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Decision Making of Liquefaction Ratio for Minimizing Specific Energy in LNG Pilot Plant Inkyu Lee, Kyungjae Tak, Sunkyu Lee, Daeho Ko, and Il Moon Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b03687 • Publication Date (Web): 09 Dec 2015 Downloaded from http://pubs.acs.org on December 15, 2015

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Industrial & Engineering Chemistry Research

Decision Making of Liquefaction Ratio for Minimizing Specific Energy in LNG Pilot Plant Inkyu Leea, Kyungjae Taka, Sunkyu Leeb, Daeho Ko*, c, and Il Moon*, a

a

Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea

b

Korea Gas Safety Corporation (KGS), 1390 Wonjung-ro, Maengdong-myeon, Eumseong-gun, Chungcheongbuk-do 369-810, Republic of Korea c

GS E&C, Gran Seoul 33 Jongro, Jongno-gu, Seoul 110-121, Republic of Korea Submitted to Industrial & Engineering Chemistry Research October 1, 2015

KEYWORDS: LNG Pilot Plant, Liquefied Natural Gas, Natural Gas Liquefaction Ratio, Propane Precooled Mixed Refrigerant Process * Corresponding authors: Tel.: + 82 2 2154 6171. E-mail address: [email protected] Tel.: + 82 2 2123 2761. E-mail address: [email protected]

ACS Paragon Plus Environment

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ABSTRACT Natural gas liquefaction is a highly energy-intensive process due to its cryogenic operation. Therefore, a major concern in designing and optimizing natural gas liquefaction processes is the enhancement of energy efficiency. A liquefaction ratio in the natural gas liquefaction process affects to the specific energy consumption, which is the energy consumption per unit mass of liquefied natural gas(LNG). However, most of previous researches have not considered the liquefaction ratio of the natural gas. This study focuses on the minimizing specific energy consumption of a LNG plant considering the liquefaction ratio. To analyze the effects of the liquefaction ratio, four different cases are set and the optimizations are performed. The objective function to be minimized includes the total energy consumption and specific energy consumption. The result of minimizing total energy consumption converges on the lower bound of the liquefaction ratio, but the result of minimizing specific energy consumption converges on between lower and upper bound. It shows that the total energy minimization does not always have same meaning of the specific energy minimization when the liquefaction ratio is not fixed. Through this analysis, the relationships between the energy consumption and the liquefaction ratio are found. As the result of the optimization, the optimal compression ratio, temperature, intermediate pressure, and refrigerant flow rate are found, and the specific power was reduced by 16.40% than the LNG pilot plant design while yielding the natural gas liquefaction ratio of 86.9%.

1. INTRODUCTION

According to the Outlook for Energy (2015), unconventional gas is forecasted to increase by


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about quadruples in the 30 years from 2010 to 2040, and LNG trade is forecasted 300% increase during the same period.1 When natural gas is liquefied, its volume is reduced by a factor of more than 600.2 Therefore, LNG is usually used for the long distance transport. To liquefy natural gas at atmospheric pressure, it must be cooled below −161 °C.3 Because of the corresponding need for cryogenic operation, the natural gas liquefaction process is highly energy-intensive.4 LNG plants are classified by the number of refrigeration cycles and the type of refrigerants used. A Cascade process developed by ConocoPhillips is the world’s first commercial LNG plant adopting three different pure refrigerants: methane, ethylene, and propane.5 A single mixed refrigerant (SMR) process uses one mixed refrigeration cycle to liquefy natural gas. Compared to the Cascade process, the SMR process uses a simplified equipment configuration. However, the SMR process has low thermodynamic efficiency.6 To improve the thermodynamic efficiency, a natural gas precooling cycle using propane was introduced by the Air Product and Chemicals, Inc. (APCI); this process is called C3MR.7 The C3MR process has become the dominant process worldwide in the LNG industry.8 A dual mixed refrigerant (DMR) process developed by Shell uses two different mixed refrigerant processes: one for natural gas precooling to about −50 °C and the other for liquefaction.9 In academia, a major concern in LNG plant research is optimal design and process optimization of mixed refrigeration systems.10 In the last two decades, many researchers have studied the optimization of LNG plants for energy savings. Lee et al.11 focused on a systematic synthesis method for the design of a mixed refrigerant system to find the optimal mixed refrigerant composition based on the optimization variables of flow rates, compressor inlet and outlet pressures, and separator temperatures. Nogal et al.12 applied a genetic algorithm (GA) as a stochastic technique to overcome local optima while optimizing the mixed refrigerant system.


