Comparison of Multistage Compression ... - ACS Publications

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Comparison of Multistage Compression Configurations for Single Mixed Refrigerant Processes Kyungjae Tak,† Inkyu Lee,† Hweeung Kwon,† Junghwan Kim,‡ Daeho Ko,*,§ and Il Moon*,† †

Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea Korea Institute of Industrial Technology, 55 Jongga-ro, Jung-gu, Ulsan 44413, Korea § GS E&C, Gran Seoul, 33 Jong-ro, Jongno-gu, Seoul 03159, Korea ‡

S Supporting Information *

ABSTRACT: This study investigates the effects of multistage compression on single mixed refrigerant processes in terms of specific work. Comparison of specific work published in the literature is not straightforward due to the variety of compression configurations and the design bases. Therefore, four configurations (two-, three-, and four-stage and pump-added three-stage compressions) along with three natural gas compositions were considered. To compare with the simulation and optimization results in the literature, these 12 cases, having the same design basis, were optimized by adjusting the optimization variables such as the flow rate and composition of the refrigerant, the compression ratio of each compressor, the inlet pressure of the first compressor, and the outlet temperatures of the hot and cold refrigerant streams. There were two important findings: (1) adding a pump reduces specific work more than adding a compressor or decreasing the minimum temperature difference value in the compressors; (2) among the four configurations, the refrigerant composition does not significantly change, although it greatly affects the efficiency. The former results from the compressor constraint of the gaseous inlet and the latter from the minimum temperature constraint of the multistream heat exchanger. Furthermore, direct comparisons to other studies were also performed showing the importance of optimization and the effect of the design basis. stage concepts were applied to their model.5 Cao et al.6 compared the optimal results of the SMR process and the N2− CH4 expander process for a small-scale LNG plant. Shirazi and Mowla7 optimized the SMR process using a GA and conducted an exergy analysis. Aspelund at el.8 proposed an optimization− simulation framework. They adopted Tabu Search for global searches and the Nelder−Mead Downhill Simplex for local searches. Optimization of the SMR and TEALARC processes using a GA was conducted by Morin et al.9 They claimed that deterministic optimization does not easily converge and is difficult to apply to complex processes such as the TEALARC process. Xu et al.10,11 highlighted the MR composition. The MR composition is the key variable of the MR cycle. However, determining the optimal composition is a difficult issue. Thus, Xu et al. performed a linear regression on the MR composition to improve the efficiency and analyzed the effect of the MR composition on the SMR process performance. Khan and Lee12 adopted a particle swarm optimization for the SMR process. Khan et al.13 proposed a knowledge-based decision-making method that considers the normal boiling point and the specific refrigeration effect of the MR components. Most of the other processes optimized in the literature used the propane precooled mixed refrigerant (C3MR) process because it is dominant in LNG plant market.2,14−18 The specific work of an NG liquefaction process, energy consumption per unit mass of LNG production, has usually

1. INTRODUCTION As the global demand for energy grows steadily, the demand for natural gas (NG) is also increasing in order to secure an alternative safe and clean fuel.1 The LNG trade is also growing due to its suitability for long-distance transportation. Since the 2000s, the market for liquefied natural gas (LNG) plants has grown to meet this demand: new LNG plants have been constructed, train capacity has been increasing, and new liquefaction processes have been applied to various LNG projects. For example, AP-X, dual mixed refrigerant, optimized cascade, and mixed fluid cascade processes have been implemented throughout the industry.2 The volume of NG in its liquid phase is 600 times smaller than in its gas phase; however, liquefying NG requires a significant amount of energy due to its low normal boiling temperature (around −161 °C). Therefore, much of the research on LNG plants has focused on the liquefaction process, with a particular emphasis on optimization to improve the efficiency of the process. Liquefaction processes for onshore LNG plants are usually classified by the number of refrigeration cycles and the types of refrigerant: pure or mixed refrigerant (MR). The majority of NG liquefaction process optimization studies have focused on the single mixed refrigerant (SMR) process, which is the simplest process and uses only one MR cycle. Lee et al.3 optimized the SMR process by employing operation variables including the pressure levels, flow rate, and composition of the MR as the decision variables for optimization. They explained that the MR composition is the most significant key design variable in the SMR process design. Nogal et al.4 used a genetic algorithm (GA) to optimize not only the SMR process but also the cascade MR cycle. Multihorizontal stage and multivertical © 2015 American Chemical Society

Received: Revised: Accepted: Published: 9992

March 11, 2015 August 27, 2015 September 29, 2015 September 29, 2015 DOI: 10.1021/acs.iecr.5b00936 Ind. Eng. Chem. Res. 2015, 54, 9992−10000

Article

Industrial & Engineering Chemistry Research

Figure 1. Four compression configurations of the SMR process.

