Energy Fuels 2010, 24, 3526–3533 Published on Web 05/13/2010
: DOI:10.1021/ef1001869
Generalized Mathematical Model for SO2 Reduction in an Oil Refinery Based on Arabian Light Crude Oil Mohammed S. Ba-Shammakh* Center of Research Excellence in Petroleum Refining and Petrochemicals, King Fahd University of Petroleum and Minerals, Post Office Box 511, Dhahran 31261, Saudi Arabia Received February 17, 2010. Revised Manuscript Received April 14, 2010
The refinery industry is a vital component of the national industry. The main task of a refinery is to efficiently produce high yields of valuable products from a crude oil feed, employing different physical and chemical processes, such as distillation, hydrotreating, reforming, cracking, and blending. The main products are liquid petroleum gas (LPG), gasoline, jet and diesel fuels, lubricants, and petrochemicals. The refinery process contributes directly to the air pollution problem, mainly from combustion sources. Because combustion is the major source of different emissions, it will be the focus of this paper. SO2 is considered as one major pollutant, from combustion to generate energy, in an oil refinery. The purpose of this paper is to develop an optimization model to select the least cost control technology for a given reduction target. The refinery is modeled on the basis of Arabian light crude oil as the feedstock to the distillation column. Three options to reduce SO2 are considered in the model, and these are fuel balancing, fuel switching, and flue gas desulfurization technology. The developed model is applied in a case study. The results show that, for a low reduction target of SO2, such as a 30% reduction target, balancing or fuel switching is the option. On the other hand, a higher reduction target requires installation of more efficient technology, such as flue gas desulfurization. The profit decreases by about 26% if all SO2 emissions are controlled by that technology. oils into valuable products, such as gasoline, jet fuel, and diesel. Expectedly, there are many decisions to be considered to achieve an optimal operation for a refinery. Linear programming (LP) is concerned with finding values for a set of variables that maximize or minimize a linear objective function of the variables, subject to a set of linear equality or inequality constraints. LP was first proposed by Dantzig in 1947 to refer to the optimization problems, in which both the objective function and the constraints are linear.2 Dantzig first proposed the most popular algorithm in LP called the simplex algorithm.2 Despite the many contributions that have been reported on planning models, few can be found that specifically address the petroleum refining industry.3,4 One of the first contributions to consider nonlinearity in production planning is that of Moro et al.5 A nonlinear planning model for refinery production was developed in their study. The model results provide guidance for improved operations that cannot be determined without application of such a model, because optimum values of many independent conditions and choices are difficult to determine by inspection. Pinto and Moro also developed a nonlinear planning model for refinery production.6 The model represents a general petroleum refinery, and its framework allows for the implementation of nonlinear process models for few units as well as
1. Introduction The refining industry plays a very important role in international economics and in our daily life. Environmental regulations are pressing the refinery industry to minimize its air emissions, such as sulfur dioxide (SO2). The environmental impact of pollution from SO2 is an area of international concern. SO2 is formed primarily from the combustion of sulfur-containing fuels and can be of major concern to all countries. Oil refineries extract and upgrade the valuable components of crude oil to produce a variety of marketable petroleum products that are vital to everyday life. Examples of these valuable products are gasoline, jet fuel, and diesel. In an oil refinery, crude is first broken up into those raw stocks that are the basis of the finished products. This breakup of the crude is achieved by separating the oil into a series of boiling point fractions, which meet the distillation requirements and some of the properties of the finished products.1 Most streams from the crude distillation units contain sulfur and other impurities and should be sent to hydrotreating units. The presence of sulfur certainly lowers the quality of the finished products. Hydrotreating the raw distillate streams removes a significant amount of the sulfur impurity. Other refining processes include catalytic cracking, hydrocracking, and reforming. These processes are used to achieve the desired product yield proportions, meeting the market specifications. The critical objective of a refinery operation, as in any other business-oriented ventures, is to generate maximum profit by converting crude
(2) Reklaitis, G. V.; Ravindram, A.; Ragsdell, K. M. Engineering Optimization Methods and Applications; Wiley: New York, 1983. (3) Shaban, H.; Elkamel, A.; Gharbi, R. Environ. Model. Software 1997, 12 (1), 51–58. (4) Elkamel, A.; Elgibaly, A.; Bouhamra, W. Adv. Environ. Res. 1998, 2 (3), 375–389. (5) Moro, L. L.; Zanin, A. C.; Pinto, J. M. Comput. Chem. Eng. 1998, 22, 1039–1042. (6) Pinto, J. M.; Moro, L. L. Braz. J. Chem. Eng. 2000, 17, 575–586.