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Shirazi and Mowla13 applied a GA built in MATLAB software to optimize the SMR process with fixed LNG temperature, -161 °C. Aspelund et al.14 employed tabu search (TS), another stochastic optimization method, in combination with the Nelder–Mead Downhill Simplex (NMDS) method. This combined optimization method reduces the number of simulations because the TS result is fine-tuned by means of the NDMS method. They also focused on the mixed refrigerant system in the SMR process with the fixed final product temperature of 163.7 °C. Kahn and Lee15 used a particle swarm paradigm as a non-traditional stochastic technique to optimize the SMR process. They set the final product temperature as the inequality constraints, below -157 °C, and compared the optimal results with specific power without analyzing the liquefaction ratio. Marmolejo-Correa and Gunderson16 studied the exergy efficiency of low-temperature processes, and especially the SMR process. They analyzed specific exergy which unit is kJ/kg LNG. Wahl et al.17 applied a deterministic optimization method, sequential quadratic programming (SQP), to optimize the SMR process, and claimed that the optimization result is better and the execution time is much shorter. However, they fixed the temperature after LNG heat exchanger as -155 °C. Li and Ju18 designed and analyzed natural gas liquefaction processes such as C3MR process, mixed refrigerant cycle and nitrogen expander cycle for the offshore associated gases with the fixed liquefaction ratio of 88.4% in their work. Mortazavi et al.19 investigated the potential of various options to improve the energy efficiency of C3MR processes; they developed a enhanced liquefaction cycle based on the C3MR process by using Aspen Plus software. The amount of LNG production was increased by the process design. They also performed case studies to find the optimal operation conditions but the liquefaction rate was fixed in their case studies. Wang et al.20 applied an SQP as an optimization method by using the Aspen Plus software, and performed a rigorous optimization of the C3MR


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process. They analyzed pure and mixed refrigerant systems thermodynamically to find opportunities to minimize energy consumption with the fixed liquefaction ratio. Alabdulkarem et al.21 applied a GA method to the C3MR process. They compared optimization results for the pinch temperatures of 1, 3, and 5 K in the spiral-wound heat exchanger. Wang et al.22 developed a mixed-integer non-linear programming (MINLP) mathematical model by using GAMS software, incorporating rigorous simulation data on the C3MR process obtained by using the Aspen Plus simulation tool. The model was simplified by using the simulation data and the optimization result was validated by using the simulation software. However they only compare the total compression energy. Castillo et al.23 especially focused on the precooling cycle. They compared pure propane and ethane/propane mixed refrigerant cycles to understand their advantages and disadvantages regarding the precooling stage. Because they focused on the precooling stage, the liquefaction ratio or final product temperature were not considered. Most previous studies have included the assumption that the liquefaction ratio is fixed or final product temperature is fixed. However, natural gas feed is not liquefied 100% in real LNG plants. The cold natural gas that is not liquefied is recycled or used for power generation and utility system. Therefore, the determination of liquefaction ratio is one of the most important factors to be considered for the design and optimization of LNG plant. The final product temperature is changed depending on the liquefaction ratio. Figure 1 shows the final product temperature as to the liquefaction ratio (0.80 – 1.00) with 200 kPa pressure by using the Aspen HYSYS software. Figure 1 also explains that the heat flow to liquefy the natural gas 5% more from 0.95 liquefaction ratio is even higher than which from 0.80 liquefaction ratio. Therefore, it can be guessed that the increasing amount of the total compression energy consumption of the LNG plant becomes bigger according as the liquefaction ratio increases. Thus, it can be guessed that


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the specific energy consumption, i.e., the compression energy consumption per unit mass of LNG product, has a minimum point within the available liquefaction ratio range. With this hypothesis, this study focuses on the minimizing specific energy consumption with various liquefaction ratio. Figure 2 shows total energy consumption and specific energy consumption with certain liquefaction range. The main purpose of this study is to find an optimal liquefaction ratio which consumes minimum compression energy per unit mass of LNG product. The target process is a real LNG pilot plant in the Republic of Korea. The pilot plant using C3MR cycle is supposed to produce 100 t of LNG/d (TPD). The natural gas feed flow rate is given as 117.65 TPD; thus, the liquefaction ratio must exceed 85% to satisfy the specification for the production capacity. The optimizations are performed by using gPROMS (version 3.7.1) commercial software based on deterministic optimization approach. Through the optimizations, the optimal operating conditions are found that yield the minimum specific energy consumption with the given optimal liquefaction ratio. Moreover, four different cases are optimized to compare the results of fixed and varying liquefaction ratio variable. These results are used to look for minimum point of specific energy demand within the given liquefaction ratio range.