varied from 914 to 1712 kJ/kg LNG7,8,10−13,19,20 because of differences in the sets in terms of the (1) configuration, (2) NG composition, (3) design basis, and (4) optimization model. However, there is a lack of work on the comparison of specific work optimizations among the different compressor config-

been selected as the process efficiency index because it inherently consumes great amounts of energy. Some researchers have compared their optimal SMR processes to the results of previous studies in terms of specific work.7,12 The specific work of optimized SMR processes in previous work has 9993

DOI: 10.1021/acs.iecr.5b00936 Ind. Eng. Chem. Res. 2015, 54, 9992−10000

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pump by replacing the subscript from comp to pump and using the flow rate of the liquid MR. The flow rate and composition of the vapor and liquid MR after the flash separator are determined by the VLE calculation.

uration systems under the same conditions such as the design basis and NG composition. Therefore, this study is pioneering in terms of first presenting SMR process optimization models with four different compressor configuration systems to investigate the configuration effect on specific work by using the equation-oriented software, gPROMS. The target configurations of the SMR process are a two-, three-, and four-compressor system and a pump-added threecompressor system. Three types of NG feed composition were applied according to methane concentration: lean gas (91.3 mol %), rich gas (82.0 mol %), and pure methane (100 mol %). These 12 cases (four configurations and three NG compositions) with the same design basis were optimized, compared, and analyzed. The purpose of this process is to obtain a standard example for comparison with previous work. The results are compared with similar cases in the literature. Some additional optimization results under the same conditions as found in the literature are also compared directly to previous results.

Wcomp = FMR ·(hcomp,out − hcomp, i n) hcomp,out =

∑ Wcomp + Wpump

ηisen

+ hcomp,in

(3)

The inlet stream of the compressor must be in a gaseous form, which means that the temperature of the inlet stream is higher than the dew point temperature. The minimum temperature difference (MTD) is added between the dew point temperature and the inlet temperature to avoid overlapping of the two; however, the last compressor of the pump-added case ignores this constraint because the liquidphase MR does not enter the compressor due to the flash separator in front of it. Since the flash separator sends saturated vapor to the last compressor, it may cause problems for the compressor.

2. MODEL DESCRIPTION 2.1. SMR Process. The NG liquefaction process (e.g., SMR process) operates on the same principle as the general refrigeration cycle, which is mainly composed of the compression and expansion of a refrigerant. Figure 1 describes the basic framework of the SMR process. A hot MR is compressed through a multicompressor system and cooled one by a cold MR in a multistream heat exchanger (MSHE). The expansion of the cooled MR via the MR valve produces cold MR due to the decreased temperature. The cold MR enters the MSHE and simultaneously exchanges heat with the NG and the hot MR. Using this closed MR cycle, NG becomes highpressure LNG, followed by its expansion via the LNG valve to be stored in an LNG tank. 2.2. Optimization Model. Compressors and MSHEs are important equipment for optimization in NG liquefaction processes where energy minimization is a major goal. Compressors consume most of the energy required by these processes, so the shaft power of the compressors is used as an objective function to be minimized. The feasibility constraint of the composite curve in the MSHEs should be taken into account to enhance the efficiency. Generally, optimal results show a shorter distance between the hot and the cold composite curves and a smaller amount of heat flow in the MSHEs than before optimization. A thermodynamic-based model of the SMR process is developed for optimization. The purpose of the optimization is to minimize the specific work. As all of the NG is assumed to be liquefied, minimization of the total power consumed by the compressors is the objective function. In the case of the pumpadded optimization, the pump power is added to the objective function despite being relatively small compared to the total power. min Wtotal =

isen hcomp,out − hcomp,in

(2)

Tin ≥ Tdew + TMTD

(4)

The temperature rises significantly during the MR compression in the compressor. These higher temperatures result in larger specific volumes of MR and higher specific compression power; therefore, the practical maximum compression ratio of the compressor is 4−5, which results in multistage compression with intercooling in MR processes.4 1 ≤ CR ≤ 4

(5)

The energy balance of the MSHE is based on the enthalpies of the streams passing through it. The total enthalpy summation of the inlet streams is equal to that of the outlet streams as an equality constraint. The inequality constraint of the MSHE is the temperature feasibility. In general, pinch technology is used for the feasibility constraint in the MSHE. Pinch technology has a focus on heat integration, and one of its major applications is a heat exchanger network.21 In the MSHE, the vertical distance between the hot and the cold composite curves must be larger than the minimum temperature approach, i.e., the minimum internal temperature approach (MITA), throughout the overall temperature range. In this model, a feasibility check is performed using 30 internal points in the MSHE as Nogal et al.4 maintained that 30 points are sufficient to confirm the constraint of the MSHE. The intervals divide the entire temperature range of the overall hot composite curve with the same temperature distance. Wahl and Lovseth22 investigated the effects of the number of intervals as well as the model formulation on the level of success of LNG plant optimizations using sequential quadratic programming. The method developed by Kamath et al. was employed to obtain the composite curves and to check the feasibility of the MSHE in this model.23 Thot, n − Tcold, n ≥ TMITA