*To whom correspondence should be addressed. E-mail: shammakh@ kfupm.edu.sa. (1) Gary, J. H.; Handwerk, G. E. Petroleum Refining: Technology and Economics, 3rd ed.; Taylor and Francis, Inc.: New York, 1994. r 2010 American Chemical Society
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: DOI:10.1021/ef1001869
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blending relations. Two real world applications are developed, and in both cases, different market scenarios were analyzed using the planning model. Zhang and Zhu showed in their paper a novel modeling and decomposition strategy for overall refinery optimization to tackle large-scale optimization problems.7 The approach was derived from an analysis of the mathematical structure of a general overall plant model. This understanding forms the basis for decomposing the model into two levels. These levels were a site level (master model) and a process model (submodels). The master model determined common issues among the processes. Then, submodels optimized the individual processes. The results from these submodels were fed back to the master model for further optimization. This procedure terminates when convergence criteria are met. Linear yield correlations were used in their study. A simultaneous optimization strategy for overall integration in refinery planning is also studied in another paper by Zhang et al.8 The paper presented a method for overall refinery optimization through integration of the hydrogen network and the utility system with the material processing system.8 Another study presented a refinery planning model that uses simplified empirical nonlinear process models with considerations for crude characteristics, yields of products, and qualities.9 The study integrated crude distillation, fluidized catalytic cracking (FCC), and product blending modules into the refinery planning model.9 A study by Elkamel et al.10 developed a refinery planning model taking into consideration meeting the demand with quality and CO2 mitigation based on Alaska crude as the feedstock. The abatement of SO2 emission is a major problem in most industrialized nations that use fuel oil or coals; therefore, numerous studies have been proposed for reducing the SO2 emission. Reid11 found that the conversion of limestone to lime is necessary as a first step in the desulfurization process, and a temperature somewhat greater than 750 °C was calculated as being necessary to bring about calcinations of calcium carbonate. Islas and Grande12 showed in a study abatement costs of different SO2 control options. The listed technologies include a lime spray dryer, fuel oil hydrotreating desulfurization, and fuel oil substitution by liquefied natural gas. The lime spray dryer technology is a dry once-through postcombustion desulfurization technology. The flue gas is treated in an absorber by mixing the gas stream cocurrently with atomized lime slurry droplets. The lime slurry is atomized through rotarycup spray atomizers or dual-fluid nozzles. The fuel oil hydrotreating desulfurization technology reduces the sulfur content in fuel oil, and with this technology, it is possible to obtain a 1% sulfur content.12 This technology requires very high hydrogen concentrations, which minimize coke formation in the catalyzer and optimize desulfurization. It requires installation of a hydrotreating desulfurization infrastructure and auxiliary equipment, including a hydrogen plant, exhaust gas treatment, and a Claus unit, as well as additional operating
personnel. As mentioned previously, a high concentration of hydrogen is needed.12 Dikshit et al.13 studied different technologies to reduce SO2 emissions from an oil refinery by source reduction (SR), tail gas treatment (TGT), or flue gas desulfurization (FGD). The objective of their research is to identify the optimum combination of these three options to minimize the total cost of overall SO2 emission from a petroleum refinery to various desired limits. The refinery model was linear. In this research, an efficient model is proposed for the production planning of refinery processes to achieve a maximum operational profit with reducing SO2 emissions to a given target by different mitigation options. Only SO2 emissions from combustion sources are considered in this paper. The options considered in this study are flow rate balancing, simply decreasing the inlet flow rate to a unit that emits more SO2, fuel switching, implying a change in operation to run with a different fuel that has low sulfur contents, and installation of SO2 control technology, such as FGD. A general mathematical programming model is described in this paper and illustrated on a case study to meet the demand of each final product with quality consideration and to find the suitable SO2 mitigation option for a given reduction target that keeps the profit at a maximum. The model is developed on the basis of Arabian light crude oil as the feedstock. 