Process 13 Heng Sun

7/26/2019 Process 13 Heng Sun

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SIMULATION STUDY OF OPERATING OPTIMIZATION OF

SMALL-SCALE LNG PLANT WITH SMR CYCLE

Heng Sun1

Hongmei Zhu2

Zengcai Li1

Feng Liu11Beijing Key Laboratory of Urban Oil and Gas Distribution Technology

China University of Petroleum, Beijing 102249, China

2Architectural Engineering Institute

North China Institute of Science and Technology, Beijing, 101601, China

KEYWORDS: operation optimization, SMR, part-load, environmental temperature

ABSTRACT

A small-scale LNG plant with SMR cycle would not work under the designed condition all the time. It isnecessary to adapt the mixture refrigerant composition and other process parameters to the changed working

conditions. Thus, the operating optimization is significant for reduction of the actual energy consumption. The

performance variations of the devices in SMR plant under changed conditions must be considered for a fixed

SMR plant when the working condition is changed. An operation optimization model is established by

combination of performance simulation of main devices with the process simulation using HYSYS software.

The performance of centrifugal compressor is calculated using fitting method. The BOX method is used to

solve the optimization problem. The effect of the variations of the feed gas and the environmental temperature

variation are considered. The case of partial load for the plant is also studied. The results validate the

significance of operating optimization for a small-scale LNG plant. The results also show how to adjust the

refrigerant composition to fit the parameters variations of feed gas. The optimized operating schemes under

different conditions are suggested.

1. INTRODUCTION

Small-scale liquefied natural gas (LNG) plants play important roles for utilizing the gas resources in remote

gas fields, offshore gas resources and unconventional natural gas resources [1]

. In recent years, the

development of small-scale LNG plants is rather rapid in many countries, special in many developing

countries [1-3]

. Mixed refrigerant cycle (MRC) is the most popular process in LNG plants. Single mixed

refrigerant cycle (SMR cycle) is expressly suitable for small-scale LNG plant due to high energy efficiency,

simple process and less devices. The small-scale LNG plant cannot always work under the designed

conditions for many reasons. This involves in the variations of feed gas pressure and composition, the

environmental temperature and load of the plant. The environmental temperature will change not only forseasonal alternation but also for day-night changes. The loads may vary due to the season fluctuations of the

gas supply or other commercial reasons. How to operate the SMR plant under changed conditions to avoid

marked increase of energy consumption is an important problem need to be investigated. Process simulation

and optimization are important ways to study and solve the problem.

Many process simulations and optimizations of MRC cycle were reported, while most of the work only were

concerned about the design problem[1-5]

. For example, Aspelund etc. [5]

developed a gradient free

optimization-simulation method for processes modeled with the HYSYS software, which could be used to find

the process parameters that minimize the energy requirement of a PRICO process. Xu etc.[6]

programmed a

genetic algorithm method coupling the process simulation software Aspen Plus and performed a linear

regression on the MR composition to derive a set of functions. The work can be used to determine theperformance of the plant and be useful to acquire the optimal MR composition. Some literatures studied the


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control problem of a MRC plant. Arjun and Ment[7]

developed a simplified model which is applicable for

control structure design without compromising. Wilco etc.[8]

discussed the potential application of modern

control technology in LNG production.

Some nearest literatures[9-11]

have involved the optimization of operations. Prue Hatcher etc.[12]

compared

the difference between the optimizations considering operation and design objects. The work focused on the

heat exchanger area and showed that the optimization with different objects leads to obviously differentresults. Hasan etc.

[13] presented a generalized model for the running of the compressors network in

large-scale AP-XTM LNG process. During operation, the efficiency of the SMR plant depends on the

performance of centrifugal MR compressors, which changes when inlet gas parameters are changed during

operation. However, this has not been considered precisely. The goal of the work here is to establish an

operation optimization model which includes this key factor and study the performance of a SMR plant under

changed working conditions.

2. OPERATION OPTIMIZATION MODEL OF SMR PLANT

The essential difference between the operation optimization and design optimization is the device

performances of the LNG plant. The device is fixed for operation optimization while the device is selectableand their performances are free variables in design optimization. Therefore, the operation optimization model

could be setup on the basis of the process simulation. Here, the simulation is also carried out by HYSYS

software. The SRK EOS was used to calculate both the phase-equilibrium and the thermal properties. The

key of operation optimization is to establish models of the devices in LNG refrigeration cycles. Since the

variation of main cryogenic heat exchanger (MCHE) is minor, its performance is treated as constant.

Therefore, the constraints of the MCHE could be described as equations (1) and (2).

