level and fully exploit the insights provided by molecular information to help refining to maximize its profit. This will lead to breakthroughs in identifying new ways for future refining (e.g., coproduction of refining and chemical products, power, etc.), as also described by Katzer et al.1 (Figure 23). 8. Conclusions This paper presents a generic methodology for incorporating molecular models into overall refinery optimization as virtually no work has been done so far in terms of optimizing a refinery by exploiting the molecular information. To achieve this goal, petroleum mixtures are modeled by molecular type homologous series matrices, which provide sufficient chemical information while maintaining a small size of the matrices. This molecular information is then used to build molecular models for processes. These models are incorporated into the overall optimization to select feedstocks, synthesize products, and optimize operating conditions for processes. Because the molecular information and models provide a good understanding of the fundamental chemistry involved in product formation and process operations, new degrees for optimizing refining operation are generated. In particular, molecular models capture environmental impacts, so that high-quality products can be produced while environmental legislation is satisfied. Major benefits of the use of molecular models in guiding refining operations have been identified in terms of producing cleaner products using existing equipment, improving existing processes, and increasing profit significantly. More importantly, the research shows exciting potential that the method developed from this work can be used to discover new approaches to future refining.

The major aspects of the work at UMIST can be summarized as follows: (a) a matrix representation for modeling refining streams, (b) modeling of several refining processes on the molecular level, (c) a consistent modeling method for modeling reactions and separations, and (d) the incorporation of molecular models into overall refinery optimization. However, several major questions have been raised by this work that will be considered in the future work: (i) How should reaction kinetics be modeled using molecular modeling techniques to avoid extensive experiments? (ii) Can the reaction pathways be modeled in the context of the overall refinery to optimize the reaction routes in terms of environmental impacts, so that the formation of desirable molecules is maximized and the formation of undesirable species? (iii) Can molecular models and optimization models be used to identify the most effective ways to produce clean fuels? (iv) Can the synthesis of speciality products be considered using molecular models to achieve coproduction? Answers to these questions will lead to several breakthroughs in terms of science and practical applications for refinery. This is largely because molecular models provide a better understanding and new insights into refinery operation, which thus generates many new degrees of freedom. Fully exploiting these degrees of freedom will lead to great opportunities for future refining operations and will eventually create a new era for optimizing refinery operations. Nomenclature a ) coefficient ai ) coefficient for sample matrix i aP ) pressure correlation coefficient for vapor-liquid equilibrium constant aT ) temperature correlation coefficient for vapor-liquid equilibrium constant A ) aromatics b ) coefficient bij ) blending ratio of feed i to product j Bc ) the vector of calculated bulk properties Bg ) the vector of given measured bulk properties ci ) mole fraction c0i ) feed mole fraction C ) carbon number CFi ) the composition matrix of blending feed i CPj ) the composition matrix of blending product j Ci,k ) cost of feed i for process k Cj,k ) price of product j from process k Cr ) cost of utility from source r CABP ) cubic average boiling point, °F d ) vector of search direction in optimization Ej ) the activation energy, kcal/mol f ) the calculation from a molecular matrix to its bulk properties Fi,n,k ) mass flow rate of component i derived from feed n for process k Fj,n,k ) mass flow rate of component j derived from feed n from process k gi ) reaction rate equation for component i i ) isoparaffins k ) the vapor-liquid equilibrium constant k′′ ) reaction rate constant kj ) effective rate constant at temperature k0j ) effective rate constant at temperature 766 K and pressure 300 psia, s-1 K ) UOP characterization factor m ) number of sample matrices

840


Mc ) calculated molecular composition matrix Mi ) samples i of molecular matrix MABP ) molar average boiling point, °F MeABP ) mean average boiling point, °F MON ) motor octane number MW ) molecular weight n ) number of bulk properties N ) naphthenes nP ) normal paraffins O ) olefins pi,k ) physical properties for inlet stream i for process k P ) pressure, psia PL ) lower limitation of pressure PU ) upper limitation of pressure PA ) partial pressure of butane PB ) partial pressure of butene Pc ) critical pressure of the mixture, psia PH ) partial pressure of hydrogen PHC ) partial pressure of hydrocarbons PH2S ) partial pressure of hydrogen sulfide PS ) partial pressure of sulfur PT ) total pressure qj,k ) physical properties for outlet stream j from process k Qr,k ) utility flow rate from source r for process k rA,τ ) hydrogenation reactionreacting rate rHDS ) hydrodesulfurization reactionreacting rate rT,σ ) hydrogenolysis reactionreacting rate R ) universal gas constant, 8.314 J/(gmol K) RON ) research octane number RONL ) lower limitation of RON RONU ) upper limitation of RON RVP ) Reid vapor pressure, psia SPGR ) specific gravity t ) time T ) temperature Tc ) critical temperature of the mixture, R TL ) lower limitation of temperature TU ) upper limitation of temperature Vc ) critical volume of the mixture, ft3/(lb mol) VABP ) volumetric average boiling point, °F Wj ) weight for bulk property j WABP ) weight average boiling point, °F x ) component flow rate Xp ) vector of optimization variables related to operating conditions Xr ) vector of optimization variables related to resource allocations Greek Symbols Rj ) the pressure effect parameter λ ) step length along search direction in optimization Superscripts L ) lower limit U ) upper limit