1.1. Overview of SO2 Control Options in an Oil Refinery. Because combustion is the major source for SO2 emissions in an oil refinery, most of the research dealing with SO2 control technologies, in an oil refinery, focused on either reducing the output from a unit that emits more SO2 or controlling the emissions after it is emitted. These may include switching to run with fuel that has low sulfur contents or to control SO2 emissions by installation of different control technologies. Each of these technologies has its own cost and will defiantly affect the profit of a given refinery if it is installed or implemented. 2. Problem Description An oil refinery is a complex entity. In general, a refinery has various processing units that separate crude oil into valuable fractions or cuts, upgrade and purify some of these cuts, and convert heavy fractions to light, more useful fractions. However, profitable operation of a refinery needs an optimization of stream flows and process feed. On the other hand, several trends in the oil refinery industry are leading to a tight production of different products with the new specifications and environmental regulations. Tightening of air regulations that ask to reduce the allowable sulfur content in final products, such as low sulfur gasoline and diesel fuel, is another challenge. The major source of SO2 emission within an oil refinery is the combustion source. An efficient model for the refinery planning will represent production planning with different SO2 mitigation options to meet the demand and quality of the product with certain SO2 reduction. The oil refinery shown in Figure 1 consists of several processing units. Refinery intermediate streams with different properties are blended to be fed to a processing unit or to be ready for sale as a final product. For both cases, the blended streams have to meet the specifications. Moreover, for the same product, such as gasoline, there are different grades that must satisfy market demands. The model starts with modeling the crude distillation unit (CDU) based on Arabian Light crude. The overall model considered the SO2 mitigation options.
(7) Zhang, N.; Zhu, X. X. Comput. Chem. Eng. 2000, 24, 1543–1548. (8) Zhang, J.; Zhu, X. X.; Towler, G. P. Ind. Eng. Chem. Res. 2001, 40, 2640–2653. (9) Li, W.; Hui, C.; Anxue, L. Comput. Chem. Eng. 2005, 29, 2010– 2028. (10) Elkamel, A.; Ba-Shammakh, M.; Douglas, P.; Croiset, E. Ind. Eng. Chem. Res. 2008, 47 (3), 760–776. (11) Reid, W. T. J. Eng. Power 1970, 92, 11. (12) Islas, J.; Grande, G. Appl. Energy 2008, 95, 80–94.
(13) Dikshit, A.; Dutta, A.; Ray, S. Clean Technol. Environ. Policy 2005, 7, 182–189.
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Figure 1. Refinery layout.
2.1. Model Formulation. In this section, a general mathematical model for an oil refinery is developed. The objective of this model is to maximize profit from selling the final products with specifications subject to reducing SO2 emissions to a certain target using the different mitigation options presented earlier in this paper. The CDU model is shown in details as an example, using Arabian light crude as feedstock, and a general model for an oil refinery is given after. 2.1.1. CDU Model. The CDU is the first major unit of any refinery. Crude oil should be characterized before being fed to the CDU. One of the key attributes for characterizing the hydrocarbons composing crude oils is by the boiling point. This is performed by measuring the temperature at which the components of the crude oil will evaporate at a given pressure (typically atmospheric pressure unless stated to be a different pressure basis). The model development starts at having a crude assay that shows crude properties and volume percentage accumulated at each given temperature among all cuts or fractions that come out from the CDU. Table 1 gives an example of the crude assay for Arabian light crude that is used as feedstock for the refinery in this study. The volume percentage accumulated for each cut is calculated by adding the volume percentage of the previous cut. For the first cut, the volume percentage accumulated is the summation of the volume percentage given for that cut plus the volume percentage of the previous cut in the crude. For the next cut, the volume percentage accumulated is the volume percentage accumulated for this cut added to the previous volume percentage accumulated. A mid volume percentage needs to be defined, to find the properties of the outlet streams, because all properties must be calculated at this mid volume percentage. The mid volume percentage is the average between the volume percentage accumulated from the previous cut and the volume percentage accumulated from the current cut.