2. LNG PILOT PLANT PROCESS DESCRIPTION The natural gas liquefaction pilot plant studied in this work is based on the C3MR cycle, in which pure and mixed refrigerants are used to liquefy natural gas. Figure 3 describes a process flow diagram of the pilot plant. Propane (C3) is used as the pure refrigerant to precool the natural gas and to cool the mixed refrigerant by means of a multistage refrigeration cycle. The lowest pressure of the pure refrigerant is 130 kPa, and it is compressed to 250, 410, 750, and 1,775 kPa through the successive stages. The compressed pure refrigerant is liquefied to 37 °C by the


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cooler. The liquid pure refrigerant is expanded through the valve and then cools down the natural gas and the mixed refrigerant at each pressure level. The temperature of the lowest-pressure pure refrigerant is −36.34 °C, and the minimum temperature difference in the heat exchanger is 3 °C. The pure refrigerant cycle uses only latent heat, so there is no temperature change between the inlet and outlet of the cold stream in the heat exchanger. Thus, the natural gas and the mixed refrigerant are cooled to −33.34 °C by means of the pure refrigerant cycle. The mixed refrigerant is compressed from 390 to 6,150 kPa by four compressors; it is cooled by the cooler to 45 °C and then cooled to −33.34 °C by means of the pure refrigerant cycle. The cooled mixed refrigerant is separated into liquid and vapor. The liquid mixed refrigerant is expanded to 440 kPa by a valve (MCHE VLV1) after the main cryogenic heat exchanger (MCHE Section1). The mixed refrigerant vapor is cooled more by the main cryogenic heat exchanger (MCHE Section1 and 2), and expanded to 490 kPa by another valve (MCHE VLV2). The expanded mixed refrigerants are used as the cold stream in the main cryogenic heat exchanger (MCHE Section 1 and 2). The pressure drop across the each section of the main cryogenic heat exchanger is 50 kPa the actual value in the LNG pilot plant, so the pressure is decreased to 390 kPa after the main cryogenic heat exchanger. The mixed refrigerant is returned to the mixed refrigerant compressor (MR Comp1), and the steps are repeated. Table 1 shows the composition and mole fractions of the mixed refrigerant. The natural gas inlet is at 15 °C and 6,260 kPa. The flow rate of the natural gas feed is 4,902 kg/hr. This flow rate of natural gas feed can produce 100 TPD of LNG provided that a 85% liquefaction ratio is maintained. Table 2 shows the component mole fraction of the natural gas feed.


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3. OPTIMIZATION MODEL DEVELOPMENT To simulate and optimize the LNG pilot plant, a mathematical model is implemented by using the commercial software gPROMS (version 3.7.1). This software can include the use of thermodynamic properties by means of the Multiflash module. Moreover, the mathematical model can be easily included in its optimization algorithms. The Peng–Robinson equation of state (EOS) is selected because it is recommended for gas, refinery and petrochemical applications.24 The optimization solution is identified by using a successive reduced quadratic programming (SRQPD) solver, which is an advanced sequential quadratic programming (SQP) solver because SQP routine is very robust and time efficient way.17 The major equipment models, the objective function, the optimization variables, and the optimization constraints are described in this chapter.

3.1. Equipment Model The working process of the compressor is modeled as isentropic compression. The isentropic efficiency was assumed to be 0.75 on the basis of industrial experience. The heat exchanger model was developed based on the heat balance. The main cryogenic heat exchanger (MCHE) contains multiple hot streams. To check the feasibility of MCHE, the hot stream temperature range is divided into 20 points for MCHE Section1, and 7 points for MCHE Section2. At each point, the temperature difference between the hot and cold stream must be larger than the minimum temperature difference. The natural gas and refrigerant are expanded isenthalpically when passing through expansion valves. The temperature of the outlet stream can be calculated by the outlet pressure, because the enthalpy is function of the temperature, pressure and the


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composition. The mixer and splitter models were developed based on the mass balance and heat balance. The computational specifications for the equipment model is shown in table 3.

