Design and comprehensive optimization of C3MR liquefaction natural gas cycle by considering operational constraints

Accepted Manuscript Design and comprehensive optimization of C3MR liquefaction natural gas cycle by considering operational constraints Hamid Sanavandi, Masoud Ziabasharhagh PII:

S1875-5100(15)30344-9

DOI:

10.1016/j.jngse.2015.12.055

Reference:

JNGSE 1204

To appear in:

Journal of Natural Gas Science and Engineering

Received Date: 5 September 2015 Revised Date:

30 December 2015

Accepted Date: 31 December 2015

Please cite this article as: Sanavandi, H., Ziabasharhagh, M., Design and comprehensive optimization of C3MR liquefaction natural gas cycle by considering operational constraints, Journal of Natural Gas Science & Engineering (2016), doi: 10.1016/j.jngse.2015.12.055. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Design and comprehensive optimization of C3MR liquefaction natural gas cycle by considering operational constraints Hamid Sanavandi, Masoud Ziabasharhagh*

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Faculty of Mechanical Engineering, k. N. Toosi University of Technology, Tehran, Iran.

Abstract

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Liquefied natural gas (LNG) plays a key role in gas transits. Hence, attempts to improve the current liquefaction technology to increase its efficiency and economic advantages have earned a place in the master plan of many countries. Propane precooled mixed refrigerant process (C3MR) is one of the most successful cycle in the liquefaction industry. In this study, an enhanced process including a three stage propane precooling cycle and two cryogenic heat exchangers with mixed refrigerant (MR) has been modeled in ASPEN-HYSYS software by 1 MTPA production capacity. Saturated temperatures of Precooling stages, outlet temperatures of heat exchangers (HE) and aftercoolers will determine the quality and efficiency of the precooling stage. Minimization of energy consumption per 1kg LNG production (specific energy consumption) has been defined as the main objective function. Optimization of the cycle variables has been performed, intended to minimize the specific energy consumption. Subsequently, comprehensive and integrated optimization has been developed. Mixed refrigerant composition has been recognized as the most effective parameter in the performance of the cycle. Therefore, mixed refrigerant composition has been optimized by two methods. HYSYS optimizer functions and a self-initiated method entitled as “Observation of governing trend in mixed refrigerant cooling curve behavior by an approach to maximization of possible fit in cryogenic heat exchangers composite curve”. Although these methods will cause a great amount of energy saving, their results are not practically achievable. Therefore the cycle has been optimized by considering operational constraints. The ultimate results of the optimization are not only theoretical, but also practical. The optimized point introduced here is obtainable in real performance, and cycle results are constant despite perturbation. Thus specific energy consumption decreases from 1028.94 kJ/kg at initial condition to 973.93 kJ/kg at sustainable optimized condition, by 5.35 percent of specific energy consumption saving.

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Keywords Liquefied natural gas, propane precooled, mixed refrigerant, specific energy consumption optimization, operational constraints

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Corresponding author. E-mail address: [email protected] or [email protected]

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1. Introduction

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Natural gas consumption has increased 62 percent from 1993 until 2011 around the world, i.e. this growth has the third place in all sources of energy. On the other hand liquefied natural gas provides 28 percent of worldwide natural gas consumption. Even in 2020, Statistics signify 72 percent of energy consumption of the world will be provided by fossil fuel and natural gas despite all scientific advancements and functional focus on the renewable energies. Natural gas consumption will not decrease until 2030 in any region of the world [1]. Profitable and low risk transit, especially 3 above 1000 kilometers distance and 1 million m per annual is just practical by liquefaction technology [2]. Natural gas changes to a colorless and odorless, non-toxic and non-corrosive liquid with very little environmental side effects when its temperature decreases to -157°C at atmospheric pressure. Accordingly, it is recognized as one of the cleanest fossil fuels. Since natural gas undergoes 600 times volume condensation in phase changing, it is important for large scale and far distance transfer. Increasing need for produce a special production causes an urgent need to enhance its features and lay more emphasis on relevant researches. Propane precooled Mixed refrigerant process is one the most important cycles of this technology which has been studied and optimized in recent research. HEs are one the main sources of exergy destruction in liquefaction processes which is inevitable due to the finite temperature difference (FTD). However meticulous design can provide us with a remarkable decrease in wastage. Indeed, exergy efficiency will improve by decreasing FTD. Increasing heat transfer area and decrease of FTD are two possible solutions; however, these solutions intensify capital investment. Therefore, mixed refrigerant has been suggested for cooling industries. If the cooling curves of the main stream and the refrigerant are more closer-fitting, HE wastage will be decreased and exergy efficiency will be improved. Composite curve provides an implement for the assessment of this factor. It shows cold and hot composites temperature according to enthalpy variation. Natural gas consists of many non-azeotrope gases. Also, special heat capacity of components changes during the cooling process. Therefore, the natural gas cooling curve shows complicated and nonlinear behavior as it is displayed in Fig. 1. For pure refrigerant cases, Natural gas and refrigerant can be more closer-fitting by providing more stage of cooling process. However, it complicates the cycle, reduces reliability and doubles the capital investment. Mixed refrigerant with non-azeotrope components is a better solution for maximum fitting [3,4]. It has been shown in Fig. 1 that mixed refrigerant curve fits better with natural gas in comparison to pure refrigerant [5]. Therefore, flexibility and reliability will be increased; moreover, the number of steps, capital investment and exergy destruction will be reduced significantly.

Fig. 1. Pure and MR cooling curve in comparison to natural gas [5]

