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Optimization of the Sulfolane Extraction Plant Based on Modeling and Simulation Yu J. Choi,† Ki W. Cho,† Byung W. Cho,‡ and Yeong-Koo Yeo*,† Departments of Chemical Engineering and Civil Engineering, Hanyang University, Seoul 133-791, Korea
The sulfolane extraction process is widely used in benzene-toluene-xylene (BTX) plants to separate aromatics from hydrotreated feedstocks. Operation of the sulfolane process affects essentially the overall efficiency of BTX plants. In this paper a model and optimization system for the sulfolane extraction plant was developed based on simulation. The parametric models obtained from the steady-state simulations were employed to develop the optimization system based on the sequential quadratic programming scheme. Optimal values of recycle streams could be determined from the present optimization system. Results of simulations led to further economic improvements over the present operation states. 1. Introduction The sulfolane extraction process is widely used to recover benzene, toluene, xylene, and C9 aromatics from aromatic-rich depentanized streams in benzene-toluene-xylene (BTX) plants. Sulfolane has an outstanding selectivity to aromatics, a high solubility to water, and a high boiling point.1 The sulfolane extraction plant consists of an extraction unit, distillation units, and other related units such as strippers and settlers. The extraction plant includes critical unit operations such as extraction, distillation, and absorption.2 The plant considered in the present study is the sulfolane extraction plant now being operated in Daelim Petrochemical Co. located in the southwestern area of Korea. Figure 1 shows a schematic diagram of the sulfolane extraction plant. As can be seen, the plant consists of six major process units. In the extraction column (extractor), desulfurized hydrocarbons from the crude oil including naphtha, aromatics, heavy polymers, and tars are fed into the middle of the column and are extracted by the mixture of solvent and sulfolane. The extract contains heavy components such as aromatics, polymers, and tars, and the raffinate contains light hydrocarbons of less than C5. The column is generally operated at 120 °C. However, the modified process is operated at 60 °C, resulting in lower energy consumption. In the wash column, the solvent and hydrocarbons are separated from the raffinates. The column is operated at 40 °C, and nonaromatics (below C5) are obtained as the overhead product. A very small amount of light hydrocarbons and water is contained in the bottom product of the extractor. To remove these components, the extractive distillation operation under vacuum is performed in the extractdistillation column. From the column, light nonaromatics and the solvent are obtained as overhead product and heavy components are produced as the bottom product. The column is operated at the temperature range of 40-200 °C. A water stripping column is * To whom correspondence should be addressed. Fax: +82-2-2291-6216. E-mail:
[email protected]. † Department of Chemical Engineering ‡ Department of Civil Engineering.
provided to remove traces of dissolved nonaromatics from the solvent-rich wash water and to generate stripping steam for the recovery column. Use of the additive water stripper instead of the distillation under vacuum is quite popular in most of the modified processes. The bottom products of the wash column and the extract-distillation column are fed into the solvent recovery column which is operated in the range of 40160 °C. Water and aromatics are obtained as the overhead product, which, in turn, is separated by density differences. The resulting water is recycled to the wash column, and aromatics are obtained as the products. The solvent and heavy components such as tars are obtained as the bottom product of the column, and the solvent is recycled to the extractor. It is not uncommon to add a solvent regenerator or a vacuum distillation equipment operated under high temperature to remove tars and impurities. The optimization of the extraction plant is by far the most important to improve the economics of the BTX plant. In this work, optimal operation conditions of the sulfolane extraction plant were identified based on modeling and simulation of the actual plant. From the results of simulations based on rigorous process models, parametric models for each plant unit were developed, which, in turn, were employed in the sequential quadratic programming (SQP) method to optimize the operation of the extraction plant. 2. Modeling of the Extraction Plant Figure 1 shows the flow diagram of the sulfolane extraction plant considered in the present study, and Table 1 shows typical operating conditions of the extraction plant. For effective flow-sheet-level calculation, suitable partitioning and tearing are inevitable. Various tearing algorithms3,4 are available, and the decomposition algorithm5 is applied in the present study. Among many numerical methods6,7 available to compute the flow-sheet-level problem, we employed the direct substitution method.8 For efficient flow-sheeting we have to identify the strong component in the digraph (directed graph) through partitioning. Partitioning operation reduces the number of processing units or equations that must be considered
10.1021/ie010435a CCC: $22.00 © 2002 American Chemical Society Published on Web 10/05/2002
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Figure 1. Flow diagram of the extraction plant. Table 1. Operating Conditions unit
stage no.
