Modelling Dual Reflux PSA Cycles Using A
Transfer Function Methodology for Separating
Nitrogen and Methane in LNG Production
Yechun Zhang1, Thomas Saleman2, Kevin Li2, Wei Yu1, Eric F. May2, Brent R. Young1*
1
The University of Auckland and 2The University of Western Australia
*Email: b.young@auckland.ac.nz
I.
Abstract
INTRODUCTION
Liquefied natural gas (LNG) is an important
energy source due to its clean combustion, low
carbon emissions and high energy density. In 2011,
330.8 million cubic metres of LNG was traded
around the world, which is a 10% increase
compared to 2010 (300.6 million cubic metres) [1].
Global LNG supply is projected to grow 4.5% p.a.
to 2030, more than twice as fast as total global gas
production (2.1% p.a.) and faster than interregional pipeline trade (3.0% p.a.). LNG will
contribute 25% of global supply growth in 2010-30,
compared to 19% for 1990-2010 [2].
Liquefied natural gas (LNG) is an important
energy source due to its clean combustion, low
carbon emissions and high energy density. The
methane-nitrogen separation process in LNG
production is significant and challenging to ensure
the nitrogen level in the LNG product is within
specification, or to upgrade the vapour overheads
from cryogenic distillation columns to meet
specifications of vent/pipeline/fuel for gas turbine.
Pressure swing adsorption (PSA) has been applied
in the industry for separating gas mixtures. Its
advanced configurations, such as stripping reflux
PSA, enriching reflux PSA and Dual Reflux PSA
(DR-PSA) have higher efficiencies which may lead
to economically improved separation of methane
and nitrogen. Literature has shown that DR-PSA
cycles can in principle achieve perfect separation,
and experimental demonstrations of DR-PSA with
other mixtures have shown how it can concentrate
dilute streams significantly even with low pressure
ratios, and simultaneously achieve high product
recovery rates. In this paper, commercial
simulation software is used to simulate stripping
reflux PSA and enriching reflux PSA according to
Ebner and Ritter’s experiments in 2002 and 2010.
A correlation based on the simulation results from
both stripping reflux PSA and enriching reflux PSA
is then developed using the transfer function
method to simulate the cyclic steady state of Dual
Reflux PSA. The results from the model were
verified by comparing with Ebner and Ritter’s
experimental data and showed a close match. The
advantages and disadvantages of the modelling
method are discussed.
Western Australia contains a large quantity of
natural gas, most of which is exported to the AsiaPacific region as LNG product. Australia is aiming
to become one of the largest LNG exporting
countries by 2020, and 140 billion Australian
dollars will be invested in LNG plants in the next
10 years [3]. For example, as one of the largest
natural gas reserves, Gorgon field will be
producing 15 million tons of LNG per annum [4].
As some of these gas fields have a high nitrogen
concentration, and the nitrogen specification in
LNG product is limited to 1%, the design and
operation of the methane-nitrogen separation
process in LNG production is increasingly
significant and challenging to ensure the nitrogen
level in the LNG product is within specification [5].
Alternative application of methane-nitrogen
separation will aim to upgrade the vapour
overheads from cryogenic distillation columns to
meet specifications of vent gas/pipeline gas or fuel
for gas turbine. Pressure swing adsorption (PSA)
has been applied in the industry for separating gas
mixtures. However, due to the fact that current
commercial adsorbents can only reach a methanenitrogen selectivity of around 3, conventional PSA
columns are far from achieving efficient
separations. For example, to decrease the methane
concentration from 3% to 100ppm in a vent stream
from a typical large scale LNG plant, a preliminary
Keywords: Natural gas, LNG, Separation,
Adsorption, Dual Reflux PSA
1
calculation revealed that 548 beds will be needed
[3]. The methane concentration in the nitrogen
enriched product could be decreased to 113ppm but
the nitrogen recovery rate was only 7.4%. To
increase the productivity and recovery rates,
alternative configurations of PSA are needed to
improve the overall separation efficiency. Dual
reflux PSA (DR-PSA) is a unique PSA
configuration which combines stripping PSA and
enriching PSA. Theoretically DR-PSA has the
potential to achieve perfect separation with high
recovery rate, and the theory was supported by
experiments in literature which have shown high
separation efficiencies in binary separations [6-9].