(1)

(6)

An isenthalpic process model was employed for the valves to calculate the outlet enthalpy and outlet temperature. For the flash separator, VLE calculation was used. In this study, optimization of SMR processes was implemented using the equation-oriented software, gPROMS V4.0. A properties package embedded in gPROMS, Multiflash, calculates the enthalpies, entropies, and VLE properties. The

The compressor power is calculated using isentropic efficiency. Since the outlet enthalpy of the compressor is obtained via the compressor power, the outlet temperature can also be determined. For the pump-added three-compressor system, the flow rate of MR in eq 2 should decrease the amount of liquid stream passing through a pump for calculating the last compressor power. Equations 2 and 3 are also used for the 9994


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the optimization, but the MR flow rate and compression ratio are also important due to their trade-off relationship. For example, when the MR flow rate increases, the compression ratio decreases to remove the given heat. Therefore, the general design variables for the NG liquefaction process optimization, which must be optimized simultaneously to reduce compressor power, are the MR composition, flow rate, and compression ratio. In this model, the outlet temperatures of the hot and cold MR are added as design variables. The outlet temperature of the cold MR affects the first compressor power by changing the specific volumes of the inlet stream. On the other hand, the outlet temperature of the hot MR affects neither the compressor power nor the energy balance in the MSHE directly because the enthalpy of the outlet hot MR is the same as that of the inlet cold MR by the isenthalpic MR valve. However, this gives flexibility to the composite curves and the minimum temperature approach constraint of the MSHE.

successive reduced quadratic programing (SRQPD) solver in gPROMS was used to minimize the specific work. This solver is a gradient-based algorithm, so it has an issue related to convergence and local optimum. The optimizations are sensitive to the formulation of the problem, bounds for the decision variables, and starting points. This effect was analyzed in detail by Wahl and Lovseth.22 As a result of these issues, dozens of optimization runs are performed in this study. The bounds and starting points are changed heuristically by considering the value change of the feasibility condition and the objective function during optimization. Although the solver has a convergence issue, it can reduce computation time significantly in comparison to derivative-free algorithms. For example, Kamath et al.23 used a derivative algorithm for SMR process optimization resulting in approximately 200 times faster optimization than the integrated genetic optimizer−simulator framework in terms of CPU time. 2.3. Design Basis and Variables. The set of parameters for the design basis is listed in Table 1. The set of optimal

3. ANALYSIS OF THE COMPRESSION CONFIGURATIONS 3.1. Compression Configurations. Compression of the gaseous form of the MR is done by compressors in the MRcycle-driven NG liquefaction process. The compression of gas requires a significant amount of energy, and the temperature of the gas passing thorough the compressor increases significantly, leading to higher specific volumes and compressor power. For these reasons, multistage compression with intercooling is common in MR cycles.4 Many compression configurations are used in the literature on the SMR process, mostly with two compressors.6,7,9,27,28 Khan and Lee12 optimized a process using four compressors; other researchers added a pump at the last compression stage.4,10,11 For a comparison and analysis of the multistage compression effect, four configuration cases are proposed in this paper. Figure 1 illustrates the proposed processes. The target process configurations for the optimization are the two-, three-, and four-compression stage processes as well as the pump-added case. 3.2. Analysis of the Effects of Configuration. The optimization to minimize the specific work for the lean gas was carried out for the four sets of configurations. The Peng− Robinson equation of state was employed to calculate the enthalpies and entropies. The number of unknown variables in the optimization model varied between configurations: From 602 variables for configuration 1 to 648 variables for configuration 4. For the degrees of freedom, Table 3 shows the list and values of the control variables for each configuration case, indicating 12−14 degrees of freedom. This study adopts a derivative-based optimization algorithm as explained in section 2.2. Therefore, the optimizations do not require much computational cost, and most of them only take a few CPU seconds. The optimal set of the design variables and some variables determined by the optimization are summarized in Tables 3 and 4, respectively. It is interesting to compare the heavy hydrocarbon compositions in the MR that butane was found to be more favorable than propane. The optimal MR compositions have negligible concentrations of propane over all configurations investigated, which is a similar trend with results in the published literature.3,19,23 The specific work has been improved with an increase in the number of compression stages since the compression process approaches the ideal case of isothermal compression by adding a compressor. Interestingly, it has been observed that the last compressor powers cause

Table 1. Design Basis parameters

values

NG temperature NG pressure NG flow rate NG composition LNG pressure after LNG valve vapor fraction after LNG valve MITA MTD MR temperature after coolers pressure drops in MSHE pressure drops in coolers isentropic efficiency internal point in MSHE