Figure 2. Flowsheet of the optimal SO2 emission reduction strategy.
A flowchart of optimal SO2 reduction strategies is presented in Figure 2. For a given oil refinery, the aim is to satisfy the demand requirement with quality specifications and determine the best SO2 mitigation techniques to achieve a certain SO2 emission reduction target at minimum cost. 3528
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: DOI:10.1021/ef1001869
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Table 1. Arabian Light Crude Oil Assay analysis of the crude gravity (deg API) sediment (vol %) sulfur (total wt %) Reid vapor pressure (psi) hydrogen sulfide (ppm) pour point (upper) (°F) wax (wt %) salt (lbs of NaCl/1000 bbl) ash (ppm) vanadium (ppm) microcarbon residue (wt %) nickel (ppm) nitrogen (ppm) true boiling range (°F)
33.2 0.05 1.82 6.4 NIL e25 5 85 17 2.47 5 803
C4 lights C5-200 200-315 315-400 400-500
yield weight (%) yield volume (%) position in crude cumulative volume mid volume (%) gravity (deg API) specific gravity, 60/60 °F
1.5 2.2 0 2.2 1.1
5.5 7 2.2 9.2 5.7 78.8 0.6727
9.8 11.3 9.2 20.5 14.9 59.5 0.7407
8.2 9.1 20.5 29.6 25 49.5 0.7819
11 11.6 29.6 41.2 35.4 42.1 0.8149
Figure 4. API as a function of mid volume percentage.
Taking any fraction as an example, if the cut temperature is predetermined, from Figure 3 or the equation given above (eq 1), we can calculate the volume percentage accumulated at this given cut temperature and then simply multiply this percentage by the inlet volume flow rate to the CDU. For the next fraction, the volume accumulated will be the current minus the previous accumulated volume. To calculate the properties, such as API, the mid volume percentage needs to be calculated. For a given cut, the mid volume percentage can be calculated as
true boiling range (°F) 500-600 600-700 700-800 800-900 900-1050 yield weight (%) yield volume (%) position in crude cumulative volume mid volume (%) gravity (deg API) specific gravity, 60/60 °F
10 10.1 41.2 51.3 46.3 35.4 0.848
9.8 9.5 51.3 60.8 56 28.6 0.8837
6.2 5.9 60.8 66.7 63.8 25.4 0.9018
9.3 8.7 66.7 75.4 71 21.5 0.9248
11.3 10.2 75.4 85.6 80.5 17.6 0.949
mid volume percentage current cut volume percentage þ previous cut volume percentage ¼ 2
ð2Þ A relation between API and mid volume percentage is given in Figure 4. API for a given cut can simply be calculated if the average volume percentage accumulated is known. It is given in this equation as API ¼ - 0:0002X 3 þ 0:032X 2 - 2:27X þ 89:4
ð3Þ
where X is the mid volume percentage. The CDU model based on Arabian light crude oil can now be summarized as follows: n X ak ðCTCDU, s Þk s ∈ SCDU - frsdg ð4Þ CDUCuts ¼ k¼0
CDUCuts represents the volume percentage vaporized (volume percentage) of all fractions (s), except the residue product, of the CDU (SCDU is the set of all CDU products). The CDU cuts are a polynomial function of the order n, as shown earlier, in product cut point CTCDU,s, which is equivalent to the end-point (EP) temperatures. The product cut point CTCDU,s or EP temperature is the temperature at which a given fraction or cut will be vaporized. The residual cut volume percentage will be expressed as
Figure 3. Volume percentage accumulated at different cut temperatures.