This may lead to economically improved separation
of methane and nitrogen. A typical layout of DRPSA unit is shown in Figure 1.
However since equilibrium theory models assume
perfect separation, it cannot be used to predict the
product concentrations. Thus the motivation is
present to develop other models that are capable of
predicting product compositions.
II. METHODOLOGY
We have used three different methods to model
DR-PSA units. First of all, the equilibrium theory
model by Kearns and Webley was enhanced. The
enhanced model is constructed on partial
differential equations which describe the adsorption
columns. The partial differential equations are
reduced to ordinary differential equations by the
method of characteristics, and are numerically and
iteratively solved. The equilibrium theory model is
able to find the optimized operating point for the
DR-PSA unit given the total throughput and
pressure ratio of the system. Some key optimized
operating parameters such as enriching/stripping
reflux ratios, feed step time and axial feed location
can be obtained rather than specified. The system
power consumption can also be calculated. A
typical equilibrium model result in terms of the
power consumption and total throughput is shown
in Figure 2. Besides these, equilibrium theory
model can be used to monitor the concentration
profile inside the column for each step. The impact
of the parameters and different configurations of
DR-PSA on the concentration curve can be visually
examined thus it helps us understanding DR-PSA
processes. However due to the equilibrium theory
model assuming perfect separation, it is overly
simplified and cannot be used to predict product
compositions.
Figure 1. (a) DR-PSA is a combination of stripping
PSA and enriching PSA with intermediate feed
either at high pressure or at low pressure. (b)
Pressure swing can either be done at the top or
bottom.
Total Throughput ()MMSCFD
7
The equilibrium theory model is the only model
available for describing DR-PSA units at the
moment. The first equilibrium theory model for
DR-PSA was first published by Ebner and Ritter in
2004. The model assumed perfect separation of
binary mixtures and proved the feasibility of sharp
separations and high recoveries by DR-PSA using
low pressure ratios. The effect of feed
concentrations, pressure ratios, total throughput,
reflux ratios and feed positions were discussed in
detail [10]. Kearns and Webley significantly
improved the equilibrium theory model in their two
companion papers in 2006 by introducing the
models for four different configurations, in terms of
feed to low/high column and purge by lean/rich gas.
The composition profile for each cycle was
numerically calculated and the productivity and
work consumption trade-off was discussed [11, 12].
6
5
4
3
2
1
0
0
50000
100000
150000
200000
Energy Consumption (kW)
Figure 2. A typical result obtained from
equilibrium theory model: the relationship between
total throughput and power consumption.
To attempt to solve the above noted problem,
Aspen Adsorption software was used to simulate
the full cycle DR-PSA units. However we
experienced difficulties when building the full
2
cycle model due an unstable and overly
complicated pressure-flow network and internal
material flow loops. To overcome this difficulty,
here we propose an alternative method to estimate
the efficiency of DR-PSA systems using the
simulation results from the combination of a
stripping PSA model and an enriching PSA model,
which can be built in Aspen Adsorption. Both
stripping and enriching PSA models in Aspen
Adsorption can be solved much easier and quicker
and thus can provide a faster prediction result and
avoid building the full DR-PSA model.
(a)
Our model started with calculating the total
material balance of the DR-PSA unit (regardless of
each component), by defining the flow rate of each
stream in Figure 3 (streams 1-7). This process
mimics the actual material flow of a DR-PSA unit
during adsorption/desorption when reaching cyclic
steady state. The core idea of our model was to
consider both the stripping PSA and enriching PSA
of a DR-PSA unit as “transfer functions”, in terms
of the relationships between their local feed
composition and lean/rich product composition
when reaching cyclic steady state. The transfer
function is usually regressed from a certain amount
of data points either from simulation models or
experimental data. A typical example of transfer
functions of stripping and enriching PSA are shown
in Figure 4.