3.2. Objective Function The objective function of the current optimization model is described as follows:

min.() = min. (∑ / ),

(1)

where SW is specific work, W is energy demand, and is the flow rate; the subscript i refers to the ith compressor and the subscript LNG refers to the liquefied natural gas product. This objective function describes the minimization of energy demand of all compressors per unit mass of LNG product.

3.3. Optimization Variables To obtain the optimal solution, the optimization model controls the compression ratio, the pure refrigerant flow rate, the mixed refrigerant flow rate, the intermediate pressure and temperature of the mixed refrigerant through MCHE Sections 1 and 2, and the mixed refrigerant composition. The model contains 20 optimization variables: Four variables for the mixed refrigerant compositions. Four variables for the compression ratio of each pure refrigerant compressor. Four variables for the compression ratio of each mixed refrigerant compressor. Two variables for the flow rate of total pure refrigerant and the flow rate of the pure refrigerant used to cool the mixed refrigerant. One variable for the mixed refrigerant flow rate. One variable for the intermediate pressure. Four variables for the mixed refrigerant temperature of the MCHE outlet.


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3.4. Optimization Constraints To obtain an optimal solution by using this model, some constraints are needed. The constraints are described as follows in Eqs 2–5.

,, − ,, ≥ ,

,

!"

(2)

where T is temperature, the subscripts out, in, HE and MTD refers to the outlet stream, the inlet stream, the heat exchanger and the minimum temperature difference. The minimum temperature difference in the heat exchanger is set as 3 °C

#,,$

−

#,,$

≥

#, !"

(3)

where the subscript MCHE refers to the main cryogenic heat exchanger and the subscript j refers to the jth point in the main cryogenic heat exchanger. For the enhancement of efficiency, multistream heat exchangers used in LNG plant are usually associated with large heat exchanging at 1-3 °C temperature differences.21 Therefore, the minimum temperature difference in the heat exchanger is set as 3 °C.for every case.

1.0 ≤ )*$ ≤ 3.5,

(4)

where CR is the compression ratio; the compression ratio must be less than 3.5.22


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-* ≥ 0.85,

(5)

where the LR is liquefaction ratio. As mentioned previously, the pilot plant is designed to have a capacity of 100 TPD and a feed rate of 4,902 kg/hr, meaning that the liquefaction ratio must be greater than 85% to satisfy the rated capacity.

4. OPTIMIZATION RESULTS AND DISCUSSION The LNG production rate and the total energy demand have a trade-off relationship; namely, the more LNG is produced, the more total energy demand is required. Thus, optimizations to seek the minimum total energy demand will generally converge at the minimum liquefaction ratio. Thus the specific energy demand is needed to be employed as the objective function. To perceive the operating condition with the minimum specific energy demand, the LNG production must increase and the total energy demand must decrease. Therefore, minimization of the specific energy demand leads to the optimal liquefaction ratio within the liquefaction ratio range. The main goal of this study is to obtain the optimal liquefaction ratio that minimizes the specific energy demand. To analyze the total and specific energy demand change with different liquefaction ratios, four different cases of optimization are performed and compared. The minimum temperature difference between hot and cold stream is given as 3 °C in the main cryogenic heat exchanger (MCHE) for every case. First case, Opt 1 is minimizing total energy consumption with the liquefaction ratio ranging from 0.85 to 1.00. As the result, the optimization was converged on the lower bound of liquefaction ratio, 0.85, with the 1,139.5 kW total energy consumption and 984.4


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kJ/kg specific energy consumption. Second case, Opt 2 is minimizing specific energy consumption with the liquefaction ratio ranging from 0.85 to 1.00. The optimization was converged on the 0.87 liquefaction ratio, the total energy consumption was 1,160.9 kW and the specific energy consumption was 981.5 kJ/kg. Comparing with the first case, the total energy consumption was increased but the specific energy consumption was reduced with 2% more LNG production. Third case, Opt 3 is minimizing the total energy consumption with fixed liquefaction ratio, 0.90. Note that, the objective function minimizing total energy consumption and minimizing specific energy consumption do the same role for the fixed liquefaction ratio because the amount of LNG product affects to the specific energy. The result of the third case was converged on the 1,214.6 kW of total energy and 988.7 kJ/kg of specific energy consumption. Opt 4, the fourth case, is minimizing the total energy consumption with 0.95 liquefaction ratio. As the result of optimization, the total energy consumption was 1,346.1 kW and the specific energy consumption was 1,040.7 kJ/kg at the optimal point. These optimization results are shown in Figure 4 which has similar shape to Figure 2. The amount of total energy consumption increase is getting larger as the liquefaction ratio is getting higher. Therefore, specific energy consumption has the minimum point within the liquefaction ratio range. As described above, the specific energy is the energy demand per unit mass of product. Therefore, it can be the process efficiency index. From this point of view, the best solution is the result of Opt 2. Resultingly, for Opt 2, the total energy demand is diminished by 14.56% and the specific energy demand is reduced by 16.40% compare to the base case; this corresponded to the liquefaction ratio of 86.9%. Note that, the base case is the simulation result using pilot plant operation conditions which are described in section 2. Table 4 lists the energy demand of each compressor and the liquefaction ratios. The refrigerant flow rate and the compression ratio of the