Figure 3 shows a plot of T versus volume percentage accumulated, for Arabian light crude, and a straight line or a polynomial is used to fit the data to use it conveniently in the model for calculating volume percentage of such a product knowing the cut temperature. This is given by the following equation: V % ¼ 0:12X - 14:14
CDUCutCDU, s ¼ rsd ¼ 100
ð1Þ
ð5Þ
Each cut volume flow rate is calculated from subtracting its volume percentage vaporized from the previous cut and multiplying the product with the crude feed to the CDU
where X is the cut temperature (°F). For simplicity, both atmospheric and vacuum distillation units are incorporated into one model (CDU). The main products from CDU, as shown in the refinery layout (Figure 1), are liquefied petroleum gas (LPG), light naphtha straight run (LNSR), heavy naphtha straight run (HNSR), kerosene, diesel, light vacuum gas oil (LVGO), heavy vacuum gas oil (HVGO), and residual.
VFCDU, s ¼ FCDU
CDUCuts - CDUCuts - 1 100
s ∈ SCDU
ð6Þ
VFCDU,s represents the volume flow rate of all of the products (s) from the CDU, where FCDU is the Arabian light crude oil fed to the CDU. 3529
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The feed for any processing unit i is represented by Fi, which is the summation of all of the possible streams VS that can be received by unit i from all other units. Flow rates are in barrels (bbl)/year. Feed Properties of Processing Units.
Properties of each product from the CDU (API, sulfur, etc.) are polynomial functions in each product mid volume percentage vaporized. The mid volume for any product can be calculated from averaging the accumulative current cut volume percentage with the previous cut volume percentage vaporized, as shown earlier in this paper PVCDU, s, p n X CDUCuts þ CDUCuts - 1 k ¼ ak, p s∈SCDU , p∈Ps 2 p k¼0
FPi ¼ f ðVS, PVÞ
Feed properties are a function of flow rates VS and properties of that stream PV. Product Flow Rates of Processing Units. Vi ¼ f ðF, FP, OVÞ
ð7Þ
PVi ¼ f ðFP, OVÞ
m∈M
Fi e Umaxi
ð9Þ
Other units in the refinery are based on data available for Arabian light crude oil. For the refinery general model, the objective function is to maximize the profit of selling the final products to meet the demand with quality specifications and reduce the SO2 emissions at minimum cost. The main focus of this study is to provide methods of determining optimal production planning for petroleum refinery processes with SO2 emission consideration. The most important variables in the production planning model will be the feed flow rate, feed properties, product flow rates, and the properties of the products. The objective is to maximize the total profit of the refinery, which is shown as follows: X X X profit ¼ SPi Fi Cf i Fi OCi Fi i∈B
-
i∈IF
w
i∈E
CSiw Xiw -
XX i∈IF
ð16Þ
w
This means that each unit has to run with a specific fuel w. SO2 Emissions. The annual total SO2 emissions from all units must satisfy a specific SO2 reduction target, % SO2 X X X 1SO2iw Xiw εik Yik eð1 - % SO2 ÞSO2 w
i∈IF
k
ð18Þ P
where wSO2iwXiw is the SO2 emissions from each furnace of a unit i in tons/year. SO2iw is the SO2 emission for unit i using fuel w. SO2iw is calculated by knowing the emission factor (EF) for fuel w multiplied by fuel consumption, which is a function of the inlet flow rate of unit i SO2iw ¼ ðEFÞw ðFCÞi
i∈I
CRTik Yik
i∈I
The feed of processing unit i cannot exceed its maximum capacity, which is represented by Umaxi. Fuel Switching. For each unit i, one fuel should be selected for each furnace of a unit. This constraint is represented by introducing a binary variable Xiw that represents the fuel selection (current or new fuel with less sulfur content) X Xiw ¼ 1 i∈IF ð17Þ
CTCDU,s is the cut-point temperature for fraction s of the CDU. Also, the crude feed to the unit cannot exceed its throughput capacity FCDU e UmaxCDU ð10Þ
XX
ð15Þ
PVi is the product property, which is a function of feed properties and the operating variables of the unit, FP and OV, respectively. Processing Unit Capacity.