CT 2min (1)

CTLMTD 4 (2)

Here, min denotes the minimum approach of temperature inside the main cryogenic heat exchanger andLMTD means logarithmic mean temperature difference. The fixing of compressor's performance is important

for the optimization results. The Q-P and Q- curves of the first and the second process stages of the

compressor could be fitted using equations (3) and (4):

2

3

0

2

2

0

1

0

1

VV QAQn

nA

n

nA

p

p+

+

=

= (3)

3

0

3

2

0

2

0

1

+

+

=

n

nQB

n

nQB

n

nQB VVV (4)

Here, p0is suction pressure, p1is outlet pressure, is the pressure ratio, Qvis volume flow rate, n is rotationalspeed. Since n is equal to the rated speed n0, n/n0is equal to 1. A1, A2, A3, B1, B2, B3are the fitting coefficients.

A typical compressor is selected and the fixed coefficients are listed in Tables 1~2, where the mean variances

are in the last column. During actual operation, the working condition of the compressors changes obviously.

Based on similarity theory, equation (3) could be used to predict the performance of the compressor under

different working conditions[9]

aa TRn

RTn

= (5)

Where R is gas constant and T is temperature.


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Table 1. Fitted coefficients of Q-p curves of 1st and 2nd stages of compressor

A1 A2 A3 R

First-stage -23.862 3.0719 -0.076537 0.15059

Second-stage 1.7931 1.1011 -0.25263 0.29789

Tab.2 Fitted coefficients of Q- curves of 1st and 2nd stage of compressorB1 B2 B3 R

First-stage 1.0012 0.42213 -0.013329 0.21897

Second-stage 52.575 -11.91 0.7649 0.07986

PRICO@

process which has been the mostly applied in small-scale LNG plant are selected to conduct the

simulation study. The PFD of the PRICO@

process is shown in Fig. 1.

J-T valve Production valve

Cryogenic heatexchanger

LNG Tank

Feed natural gasPurifying unit

Suction vessel

Refrigerant pumpRefrigerant pump

SeparatorSeparator

CoolerCooler

Figure 1. PFD of the PRICO

@liquefaction process of BV

The standard working condition (design condition) is as follows: the flow rate of the feel natural gas is 1050

kmol/h, the temperature is 45 and the pressure is 4.5 MPa. The composition of the natural gas is gas 1

listed in Tab. 3. The rotary speed of compressor is 10850 rpm. The MR composition for design optimization is

listed in Tab. 4. The suction volume flow rate of MR of compressor is 2.43104m

3/h, the outlet pressure of the

compressor is 3403 kPa (Absolute pressure). The total power which also includes the MR pump is 6363.2kW.

Since the LNG produced is 984.05kmol/h, the unit energy consumption of LNG is 23.279kJ/mol.

Table 3. Components of natural gas (Mole percentages)

Component [%] Gas 1 Gas 2 Gas 3

CH4 92.67 96.63 93.46

C2H6 3.9 1.54 2.03

C3H8 1.95 0.85 1.13

C4H10 0.98 0.43 0.74

N2 0.5 0.55 2.64


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Table 4. Components of MR of design optimization (Mole percent)

Component Molar percentage [%] Component Molar percentage [%]

CH4 20.79 C2H4 36.21

C3H8 12.62 C5H12 19.58

N2 10.8

The MR composition for design optimization is already known. However, it may not be available for changed

conditions including different environmental temperature, changed feed gas conditions and partial load.

Therefore, an operation optimization model is necessary to be established to obtain the best operating

parameters under actual conditions. Here, the optimized variables are pthroatand coi ni 2,1= .

Here, co means the molar fraction of i-component. The constraint involves that the pressure of suction gas of

the compressor could not be too low. The constraint conditions could be expressed as:

11

==

n

i

ico (6)

130suctionp (7)

The objective function is to minimize the energy consumption:

2121 cccctotal WWWWW += (8)

This optimization problem could be solved using BOX method.

3. OPERATION OPTIMIZATION UNDER CHANGED FEED GAS

On a long view, the pressure of the gas will decrease slowly. Thus, it is needed to study how to operate a

SMR plant under different feed gas pressure. The problem can be solved based on the above operation

optimization model. The results are illustrated in Fig. 2. It can be drawn that it is almost impossible to reducethe unit power of the SMR plant when the pressure gas is increased from the design point. This is because

the decrease of efficiency of the plant offsets the influence of high-pressure feed gas. When the pressure of

feed gas is decreased from the design point, the operation optimization can get a little lower unit power

consumption than without any adjustments, while the actual unit power is increased.