(b)
Figure 4. Examples of transfer functions of (a) a
stripping PSA model and (b) an enriching PSA
model, and their regressed curves.
In our research work, Aspen Adsorption was
adopted to build stripping/enriching PSA models
and rigorously solve them to provide data points for
regressing transfer functions. Then, the transfer
functions of stripping/enriching PSA are combined
according to the calculated total material balance
for the DR-PSA unit to predict the product
concentrations. An algorithm was developed in
Matlab for the purpose of the iterative combination
calculations. The detailed steps are illustrated in
Figure 3 and as follows (Feed to HP column
example):
Step1: Set the flow rate at the local feed to the
stripping PSA (stream 1) to desired value and make
an initial assumption of its composition.
Step 2: According to the regressed transfer function
of the stripping PSA and the reflux ratio, calculate
the flow rates and the compositions of lean product
(stream 2) and waste (stream 3).
Figure 3. Illustration of the algorithm steps in
combining transfer functions of stripping/enriching
PSA (Feed to high pressure configuration).
Step 3: Transport the waste (stream 3) from
stripping PSA part to the feed (stream 4) of
enriching PSA part, then calculate the flow rates
and compositions of rich product (stream 5) and
waste (stream 6) according to the transfer function
and reflux ratio of enriching PSA.
3
Step 4: Calculate the required flow rate of actual
feed (stream 7) in order to mitigate the difference
between stream 6 and stream 1.
extended Langmuir 2 according to the
equation/parameter
definitions
in
Aspen
Adsorption. Despite the fact that the dynamic
material balance could not achieved due to
adsorption/desorption processes, QLR, QLP, QHR and
QHP were always guaranteed during simulation to
ensure a stable productivity.
Step 5: Since the composition of actual feed
(stream 7) is given, the composition of stream 1
can be calculated and updated to start the next
iteration.
The simulation results and their comparison
with experimental data are illustrated in Table 2.
The result showed that the deviation was never
greater than 0.1% for ethane concentration in lean
product and never greater than 5.1% for ethane
concentration in rich product. As the lean product
and rich product recovery rates can be calculated
by the lean/rich product split ratios and their
compositions, they also exhibited a close match.
The error in nitrogen recovery rate prediction was
no more than 2% and the ethane recovery rate
prediction error was no more than 6%. We believe
that the main reasons for the deviations were
because of the semi-isothermal assumption for
simulation, and using a different technique for the
pressure inverse step. Besides this, regression of
isotherm curve and concentration curve for the
transfer function can have a significant impact on
the prediction accuracies. Obtaining more data
points for regressing the concentration curve can
increase the accuracy of the model, but the extra
effort would be a trade-off. Nevertheless the
simulation results showed a close match to the
experimental data under different combinations of
parameters.
After the model is iteratively solved and
converged, the concentrations of rich product and
lean product can be obtained.
III. RESULTS AND DISCUSSION
Before applying the new model to nitrogen and
methane separations, it is essential to test the
validity of the transfer function model. In order to
do so we selected a series of DR-PSA experiments
done by McIntyre et al. for separating ethane and
nitrogen in 2002 and 2010 for comparison. The
main reason for selecting them is that these
experiments covered a wide range of different key
parameters, such as pressure ratio (defined as the
ratio between adsorption pressure and desorption
pressure: PA/PD), feed composition (yF), stripping
reflux ratio (RS), enriching reflux ratio (RE) and
feed step time (tf). We have picked 2 scenarios
from the 2002 experiments and 2 scenarios from
the 2010 experiments, and their key parameters are
listed in Table 1 [8, 9].
Table 1. Key parameters of selected McIntyre et al.
experiment scenarios [8, 9].