Acknowledgment S.H. and (F.)X.X.Z. thank The British Royal Society and Chinese National Science Foundation (No. 29836140) for their financial support. Literature Cited (1) Katzer, J. R.; Ramage, M. P.; Sapre, A. V. Petroleum refining: Poised for profound changes. Chem. Eng. Prog. 2000, July, 41-51. (2) Peng, B.; Towler, G. (Supervisor). Molecular Modelling of Petroleum Processes. Ph.D. Dissertation, University of Manchester Institute of Science and Technology, Manchester, U.K., 1999; pp 22-41.

(3) Zhang, N.; Zhu, X. X. A novel modeling and decomposition strategy for overall refinery optimisation. Comput. Chem. Eng. 2000, 24, 1543-1548. (4) Weekman, V. W.; Nace, D. M. Kinetics and catalytic cracking selectivity in fixed, moving, and fluid-bed reactors. AIChE J. 1970, 16, 397. (5) Jacob, S. M.; Gross, B.; Voltz, S. E. A lumping and reaction scheme for catalytic cracking. AIChE J. 1976, 22, 701-713. (6) Gray, M. R. Lumped kinetics of structural groups: Hydrotreating of heavy distillates. Ind. Eng. Chem. Res. 1990, 29, 505-512. (7) Korsten, H.; Hoffmann, U. Three-phase reactor model for hydrotreating in pilot trickle-bed reactors. AIChE J. 1996, 42, 1350-1360. (8) Stangeland, B. E. Kinetic model for prediction of hydrocraker yields. Ind. Eng. Chem. Process Des. Dev. 1974, 13, 72. (9) Koseoglu, R. O.; Phillips, C. R. Kinetic models for the noncatalytic hydrocracking of Athabasca bitumen. Fuel 1988, 67, 906-915. (10) Beaton, W. I.; Betolacini, R. J. Resid hydroprocessing at Amoco. Catal. Rev. Sci. Eng. 1991, 33, 281-317. (11) Laxminarasimhan, C. S.; Verma, R. P.; Ramachandran, P. A. Continuous lump model for simulation of hydrocracking. AIChE J. 1996, 42, 2645-2653. (12) Parnas, R. S.; Allen, D. T. Compound class modeling of hydropyrolysis. Chem. Eng. Sci. 1988, 43, 2845-2857. (13) Neurock, M.; Nigam, A.; Trauth, D.; Klein, M. T. Asphaltene pyrolysis pathways and kinetics: Feedstock dependence. Tar sand and oil upgrading technology. AIChE Symp. Ser. 1991, 7279. (14) Katz, D. L.; Brown, G. G. Vapor Pressure and Vaporization of Petroleum Fractions. Ind. Eng. Chem. 1933, 25, 1373-1384. (15) Poettmann, F. H.; Mayland, B. J. Equilibrium Constants for High Boiling Hydrocarbon Fractions of Varying Characterization Factors. Pet. Refiner 1949, 28 (7), 101-112. (16) Edmister, W. C. Applied Hydrocarbon Thermodynamics; Gulf Publishing Co.: Houston, TX, 1961; p 50. (17) Hariu, O. H.; Sage, R. C. Crude Split Figured by Computer. Hydrocarbon Process. 1969, 48 (1), 143-148. (18) Hou, K.; Li, Y.; Shen. J.; Hu, S. Online simulation, parameter estimation and optimisation of crude unit. Pet. Process. Petrochem. (Chinese) 1999, 30 (10), 29-34. (19) Yu, H.; Shen, J.; Li, Y.; Hu, S. Inside-out infeasible path optimisation method for crude distillation column. J. Chem. Ind. Eng. (Chinese) 2000, 51 (2), 215-220. (20) Harding, R. H.; Mavrovouniotis, G. M.; Neurock, M.; Liguras, D. K.; Klein, M. T. Molecular Modeling of Fluid Catalytic Cracking. Presented at the AIChE Annual Meeting, Los Angles, CA, Nov 1994; Paper 77a. (21) Watson, B. A.; Klein, M. T.; Harding, R. H. Mechanistic modeling for n-heptane cracking on HZSM-5. Ind. Eng. Chem. Res. 1996, 35, 1506-1516. (22) Watson, B. A.; Klein, M. T.; Harding, R. H. Mechanistic modeling of a 1-phenylotane/n-hexadecane mixture on rare earth Y zeolite. Ind. Eng. Chem. Res. 1996, 36, 2954-2963. (23) Watson, B. A.; Klein, M. T.; Harding, R. H. Catalytic cracking of alkylbenzenes: Modeling the reaction pathways and mechanisms. Appl. Catal. A: Gen. 1997, 160, 13-39. (24) Watson, B. A.; Klein, M. T. Catalytic cracking of alkylcyclohexanes: Modeling the reaction pathways and mechanisms. Int. J. Chem. Kinet. 1997, 29, 545-560. (25) Joshi, P. V.; Iyer, S. D.; Klein, M. T. Computer-assisted modeling of gas oil fluid catalytic cracking (FCC). Presented at the 214th National Meeting of the American Chemical Society, Las Vegas, NV, Sep 7-12, 1997. (26) Russell, C. L.; Klein, M. T.; Quann, R. J.; Trewella, T. Catalytic hydrocracking reaction pathways, kinetics, and mechanisms of n-alkylbenzenes. Energy Fuels 1994, 8, 1394-1400. (27) Hou, G.; Klein, M. T. Automated molecular-based kinetic modeling of complex processessA hydroprocessing application. Presented at the AIChE Spring National Meeting, Houston, TX, Mar 14-18, 1999. (28) Nigam, A.; Klein, M. T. A mechanism-oriented lumping strategy for heavy hydrocarbon pyrolysissImposition of quantitative structure-reactivity relationships for pure components. Ind. Eng. Chem. Res. 1993, 32, 1297-1303. (29) Broadbelt, L. J; Stark, S. M.; Klein, M. T. Computergenerated pyrolysis modeling: On-the-fly generation of species, reactions, and rates. Ind. Eng. Chem. Res. 1994, 33, 790-799.