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compressor directly affects the compression energy. Table S1 lists the pure and mixed refrigerant flow rates. The mixed refrigerant flow rate decreased by 15.16% for Opt 2; these declines greatly influence the compression energy savings. The decreases of the pure refrigerant flow rate are not as great as those of the mixed refrigerant. Regarding total pure refrigerant, the decrease in flow rate is 12.81%. At the same time, the pure refrigerant flow rate for natural gas precooling decreases by only 4.60%. This shows that a large amount of the pure refrigerant cold heat is used to cool the mixed refrigerant. Tables S2 and S3 list the compression ratios of the pure refrigerant compressors and the mixed refrigerant compressors respectively. The lowest pressure of the pure refrigerant cycle was 130 kPa for both cases. The highest pressure was 1,775 kPa for the base case and 1,360 kPa for Opt 2 case. The total compression ratio of Opt 2 is 23.39% smaller than that of the base case. For the mixed refrigerant compressors, the total compression ratio decreases by 22.39% compared to the base case. The composite curves are very efficient tool for chemical plant.25 he feasibility of the MCHE can be checked in the composite curve and the distance between hot and cold streams decreases through optimization generally.26 A large temperature difference in the heat exchanger leads to irreversibility, and the high irreversibility reduces the heat exchange efficiency.11 Thus, decreasing the area between the hot and cold streams results in an increase in heat exchange efficiency. Figure 5 shows composite curves of the hot and cold streams for the base case and Opt 2 case. The area between the hot and cold streams are reduced through the optimization. The temperature differences between the hot and cold streams of these two cases are represented in Figure 6. The difference is decreased inside the main cryogenic heat exchanger, especially in MCHE Section 1. The mixed refrigerant composition is included in the optimization model as one of the optimization variables. Changing mixed refrigerant composition plays a key role in


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the improvement of mixed refrigerant cycle.26 Figure 7 illustrates the mixed refrigerant composition for the base and Opt 2 case studied. For the base case, the portions of the components are 0.0586:0.4693:0.3393:0.1328 for nitrogen (N2), methane (C1), ethane (C2), and propane (C3), respectively; these portions are 0.0623:0.4452:0.3144:0.1781 for Opt 2 case. The portion of nitrogen was slightly increased compare to the base case. The portion of methane and ethane decreased but the portion of propane increased about 6% from the base case to Opt 2 case. The results shows that increase of the propane component in the mixed refrigerant causes the decrease of the highest pressure for the mixed refrigerant. Because the pressure directly influences on the compression energy, the energy demand of the mixed refrigerant cycle is reduced. Moreover, as the heat exchanging efficiency is improved, the flowrate of the mixed refrigerant can be greatly reduced. It also greatly affects the compression energy savings.

5. CONCLUSION Cryogenic refrigeration systems are very energy-intensive due to the huge compressor power consumption. Therefore, minimizing the compression energy is a major concern for the design and optimization. When the liquefaction rate is fixed, the minimization of the compression energy can be obtained by improving energy efficiency. However, when it is not fixed, the amount of the product can be changed and the liquefaction rate is another major variable in point of economic view. Therefore, the energy consumption per unit mass of LNG product has to be minimized to maximize the economic profit. This research focuses not only on minimizing compression energy but also on maximizing the LNG product by considering the natural gas liquefaction ratio. This study analyzes that there is minimum point of specific energy demand within the liquefaction ratio range compare to the total energy demand minimization which is