temp (°C)
pressure (atm)
extractor wash column stripper column solvent recovery column water stripper solvent regenerator
105 40 44 27 absorb 4
58.9-73.3 30.6-44.2 101.4-170.8 76.0-170.7 117.0-117.3 168.5-172.1
6.3-9.14 3.0-4.5 2.08-2.54 0.4-0.6 2.84 1.81
a
feed (ton/h)a 28.7(47) 9.8(40) 18.9(1) 0.066(C) 2.3(T) 2.2(1)
14.5(105) 174.0(9) 2.0(R) 1.4(4)
6.5(27) 1.7(R)
(no.): stage number. (C): condenser. (T): top. (R): reboiler.
simultaneously at any time. For partitioning operation, we usually employ the graphical representation of the process flow sheet using nodes and directed edges. The digraph of the sulfolane extraction process being considered is shown in Figures 2 and 3. For the sulfolane extraction process being considered here, we have four tearing variables (x3, x17, x24, and x25) obtained from the partitioning and tearing operations. Because of the high nonlinearity of the complicated operating constraints and of the unknown physical properties of the key components, it is well-known that the extractor cannot be modeled by commercial simulation packages such as HYSYS and ASPEN. However, most of the process units except the extraction column can be modeled by any commercial package, and HYSYS was used to model process units except the extraction process in the present study. In the modeling of the extraction process, it is imperative to choose an appropriate thermodynamic model. Although equations of state models have been proven to be very reliable in the prediction of properties of most hydrocarbon fluids over a large range of operation conditions, their applications have been limited to primarily nonpolar or slightly polar components. Because of the high polarity of sulfolane and water contained in virtually all of the streams of the extraction plant, we can see that equations of state are not suitable for the modeling of the extraction unit. In the present study, an activity model was employed in the modeling. The characteristics of various activity models are summarized in Table 2. Because of its wide application range, the well-known UNIQUAC was chosen for the computations of related thermodynamic properties. The results of simulations for the extractor are shown in Figure 4. In the figures, the numbers in the component axis denote the following: 1, cyclopentane; 2, hexane; 3, methylcyclopentane; 4, benzene; 5, 3-meth-
Table 2. Comparison of Activity Modelsa application binary systems multicomponent systems azeotropic systems liquid-liquid equilibria dilute systems self-associating systems polymers extrapolation
Van Margules Laar Wilson NRTL UNIQUAC A LA
A LA
A A
A A
A A
A A
A A
A N/A
A A
A A
? ?
? ?
A A
A A
A A
N/A ?
N/A ?
N/A G
N/G G
A G
a A ) applicable; N/A ) not applicable; ? ) questionable; G ) good; LA ) limited application.
ylhexane; 6, methylcyclohexane; 7, toluene; 8, n-octane; 9, dimethylcyclohexane; 10, ethylbenzene; 11, p-xylene; 12, m-xylene; 13, o-xylene; 14, sulfolane; 15, water. The results of simulations of all of the other units of the extraction plant except the extractor are summarized in Figures 5-8. As can be seen, the results of simulations show a good agreement with the actual operation data. Stream 24 (Figure 1) in Figure 8 is the final product stream in the sulfolane extraction plant. Stream 24 (Figure 1) is fed to the fractionation plants to give benzene with high purity. 3. Optimization of the Extraction Plant The purity of benzene in final product streams is most severely influenced by the recycle streams within the plant. Therefore, the primary task in the operation is to maintain the optimal levels of flow rates of recycle streams. For this reason, the major effort in the optimization should be given to identifying the optimal recycle flow rates in the plant to maximize the benzene
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Ind. Eng. Chem. Res., Vol. 41, No. 22, 2002
Figure 2. Digraph of the sulfolane extraction process.
Figure 3. Diagram of unit modules and streams.
content in the product stream. Optimization of the sulfolane plant requires repetitive model computations
and numerous numerical differentiations that consume a lot of computing time. In the actual operation of the
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Figure 4. Results of simulations and operation data of the extraction plant.