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Numerous researches and studies have been carried out about C3MR process. Helgestad [5] studied C3MR cycle and optimized mixed refrigerant components by genetic algorithm and ASPENHYSYS computational modelling. Bukowski et al. [6] studied industrial cycles of APCI and demonstrated the weak and strong points of each of them from an operational viewpoint. Also, in another report, they have analyzed three enhanced cycles of APCI, including SMR, DMR and C3MR/N2. Then, they have discussed the advanced main cryogenic heat exchangers (MCHE) and companders for FLNG application and some experimental results. Abdolkarim et al. [7] have minimized total energy consumption of the cycle by genetic algorithm and pinch technology. Results have shown that precooling stage has 13 percent and mixed refrigerant has 17 percent reduction of energy consumption. Mortazavi et al. [8] have introduced innovative C3MR cycle and have analyzed 14 different driver cycle enhancement options. Five states have better efficiency versus ordinary systems; moreover, optimized state has a 38 percent advantage as compared to the initial state. Also, they have shown that throttling valves had to be replaced with recovery turbines. It reduced specific energy consumption by 3.7 percent in simple case. Lee et al. [9] have studied and analyzed cooling systems using superstructure, integrating process core and design the system using pinch and exergy analysis. Then, they introduced an optimization method, mixing power of computational programming and pinch technology. System energy consumption was targeted using grand composite curve. The number of temperature levels and their intervals was determined. Objective function is minimization of capital and running investment (pinch and exergy analysis). Afterwards, other parameters of the cooling system were determined using MINLP model and advanced disjunctive programming; therefore, optimized ordering has been obtained (mathematical method). A better initial speculation is the advantage of this method and there is no need to form superstructure. Gao, Lin and Gu [10] have optimized C3MR cycle using ASPEN-HYSYS software. They have considered the main parameters of system such as mixed refrigerant composition, inlet and outlet pressure, and temperature of HEs. Propane quantity has been assumed to be constant and other parameters can be changed to reach the minimum of objective function (compressors work). As summarized, most of the previous researches have concentrated on mixed refrigerant composition and dominant method is mathematical optimization. Nonetheless recent observations reveal mixed refrigerant has an abnormal and a nonlinear behavior, and mathematical methods are intensively under the influence of the initial state, convergence intervals and

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analysis and optimization with considering interaction of variables have not been developed properly. Moreover, an optimization free from limits of mathematical methods, trying a huge set of compositions for developing the governing trend in natural gas behavior and finally, introducing a sustainable optimized point with regard to some important functional constraints are distinct features of the recent work. It means optimal results of this research are more practical compared to those of previous mathematical methods which is the critical and distinguishing feature of the recent research.

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function behavior. Hence, having a profound knowledge about natural gas behavior is essential for optimizing its composition. Recent research has studied some methods of optimization, free from these obstructions. Also, previous works did not study and analyze the whole cycle as a single entity; accordingly, a comprehensive analysis and optimization has been performed in the recent work. All of th previous works have reported optimal mixed refrigerant composition by three decimal place precision. This preciseness is absolutely theoretical. Controlling and maintaining this degree of precision in industrial applications is impossible to reach. Therefore, corresponding results are not achievable in the real world performance. In addition, perturbations and leakages will cause considerable curtailment of efficiency. In the recent work, temperature and pressure of every stream and mixed refrigerant components mole fraction have been controlled by logical precision. However the cycle is extremely sensitive to mixed refrigerant composition and this inevitable change in mole fractions (rounding amounts by 1 percent mole fraction to be logically controllable) will severely reduce specific energy consumption. Accordingly, pressure and temperature of some streams have been changed to counterbalance this effect. Thus optimal results of the cycle are sustainably achievable, despite the perturbation and fluctuation in parameters. Final optimization has been done according to the above-mentioned subjects.

2. Propane precooled mixed refrigerant process

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Simple C3MR cycle includes a single level of propane precooling stage and a single liquefaction heat exchanger. The temperature of the compressed propane is reduced by air-cooled or water-cooled HEs; therefore, propane reaches the bubble point. At this state, the refrigerant crosses the throttling valve, undergoes intensive pressure and temperature reduction, and enters the saturated region again. Afterwards, two phases are divided in the separator. The gaseous phase will return to the compressor and the liquid phase will enter the HE which is used for precooling natural gas and mixed refrigerant. Then, mixed refrigerant enters the separator. Two phases have different compositions, because of the inequality in boiling points of components. Thus two streams of mixed refrigerant enter MCHE. During the process, temperature of natural gas decreases sharply, enters the saturated phase. Mixed refrigerant cools across the MCHEs, then undergoes pressure and temperature reduction in the throttling valves, making a returning cryogenic stream that will cause natural gas to liquefy.

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In the recent work, pressure of natural gas feed stream, temperature of precooling three levels, natural gas and mixed refrigerant temperature after each precooling level and discharged temperatures of aftercoolers, have been recognized as the effective parameters in performance of the precooling stage. Furthermore, the parameters that are involved in the liquefaction stage have been studied such as mixed refrigerant mass flow rate and its outlet pressure from compressor. These parameters are significantly under the influence of mixed refrigerant composition. The effects of all the effective variables on the objective functions (cycle energy consumption and total LNG production in kg) have been exactly determined. However these two objective functions are not specific; thus the specific energy consumption has been introduced as the energy used for the 1kg pure LNG which is produced. In this way, best value for each variable has been obtained in order to minimize specific energy consumption.

Mixed refrigerant composition has been recognized as the most important variable in performance of the cycle; therefore, its best mixture has been achieved by two methods. First, utilization of HYSYS optimizer functions that provide us with an advanced mathematical algorithm while some of them have been specifically developed for cryogenic processes. The second method is an innovative procedure named “Observation of governing trend in mixed refrigerant cooling curve behavior by an approach to maximization of possible fit in cryogenic heat exchangers composite curve”.

An advanced cycle with three temperature levels of precooling stage and two cryogenic heat exchangers has been considered in the recent work. The propane separators are in three temperature levels, the gaseous phases always returns to compressors and the liquid phases is divided into three streams. Two stream will be used for precooling natural gas and mixed refrigerant and one of them will enter the throttling valve to reach the next temperature and pressure level. Therefore, propane will be in the lowest temperature in the third level. There is a functional constraint on using refrigerants. Every refrigerant in a cooling process, could undergo a temperature reduction until its saturated temperature at atmospheric pressure, independent of the functional pressure [3]. It is -42°C for propane, accordingly, the third level of the precooling stage is set on -42°C. Mixed refrigerant compresses and cools in aftercooler, then enters three precooling levels. Afterward, it separates and enters MCHEs. The gaseous phase cools and turns to liquid, then enters the second level. In the second MCHE, it cools down more and outlet stream enters the throttling valve and makes a cryogenic stream which causes cooling process in the second MCHE. The liquid phase becomes cool in the first MCHE, then enters the throttling valve. Then this stream and returning stream from the second MCHE will mix and cool other streams in the first MCHE. All

There are plenty of works about optimization of liquefaction cycles and their different variables, however, comprehensive

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of the streams will mix finally and enter the compressor. It is important that the inlet stream must be superheat, no liquid must enter the compressors.

Cryogenic processes have many functional complications, however their governing rule is expressible by the thermodynamics equations.

Natural gas feed stream enters the cycle with a specific pressure that plays an important role in the system performance and efficiency. After three precooling levels and mixed refrigerant stage, natural gas will cross a throttling valve to reach atmospheric pressure, prepares to be send to transport stations. It must be under -157°C after the final throttling valve. The gaseous phase and its cold exergy can be utilized as a cold stream for some industrial usages or being returned to cycle. This cycle has been designed for 1 MTPA production.