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converged on the lower bound of the liquefaction ratio range. Through this analysis, the optimal operating condition and heat exchanging profile is found for the LNG production with minimum energy consumption. The optimization objective is to minimize the energy demand per unit mass of LNG product. The design capacity of the target process, the LNG pilot plant, is 100 TPD. To satisfy this design capacity, the liquefaction ratio of the natural gas feed has to be larger than 85%. The optimization yielded an optimal operating condition that simultaneously minimized compression energy and maximized the product. The results reveals that the optimization greatly reduces energy consumption by decreasing the compression ratio and refrigerant flow rate as well as increasing the heat exchange efficiency. In the optimization results, the total energy demand diminishes by 14.56% and the specific energy demand goes down by 16.40% when the 3 °C minimum temperature difference in the main cryogenic heat exchanger is used with 86.9% liquefaction ratio. This mathematical programming approach used in this work to analyze the natural gas liquefaction process will contribute to the LNG plant market and LNG industry.


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AUTHOR INFORMATION Corresponding Author *To whom correspondence should be addressed. Tel.: +82 2 2123 2761. Fax: +82 2 312 6401. Email: [email protected]. 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 also by the BK 21 Program, funded by the Ministry of Education (MOE) of Korea.

NOMENCLATURE Variables SW

specific work (kJ/kg)

W

work (kJ/hr)

mass flow rate (kg/hr)

T

temperature (°C)

CR

compression ratio

LR

liquefaction rate

Subscripts i

ith compressor

j

jth point in the main cryogenic heat exchanger

in

inlet stream

out

outlet stream

hot

hot stream

cold

cold stream

LNG

liquefied natural gas stream


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HE

heat exchanger

MTD

minimum temperature difference

MCHE

main cryogenic heat exchanger


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REFERENCES (1) The Outlook for Energy: A view to 2040; ExxonMobil, 2015. (2) Kumar, S.; Kwon, H. T.; Choi, K. H.; Lim, W.; Cho, J. H.; Tak, K.; Moon, I. LNG: An eco-friendly cryogenic fuel for sustainable development. Appl. Energy 2011, 88, 42644273. (3) Kirillov, N. G. Analysis of Modern Natural Gas Liquefaction Technologies. Chem. Pet. Eng. 2004, 40 (7-8), 401-406. (4) Hatcher, P.; Khalilpour, R.; Abbas, A. Optimisation of LNG mixed-refrigerant processes considering operation. Comput. Chem. Eng. 2013, 41, 123-133. (5) Investors’ Handbook. http://reports.shell.com/investors-handbook/2013/servicepages/ downloads/files/entire_shell_ih13.pdf (accessed April 13, 2015). (6) Lim, W.; Choi, K.; Moon, I. Current Status and Perspectives of Liquefied Natural Gas (LNG) Plant Design. Ind. Eng. Chem. Res. 2013, 52, 3065-3088. (7) 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 (25), 10397-10403. (8) Mortazavi, A.; Somers, C.; Alabdulkarem, A.; Hwang, Y.; Radermacher, R. Enhancement of APCI cycle efficiency with absorption chillers. Energy 2010, 35, 3877-3882. (9) Mokhatab, S.; Economides, M. J. Process Selection Is Critical to Onshore LNG Economics, World Oil 2006, 227, 95-99.


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(10) 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 (5), 1973-1985. (11) Lee, G. C.; Smith, R.; Zhu, X. X. Optimal Synthesis of Mixed-Refrigerant Systems for Low-Temperature Processes. Ind. Eng. Chem. Res.2002, 41, 5016-5028. (12) Nogal, F. D.; Kim, J.; Perry, S.; Smith, R. Optimal Design of Mixed Refrigerant Cycles. Ind. Eng. Chem. Res.2008, 47, 8724-8740. (13) Shirazi, M. M. H.; Mowla, D. Energy optimization for liquefaction process of natural gas in peak shaving plant. Energy 2010, 35, 2878-2885. (14) 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, 1606-1617. (15) 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, 146-155 (16) Marmolejo-Correa, D.; Gundersen, T. A comparison of exergy efficiency definitions with focus on low temperature processes, Energy 2012, 44, 477-489 (17) Wahl, P. E.; Løvseth, S. W.; Mølnvik, M. J. Optimization of a simple LNG process using sequential quadratic programming. Comput. Chem. Eng. 2013, 56, 27-36.