Figure 6. Results of simulations for the stripper.
Results of the computations are regarded as plant data. The computational results can then be represented by parametric models, which can be written as n
y)
Figure 5. Results of simulations for the wash column.
plant, the optimization system with large computing time is not effective, especially when process variables change with time. To reduce the computing time and to avoid numerical problems while maintaining computational accuracy, we developed a parametric model which can represent the behavior of the actual plant. To develop the parametric model, we first computed the benzene composition in the product by using the HYSYS plant model. For a given operational range of each of the recycle streams, HYSYS and the extractor model gave the benzene compositions in the product stream.
b[i]xi ∑ i)0
(1)
where y is the mass fraction of benzene, x is the recycle stream, and b[i] is the coefficient of the model. Values of b[i] for each recycle streams (see Figure 1) are given in Table 3. In short, the rigorous simulator based on HYSYS acts as the plant model, and the parametric model given by (1) is the “plant model” to be used in the optimization. The optimization depends only on the parametric model, not on the rigorous simulator. The parametric models are employed in the SQP optimization method9 to give optimal recycle rates. The objective function for the optimization is very simple and consists of the pumping and utility costs subtracted by benzene values. Constraints can be summarized as
s1 > 0, s2 > 0, s3 > 0, s4 > 0, s5 > 0, s6 > 0 0.5 < f1(s1) < 1.0, 0.5 < f2(s2) < 1.0, 0.5 < f3(s3) < 1.0 0.5 < f4(s4) < 1.0 , 0.5 < f5(s5) < 1.0, 0.5 < f6(s6) < 1.0
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Table 3. Coefficients of Parametric Models recycle stream
n
b[i] (i ) 0, 1, ..., n)
recycle 1
10
recycle 2
10
recycle 3
7
recycle 4
7
recycle 5 recycle 6
1 10
b[0] ) 0.4687, b[1] ) 4.8043 × 10-6, b[2] ) -1.7275 × 10-10, b[3] ) 3.2146 × 10-15, b[4] ) -3.5042 × 10-20, b[5] ) 2.3836 × 10-25, b[6] ) -1.0406 × 10-30, b[7] ) 2.9145 × 10-36, b[8] ) -5.0610 × 10-42, b[9] ) 4.9582 × 10-48, b[10] ) -2.0941 × 10-54 b[0] ) 0.4398, b[1] ) 6.9078 × 10-5, b[2] ) -2.0409 × 10-8, b[3] ) 2.6759 × 10-12, b[4] ) -1.5549 × 10-16, b[5] ) 1.0691 × 10-21, b[6] ) 3.6284 × 10-2, b[7] ) -2.1183 × 10-29, b[8] ) 5.4800 × 10-34, b[9] ) -7.0043 × 10-39, b[10] ) 3.5915 × 10-44 b[0] ) 0.4852, b[1] ) 2.0146 × 10-5, b[2] ) -4.5018 × 10-9, b[3] ) 5.1123 × 10-13, b[4] ) -3.2269 × 10-17, b[5] ) 1.1459 × 10-21, b[6] ) -2.1418 × 10-26, b[7] ) 1.6387 × 10-31 b[0] ) 0.4956, b[1] ) 7.4640 × 10-6, b[2] ) -7.7213 × 10-10, b[3] ) 3.5426 × 10-14, b[4] ) -8.5556 × 10-19, b[5] ) 1.1340 × 10-23, b[6] ) -7.8100 × 10-29, b[7] ) 2.1854 × 10-34 b[0] ) 0.5090, b[1] ) 2.0053 × 10-6 b[0] ) 1.1493, b[1] ) -5.3256 × 10-5, b[2] ) 1.7662 × 10-9, b[3] ) -3.0459 × 10-14, b[4] ) 3.1266 × 10-19, b[5] ) -2.0364 × 10-24, b[6] ) 8.6357 × 10-30, b[7] ) -2.3769 × 10-35, b[8] ) 4.0937 × 10-41, b[9] ) -4.0077 × 10-47, b[10] ) 1.7018 × 10-53
Figure 7. Results of simulations for the solvent regenerator. Table 4. Results of Optimizations: Recycle Rates case stream
operation
optimization
recycle 1 (kg/h) recycle 2 (kg/h) recycle 3 (kg/h) recycle 4 (kg/h) recycle 5 (kg/h) recycle 6 (kg/h)
4.750 × 1709 1580 2954 30.85 3.997 × 104
3.376 × 104 1709 1576 2728 44.73 5.796 × 104
104
where s means mass flow rates of recycle streams and fi(si) values (i ) 1, 2, ..., 6) represent identified parametric models relating recycle rates and benzene fractions. The results of optimization computations are summarized in Tables 4 and 5, where the optimal results
Figure 8. Results of simulations for the solvent recovery column.