Peng-Robbinson equation of state is a semi-experimental one, which is derived from Van der Waals form. Its algebraic form has been shown by Eq. (1).   −  −    +  +   − 

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=

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In Eq. (1) a and b are Peng-Robinson constants and α is dimensionless coefficient, dependent on the reduced temperature that is calculated by Eq. (2). = 1 +  1 −  



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3. Model development Mathematical modelling or simulation is changing physical quality and interaction between them to numerical quantity and equation. A mathematical modelling can predict a simple system behavior before it really exists and works. Also, it can help to predict the system’s new behavior in the new state. ASPEN-HYSYS software is able to cope with most of the petroleum, gaseous, oil and other such industrial processes. Here, after defining all streams and equipment, HYSYS software has been utilized to model the cycle. Fig. 2 has shown the cycle schematically. Peng-Robbinson equation of state has been used which is proper for cryogenic problems [11]. Pressure has been assumed constant at inlet and outlet of mixers, pressure drop has been assumed according to the stream temperature and pressure and HEs’ type. Throttling valves have been modelled by the Joule-Thomson effect. Water-cooled type has been selected for aftercoolers.

(2)

Coefficient of m is defined by Eq. (3).

 = 0.3796 + 1.5422 − 0.2699

(

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Also, a and b can be calculated by Eq. (4).

   ,
  = Ω" ,
 = Ω

Ω = 0.4572

Ω" = 0.0778

( 4)

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Enthalpy departure function of mixed refrigerant can be calculated by Eq. (5). %

ℎ∗ − ℎ ℎ∗ − ℎ )  ℎ∗ − ℎ * ℎ∗ − ℎ ) (=% ( + * +% ( −% ( ,     

(5)

Entropy departure function of mixed refrigerant can be calculated by Eq. (6). -∗ − - ∗ − - )  - ∗ − - * - ∗ − - ) % (=% ( + * +% ( −% ( ,     

(6)

Enthalpy difference between 2 states of ideal gas is calculated by enthalpy departure function according to Eq. (7).

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ℎ
 ,  − ℎ
. , . = /ℎ
 ,  − ℎ∗
∗ ,  0 +ℎ∗
∗ ,  − ℎ∗
∗ , . − /ℎ
. , . − ℎ∗
∗ , . 0

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Enthalpy difference of Ideal gas is defined by Eq. (8). 45

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45

ℎ ∗
∗ ,  − ℎ∗
∗ , . = 1 23 ∗ 7 = 1 23 ∗ 7 − 1 23 ∗ 7 46

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T0 is index temperature, mostly assumed to be 298K. Therefore, enthalpy at any temperature and pressure may be calculated as the summation of enthalpy departure at desired temperature and pressure with ideal gas enthalpy at this temperature. Ideal gas enthalpy is calculated by integration of special heat capacity at constant pressure (Cp) from index

Fig. 2. C3MR liquefaction cycle plan

3.1. Governing equation

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temperature until desired temperature. Cp can be calculated by Eq. (9). @

23,9:;<= ∗ = > ?: . 23,: ∗ :A.

(9)

Cp of each component is assumed to be a third degree function of temperature according to Eq. (10). 23 = ) + .  + 



+ B

B

Table 2 Samples of entered data and calculated data in HYSYS

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Name Vapor Fraction Temperature [C] Pressure [kPa] Mol. Flo. [kgmol/h] Mass Flow [kg/h] Vol. Flow [m3/h] Heat Flow [kJ/h] * entered data

Coefficients of Eq. (10) have been shown for mixed refrigerant in Table 1.

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Therefore, enthalpy of real multicomponent mixture can be expressed by Eq. (12). ℎ
,  = ℎ=<3FG<
,  + ℎ∗ 

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Moreover, entropy of multicomponent mixture when it is defined as an ideal gas is calculated by Eq. (13). 48



@


 7 − . HI % ( −  > ?: . ln ?:
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- ∗
,  = 1

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And entropy of real multicomponent mixture can be expressed by Eq. (14).

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,  = -=<3FG<
,  + - ∗
, 

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MRL-1 0.0000 -130.3* 3300 5763 1.654e+5 439.4 -5.97e+8

Changing a variable may affect a part or the whole of the cycle, while it may also have a positive or negative effect on the outlet results. Moreover, alterations may be against each other, and the cycle undergoes advantages in some parts and disadvantages in other parts. Therefore, results must be analyzed comprehensively. Alteration can also lead to errors in the cycle, such as liquid phase in compressors, temperature cross in HEs, over-specified and underspecified of equipment, operational failure of throttling valves, compressor polytrophic efficiency being out of range and low LMTD correction factor that need to be prevented. Furthermore, production temperature must be maintained under -157°C in any case. The initial condition by specific energy consumption of 1028.94 kJ/kg has been shown in Table 3. Heat loos (leakage) has been assumed to be equal to zero in all of the heat exchangers [5,12]. Natural gas and initial mixed refrigerant composition have been shown in Table 4. Each parameter has been analyzed one by one and other parameters have been considered to be constant each time; therefore, effects of each parameter on the results have been studied.

Compressor adiabatic efficiency has been assumed to be 75 percent, thus the compressor work is calculated by Eq. (15). N
 PQ. L =
. M. O% ( − 1R /T= N − 1
.

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MRV-2 0.0000 -140.0* 2800 3831 8.459e+4 214.0 -2.82e+8

After modelling the cycle and its main parameters, finding errors and amending some out-of-range values, finally, the cycle achieves steady function and a fault-free condition. Then, the sensitivity analysis and optimization of variables have been performed by trial and error of the involved parameters and assessment of their effects on the outlet parameters.

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MR-6 1.0000 112.6 4000* 9593 2.500e+5 653.4 -6.71e+8

4. Results and discussion

Enthalpy of multicomponent mixture when it is defined as an ideal gas is calculated by Eq. (11). ℎ∗  = 1 > ?: . 23,: 7

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NG-1 1.0000 25.00* 5850 7059 1.250e+5* 391.0 -5.30e+8

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B × 10E -11.32 8.713 32.15 -11.68

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C3-1-1 1.0000 -41.85 92.16 2181 9.619e+4 189.9 -2.37e+8

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Table 1 Coefficient of Cp equation for mixed refrigerant [4] Component ) . × 10  × 10D Methane 19.25 5.213 1.197 Ethane 5.409 17.81 -6.938 Propane 4.224 30.63 -15.86 Nitrogen 31.15 1.357 2.068

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after compressor), propane temperature at three precooling levels, intermediate temperature (after the first MCHE and before the second), partition temperature and mixed refrigerant compositions are the key variable of the cycle. Minimizing Specific energy consumption is the objective function and sustainability of LNG outlet temperature under -157°C is obligatory. Some of the entered data and calculated data have been included in Table 2.