Ind. Eng. Chem. Res., Vol. 41, No. 4, 2002 841 (30) Walter, T. D.; Klein, M. T. A mechanistic model of the pyrolysis chemistry of 4-(1-naphthylmethyl) bibenzyl as a probe of hydrocarbons structure/reactivity relations. Ind. Eng. Chem. Res. 1995, 34, 4244-4253. (31) Fake, D. M.; Nigam, A.; Klein, M. T. Mechanism-based lumping of pyrolysis reactions: Lumping by reactive intermediates. Appl. Catal. A: Gen. 1997, 160, 191-221. (32) Baltanas, M. A; Froment, G. F. Computer generation of reaction networks and calculation of product distribution in the hydroisomerization and hydrocracking of paraffins on Pt-containing bifunctional catalyst. Comput. Chem. Eng. 1985, 9, 71-81. (33) Baltanas, M. A; Van Raemdonck, K. K; Froment, G. F.; Mohedus, S. R. Fundamental kinetic modeling of hydroisomerization and hydrocracking on noble-metal-loaded faujasites. Ind. Eng. Chem. Res. 1989, 28, 899-910. (34) Clymans, P. J.; Froment, G. F. Computer generation of reaction paths and rate equations in thermal cracking of normal and branched paraffins. Comput. Chem. Eng. 1984, 8, 137-142. (35) Hillewaert, L. P.; Dierickx, J. L.; Froment, G. F. Computer generation of reaction schemes and rate equations for thermal cracking. AIChE J. 1988, 34, 17-24. (36) Froment, F.; Depauw, G. A.; Vanrysselberghe, V. Kinetic modeling and reactor simulation of hydrodesulfurisation of oil fractions. Ind. Eng. Chem. Res. 1994, 33, 2975-2988. (37) Feng, W.; Vynckier, E.; Froment, G. F. Single-event kinetics of catalytic cracking. Ind. Eng. Chem. Res. 1993, 32, 2997-3005. (38) Svoboda, G. D.; Vynckier, E.; Debrabandere, B.; Froment, G. F. Single-event rate parameters for paraffin hydrocracking on a Pt/US-Y zeolite. Ind. Eng. Chem. Res. 1995, 34, 3793-3800. (39) Dewachtere, N. V.; Santaella, F.; Froment, G. F. Application of a single-event kinetic model in the simulation of an industrial riser reactor for the catalytic cracking of vacuum gas oil. Chem. Eng. Sci. 1999, 54, 3653-3660. (40) Quann, R. J.; Jaffe, S. B. Structural-oriented lumping: Describing the chemistry of complex mixtures. Ind. Eng. Chem. Res. 1992, 31, 2483-2497. (41) Quann, R. J.; Jaffe, S. B. Building useful models of complex reaction systems in petroleum refining. Chem. Eng. Sci. 1996, 51, 1615-1635. (42) Christensen, G.; Apelian, M. R.; Hickey, K. J.; Jaffe, S. B. Future directions in modeling the FCC process: An emphasis on product quality. Chem. Eng. Sci. 1999, 54, 2753-2764. (43) Zhang, Y. A molecular approach for charaterisation and property preditions of petroleum mixtures with applications to refinery modelling. Ph.D. Dissertation, University of Manchester Institute of Science and Technology, Manchester, U.K., 1999; pp 22-57. (44) Altgelt, K. H.; Gouw, T. H. Chromatography in Petroleum Analysis; Marcel Dekker: New York, 1979.