Case
Scenario
QF (sccm)
PA (bar)
PD (bar)
yF
RS
RE
tf (s)
1
2002,
scenario 2
580.0
2.10
0.27
0.80%
0.82
85
95
2
2002,
scenario 3
544.0
2.10
0.27
0.78%
0.56
123
95
3
2010,
scenario 1
583.6
1.84
0.26
1.35%
0.38
72
45
Some of the distillation-like characteristics of
DR-PSA units can be observed through the
experiment result and model predictions with
respect to different parameter values. For example,
RS was reduced and RE was increased dramatically
in case 2 compared to case 1, with tf kept constant.
yE increased from 41.0% to 63.9%, suggested by
the experiment result and from 38.1% to 59.3%,
both showing that decreasing RS and increasing RE
will result in increased heavy gas composition rich
product while sacrificing the purity in the lean
product, which agrees with similar results from
distillation columns. Another example was the
increased RS and RE in case 4 compared with case 3.
RS was increased from 0.38 to 1.57, while RE was
increased from 72 to 149. This resulted in a
decrease of yL from 2900ppm to 2000ppm and an
increase in yE from 61.6% to 70.7%, both
indicating purer lean and rich products. As RS and
RE can be translated into power input in actual
operations, this proved that the purity of products
can be elevated by increasing the total power input
of DR-PSA units.
4
2010,
scenario 6
583.9
1.84
0.26
1.36%
1.57
149
45
where RS and RE are defined as:
RS=QLR/QLP
(1)
RE=QHR/QHP
(2)
All these scenarios had a feed to low-pressure
column configuration, and the column dimensions
were given as 0.87m high and 0.0121m radius.
Axial feed position was set to be at the middle of
the column. MeadWestvaco BAX-1500 activated
carbon was used as the adsorbent in all the
scenarios, and its adsorption isotherm data were
obtained from McIntyre’s paper [8]. The isotherm
curves of ethane and nitrogen follow a typical
Langmuir isotherm, and they were fitted to
Since this is on-going work, we only performed
a preliminary run of methane/nitrogen separation
after the model was validated using experimental
4
data from the literature. The preliminary run was
conducted for the separation of 3% methane and 97%
nitrogen. The adsorbent used was Norit RB3
activated carbon. RE and RS were both set to 10,
and a total throughput was set at 0.85MMscfd. The
column is 20 metres high and 3 metres in diameter
which is at industrial scale. A pressure ratio of 3
and 5, feed temperature at 240K and 302K and feed
to high/low pressure column configuration were
simulated. The detailed results are illustrated in
Figure 5.
IV. CONCLUSIONS AND FUTURE WORK
In our research, a new modelling methodology
for DR-PSA units has been proposed. The model
combines transfer functions of stripping and
enriching PSA units and is iteratively solved
according to the material balance of the DR-PSA
unit. The validity of the model was verified by
comparing the simulation results with the
experimental results in the literature in a variety of
scenarios which covered a wide range of parameter
values. The comparison confirmed that the model is
capable of predicting the product concentrations of
DR-PSA units as they closely match the
experimental results in all scenarios. A preliminary
simulation run was done for separating methane
and nitrogen in industrial scale. The results were
promising as DR-PSA units showed high
separation efficiencies in the run.
Future work will focus on linking the
equilibrium theory model with the transfer function
model, as equilibrium theory model is able to
optimize the operating parameters and the transfer
function model is able to predict the product
compositions. Optimization of the transfer function
model in methane/nitrogen separations will be also
carried out to obtain purer products and lower
power consumption. The transfer function model
also has the potential to be used in other gas
separations, such as CH4/CO2 separations and
N2/O2 separations.
Figure 5. Results for the comparison of changing
pressure ratio, temperature and configuration in
terms of yE, yL and RecHP. The parameter
combinations are: adsorption pressure (3bar and
5bar), feed temperature (302K and 240K), and DRPSA configuration (feed to high pressure column
configuration as HP, feed to low pressure column
as LP).