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(18) Li, Q. Y.; Ju, Y. L. Design and analysis of liquefaction process for offshore associated gas resources. Appl. Ther. Eng. 2010, 30, 2518-2525. (19) Mortazavi, A.; Somers, C.; Hwang, Y.; Radermacher, R.; Rodgers, P.; Al-Hashimi, S. Performance enhancement of propane pre-cooled mixed refrigerant LNG plant. Appl. Energy 2012, 93, 125-131. (20) Wang, M.; Zhang, J.; Xu, Q.; Li, K. Thermodynamic-Analysis-Based Energy Consumption Minimization for Natural Gas Liquefaction. Ind. Eng. Chem. Res.2011, 50, 12630-12640. (21) Alabdulkarem, A.; Mortazavi, A.; Hwang, Y.; Radermacher, R.; Rogers, P. Optimization of propane pre-cooled mixed refrigerant LNG plant. Appl. Therm. Eng. 2011, 31, 10911098. (22) Wang, M.; Zhang, J.; Xu, Q. Optimal design and operation of a C3MR refrigeration system for natural gas liquefaction. Comput. Chem. Eng. 2012, 39, 84-95. (23) Castillo, L.; Dorao, C. A. On the conceptual design of pre-cooling stage of LNG plants using propane or an ethane/propane mixture. Energy Convers. Manage. 2013, 65, 140-146. (24) Aspen Physical Property System—Physical Property Methods, version 7.2; AspenTech, Inc.: Burlington, MA, 2010. (25) Ebrahim, M.; Kawari, A. Pinch technology: an efficient tool for chemical-plant energy and capital-cost saving. Appl. Energy 2000, 65, 45-49.


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(26) Tak, K.; Lee, I.; Kwon. H.; Kim. J.; Ko, D.; Moon, I.; Comparision of multistage compression configurations for single mixed refrigerant processes. Ind. Eng. Chem. Res. 2015, 54, 9992-10000.


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FIGURE LIST

Figure 1. Final product temperature with liquefaction ratio


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Figure 2. Hypothesis of total and specific energy consumption with certain liquefaction ratio range


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Figure 3. Process flow diagram of LNG pilot plant


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Figure 4. Optimization results of total and specific energy consumption with unfixed liquefaction ratio


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Figure 5. MCHE composite curves: (a) base case, and (b) optimum for Opt 2 case


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Figure 6. MCHE temperature difference curves: (a) base case, and (b) optimum for Opt 2 case


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Figure

7.

Mixed

refrigerant

composition:

(a)

base

case,

and

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(b)

optimum

for

Opt

2

case


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TABLE LIST Table 1. Mixed refrigerant composition Component

Mole fraction

nitrogen (N2)

0.0586

methane (C1)

0.4693

ethane (C2)

0.3393

propane (C3)

0.1328


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Table 2. Natural gas feed composition Component

Mole fraction

nitrogen (N2)

0.0497

methane (C1)

0.8689

ethane (C2)

0.0510

propane (C3)

0.0213

i-butane (i-C4)

0.0044

n-butane (n-C4)

0.0045

i-pentane (i-C5)

0.0001

n-pentane (n-C5)

0.0001


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Table 3. Computational specifications for equipment model Compressor efficiency

0.75

Node for MCHE Section 1

20

Node for MCHE Section 2

7


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Table 4. Energy consumption of compressors and liquefaction ratios Base case

Opt 1 case

Opt 2 case

Opt 3 case

Opt 4 case

C3 Comp1 (kW)

32.0

23.8

19.5

19.6

23.5

C3 Comp2 (kW)

51.6

41.4

37.3

38.5

41.4

C3 Comp3 (kW)

119.9

89.0

82.9

87.9

95.4

C3 Comp4 (kW)

246.9

181.1

193.2

195.8

212.7

MR Comp1 (kW)

265.0

237.2

246.1

260.5

293.8

MR Comp2 (kW)

219.6

197.3

204.0

219.4

247.1

MR Comp3 (kW)

220.7

193.9

198.3

205.0

225.5

MR Comp4 (kW)

204.0

175.8

179.6

187.9

206.7

Total energy (kW)

1,358.7

1,139.5

1,160.9

1,214.6

1,346.1

Liquefaction ratio (%)

85.0

85.0

86.9

90.0

95.0

LNG production (TPD)

100.00

100.00

102.20

105.88

111.76

Specific energy (kJ/kg)

1,174.0

984.4

981.5

988.7

1,040.7


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Decision Making on Liquefaction Ratio for ... - ACS Publications

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