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V is the flow rate at discharge pressure and T is the compressor adiabatic efficiency.

Table 3 Cycle initial condition [9] NG inlet Pres. (kPa) MR discharge Pres. (kPa) MR flow rate (kg/s) 1st precool. lv. Temp. (C) nd 2 precool. lv. Temp. (C) 3rd precool. lv. Temp. (C)

3.2. Functional condition Inlet temperature and pressure of natural gas, mixed refrigerant mass flow rate and its functional pressure (before and

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5500 4000 64.63 -2.3 -22.5 -40

1st comp. pwr. (kW) 2nd comp. pwr. (kW) 3rd comp. pwr. (kW) MR comp. pwr. (kW) LNG production (kg/s) NG outlet Temp. (C)

956 2466 6408 16130 25.23 -161.6

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Table 4 Natural gas and initial mixed refrigerant composition [9] Components Natural gas Mixed refrigerant Methane 0.897 0.36 Ethane 0.055 0.455 Propane 0.018 0.049 Butane 0.001 0.021 Nitrogen 0.029 0.115

4.1. Inlet pressure of Natural gas feed stream First, the cycle has been modelled to study the effects of natural gas inlet pressure on the cycle performance. For example, the effects of pressure increase on the first compressor of precooled stage, LNG production in one hour and specific energy consumption has been shown in Fig. 3.

Fig. 4. Second and third precooling compressors work and specific energy consumption according to temperature of third precooled level

Temperature (C)

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MR compressor work and LNG production remain constant by the third level temperature rising. Work of precooling stage compressors will reduce because propane cooling potency will increase, equal to the positive slope of the propane saturated curve at its right side. For more clarification, propane temperatureenthalpy diagram is needed that has been shown in Fig. 5.

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Fig. 3. First precooled compressor work, LNG production and specific energy consumption according to NG inlet pressure

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Energy consumptions of all three precooling compressors reduce by an increase in natural gas inlet pressure, because Cp of natural gas will rise at higher pressure. Therefore, more propane flow rate is needed and it means higher compressor energy consumption. Temperature of LNG after last throttling valve reduces; therefore, LNG production will increase and fluid quality will reduce. Finally, the specific energy consumption will reduce which means an increase in natural gas inlet pressure will cause energy saving internally. Out of this pressure range, under 5000 kPa and above 6700 kPa, temperature cross happens in the first MCHE that is unacceptable.

Enthalpy Fig. 5. Temperature-enthalpy saturated diagram of propane

Moreover, according to this diagram, fluid quality and gaseous phase will reduce which means compressors flow rate and energy consumption will reduce. On the whole, Specific energy consumption improves by temperature rising and -40.5°C is the best state. Out of this temperature range, temperature cross will happen in the third level of the precooling HEs.

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Unfortunately, while the saturated temperature rises, natural gas and mixed refrigerant outlet temperature of third precooling level (partition temperature) would rise and have new effects on the cycle that must be taken into consideration. Partition temperature (PT) is the temperature that separates each stage, and is equal to the lowest evaporating temperature level of the upper stage [9].

4.2. Third level of precooled stage

The effects of saturated temperature of the third precooling level on the second and the third compressors of precooling stage and specific energy consumption have been shown in Fig. 4.

Results of the partition temperature rising on the second compressor of precooling stage, LNG production in one hour and specific energy consumption have been shown in Fig. 6.

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How the best temperatures for three levels of precooling stage can be determined must be analyzed, indeed by preserving a minimum approach temperature of 0.5°C [5].

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In order to determine these three saturated temperatures, alterations have been orderly made for all levels, by 0.25°C as steps, natural gas and MR outlet temperature from HEs have been set 0.5°C above it (preserving minimum approach temperature). Specific energy consumption has been calculated each time. Thus, study and optimization of the precooling stage have been simultaneously performed and the results have been shown in Table 5. Table 5 Comprehensive optimization of precooling stage precooling levels Temperature (C) 1st level -1.5 2nd level -22.5 rd 3 level -42

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Fig. 6. Second precooling compressor work, LNG production and specific energy consumption according to PT

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Under the range, temperature cross will happen in the first MCHE and above the range, in the second MCHE. For example, the first MCHE composite curve has been shown in Fig. 7. Temperature cross has been clearly demonstrated on it.

4.3. Mixed refrigerant flow rate

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The effects of MR flow rate increase on the third precooling compressor, LNG production and specific energy consumption have been shown in Fig. 8.

Fig. 7. First MCHE composite curve and Temperature cross

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Mixed refrigerant compressor work remains constant by an increase in PT. Three precooling compressors work will reduce because the third HE’s duty and its flow rate decrease; thus all of precooling compressors flow rates decrease. LNG production will reduce because the MCHEs’ duty and natural gas outlet temperature increase. Therefore, natural gas quality increases which means lower production per hour. However specific energy consumption improves in general. It is obvious that the orientation of results toward the optimized point for PT and precooling third level temperature are against each other. Increasing temperature of the third level will reduce the specific energy consumption, but it increases PT and it increases the objective function. Thus a more comprehensive view is needed to optimize this section. Moreover, final precooling level affects mixed refrigerant and natural gas cooling curve. Changing the saturated temperature of each precooling level will change the lowest operationally reachable temperature for natural gas and MR at the same level and it affects temperature cross boundary conditions. Those issues are needed to be taken into consideration.

Fig. 8. Third precooling compressor work, LNG production and specific energy consumption according to MR flow rate

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Energy consumption of all of the compressors will increase by the flow rate augmentation which is obvious and LNG production will reduce accordingly. Moreover, specific energy consumption reduces and improves. Under the range, temperature cross will occur in the second MCHE and above it, in the first MCHE which are unacceptable. 4.4. Mixed refrigerant composition Analysis and optimization of mixed refrigerant composition is more complicated because of its non-linear and unstable behavior. A cooling system with a mixed refrigerant behaves deeply under the influence of materials and mole fraction of its refrigerants. Recent observation signifies that a slight alteration about 0.1 percent of mole fraction in one of the components can affect the cycle sensibly

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constraints. The BOX Method is not very efficient in terms of the required number of function evaluations. It generally requires a large number of iterations to converge on the solution. However, if applicable, this method can be very robust [11]. The Mixed method attempts to take advantage of the global convergence characteristics of the BOX method and the efficiency of the SQP (The Sequential Quadratic Programming) method. It starts the minimization with the BOX method using a very loose convergence tolerance (50 times larger than the desired tolerance). After convergence, the SQP method is then used to locate the final solution using the desired tolerance. The Mixed Method also handles only inequality constraints [11].

