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Simulation and Optimization of the Acid Gas Absorption Process by
an Aqueous Diethanolamine Solution in a Natural Gas Sweetening
Unit
Nasrin Salimi Darani,*Reza Mosayebi Behbahani, Yasaman Shahebrahimi, Afshin Asadi,
and Amir H. Mohammadi*
Cite This: ACS Omega 2021, 6, 12072−12080
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ABSTRACT: The presence of carbon dioxide in natural gases can
lower the quality of natural gas and can cause CO2freezing
problems. Therefore, using reliable techniques for the reduction
and elimination of carbon dioxide from natural gases is necessary.
The aqueous diethanol amine (DEA) solution’sabilityto
simultaneously absorb H2S and CO2from sour natural gases
makes it possible to use this solution in the natural gas sweetening
process. The goal of this work was to determine the maximum
amount of the removed CO2by an aqueous DEA solution in one
of the gas sweetening plants of the National Iranian South Oilfields
Company (NISOC). For this purpose, based on the obtained
designed experiment results using the L9 orthogonal array Taguchi
method, the experiments were conducted and three levels of amine
concentrations (25, 28, and 30 wt %), temperatures (40, 50, and 60 °C), and circulation rates of lean amine (220, 240, and 260 m3
h−1) were considered as the key operational parameters on CO2removal. To evaluate the ability of the HYSYS simulation software
and the Kent−Eisenberg thermodynamic model to predict CO2absorption by an aqueous DEA solution in the gas sweetening
process, the field data were compared with the results of the simulation. It was observed that the maximum removal of CO2is
achieved at a lean amine concentration of 30 wt %, a temperature of 40 °C, and a circulation rate of 260 m3h−1. Also, the
experimental results indicate that the effects of the selected process variables on CO2absorption are not linear and the most effective
parameter on carbon dioxide removal is the concentration of amine in an aqueous solution and the temperature of the lean amine
has the least effect. Besides, the obtained simulation results are in the range of the unit design basis but have some deviations from
field data. The findings of this study can help in better understanding of the selection of the effective variables in the natural gas
sweetening process and obtaining their appropriate values to achieve the highest efficiency.
■INTRODUCTION
Natural gases normally contain some impurities such as
hydrogen sulfide, carbon dioxide, water vapor, heavy hydro-
carbons, and mercaptans. It is desirable to remove both H2S
and CO2(known as acid gases) to prevent corrosion problems
and increase the heating value of the gas.
1
Because of health
hazards, sale contracts, CO2freezing, and corrosion problems,
removing any sulfur compound and acid gas from natural gas
(called natural gas sweetening) is one of the most important
steps in natural gas processing. Some natural gas sweetening
methods such as adsorption,
2
chemical and physical
absorption,
3
and membrane separation
4
have been proposed
and their capabilities have been investigated. It has been
observed that factors such as gas flow rate, temperature,
pressure, acid gas selectivity required, and economics play
important roles in choosing an appropriate technique for
natural gas sweetening.
5−7
The most commonly used method
in acid gas removal is the absorption−desorption process, and
the most appropriate solvents are aqueous alkanolamine
solutions. The low operating cost, reactivity, and flexibility of
tailoring the solvent composition to suit gas compositions have
ledtoanincreaseintheusageofthisprocess.
5,6,8
Economically, the most important factor in designing an
absorption−desorption process is the solvent circulation rate.
A lower circulation rate leads to lower pumping energy cost
and therefore reduction of the regeneration energy required
Received: February 9, 2021
Accepted: April 6, 2021
Published: April 30, 2021
Article
http://pubs.acs.org/journal/acsodf
© 2021 The Authors. Published by
American Chemical Society 12072
https://doi.org/10.1021/acsomega.1c00744
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that can include about 70% of the total cost of operation of the
gas purification process.
9
Besides, vapor−liquid equilibrium
(VLE) modeling of acid gas−aqueous amine systems is
necessary for the synthesis, design, and analysis of gas
sweetening units. There are two categories of VLE models
for the description of gas−aqueous amine systems: the
empirical models based on the Kent−Eisenberg model and
activity coefficient- or excess Gibbs energy (Gex)-based models.
Kent and Eisenberg proposed a VLE model to predict the
equilibrium partial pressures of H2S and CO2in aqueous
monoethanolamine (MEA) and diethanolamine (DEA)
solutions.
10
Jou et al. applied such an approach for the
correlation of H2S and CO2solubilities in aqueous methyl
DEA (MDEA) solutions.
3
Moreover, Chakma and Meissen
extended the Kent−Eisenberg approach for the system of
CO2−DEA−H2O.
11
According to Weiland et al., the Kent−
Eisenberg correlation results show a good agreement with
experimental data only in the loading range of 0.2 to 0.7 moles
acid gas per mole of amine, and the model gives inaccurate
results for mixed acid gases.