25 °C 55 bar 1 kg/s fixed (Table3) 1 bar 0 3 °C 3 °C 30 °C 1 bar 0 bar 0.8 30

operating conditions is determined given a set of equipment parameters such as efficiency and pressure drop as well as a set of NG parameters such as flow rate and composition. Optimization to minimize the specific work was made for three different NG compositions under the assumption of 100% liquid phase in the stream passing through LNG valve. Three quite different NG compositions were chosen according to their methane concentration: 91.3 mol % for lean gas (NG1), 82.0 mol % for rich gas (NG2), and 100 mol % for pure methane (NG3) as shown in Table 2. As the lean gas and rich gas are used in previous work in this field, some results in our study are comparable. The set of key design variables includes the composition and flow rate of the MR, the compression ratio of each compressor, the inlet pressure of the first compressor, and the outlet temperatures of the hot and cold MR streams in the MSHE. Here, the MR composition is the most significant variable in Table 2. NG Compositions (mol %) type NG124−26 NG26,7 NG3

lean gas rich gas pure methane

N2

CH4

C2H6

0.2 0.7 0

91.3 82.0 100

5.4 11.2 0

iC3H8 C4H10 2.1 4.0 0

0.5 1.2 0

nC4H10 0.5 0.9 0

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Industrial & Engineering Chemistry Research Table 3. Optimal Design Variables in NG1 MR flow rate (mol/s) MR composition (mol %) N2 CH4 C2H6 C3H8 i-C4H10 n-C4H10 compression ratio compressor 1 compressor 2 compressor 3 compressor 4 inlet pressure of compressor 1 (bar) outlet temperature of the hot MR (°C) outlet temperature of the cold MR (°C)

configuration 1

configuration 2

configuration 3

configuration 4

165.4

182.1

169.8

161.1

14.0 23.8 35.7 0.1 4.5 21.9

13.3 26.1 32.7 0.0 5.0 22.9

13.3 25.0 34.4 0.0 0.0 27.2

14.9 25.0 35.1 0.0 0.3 24.7

3.19 2.78

1.86 1.61 2.40

3.35 −164.9 26.9

3.42 −166.7 27.0

1.51 1.43 1.41 2.57 3.34 −170.4 27.0

2.35 2.02 1.85 3.85 −168.6 27.0

Table 4. Variables Determined by the Optimization in NG1 compressor 1 (kW) compressor 2 (kW) compressor 3 (kW) compressor 4 (kW) pump (kW) specific work (kJ/kg LNG) inlet pressure of the last compressor (bar) dew point temperature of the inlet stream in last compressor (°C)

configuration 1

configuration 2

configuration 3

configuration 4

622.7 507.5

351.8 268.3 475.3

211.1 186.1 175.8 482.4

437.0 345.4 251.3

1130.2 10.69 26.0

1095.4 10.27 26.3

1055.5 10.14 27.0

small changes in comparison to the power of the other compressors over configurations 1−3 as the compression ratios of the last compressors are almost the same. The reason for the nonuniformity of the compression ratios comes from the constraint that a compressor can only compress gas and not liquid (eq 4). As shown in Table 4, the inlet pressure of the last compressor shows a slight decrease with an increase in the number of compressors (configurations 1−3) because the dew point temperature is reached at the upper bound (27 °C) in configuration 3. Changing the MR composition is a possible solution of the nonuniformity problem by increasing the low boiling component concentration in order to lower the dew point temperature. However, this solution has undesirable results due to the fact that the components with low boiling points, such as nitrogen and methane, exhibit a low specific refrigeration effect according to Khan et al.13 They analyzed the relationship between the feasibility of the composite curves and the refrigerant component effect and claimed that a decreasing concentration of the low boiling components provides a better solution; however, this solution has a constraint coming from the temperature feasibility of the composite curves in the lowtemperature region, and the pinch point is usually obtained in the low-temperature region. Therefore, the further composition change gives a result that is not feasible. As a practically feasible alternative solution, adding a flash separator and a pump to the last compression stage relaxes the constraint related to the dew point. The dew point temperature of the inlet stream entering the separator (42.1 °C) can be higher than the MR temperature after the cooler (30 °C) in that case. Consequently, the pumpadded three-stage compression exhibits uniform distribution of

3.5 1037.1 18.27 30.0

the compression ratio over the entire stage, as shown in Table 3. This provides a justification for the use of the pump-added multistage compression, although the saturated vapor of the last compressor inlet stream may still cause problems. In addition, employing a pump can be beneficial due to a pump consuming less energy, as it uses liquid rather than gas. For analyzing the MTD effect, additional optimization of configuration 2 with an MTD of 0 °C was also performed. The specific work was 1068.2 kJ/kg LNG, located between the values of the configuration 2 and 3 cases. 3.3. Comparison to Other Studies. Despite differences in the design basis and physical properties packages, the results were compared with other published results to enhance the