VSCDU,s,m represents the volume flow rate of all of the streams split from the CDU products (s) to different destinations (m). All fractions for the CDU, except the residue, have upper and lower limits for their cut point s∈SCDU - frsdg
ð14Þ
The product flow rate from unit i is represented by Vi and is a function of the feed quantity F and feed property FP as well as the operating variables OV. Product Properties of Processing Units.
PVCDU,s,p represents different properties (p) for each product (s) from the CDU. Ps is the set of all of the properties calculated for the specified stream (s). X VCDU, s ¼ VSCDU, s, m s∈SCDU ð8Þ
CTLCDU, s e CTCDU, s e CTU CDU, s
ð13Þ
ð11Þ
k
FC is fuel consumption and a function of the inlet flow rate. εik is a fraction of SO2 removed using technology k. The binary variable Yik indicates the existence/non-existence of SO2 control technology. If there is no SO2 control technology applied, Yik= 0, then all SO2 emitted from the furnace of the unit is released to the atmosphere. In the above equation, the nonlinearity is due to the multiplication of continuous variable SO2iw and binary variable Xiw and also the multiplication of two binaries Xiw and Yik. Selection of SO2 Control Technology To Be Installed. This constraint let the model select only one control technology for each unit i belonging to unit furnace set IF X Yik e1 i∈IF ð19Þ
The refinery profit is expressed as revenues from selling products minus the costs of purchasing feedstock and costs of operating the process units in the refinery. In eq 11, B represents the set of blending units for the final products and their sales price SPi (U.S. $/bbl). The cost Cfi (U.S. $/bbl) of the feedstock purchased from external sources is defined under the set E for all of the units that receive such material from outside. The third term represents the operating cost OCi (U.S. $/bbl) for each processing unit i in the refinery, where it is usually expressed as a function of the quantity fed to the running unit. The cost of applying fuel switching is shown in the fourth term, where X is a binary variable that represents which fuel w to select. CSiw (U.S. $/year) represents the cost of switching if fuel w is being chosen. Finally, the profit should be affected by applying SO2 reduction technology k with cost CRTik (U.S. $/year) if necessary to meet a given SO2 reduction target. A general model for an oil refinery consists of the following sets of constraints: Feed Flow Rate of Processing Units. X Fi ¼ VS ð12Þ
k
The developed model for an oil refinery is applied to a case study. 2.2. Case Study. The refinery layout shown in Figure 1 is used to illustrate the model. As with most crude oil refineries, the major units exist in the selected refinery for the case study. A 100 000 bbl/day Arabian light crude oil was selected to be the basis 3530
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: DOI:10.1021/ef1001869
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Table 2. Product Demands final product
demand (bbl/day)
price (U.S. $/bbl)
gasoline kerosene (jet fuel) diesel fuel oil
20000 20000 20000 15000
110 84 76 66
Table 3. Product Specifications final product gasoline kerosene (jet fuel) diesel fuel oil
property
specification requirement
specific gravity sulfur (wt %) RON specific gravity sulfur (wt %) smoke point specific gravity sulfur (wt %) CN specific gravity sulfur (wt %)
e0.8 e0.05 g90.0 e0.85 e0.20 g20.0 e0.87 e0.5 g45 e1.0 e1
Figure 7. SO2 reduction (10%).
Table 4. Production Rate for Final Products final product
production (bbl/day)
gasoline kerosene (jet fuel) diesel fuel oil
28426 25843 25547 18805
Figure 8. SO2 reduction (30%).
(fuel oil grade 2)], and SO2 control technology [FGD technology is an option to be considered in this study because it is a common SO2 reduction technology]. The last two options have cost, and they are implemented in the model. The model was mixed-integer nonlinear programming (MINLP) and was written in the general algebraic modeling system (GAMS). The model has 122 continuous variables and 24 discrete variables, and the solver was simple branch and bound (SBB). The GAMS model optimizes all intermediate and final product streams across the refinery subject to connectivity, capacity, demand, and quality constraints with a certain SO2 reduction target. These constraints can be easily modified to either incorporate new data or steer the model to acceptable solutions.