A mature comprehension of the cycle is necessary for optimizing the mixed refrigerant Independent of chosen method. Profound perception of cycle behavior and effects of each refrigerant on system, interaction of components and change of the objective function according to the fluctuation in components are essential prior knowledge for optimization. In recent work, optimization has been done by two methods, HYSYS optimizer function and an innovative method, defined as observation of mixed refrigerant and cycle behavior. The first method is HYSYS standard functions whose main advantage is its lawful procedure which is based on mathematical postulates. The program cannot cross a point that errors occur in it more than once which is its main drawback [11]. Moreover, those functions are very sensitive to the initial domain. This disadvantage can be turned to a benefit by observing the system behavior watchfully, in a wide domain of components’ alteration and numerous iterations of different mole fractions. The second method has been based on observation of the cycle performance by a huge number of different mixtures of refrigerant and finding the trend systematically. Although the function behavior is abnormal, some general points can be defined by many iterations of different compositions, trial and error process by changing the composition slightly. In this way, composition can move manually towards better efficiency and during the process, a number of general governing rules about mixed refrigerant can be developed. This method in some features is analogous to GA while the alteration of every generation is based on a meticulous observation of the cycle (especially MCHEs composite curve) in previous composition (previous generation). When these rules are defined and validated, the results can be more efficient than GA, because each generation has been determined directly, according to the governing rules.

Results have been achieved after performing these steps: defining problem parameters and a feasible domain of alterations, determining minimizer or maximizer function in the optimizer section of the program and equal and non-equal constraints and then entering governing equations, determining the method of optimization and convergence condition. The outcome has been shown in Table 6. The mixed method has yielded better results with smaller number of iteration. These amounts have been obtained by a condition in which all of the parameters are based on that initial condition, except for the mixed refrigerant composition.

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and bring the cycle to improvement or unsustainability. In other words, cycle response function to fluctuations in components is extremely sensitive and nonlinear. This behavior intensifies by reducing the molecular weight of components. It can be testified by nitrogen in that a very small alteration of its fraction has a great effect on the cycle. Optimizing this function is very difficult because of this behavior. Sharp gradient of function paralyzes many optimization methods; moreover, local extremum trap and dependency of results on initial intervals deteriorate the situation. Therefore, utilizing ordinary optimization methods such as genetic algorithm may have a remarkable risk that needs to be taken into consideration.

Table 6 Result of HYSYS optimizer function

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Iteration

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Mixed

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Components Methane 0.3805 Ethane 0.4683 Propane 0.041 Iso-butane 0.0137 n-butane 0.0088 Nitrogen 0.0878 Methane 0.3812 Ethane 0.4673 Propane 0.041 Iso-butane 0.0136 n-butane 0.0086 Nitrogen 0.0884

Specific energy consumption

951.8

947.9

4.4.2. Observation of governing trend in mixed refrigerant behavior

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The sensibility analysis with slight alterations in the components has been performed, and the results have been shown in Table 7. The specific energy consumption of the initial composition is 985 kJ/kg. Table 7 Sensibility analysis for MR components Mole fraction alteration

4.4.1. Aspen-HYSYS optimizer function method

According to problem condition, BOX method and Mixed method have been chosen since they are effective and applicable to a problem with inequality constraints such as the recent one (inequality constraints are defined as each compressor’s energy consumption and LNG production must be bigger than zero) [11]. The BOX Method only handles inequality constraints, a sequential search technique which solves problems with non-linear objective functions, subject to nonlinear inequality constraints. No derivatives are required. It Handles inequality constraints but not equality

Methane Ethane Propane Butane Nitrogen

Down | Up (1 percent) Down | Up (1 percent) Down | Up (0.5 percent) Down | Up (0.3 percent) Down | Up (0.7 percent)

Specific energy consumption 1001.3 | 1026.0 1024.5 | 1002.8 1027.1 | 1000.9 1025.5 | 1012.8 997.10 | 1029.9

The sensibility analysis is an initial idea for reaching optimized composition. Subsequently, more than 20000 different

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cold composites of the second MCHE can be observed). The nine rules are criteria of optimization. By paying close attention to every clause, it is conceivable that each area throughout composite curves, especially the first MCHE is adjustable by lessening or increasing one or more components. The distance between hot and cold composites, their curvature, and convergent and divergent areas can be amended to reach an almost parallel composite curve according to rules 3,5,5,7, and 8. Subsequently, by adjusting nitrogen according to rules 1 and 6, the parallel distance can be eliminated. The desired composite curve by maximum close-fitting lines will be achieved through this process. Data has been coupled by office-excel for more precision and a better assessment.

4.

5.

6. 7. 8.

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According to MCHEs’ composite curve, many sets of compositions with errors or significant exergy destruction (temperature cross and/or overpassing minimum approach temperature, noticeable distance between hot and cold composites, respectively) have been excluded immediately. Subsequently, acceptable and visually analogous data have been entered office-excel and results have been compared by an approach to minimum specific energy consumption. After numerous iterations, improving composition step by step and considering the nine clauses, the final results have been shown in Table 8.

Lack of nitrogen causes the cycle to undergo functional faults and unsustainability. Liquid leaks to mixed refrigerant compressor if propane or butane increases. Near the axes, the composite curve of first MCHE converges or even intersects when ethane mole fraction increases while LNG production reduces. Lack of propane or butane brings the second MCHE to temperature cross and in first MCHE composite curve, cold composite shows a sudden turn to hot composite, far from the axes. Reduction of ethane mole fraction and increase in methane mole fraction causes Composite curve of the second MCHE to diverge far from the axes. An increase in nitrogen mole fraction causes both composite curves to diverge throughout the curve. Lack of propane or butane causes composite curve of second MCHE to converge far from the axes. An increase in Natural gas inlet pressure or mixed refrigerant flow rate only affects the beginning of composite curves and makes it to converge.

Table 8 Result of innovative method Mixed refrigerant composition Methane 0.384 Ethane 0.04674 Propane 0.0477 i-Butane 0.018 n-Butane 0.007 Nitrogen 0.076

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compositions have entered the software and the effects of each change on MCHEs composite curve have been observed meticulously. These two composite curves are the bases of assessment. If the cold composite and the hot composite are more parallel and close to each other, exergy destruction will be lower and cycle efficiency will be improved. Closest feasible position of cold and hot composite curves is desired target while the temperature cross is prevented and a minimum approach temperature of 0.5°C for MCHEs is maintained. In what follows, the consequence of the recent observations to reach a systematic method have been explained. These clauses have been defined as the governing rules, according to more than 20000 observation.

