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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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