are compared with operating conditions. The optimal recycle rates are given in Table 4, and Table 5 shows optimal component fractions. The computational results shown in Table 5 were obtained by plugging the values of the optimal recycle rates in Table 4 into the rigorous simulator. From Table 4, we can see that almost a 10% increase of the purity of benzene was achieved by the optimization. 4. Conclusions In the modeling of the sulfolane extraction plant, it is well-known that commercial process software pack-
Ind. Eng. Chem. Res., Vol. 41, No. 22, 2002 5509 Table 5. Results of Optimizations: Component Fractions benzene toluene ethylbenzene p-xylene m-xylene o-xylene dimethylcyclohexane sulfolane water
operation
optimization
0.5091 0.3003 0.0963 0.0192 0.0545 0.0199 0.0005 0.0000 0.0003
0.5562 0.3302 0.1088 0.0010 0.0027 0.0010 0.1088 0.0000 0.0002
ages do not give an adequate model for the sulfolane extraction unit. In the present study, a steady-state model for the sulfolane extraction plant was developed based on HYSYS and the extractor model. The purity of the products from the sulfolane extraction plant is highly dependent upon the rates of recycle streams that can be manipulated during the operation. To identify the optimum values of the rates of recycle streams, parametric models relating the recycle stream with the product quality were obtained first based on the rigorous steady-state models aforementioned. Optimum recycle rates were found by using the SQP algorithm. It was found that almost a 10% increase of the purity of benzene could be achieved by the application of the present optimization system. Extension to the dynamic modeling and optimization is yet to be investigated. Acknowledgment This work was supported by the Korea Science and Engineering Foundation (No. R01-2001-00409) and in part by Hanyang University, made in the program year of 2001.
Nomenclature b ) model parameter fi(si) ) relation equation about the purity of benzene and variation in the i recycle rate n ) total number of components si ) recycle rate of i [kg/h] x ) recycle streams [kg/h]
Literature Cited (1) Bailes, C.; Hughes, M. A. Liquid-Liquid Extraction: Nonmetalic Materials. Chem. Eng. 1976, 10, 115. (2) Cho, K.-W.; Yeo, Y.-K.; Kim, M. K. Application of Partitioning and Tearing Techniques to Sulfolane Extraction Plant. Korean J. Chem. Eng. 1999, 16, 462. (3) Upadhye, R. S.; Grens, E. A., II. Selection of Decompositions for Chemical Process Simulation. AIChE J. 1975, 21, 136. (4) Genna, P. L.; Motard, R. L. Optimal Decomposition of Process Networks. AIChE J. 1975, 21, 656. (5) Ollero, P.; Amselem, C. Decomposition Algorithm for Chemical Process Simulation. Chem. Eng. Res. Des. 1983, 61, 303. (6) Crowe, C. M.; Nishio, M. Convergence Promotion in the Simulation of Chemical ProcessessThe General Dominant Eigenvalue Method. AIChE J. 1975, 21, 528. (7) Motard, R. L.; Shacham, M.; Rosen, E. M. Steady-State Chemical Process Simulation. AIChE J. 1975, 21, 417. (8) Metcalfe, S. R.; Perkins, J. D. Information Flow in Modular Flowsheeting Systems. Trans. Inst. Chem. Eng. 1978, 56, 210. (9) Edgar, T. F.; Himmelblau, D. M. Optimization of Chemical Process; McGraw-Hill: New York, 1988.
Received for review May 14, 2001 Revised manuscript received July 22, 2002 Accepted August 9, 2002 IE010435A