Figure 2. Hot and cold composite curves of configuration 1 in NG1. 9996


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configuration 1

configuration 2

configuration 3

configuration 4

167.4

158.7

176.0

168.0

17.1 25.1 32.6 0.0 7.0 18.3

15.9 25.1 32.6 0.8 3.8 21.8

16.1 25.5 32.2 1.3 2.7 22.1

17.0 25.3 32.9 1.3 0.2 23.3

2.46 2.85

1.66 1.60 2.91

4.92 −165.4 26.6

4.26 −165.2 27.0

analysis of the optimization. Lim et al.25 used lean gas (NG1) with configuration 4 in Figure 1 to simulate the SMR processes. The specific work in their case was 1249 kJ/kg LNG, which is approximately 200 kJ/kg LNG higher than the result of the similar case in our study. The differences in the design basis are the NG temperature (37 °C), NG pressure (50 bar), MR temperature after coolers (37 °C), a pressure drop in the MSHE (2 bar for NG, 1.5 bar for hot MR, and 0 bar for cold MR), a pressure drop in the coolers (0.2 bar), the efficiency of the compressors (0.82) and the pump (0.82), the LNG pressure after the LNG valve (1.5 bar), and the absence of ibutane in the MR component. For a more accurate comparison, optimization with the design basis of Lim et al. was performed, and it gave the specific work as 1016.4 kJ/kg LNG, which is 20.7 kJ/kg LNG less than the result of configuration 4 in the lean gas case. Considering that the result of Lim et al. was not optimized, the optimization significantly contributes to a reduction of the specific work of the SMR process. Changing the MR composition plays a key role in the improvement, although finding the optimal composition remains difficult. The detailed results are listed in Tables S3 and S4. Khan and Lee12 optimized configuration 3 as shown in Figure 1 with an NG composition, which is almost the same as the lean gas (NG1). The similar case result in our study, 1055.5 kJ/kg LNG, seems to be an enormous improvement in that the specific work of their study is 1370.0 kJ/kg LNG. However, they optimized the processes in harsh conditions, including higher MR temperatures after coolers (40 °C), lower equipment efficiency (0.75), and the absence of butane in the MR. Although high MR temperature after the cooler can relax the gas-phase constraint of the compressors, it means the high specific volume of the MR that enters the compressors. As a result, the compressors consume more energy, indicating higher specific work. Therefore, optimization using their case is impossible due to a lack of information such as the lowest pressure of the MR and the final LNG pressure. Moreover, their optimization considers the liquefaction ratio, but its value is unknown in the literature.

1.35 1.41 1.28 2.60 4.72 −165.0 27.0

2.10 1.84 1.78 4.99 −165.0 27.0

sets of NG compositions: a lean gas with 95.89 mol % methane and a rich gas with 88.80 mol % methane. Their results show a consistent trend over different composition sets where the lean gas requires approximately 20 kJ/kg LNG of the specific work more than the rich gas, although the specific work varies along with the different objective functions and constraints. A variety of different NG compositions have been employed for simulations and optimizations of LNG plants in the literature.6−8,10−14,18,19,24−26,29−37 Moreover, it is not straightforward to compare the specific work over the different sets of NG compositions published in the literature because they do not use the same design basis. As a result, this study takes into account three NG compositions based on the same design basis: a lean gas (NG1), a rich gas (NG2), and pure methane (NG3). The results from the lean gas were already explained in section 3. The optimization models for the rich gas and pure methane were the same as the models for the lean gas, including the number of unknown variables and the degrees of freedom. 4.2. Rich Gas Results. The compositions of NG2 in Table 2 were used as the rich gas. The key design variables and a set of variables simulated under optimal operating conditions are shown in Tables 5 and S1, respectively. The specific work in the rich gas is approximately 7−10% lower than that in the lean gas because the rich gas requires less heat removal to be liquefied than the lean gas. This lower amount allows a smaller total compression ratio. The results, however, show no significant changes in the compression ratio and inlet pressure of the last compressors compared to the lean gas cases. These lead to the higher inlet pressure of compressor 1, which means that the higher pressure cycle of the MR is more favorable than the lower pressure cycle. For this reason, the dew point temperature constraint of the last compressor for the rich gas is actively applied over all multistage compressions as in the lean gas cases. Therefore, the addition of a pump to the multistage compression makes a significant contribution to obtain the minimum specific work by relaxing the dew point temperature constraint, indicating the dew point temperature of the separator inlet stream of 41.9 °C. However, an increase in the number of compressors over stages has less of an effect on the specific work in the rich gas compared to the lean gas. For an MTD of 0 °C in configuration 2, such as the lean gas case,