To evaluate the adaptation capacity of the SMR plant under changed feed gas compositions, three presumed

gas compositions which are listed in Tab. 1 are employed. The composition 1 is the one which is used in the

design optimization. The operation optimization results are shown in Fig. 3. The results show the plant has

good adaptation capacity when the fraction of ethane increases. However, the unit energy consumption is

obviously increased for rich-nitrogen gas.

4. OPERATION OPTIMIZATION UNDER CHANGED ENVIRONMENTAL TEMPERATURE

To ensure the performance of a LNG plant, the plant must provide enough refrigerating capacity under relative

high temperature in summer. In other seasons, the environmental temperature is lower than that in summer. It

is possible to adjust the operation of the platn to reduce the energy consumption. The operation optimization

results are shown in Fig. 4, which shows that operation optimization can get satisfactory effect when the

environmental temperature is higher than -20.


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18

20

22

24

26

3 3.5 4 4.5 5 5.5 6 6.5

Feed Gas Pressure (MPa)

Unitenergyco

nsumption

(kJ/mol)

Figure 2. Variations of unit energy consumption vs. the pressure of feed gas

0

10

20

30

1 2 3

Feed Gas Composition

Unitenergyconsumption(kJ/mol)

Figure 3. Unit energy consumption of the SMR plant under different gas compositions

However, the reduction of energy consumption becomes inapparent when the environmental temperature

decreases further. This could be explained by the working condition is far away from the design condition

when temperature is lower than -20. The main process parameters and MR compositions of the operation

optimization are listed in Tab. 5 and Tab. 6, respectively. An available adjustment strategy could be drawn

from the process parameters achieved. The adjustment strategy is to ensure the high efficiency of the first

stage compressor primarily since it is the key factor effecting the energy consumption. After that, the LMTD of

MCHE increased rapidly when the temperature is lower than -20. This causes the efficiency of the total

cycle decreased correspondingly.


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Table 5 Process parameters and simulation results under different loads for throttle adjustment

Temperature () 40 20 10 0 -10 -20 -30 -40

MR molar flow (kmol/h) 2615.9 2587.0 2338.8 2165.3 2157.3 2155.2 2152.1 2155.0

Pressure after 1ststage

(kPa)1549.8 1435.2 1535.8 1500.9 1426.6 1327.2 1256.9 1251.1

Pressure after 2

nd

stage(kPa)

3411.7 3115.6 3693.6 3765.7 3542.2 3265.9 3102.6 3090.7

LMTD () 4.44 6.00 5.32 4.76 5.61 5.56 7.74 7.81

1stpolytropic efficiency (%) 82.474 82.867 84.332 82.454 82.452 82.467 82.454 82.505

2nd

polytropic efficiency (%) 70.742 70.796 70.768 69.555 69.557 70.193 70.504 70.614

Total power (kW) 6339.1 5948.6 5646.8 5330.3 5136.7 4909.7 4760.8 4756.9

0

5

10

15

20

25

-40 -20 0 20 40 60

Temperature ()

Unitenergyconsumption

(kJ/mol)

Figure 4. Unit energy consumption of the SMR plant under different environmental temperature

Table 6. Optimized composition of MR for plants with throttle facility

under different loads (molar fraction)

Temperature () 40 20 10 0 -10 -20 -30 40

Methane 0.229 0.252 0.333 0.345 0.373 0.420 0.425 0.425

Ethylene 0.334 0.367 0.271 0.260 0.267 0.240 0.281 0.279

Porpane 0.143 0.143 0.194 0.247 0.223 0.238 0.206 0.215

i-Pentane 0.192 0.144 0.107 0.063 0.056 0.028 0.013 0.008

Nitrogen 0.101 0.094 0.094 0.085 0.082 0.074 0.075 0.073

Molecular weight 36.05 33.66 31.89 30.65 29.62 28.05 26.85 26.75

5. OPERATION OPTIMIZATION OF LOAD ADJUSTMENT

The small-scale LNG plant can not always work under the designed NG flow rate for many reasons, such as

the season fluctuations of the gas supply or other commercial reasons. Thus, the load adjustment ability of

LNG plant is very important. However, small-scale SMR plants usually only have simply load adjustment

facility of throttle method or have no such facilities. Operation optimization is specially significant for such

small-scale LNG plant. The optimization is carried out and the results are shown in Fig. 5 and Fig. 6. Here, the

load of the plant is proportional to the flowrate of feed gas. "Op" means the case that optimized variables


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includes MR composition, throttle pressure and MR flowrate. "Th" means fixed MR composition case in which

only MR flowrate and throttle pressure are optimized variables.