Figure 5. Base case.
3. Results and Discussion The refinery planning model is run at base case with a zero SO2 reduction target. The model was mainly solved to meet demand and quality specifications. The profit at 0% reduction of SO2 is 540 million U.S. $/year, with total SO2 emissions of 407.2 tons/year. The results from the planning model show that the model tries to meet the demand requirement for each product and the properties required for meeting the quality constraint for each final product. The most profitable product among the oil refinery is gasoline. Therefore, the model tends to produce more from such a product, but meeting the demand for other products is also mandatory. For example, the kerosene produced by the diesel hydrotreater (KDHT) is selected to be blended with the kerosene pool rather than diesel because the kerosene selling price is higher than that for diesel fuel. The model also tried to reduce the production rate of a certain unit if it emits more SO2 emissions than other units, provided that the demand for each product is met. Table 4 shows the production rate for each final product, and it is clear that the model tends to produce more from the
Figure 6. SO2 reduction (5%).
feed for the refinery. The refinery has to meet the market demands for different products (Table 2). Also, product specifications have to be met (Table 3), and a certain SO2 emission reduction target is set. All prices are in U.S. dollars. The options considered, in this study, for SO2 reduction are flow rate balancing, fuel switching [the model will chose the current fuel (fuel oil grade 6) or switch to less sulfur content fuel 3531
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product that has a high selling price, provided that the demand is met for all other products. Figure 5 illustrates the SO2 emissions from each unit without any mitigation options (base case). The fuel used in combustion for all units is fuel oil grade 6. The profit is 540 million U.S. $/year, with total SO2 emission of 407.2 tons/year. Figure 6 shows the results for the 5% SO2 reduction target. In this case, the model chose to switch one unit to run with fuel oil grade 2 that has lower sulfur contents and this is the GOHT unit. The optimization model decided to switch a medium size unit in terms of SO2 emissions. The base case is shown in the figure for comparison. The product quantity and quality remain almost unchanged, except the profit, which decreases with a higher SO2 reduction target because of the retrofit cost for switching. The profit dropped by about 1% to 535 million U.S. $/year. For a higher reduction target, such as 10%, the model tends to switch more units to run with fuel that has lower sulfur contents. Figure 7 shows the result for this case. It is clear from the figure that only three units are switched and chosen to run with fuel that has low sulfur contents. These units are gas oil hydrotreater (GOHT), diesel hydrotreater (DHT), and fluid catalytic cracking (FCC). The profit dropped to 528 million U.S. $/year). As seen in Figure 8, for a 30% reduction target, even more units should be switched to run with the fuel that has low sulfur contents. Figure 8 shows that all units, except DHT and naphtha hydrotrater (NHT), are switched to run using the low sulfur content fuel. These units have the highest SO2 emissions compared to other units. The product flow rate and product specification are all almost the same, and only switching is considered as a promising option to reduce SO2 emission; however, the profit will decrease to 510 million U.S. $/year. To go beyond 30% reduction in SO2 emissions, other mitigation options must be considered. Figure 9 shows the results for 50% reduction, in which emissions from three units, CDU, hydrocracker (HC), and FCC, are controlled by FGD technology. This technology is much more expensive than the other mitigation options, in which the profit of the refinery will decrease to 465 million U.S. $/year. Other units are chosen to run with the fuel that has less sulfur content.
Figure 9. SO2 reduction (50%).
Figure 10. SO2 reduction (80%). Table 5. Summary of the Results percent reduction
profit (million U.S. $/year)
SO2 emission (tons/year)
percent reduction in profit
base 5 10 30 50 80
540 535 528 510 465 400
407.2 382.4 366.5 280.7 199.4 81.4
1 2.2 5.6 13.9 26
Figure 11. Profit versus % SO2 reduction target.