4. NG COMPOSITION EFFECT 4.1. NG Compositions. The NG composition affects the specific work in accordance with the different specific enthalpy of each component. Aspelund et al.8 optimized two different 9997


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configuration 1

configuration 2

configuration 3

configuration 4

177.2

169.8

169.0

156.1

13.6 25.9 36.3 0.0 0.0 24.3

13.0 25.3 36.2 0.0 0.0 25.5

12.9 25.2 36.2 0.0 0.0 25.7

13.3 24.6 36.6 0.0 0.0 25.5

3.60 2.84

2.04 1.95 2.80

3.13 −163.6 27.0

2.73 −163.6 27.0

1.60 1.59 1.59 2.80 2.68 −163.6 27.0

2.77 2.43 2.03 2.49 −162.1 27.0

4.4. Comparison to Other Studies. An optimization study of SMR processes with rich gas was carried out by Cao et al.,6 who optimized the two-compressor system (configuration 1 in Figure 1) with two-stage MSHEs. In contrast, Shirazi and Mowla7 optimized the same system with a single-stage MSHE. The specific work obtained by Cao et al. was 122.3 kJ/mol, which can be converted to approximately 6164 kJ/kg LNG. Since there is little information about the design basis and key design variables in the literature, it is hard to determine the reason for that high specific work. One possible reason could have been the MR composition. Their purpose was to design small-scale LNG plants, so their SMR process was based on a C3MR process with elimination of the propane precooled cycle. Therefore, the MR has a high concentration of methane and ethane, 40 mol % each. It is similar to the MR compositions for the MR cycle in the C3MR processes in previous work.18,31 Consequently, their result is far from optimal MR compositions for SMR processes. Shirazi and Mowla7 showed an SMR process with a specific work of 1092.4 kJ/kg LNG using optimization. Their result was 84.1 kJ/kg LNG higher than the result of configuration 1 in the rich gas. The differences include a pressure drop in the coolers (0.2 bar), a pressure drop in the MSHE (5 bar for hot streams and 0.3 bar for the cold stream), the efficiency of the compressors (0.75), MTD (0 °C), and MITA (1.5 °C). Using these values, the optimization shows the specific work of 992.2 kJ/kg LNG. The detailed results are listed in Tables S5 and S6.

the specific work is located between the values of the cases of configurations 2 and 3 (984.4 kJ/kg LNG). 4.3. Pure Methane Results. Pure methane (NG3) was also used for analyzing the NG composition effect. Tables 6 and S2 present the results of the pure methane cases. Unlike the rich gas cases, the pure methane case shows higher specific work over different sets of compression configurations compared to the lean gas case. The results show larger amounts of the total compression load in pure methane than the lean gas and rich gas cases. To obtain this higher total compression ratio, the inlet pressure of the first compressor was lowered instead of increasing the inlet pressure of the last compressor. The reason is that the last stage has a dew point temperature constraint. Similar to the lean gas and rich gas cases, increasing the number of compression stages in pure methane is more effective for improvement of specific work, and the pump-added three-stage compression in pure methane leads to the minimum specific work. The dew point temperature of the separator inlet stream in configuration 4 is 40.5 °C. In the case of the MTD of 0 °C in configuration 2, the specific work was 1264.9 kJ/kg LNG. The overall optimal level of specific work over different sets of compression configurations along with different NG composition sets are summarized in Figure 3. They show a similar tendency of specific work. On the basis of configuration 2, adding a pump shows the greatest reduction of the specific work followed by adding a compressor. On the other hand, increasing the MTD value to 0 °C provides the least amount of improvement in terms of specific work.

5. CONCLUSION To give a standard example for comparison of the SMR processes optimized in the literature, the configuration effects of multistage compression were analyzed. Two-, three-, and four-compressor systems and a pump-added three-compressor system were investigated with three NG compositions of lean gas, rich gas, and pure methane. An increase in the number of compressors reduces the energy consumption by getting closer to the ideal case of isothermal compression. However, adding a pump was found to provide the best solution among all NG cases rather than adding a compressor or decreasing the minimum temperature difference value in the compressors from 3 °C to 0. Two major reasons for this are the dew point temperature constraint in the compressors and the minimum temperature approach constraint in the MSHE. The former

Figure 3. Comparison of the specific work optimized in this study. 9998


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Industrial & Engineering Chemistry Research isen n out pump total MITA

isentropic process internal point in the multistream heat exchanger outlet stream pump for compressing liquid mixed refrigerant total amount minimum internal temperature difference in the multistream heat exchanger MR mixed refrigerant MTD minimum temperature difference

constraint creates a nonuniform compression ratio with the high compression ratio of the last compressor in the pumpabsent cases, preventing these cases from approaching isothermal compression. Two possible solutions to relax the constraint in the last compressor were (1) the changing MR composition to lower the dew point and (2) adding a pump to treat the liquid part. Although the low boiling components give the temperature feasibility between the composite curves in the low-temperature region, these components have small specific refrigeration effects. The minimum temperature approach constraint in the MSHE prevents a significant change of the MR composition. As a result, adding a pump becomes the best solution. Furthermore, additional optimizations were performed using the same conditions as found in other studies. The optimization of the process was found to significantly reduce the specific work by adjusting the MR composition in comparison to the results that were not optimized. The specific work was affected by only 1−2% of its value by different design bases such as pressure drops and equipment efficiency. On the basis of this standard example, other published results can be easily compared and analyzed, even though most previous studies have used quite different configurations, NG compositions, and design bases.