It can be observed from Figure 5 that feed to
low pressure column configuration can obtain a
better result in yL and RecHP, with a minor sacrifice
in yE. Either high adsorption pressure (pressure
ratio) or low temperature contributed to yL and
RecHP, with the lowest yL and highest RecHP
obtained in 3bar 240K LP configuration, being
880ppm and 98.7% respectively. We performed
simulation runs for adsorption pressure at 5bar and
temperature 240K as they seemed to contribute to
lowering yL individually, but resulted in an increase
in yL compared to the 5bar 302K and 3bar 240K
scenario, indicating the large nonlinearity between
adsorption pressure and temperature. Due to the
low selectivity of the adsorbent and the absence of
optimization of the model, the above results still
cannot achieve the separation objective. We believe
optimization needs to be carried out in search of the
best combinations of pressure ratio and temperature,
axial feed position, reflux ratios and feed step time.
Considering that this is only a preliminary run with
no optimization, the result is encouraging and again
proved that DR-PSA has the potential for efficient
separation, in terms of the low yL and high RecHP.
5
Output
Input
Table 2. Simulation results and comparison with experimental data
QF (sccm)
QLP (sccm)
QHP (sccm)
OT (sccm)#
yF
yL (ppm)
yE
RecLP
RecHP
2002 Scenario 2
Experiment
Model
Deviation*
580
555
-4.31%
548
544
-0.73%
11.8
11.0
-6.78%
1010
1010
0.00%
0.80%
0.80%
0.00%
<1
467
0.05%
41.0%
38.1%
-2.90%
99.9%
98.2%
-1.70%
100.0%
94.3%
-5.70%
2002 Scenario 3
Experiment
Model
Deviation*
544
543
-0.18%
538
536
-0.37%
6.8
6.8
0.00%
843
843
0.00%
0.78%
0.78%
0.00%
7
380
0.04%
63.9%
59.3%
-4.60%
99.9%
99.5%
-0.40%
99.9%
95.2%
-4.70%
2010 Scenario 1
Experiment
Model
Deviation*
583.6
583.3
-0.05%
573.0
573.1
0.02%
10.6
10.3
-3.10%
733.0
722.7
-1.41%
1.35%
1.35%
0.00%
2900
2000
-0.09%
62.6%
63.9%
1.27%
99.2%
99.4%
0.14%
80.8%
83.2%
2.42%
2010 Scenario 6
Experiment
Model
Deviation*
583.9
583.9
0.00%
574.0
574.0
0.00%
9.9
9.9
0.00%
1483.9
1483.9
0.00%
1.36%
1.36%
0.00%
1900
2520
0.06%
70.7%
65.6%
-5.10%
99.5%
99.4%
-0.10%
86.9
81.8%
-5.10%
* The percentage deviations of QF, QLP, QHP and QT are calculated as ((Model-Experiment)/Experiment*100%). For the concentrations and recoveries, the percentage deviations are directly
calculated by (Model-Experiment), as they are already in percentage.
#
QT was defined as the maximum flow rate in the DR-PSA internal material loop. The maximum flow rate value appears in the local feed stream to the first stripping/enriching PSA column
downstream of the actual feed, i.e. stream 1 in Figure 2.
6
NOMENCLATURE
3.
QF = volumetric feed rate, cm3(STP)min-1
QHR = volumetric flow rate of rich product,
cm3(STP)min-1
4.
QHP = volumetric flow rate of enriching reflux
stream, cm3(STP)min-1
QLR = volumetric flow rate of lean product,
cm3(STP)min-1
QLP = volumetric flow rate of stripping reflux,
cm3(STP)min-1
5.
QT = maximum volumetric flow rate in a DR-PSA
unit, cm3(STP)min-1
6.
PA = adsorption pressure, bar
PB = desorption pressure, bar
RS = stripping reflux ratio
RE = enriching reflux ratio
RecHP = recovery of heavy gas
7.
RecLP = recovery of light gas
tf = adsorption/desorption step time, s
yE = molar fraction of heavy gas in rich product
yF = molar fraction of heavy gas in feed
8.
yL = molar fraction of heavy gas in lean product
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