And finally, an important rule of separators:

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Alterations of propane or butane mole fractions deeply affects the first MCHE’s cooling process. According to the temperature of mixed refrigerant’s separator and the boiling points of these two components, the second MCHE’s streams are almost free from propane and butane. Thus nitrogen plays a key role in the second MCHE’s cooling process.

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9.

Specific energy consumption[kJ/kg] 936.98

The objective function in HYSYS function, both BOX and mixed method is minimizing the amount of specific energy consumption. On the other hand all attempts in innovative method have been concentrated on the most close-fitting composite curves; however, better fitting means lower exergy destruction and lower specific energy consumption. The innovative method has better results in comparison with HYSYS optimizer functions and it shows the power of the recent method. Furthermore, its result has been utilized for choosing the best possible domain for initial intervals in optimizer function method. Results show a saving potency in each part of the cycle, however all of them cannot be implemented simultaneously. Therefore, some modifications are necessary for preventing temperature cross and gaining stable performance of the cycle. By applying the new composition and setting three precooling levels at optimized temperatures and other variables in their best feasible condition, specific energy consumption of 931.89 kJ/kg LNG has been accomplished that is the best state in recent research and has improved 9.43 percent in comparison with the initial condition [9]. Recent results has been shown in Table 9.

The first MCHE composite curve is more important and more complicated for optimizing because it has more cooling duty, its temperature range is bigger, all of its components are involved (referring to rule 9) and this makes it more flexible and increases the degree of freedom by two because of the presence of propane and butane [4]. Therefore, its composite curve has more potential for optimizing and improvement, and concentration on it will return more benefit in terms of energy saving. Thus, here, it has been attempted to optimize the first MCHE composite curve as best as possible and after that the second MCHE has been noted for any signs of saving potential (if a noticeable distance between hot and

Table 9 Final results of the cycle optimization NG inlet pressure (kPa)

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1st precooling lv. Temp. (C) 2nd precooling lv. Temp. (C) 3rd precooling lv. Temp. (C) PT (C) MR flow rate (kg/s) MR discharge pressure (kPa) 1st comp. power consumption (kW) 2nd comp. power consumption (kW) 3rd comp. power consumption (kW) MR comp. power consumption (kW) Coefficient of performance (COP) LNG production (kg/s) Specific energy consumption (kJ/kg)

Table 10 Second law efficiency of heat exchangers Heat exchanger Eff. (initial condition) HX1 22.09 HX2 53.87 HX3 69.56 HX4 38.32 HX5 72.81 HX6 74.64 MR after-cooler 7.40 C3 after-cooler1 43.21 C3 after-cooler2 14.48 MCHE1 88.04 MCHE2 77.91 Cycle 39.16

4.5. Exergy analysis

Comprehensive optimization of the cycle improves exergy efficiency of the cycle by 6.36 percent. Although the new composition is just suitable for the recent cycle by its specific features and another composition is needed for another cycle or liquefaction technology, both methods can be implemented on every cycle and every functional conditions.

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Exergy analysis is a powerful tool to study thermodynamic systems based on both the first and the second laws. Exergy can be defined as the useful work potential of a given amount of energy at some specified state. Exergy analysis can signify how much of the supplied exergy has been consumed and destructed. The main purpose of exergy analysis is to determine causes and the amount of irreversibility during a process and subsequently finding a solution for increasing the second law efficiency.

5. Optimization by considering functional constraints

Exergy destruction of heat exchangers can be defined as the difference between inlet and outlet exergy according to Eq. (16). V = YZV:@ − YZV[GF = UWX

V [\= ] [\= + V^[F ]^[F :@ − V V^[F ]^[F [GF

[\= ] [\=

+

(1 6)

V [\= ] [\= [GF − V [\= ] [\= :@

V^[F ]^[F [GF − V^[F ]^[F :@

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Exergy efficiency of HEs can be defined as Eq. (17). T_,WX =

(17)

Exergy destruction of MCHEs can be defined as the difference between inlet and outlet exergy according to Eq. (18).

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V = YZV:@ − YZV[GF = > V: ]: :@ − > V: ]: [GF UWX

(18)

T_,WX =

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Ultimately exergy efficiency of MCHEs can be defined as Eq. (19).

∑/ V [\= ] [\= [GF − V [\= ] [\= :@ 0 ∑/ V^[F ]^[F [GF − V^[F ]^[F :@ 0

Eff. (recent condition) 61.26 42.11 75.36 29.73 74.28 69.91 11.32 52.61 10.95 92.63 85.70 45.52

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-1.5 -22.5 -41.5 -41 64.8 4000 1135 2706 6763 16160 0.966 28.72 931.89

(19)

According to equations, the second law efficiency of heat exchangers in the initial and the recent condition have been compared in Table 10. Low exergy efficiency of equipment signifies that the cycle can be improved by further analysis. The recent condition has better exergy efficiency because of the performed optimization. It is worth mentioning that the second law efficiency of the cycle and MCHEs, the most important heat exchangers have been increased significantly because of recent improvements.

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Although the exhibited optimized point has low specific energy consumption and brings significant energy saving theoretically, it has some functional problems. Determining, adjusting and maintaining pressure, temperature and composition to such a degree of precision is not practical. Therefore, 25 kPa for pressure, 1°C for temperature and 1 percent for each components’ mole fraction have been considered as minimum amounts that can be controlled [4]. Moreover, Leakages and perturbations are inevitable in the real world performance. The determining parameters, including temperature, pressure and mole fraction of mixed refrigerant may change slightly in any part of the cycle Because of this unavoidable detrimental fluctuation. Most of composite curves are in critical condition, hot and cold composites are as close as possible in many cases and areas because it is the purpose of optimization. Because of this tight close-fitting, fluctuations can cause the cycle to confront with the aforementioned errors, especially temperature cross and/or overpassing minimum approach temperature. Temperature cross is an imaginary condition and impossible to happen, signifying a problem in thermal systems. In real application, temperature cross will not appear. Thus the efficiency will fall if fluctuations causes the recent problem. In the recent work, this situation has been entitled as “non-feasible or unsustainable condition”. It will show itself as errors in the modeled systems, however, perturbations have been neglected in most of the researches. Therefore, results are not fully reliable because these points may lead the system to a non-feasible condition and may cause misinterpretation of the results, because they are not achievable in the real performance. The optimized point must remain feasible and sustainable, if temperature changes 0.5°C around the operating point, 12.5 kPa for pressure and the cycle has to remain without temperature cross and overcrossing minimum approach, even if components’ mole fraction changes 0.5 percent