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Superscript

isen isentropic process Parameter

η equipment efficiency Variables

h CR F T W

■

ASSOCIATED CONTENT

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.5b00936. Variables determined by the optimization in NG2; variables determined by the optimization in NG3; optimal design variables of configuration 4 in NG1 under the same conditions as the literature; variables determined by optimization of configuration 4 in NG1 under the same conditions as the literature; optimal design variables of configuration 1 in NG2 under the same conditions as the literature; variables determined by optimization of configuration 1 in NG2 under the same conditions as the literature (PDF)

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by a grant from the LNG Plant R&D Center funded by the Ministry of Land, Infrastructure, and Transport (MOLIT) of the Korean government, and was also supported by the Engineering Development Research Center (EDRC) funded by the Ministry of Trade, Industry, and Energy (MOTIE).

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NOMENCLATURE

Subscripts

cold comp dew hot in

REFERENCES

(1) Kumar, S.; Kwon, H.; Choi, K.; Lim, W.; Cho, J. H.; Tak, K.; Moon, I. LNG: An eco-friendly cryogenic fuel for sustainable development. Appl. Energy 2011, 88, 4264. (2) Lim, W.; Choi, K.; Moon, I. Current Status and Perspectives of Liquefied Natural Gas (LNG) Plant Design. Ind. Eng. Chem. Res. 2013, 52, 3065. (3) Lee, G. C.; Smith, R.; Zhu, X. X. Optimal Synthesis of MixedRefrigerant Systems for Low-Temperature Processes. Ind. Eng. Chem. Res. 2002, 41, 5016. (4) Nogal, F. D.; Kim, J.; Perry, S.; Smith, R. Optimal Design of Mixed Refrigerant Cycles. Ind. Eng. Chem. Res. 2008, 47, 8724. (5) Vaidyaraman, S.; Maranas, C. D. Synthesis of Mixed Refrigerant Cascade Cycles. Chem. Eng. Commun. 2002, 189, 1057. (6) Cao, W.; Lu, X.; Lin, W.; Gu, A. Parameter comparison of two small-scale natural gas liquefaction processes in skid-mounted packages. Appl. Therm. Eng. 2006, 26, 898. (7) Shirazi, M. M. H.; Mowla, D. Energy optimization for liquefaction process of natural gas in peak shaving plant. Energy 2010, 35, 2878. (8) Aspelund, A.; Gundersen, T.; Myklebust, J.; Nowak, M. P.; Tomasgard, A. An optimization−simulation model for a simple LNG process. Comput. Chem. Eng. 2010, 34, 1606. (9) Morin, A.; Wahl, P. E.; Molnvik, M. Using evolutionary search to optimize the energy consumption for natural gas liquefaction. Chem. Eng. Res. Des. 2011, 89, 2428. (10) Xu, X.; Liu, J.; Jiang, C.; Cao, L. The correlation between mixed refrigerant composition and ambient conditions in the PRICO LNG process. Appl. Energy 2013, 102, 1127. (11) Xu, X.; Liu, J.; Cao, L. Optimization and analysis of mixed refrigerant composition for the PRICO natural gas liquefaction process. Cryogenics 2014, 59, 60. (12) Khan, M. S.; Lee, M. Design optimization of single mixed refrigerant natural gas liquefaction process using the particle swarm paradigm with nonlinear constraints. Energy 2013, 49, 146. (13) Khan, M. S.; Lee, S.; Rangaiah, G. P.; Lee, M. Knowledge based decision making method for the selection of mixed refrigerant systems for energy efficient LNG processes. Appl. Energy 2013, 111, 1018. (14) Alabdulkarem, A.; Mortazavi, A.; Hwang, Y.; Radermacher, R.; Rogers, P. Optimization of propane pre-cooled mixed refrigerant LNG plant. Appl. Therm. Eng. 2011, 31, 1091. (15) Wang, M.; Zhang, J.; Xu, Q.; Li, K. Thermodynamic-Analysisbased Energy Consumption Minimization for Natural Gas Liquefaction. Ind. Eng. Chem. Res. 2011, 50, 12630. (16) Wang, M.; Zhang, J.; Xu, Q. Optimal design and operation of a C3MR refrigeration system for natural gas liquefaction. Comput. Chem. Eng. 2012, 39, 84.