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GOHT=gas oil hydrotreater DHT=diesel hydrotreater NHT=naphtha hydrotreater HC=hydrocracker FCC=fluidized catalytic cracking ref=reformer
However, it is found that 80% is the maximum possible reduction target when all SO2 emissions from all units are controlled by flue gas desulfrization technology. This is based on an assumption that the efficiency of flue gas desulfrization technology is 80%. This result is shown in Figure 10, with a profit of 400 million U.S. $/year. Table 5 gives a summary of the results for different reduction targets. It shows the profit for each reduction target with percent reduction in the profit. For 5% reduction, the profit decreases by about 1% because only one unit is switched to run with fuel oil grade 2 that has lower sulfur contents. As more units are switched to run with this fuel, the profit decreases by a noticeable percentage. At 30% SO2 reduction, for example, the profit decreases by about 6%. The desulfurization technology will dramatically affect the profit at the reduction target beyond 30%. For 50% reduction, for example, the profit decreases by about 14%. This is expected because the cost of this technology is much higher than other control technologies mentioned in this paper. The next figure (Figure 11) shows the profit for each reduction target. As the reduction target increases, the profit decreases because more units are selected to run with the low sulfur content fuel. A decrease in profit seems more pronounced for the SO2 reduction target above 30% because more costly technology is being implemented.
Streams ALC=Arabian light crude oil LSRN=light straight run naphtha TLN=treated light naphtha REFORMATE=reformate LNHC=light naphtha from hydrocracker LNFCC=light naphtha from FCC HNFCC=heavy naphtha from FCC Kero (CDU)=kerosene from CDU KHC=kerosene from HC KDHT=kerosene from diesel hydrotreater TDiesel=treated diesel KDHT=kerosene from DHT DHC=diesel from HC DGOHT=diesel from gas oil hydrotreater LCOFCC=light cycle oil from FCC DRDHT=diesel from residual hydrotreater HCOFCC=heavy cycle oil from FCC LSFO=low sulfur fuel oil
4. Conclusion
Indices
A general mathematical model for an oil refinery was developed on the basis of Arabian light crude oil as the feedstock to the refinery. The model considered product demands with quality specifications and at the same time meet certain SO2 reduction targets. Three SO2 mitigation options were considered. These are flow rate balancing, fuel switching, and FGD. It was shown that, to reduce SO2 without fuel switching or installation of FGD technology, the model tends to blend streams into the most profitable pool unless the demand of such other products needs to be met. For higher reduction targets up to 30%, fuel switching is the option of choice. The final product quantity and quality remain unchanged. The profit is affected by the retrofit cost. To achieve a SO2 reduction target of more than 30%, installation of FGD is a promising option because it can achieve up to 80% reduction. The profit is affected by this option because it is more expensive than fuel switching. The profit decreases by about 26% if all SO2 emissions are controlled by installation of FGD technology.
i, j, m, and b=for refinery unit s=for stream p=for property w=fuel type k=SO2 control technology Parameters Umaxi =maximum capacity of unit i L CTU i and CTi =upper and lower bounds of the end point (EP) of product s from CDU (°F) Spi =selling price of final product from unit i Cfi =cost price of feed to unit i OCi =operating cost of unit i Csi =cost price for switching for unit i CRTi =cost of reduction technology CTCDU,s =EP cut temperature for product s of CDU CDUCuts =volume percent vaporized of product s at the CTs εik =percent of SO2 reduced if technology k is applied on unit i
Acknowledgment. The author expresses his appreciation to the support from the Ministry of Higher Education, Saudi Arabia, in establishment of the Center of Research Excellence in Petroleum Refining and Petrochemicals at King Fahd University of Petroleum and Minerals (KFUPM). The author also acknowledges the financial support of KFUPM to finish this project.
Variables Fi =volume flow rate of feed to unit i, BPD Vi =volume flow rate of product from unit i, BPD PVi =property for a stream from unit i FPi =property p of feed to unit i Xiw =binary variable represents switching for unit i or not Yik = binary variable represents existing or not of SO2 control technology k on unit i SO2iw=SO2 emissions from unit i using fuel w (tons/year)
Nomenclature Units CDU=crude distillation unit RDHT=residual hydrotreater
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