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around principal amounts. In prior sections, it was shown that Liquefaction cycles are very sensitive to fluctuations, specially mixed refrigerant composition. Therefore, optimizing the cycle without considering the recent issue is uncertain or even useless.

temperature, precooling stage, utilization of new equipment in cycles, etc. Lee et al.’s [13] study has a powerful optimization method. Fundamental assumptions of the recent study has been derived from this study and it has some leading features that have been used by many subsequent researchers. However, it has failed to address the amount of LNG production. Total shaft work is a general parameter and cannot be compared in different cycles and applications. The specific energy consumption is more specific and comparable. Moreover, this is not a favorable basis for assessment and can be deceptive, in that a cycle can be of less shaft work versus by another one, but more specific energy consumption. Furthermore, LNG product of this study is pressurized and it will have a gaseous phase if its pressure reduces to atmospheric state. Therefore, LNG production will decline. The lowest temperature of propane has been assumed to be -45 °C In this study and with regard to the lowest feasible temperature for a refrigerant according to ambient pressure, this assumption needs more consideration.

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The optimized point determines maximum or minimum amount of each component, because previous optimization has adjusted all of the components in critical condition which means very small change in any component can cause error (overpassing minimum approach temperature) or more distance between hot and cold composites. These two occurrences are mutually exclusive, therefore, any kind of change has a clear effect: more divergence or more convergence. These critical amounts of components have been named as boundary values. Thus on one side of each boundary, the value is safe and on the other side, the minimum approach temperature will be overpassed and subsequently, temperature cross will happen categorically. Sensibility analysis that has been performed previously will determine safe and unsafe sides. Accordingly, safe neighbor domain has been introduced as allowed direction for change in components and in this domain, 0.5 percent fluctuation in mole fraction cannot cause the cycle to confront with errors. Subsequently, values have been rounded by 0.5 percent mole faction to their safer amount, causing much more penetration in safe domain far from temperature cross, strengthening sustainable position. Iso-butane has been removed because of its ignorable amount. The final results have been shown in Table 11, the cycle will work sustainably in the recent state and associated specific energy consumption will be permanently reachable in this new optimized condition.

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Taleshbahrami and saffari’s [14] study has suggested a special plan for C3MR cycle by a precooling stage of propane, a liquefaction and a subcooling stages of mixed refrigerant. It has applied a genetic algorithm code to minimizing the area between hot and cold composite in liquefaction and subcooling MCHEs’ composite curve. It has concentrated just on the best mixed refrigerant composition.

MR composition Methane, Ethane, Propane Butane, Nitrogen

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Table 11 Final condition for sustainable optimization of C3MR cycle Parameter Amount MR flow rate (kg/s) 69.44 MR discharge pressure (kPa) 4575 1st precooling lv. Temp. (C) -1 2nd precooling lv. Temp. (C) -22 rd 3 precooling lv. Temp. (C) -41

Mole fraction 0.39, 0.46, 0.04 0.02, 0.09

Wang et al.’s [16] study has used a C3MR and an enhanced C3MR cycle, named as C3MR-Split Propane (precooling stage has 5 compressors) applying four objective functions including shaft work consumption, two different exergy efficiency expressions, and operating expenditure to improve the performance of the cycle and identify the best objective function with regard to lower specific energy consumption. A comparison between its final composite curve of C3MR cycle and recent study has been made in Fig. 19. Discussion has been summarized in Fig. 9. Comparison between

973.93

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Specific energy consumption (kJ/kg L.N.G)

Helgestad’s study is more comprehensive which have used the genetic algorithm. However, it did not include the MR composition in its process of optimization. Makarizade and Mowla’s [15] study is about the single-stage mixed refrigerant cycle. It has focused on optimization of specific energy consumption of the cycle and its product is at atmospheric pressure. Moein et al.’s [16] study is also about SMR process, trying to optimize 11 variables including MR composition in order to achieve the lowest total work, using genetic algorithm and ASPEN-HYSYS for its purpose.

The recent specific energy consumption has improved 5.35 percent compared with initial conditions [9], even though functional constraints have not been noted in the initial research at all. 5.1. Comparison of optimized results with literature The optimal results of recent work can be compared with those of liquefaction technology studies with mixed refrigerant in other literature. In many cases, recent study has more comprehensive analysis and better results. The mixed refrigerant composition that is the most important factor of the cycle performance has been emphasized almost in all studies. However, there are many other works concentrated on other parameter, such as ambient

composite curves of a) the recent study. b) Wang et al.

. According to the aforementioned discussion, a paucity of comprehensive optimization considering all variables altogether,

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is important for analysis of the cycle which has been omitted in many works. These facts have been the real purpose of the recent work for studying the C3MR process more profoundly.

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based on an absolute criterion for assessment of cycle performance, and an absence of considering functional constraints of the real world application is noticeable in literature. Moreover, the amount of LNG production and the amount of gaseous phase in the output

(a)

(b)

Helgestad (2009) 4000 4800 32.53 0.7 -18.6 -37 -35.8

Taleshbahrami and Makarizadeh and Saffari (2010) Mowla (2010) 5500 5500 3950

326664

196000

-156.9 295.3

-150

986.5 -165 1.1

6. Conclusion

36 45.5 4.9 2.1 11.5

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MR Composition (mole fraction) Methane (%) Ethane (%) Propane (%) Butane (%) Nitrogen (%)

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Table 12 Optimal result obtained from literature parameter Lee et al. (2002) 5500 NG inlet pressure (kPa) 4000 MR discharge pressure (kPa) 2.3 MR flow rate (kmol/s) st -2.3 Temp. of 1 precooling lv. (C) -22.5 Temp. of 2nd precooling lv. (C) -45 Temp. of 3rd precooling lv. (C) PT (C) -40 1358 1st comp. pwr. consumption (kW) 2240 2nd comp. pwr. consumption (kW) 5946 3rd comp. pwr. consumption (kW) MR comp. pwr. consumption (kW) 13509 -163 NG outlet Temp. (C) LNG production (kg/s) Coefficient of performance (COP) Specific energy consumption (kJ/kg)

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Fig. 9. Comparison between composite curves of a) the recent study. b) Wang et al.