S Supporting Information *

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molar enthalpy (J/mol) compression ratio molar flow rate (mol/s) temperature (K) compression power (W)

cold stream compressor dew point hot stream inlet stream 9999


Article

Industrial & Engineering Chemistry Research (17) Hatcher, P.; Khalilpour, R.; Abbas, A. Optimisation of LNG mixed-refrigerant processes considering operation and design objectives. Comput. Chem. Eng. 2012, 41, 123. (18) Lee, I.; Tak, K.; Kwon, H.; Kim, J.; Ko, D.; Moon, I. Design and Optimization of a Pure Refrigerant Cycle for Natural Gas Liquefaction with Subcooling. Ind. Eng. Chem. Res. 2014, 53, 10397. (19) Jacobsen, M. G.; Skogestad, S. Active constraint regions for a natural gas liquefaction process. J. Nat. Gas Sci. Eng. 2013, 10, 8. (20) Lee, G. C. Optimal design and analysis of refrigeration systems for low temperature processes. Ph.D. Thesis, University of Manchester Institute of Science and Technology, U.K., 2001. (21) Duran, M. A.; Grossmann, I. E. Simultaneous Optimization and Heat Integration of Chemical Processes. AIChE J. 1986, 32, 123. (22) Wahl, P. E.; Lovseth, S. W. Formulating the optimization problem when using sequential quadratic programming applied to a simple LNG process. Comput. Chem. Eng. 2015, 82, 1. (23) Kamath, R. S.; Biegler, L. T.; Grossmann, I. E. Modeling Multistream Heat Exchangers with and without Phase Changes for Simultaneous Optimization and Heat Integration. AIChE J. 2012, 58, 190. (24) Chang, H.; Chung, M. J.; Lee, S.; Choe, K. H. An efficient multistage Brayton-JT cycle for liquefaction of natural gas. Cryogenics 2011, 51, 278. (25) Lim, W.; Lee, I.; Tak, K.; Cho, J. H.; Ko, D.; Moon, I. Efficient Configuration of a Natural Gas Liquefaction Process for Energy Recovery. Ind. Eng. Chem. Res. 2014, 53, 1973. (26) Lim, W.; Lee, I.; Lee, K.; Lyu, B.; Kim, J.; Moon, I. Design and analysis of multi-stage expander processes for liquefying natural gas. Korean J. Chem. Eng. 2014, 31, 1522. (27) Chang, H.; Lim, H. S.; Choe, K. H. Effect of multi-stream heat exchanger on performance of natural gas liquefaction with mixed refrigerant. Cryogenics 2012, 52, 642. (28) Marmolejo-Correa, D.; Gundersen, T. A comparison of exergy efficiency definitions with focus on low temperature processes. Energy 2012, 44, 477. (29) Rameljej, C. W.; Hoadley, A. F. A. An exergy analysis of smallscale liquefied natural gas (LNG) liquefaction processes. Energy 2006, 31, 2005. (30) Shah, N. M.; Hoadley, A. F. A. A Targeting Methodology for Multistage Gas-Phase Auto Refrigeration Processes. Ind. Eng. Chem. Res. 2007, 46, 4497. (31) Castillo, L.; Dorao, C. A. Consensual decision-making model based on game theory for LNG processes. Energy Convers. Manage. 2012, 64, 387. (32) Wang, M.; Zhang, J.; Xu, Q.; Li, K. Thermodynamic-AnalysisBased Energy Consumption Minimization for Natural Gas Liquefaction. Ind. Eng. Chem. Res. 2011, 50, 12630. (33) Wang, M.; Zhang, J.; Xu, Q. Optimal design and operation of a C3MR refrigeration system for natural gas liquefaction. Comput. Chem. Eng. 2012, 39, 84. (34) Castillo, L.; Dahouk, M. M.; Scipio, S. D.; Dorao, C. A. Conceptual analysis of the precooling stage for LNG processes. Energy Convers. Manage. 2013, 66, 41. (35) Mortazavi, A.; Somers, C.; Alabdulkarem, A.; Hwang, Y.; Radermacher, R. Enhancement of APCI cycle efficiency with absorption chillers. Energy 2010, 35, 3877. (36) Mortazavi, A.; Somers, C.; Hwang, Y.; Radermacher, R.; Rodgers, P.; Al-Hashimi, S. Performance enhancement of propane precooled mixed refrigerant LNG plant. Appl. Energy 2012, 93, 125. (37) Hatcher, P.; Khalilpour, R.; Abbas, A. Optimisation of LNG mixed-refrigerant processes considering operation and design objectives. Comput. Chem. Eng. 2012, 41, 123.

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Comparison of Multistage Compression ... - ACS Publications

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