(45) Oka, M.; Chang, H. C.; Gavales, G. R. Computer-assisted molecular structure construction for coal-derived compounds. Fuel 1977, 56, 3-8. (46) Petrakis, L.; Allen D. T.; Gavales, G. R.; Gates, B. C. Analysis of synthetic fuels for functional group determination. Anal. Chem. 1983, 55, 1557-1564. (47) Jones, D. S. J. Elements of Petroleum Processing; John Wiley & Sons: New York, 1995; pp 181-205. (48) Standard Data Book of Lummus; Lummus Co.: Atlanta, GA, 1971. (49) PRO II Reference Manual; Simulation Sciences Inc.: Brea, CA, 1994; pp 19-134. (50) Baird, C. T. Guide to Petroleum Product Blending; HPI Consultants, Inc.: Houston, TX, 1989; pp 13-30. (51) Hwang, S.; Hu, S.; Zhu, X. X. (Supervisor), Molecular modelling and optimisation of naphtha hydrotreater and gasoline stabiliser. M.Sc. Dissertation, University of Manchester Institute of Science and Technology, Manchester, U.K., 2000; pp 28-75. (52) Hu, S.; Zhu, X. X.; Zhang, N. Molecular modeling and optimization for refineries: An application to the reforming process. Presented at the AICHE Annual Meeting, Los Angeles, CA, 2000. (53) Ancheyta-Juarez, J; Aguilar-Rodriguez, E. New model accurately predicts reformate composition. Oil Gas J. 1994, 92 (5), 93-95. (54) Gates, B. C.; Katzer, J. R.; Schuit, G. C. A. Chemistry of Catalytic Processes; McGraw-Hill: New York, 1979. (55) Van Parijs, I. A.; Froment, G. F. Kinetics of Hydrodesulfurization on a Co-Mo/γ-Al2O3 Catalyst. Ind. Eng. Chem. Prod. Res. Dev. 1986, 25, 431-443. (56) Phillipson, J. J. Kinetics of hydrodesulphurisation of light and middle distillates. Presented at the AIChE Meeting, Houston, TX, 1971. (57) McWilliams, M. L. An equation to relate the k-factor to pressure and temperature. Chem. Eng. 1973, 80 (25), 138-140. (58) Treybal, R. E. Mass Transfer Operations, 3rd ed.; McGrawHill: New York, 1980; pp 358-439. (59) Douglas, J. M. Conceptual Design of Chemical Processes; McGraw-Hill: New York, 1988; pp 439-441. (60) Kirkbride, C. G. Pet. Refiner 1944, Vol. 9, No. 23, 87. (61) Zhu, X. X.; Hu, S. Molecular modeling for overall refinery optimization. Presented at the AICHE Annual Meeting, Los Angeles, CA, 2000.

Received for review November 28, 2000 Revised manuscript received April 7, 2001 Accepted October 16, 2001 IE0010215

Combine Molecular Modeling with Optimization to Stretch Refinery

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