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1106 45 45 2 8

MR1 20.9 34 45.1 -

MR2 57.4 41.4 1.2

Wang et al. (2013) 5000 2376 11.2 3.6 -15.2 -20.36

144500 -161.3 98.7

1092.4

1046.4

82 11.2 4 2.1 0.7

37 48.36 0.26 8.55 5.84

Moein et al. (2015) 6021

recent

128325 -155

5500 4575 2.72 -1 -22 -41 -40 1140 2822 6947 19020 163.3 30.722 0.96 973.93

27.3 33.43 25.67 7.87 5.74

39 46 4 2 9

refrigerant composition is the most important factor. On the other hand, mixed refrigerant behaves in a very complicated and out-ofdiscipline way. Most of ordinary optimizing methods have been developed for normal function. Therefore, suitable mathematical methods and an innovative one have been utilized for optimization of mixed refrigerant. Most of the variables in the cycle have effects on the other ones, and they often have inconsistent effects on the objective function. Therefore, a comprehensive analysis is needed and has been done here.

Natural gas liquefaction cycles are energy-intensive facilities; nonetheless, many countries, companies and researchers proceed with researches about it, because of LNG advantages. High demand and being energy-intensive mean more work and research is needed to improve the existing liquefaction science and technology. All of the effective parameters in performance and efficiency of natural gas liquefaction cycle (C3MR) have been analyzed and optimized in the recent work. According to the results, mixed

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References

Nomenclature

* (R) (0)

Ideal gas property Reference fluid Simple fluid

Subscripts Reduced property Critical property Index state Multicomponent mixture Departure function Cold stream Hot stream Inlet Outlet

Abbreviation LNG C3MR MR MTPA HE FTD APCI SMR DMR MCHE

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r c 0 mixed departure cold hot in out

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Superscript

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Propane Pressure (kPa) Molar gas constant, 8.314 (J/mol/K) Temperature (K) 3 Molar volume (m /mol) Peng-Robinson constants Acentric factor Specific enthalpy (J/mol) Specific entropy (J/mol/K) Specific heat capacity at constant pressure (J/K) Mole fraction Work (kW) Volume (m3) Adiabatic efficiency Heat capacity ratio Exergy destruction rate (kW) Stream’s exergy rate (kW) Mass flow rate (kg/s) Specific exergy (kJ/kg) Exergy efficiency

[1] Gadonneix, P. et al., 2013. World Energy Resource 2013 Survey, World Energy Council, London, England. [2] Gudmundsson, J.S., Mork, M., 2001. Stranded gas to hydrate for storage and transport, International Gas Research Conference, Amsterdam, 5-8. [3] Smith, R.R., 2004. Chemical process design and integration, John Wiley & Sons, New York. [4] Mafi, M., 2010. Development in mixed refrigerant cycle for separation systems of petrochemical industries and thermoeconomic optimization through combined pinch and exergy analysis , PhD thesis, K.N Toosi University of technology, Tehran, Iran. [5] Helgestad, D.E., 2009. Modelling and optimization of the C3MR Process for Liquefaction of Natural Gas, Specialization Project, TKP 4550 Process Systems Engineering, Norges teknisknaturviten skapelige university (NTNU), Stavanger, Norway. [6] Bukowski, D.J. et al., Natural Gas Liquefaction Technology For Floating LNG Facilities, Air Products and Chemicals Inc., Allentown, PA, USA 18195-1501. [7] Alabdulkarem, A. et al., 2011. Optimization of propane precooled mixed refrigerant LNG plant” Applied Thermal Engineering, Vol 31, 1091-1098. [8] Mortazavi, A. et al., 2014. Novel combined cycle configurations for propane precooled mixed refrigerant (APCI) natural gas liquefaction cycle, Applied Energy, Vol 117, 76–86. [9] Lee, G.C., 2001. Optimal Design and Analysis of Refrigeration for Low Temperature Processes, PhD thesis, Manchester, University of Manchester. [10] Gao, T., Lin, W., Gu, A.,Gu, M., 2009, Optimisation of coalbed methane liquefaction process adopting mixed refrigerant cycle with propane precooling, Journal of Chemical Engineering Japan, Vol. 42, 893–901. ® [11] HYSYS 2004.2, 2005. Operations Guide, Aspentech, Cambridge, MA, 02141-2201, USA. [12] Moein, P. et al., 2015. APCI- LNG single mixed refrigerant process for natural gas liquefaction cycle: Analysis and optimization, Journal of natural gas science and technology, Vol. 26, 470-479. [13] Lee, G. C., Smith, R.R. and Zhu, X.X., 2002. Optimal synthesis of mixed-refrigerant systems for low-temperature processes, Industrial and Engineering Chemical Research, Vol. 41, No. 20, 5016-5028. [14] Taleshbahrami, H., Saffari, H., 2010. Optimization of the C3MR cycle with genetic algorithm,Transactions of the Canadian Society for Mechanical Engineering, Vol. 34, No. 3–4. [15] mokarizadeh, M., mowla, D., 2010. Energy optimization for liquefaction process of natural gas in peak shaving plant, Energy, Vol. 35, 2878-2885.

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Symbols C3 P R T  a, b, α  h s Cp y W V Tad N UV YZV V e T_

Floating liquefied natural gas Mixed Integer Nonlinear Programming Logarithmic mean temperature difference Partition temperature Genetic algorithm Sequential Quadratic Programming Natural gas Coefficient of performance

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FLNG MINLP LMTD PT GA SQL NG COP

Although theoretical results have high saving-potency, functional constraints are the most important obstacles for reaching the desired results. On the other hand, those constraints are inevitable, because of leakages and perturbations. Therefore, the final study has been performed by considering some of the important industrial constraints which is the main distinguishing feature of recent research. Specific energy consumption of 973.93 kJ/kg LNG in sustainable mode analysis is the final result and has 5.35 percent improvement compared to the initial condition, based on the initial research.

Liquefied natural gas Propane precooled mixed refrigerant process Mixed refrigerant Million ton per annual Heat exchanger Finite temperature difference Air Products and Chemicals, Inc. Single mixed refrigerant Dual mixed refrigerant Main cryogenic heat exchanger

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Journal of Natural Gas Science and Engineering, Vol. 15, 93-105.

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[16] Wang, M., Khalilpour, R., Abbas, A., 2013. Operation optimization of propane precooled mixed refrigerant Processes,

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•

An innovative systematic method of optimization for mixed refrigerant composition has been introduced which tested, compared and validated meticulously.

•

Comprehensive optimization of the cycle improves exergy efficiency of the cycle by 6.36 percent.

•

Some operational constraints of the cycle, caused by leakages and perturbations in the real applications

Specific energy consumption of optimal condition is 973.93 kJ/kg

LNG

by 5.35 percent improvement

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compared to initial condition.

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cause specific energy consumption to reduce by 4.08 percent, compared to theoretical results.