40

60

80

100

0 20 40 60 80 100 120

Load percentage (%)

Totalpowerpercentage(%) Th

Op

Figure 5. Comparison between the relative power consumption

with and without component optimization of MR

20

25

30

35

40

0 20 40 60 80 100 120

Load percentage(%)

Unitpower(kJ/mol)

Th

Op

Figure 6. Comparison between the unit power with

and without component optimization of MR

As Fig. 5 and Fig. 6 show, the total power could be decreased for throttle method under part-load condition

while the unit energy consumption is increased. It can be drawn that throttle method could get a relative good

adjustment effect in the load range of 60%~100%. When the load is less than 60%, the throttle method also

works, but the effect is not so satisfactory. In any case, the lower energy consumption could be gotten under

all loads. The efficiency with MR composition optimization is obviously higher than that without MR

composition optimization is obtained only in the range of 40% to 60%. An energy save of about 5% could be

gotten in this range.

The main process parameters in case "Th" are listed in Tab. 7, also. The similar regulation of operation

strategy can be drawn that the high efficiency of the first stage compressor should be primarily ensured. The

low suction pressure means low vapor density, which can make the volumetric flowrate maintain the

reasonable range when the molar flowrate is reduced marked.


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Table 7. Main process parameters and simulation results

under different loads for throttle adjustment

Loads (%) 100 90 80 70 60 50 40 30

NG molar flow (kmol/h) 1050 945 840 735 630 525 420 315

MR molar flow (kmol/h) 2623.4 2365.6 2093.1 1828.6 1571.4 1494.6 1292.7 1013.7

Pressure before throttle (kPaA) 280 251 228.5 206.9 189.9 166.9 147.1 1301

stpolytropic efficiency (%) 82.33 82.95 83.8 84.39 84.02 84.09 83.60 83.47

2nd

polytropic efficiency (%) 70.70 71.11 71.35 71.02 69.85 71.28 70.71 70.70

Total power (kW) 6363.2 5905.1 5429.7 4939.9 4451.1 4074.3 3594.1 3080.9

Some small-scale SMR plants which have not been designed for operations under part-loads have no load

adjustment facilities such as throttle valves. Since such plants do may occasionally work under part-loads, it is

also significant to study how to save the energy consumptions for such SMR plants. The adjustment of the

composition of MR is a potential method. The simulation study of this method is also conducted. The methods

are the same to the above except for the throttle pressure is set to be always zero. The energy consumption is

obviously decreased by changing the composition of MR, which is illustrated in Fig. 7. A more than 10%

energy save under 70% load and a more than 16% energy save could be obtained using this method. The

results also indicate the possibility of extend the load adjustment range of throttle by using composition

optimization exists.

0

10

20

30

40

50

0 20 40 60 80 100 120

Load percentage (%)

Unitpower

(kJ/mol)

Component Op

Throat

No Adjust

Figure 7. Comparison between the unit power with and without throttle facility

6. CONCLUSIONS

A model of operation optimization is established based on the consideration of performance variations of the

devices in SMR plant under changed conditions. The operation optimization model is established bycombination of performance simulation of main devices with the process simulation using HYSYS software.

The performance of centrifugal compressor is calculated using fitting method. The model can be used to

study the performance of SMR plant under different conditions, such as variations of feed gas parameters,

environmental temperature and part-load conditions. The optimized operation strategy could also be achieved

based on the model. The available adjustment strategy is that the high efficiency of the first stage compressor

must be primarily ensured. Since the efficiency of compressor is a key factor for energy consumption, this is

suitable for almost every changed working condition.

The simulations and operation optimizations of the SMR plant working under changed gas parameters and

environmental temperature was carried out. The results show that the operation optimization can provide a

good adaptation capacity for SMR plant when the working condition slightly deviated from the design point. If

the deviation is large, the efficiency of the plant will decrease. Besides, the satisfactory energy consumption


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could be achieved when the fraction of ethane increases while the unit energy consumption is obviously

increased for rich-nitrogen feed gas.

The simulation of a LNG plant of PRICO process with throttle facility of load-adjustment was implemented.

The results show that throttle method could get a relative good adjustment effect in the load range of

60%~100%. The unit power for 30% load is about 60% more than that under design condition. The

composition optimization of MR can slightly decrease the power further. The composition optimization canadjust the load effectively for the simply SMR plant without any load adjustment facility. A more than 10%

energy save under 70% load and a more than 16% energy save could be obtained using this method.

ACKNOWLEDGEMENTS

The authors are grateful for funding by National Natural Science Foundation of China (No. 51004111:

Research on the mechanism and regulation of the liquefaction process of nature gas in a supersonic swirling

separator) and Special Foundation of Young Teacher of CUPB, China (Research on cryogenic power cycle to

recovery LNG cold energy and waste heat and optimization study)

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Process 13 Heng Sun