12
Haji-Sulaiman et al. extended the
Kent−Eisenberg model to estimate the CO2loading in the
aqueous mixtures of DEA, MDEA, and DEA−MDEA, and it
was observed that this model forecasts a relatively accurate
carbon dioxide loading over a wide range of operating
conditions.
13
Ebenezer evaluated the HYSYS capability to
estimate the CO2removal at operating conditions of
minimizing hydrocarbon and chemical losses.
14
Also, Aliabad
and Mirzaei studied the accuracy of HYSYS and ASPEN
simulators in gas sweetening forecasting by aqueous amine
solvents.
15
The Aspen HYSYS software was also applied to
simulate and optimize the gas sweetening process using several
amine types and blends, for example, DEA,
16
MEA,
16
MDEA,
16−18
DGA,
19
and MEG
20,21
aqueous solutions and
the effects of operating conditions such as circulation rate,
concentration, and inlet temperature of amine on the
regeneration reboiler temperature and duty were studied.
So far, no comprehensive study has been conducted on the
effects of process parameters on increasing the efficiency of
natural gas sweetening units in Iran, and it seems that the study
on this issue is very important to reduce production and
processing costs and solve existing problems. This study aimed
to investigate the maximum CO2removal from the sour
natural gas by an aqueous DEA solution in the Amak gas
treating unit (GTP) of the National Iranian South Oilfields
Company (NISOC). This removal process was optimized in
different levels of the amine concentration, temperature, and
circulation rate of lean amine using the Taguchi Method.
Furthermore, the accuracy of the Kent−Eisenberg thermody-
namic model in the Aspen HYSYS v.8 simulator to estimate
CO2absorption by an aqueous DEA solution in the gas
sweetening process was investigated. The flowchart summariz-
ing the applied method and the steps is demonstrated in Figure
1.
Studying the gas sweetening unit and providing field data
can be considered as one of the advantages of this work. Also,
applicable conditions allow using the obtained results to
increase the performance efficiency of related units. On the
other hand, the unit equipment was designed for specific
conditions; therefore, the possibility of changing the process
variables is not perceptible and these changes can be applied in
a limited range. Besides, significant alterations in the operating
variables can lead to changes in the quality of output products
and disruption in downstream units. Thus, these limitations
have prevented a more complete study by investigation on
further variables with a wide range of variations.
In this article the definitions are presented. Then, the
operating conditions and control are presented. In the
Experimental Procedure and Sampling section, different stages
of the experiments and sampling are described, and in the
Steady-State Simulation and Optimization section, the
capabilities of Aspen HYSYS and the Kent−Eisenberg
thermodynamic model in the CO2absorption estimation by
an aqueous DEA solution in this case study are investigated.
The results of the experimental and modeling study are
reported as tables and figures in the Results and Discussion
section and their evaluations are presented in this section. In
the Conclusion section, an overview of the obtained results is
provided.
■RESULTS AND DISCUSSION
Experimental and Analysis Results. The measured CO2
concentration in the sweet natural gas for each trial and sample
is listed in Table 1. It can be observed that trial 2 has the
highest variation in its results and also the proposed
operational conditions in trial 7 including a temperature of
40 °C, a DEA solution concentration of 30 wt %, and an amine
circulation rate of 260 m3h−1lead to the best CO2removal
result. The results of the signal-to-noise (S/N) ratio for each
trial are presented in Table 2.
The effect of each factor was estimated by calculating the
average value of the S/Nratios at the total levels of the factor.
The factor effect was the arithmetic difference between the
maximum and minimum amounts of S/Nratios and is listed in
Table 3. The magnitude of the values in the last column
(Lmax−Lmin) shows the influence of the factors and the minus
Figure 1. Flowchart summarizing the steps applied.
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sign indicates a decrease in the S/Nratio as a variety of levels.
Preliminary reviews show that the amine concentration has the
greatest difference in the levels, and consequently, it can affect
the CO2concentration in sweet natural gas as the target,
significantly.
The evaluation of the influence of the individual factor was
obtained from the analysis of variance (ANOVA) and is shown
in Table 4. It can be revealed that the amine concentration
with a contribution percent of 46.14% plays the most
significant influence on the process of CO2removal. The
percentage of error contribution points to the accuracy of the
experiments and experimental error, interactions, or uncontrol-
lable factors causing it. The ANOVA results show that the
percent contribution of error is low, 11.414%, and with the
best estimate, it could be assumed that unconsidered factors
did not vary during the experiments, and the experiments were
performed under controlled conditions.
22,23
To investigate the main effects of the factors on the trail
conditions, the S/N ratio average for each parameter was
plotted versus the various levels and is illustrated in Figure 2.
According to the slope of the lines, the concentration of lean
amine has the highest impact on the response and an increase
in lean amine concentration causes CO2removal to increase
strongly. Also, it can be observed that the increasing
concentration of the aqueous DEA solution from 25 to 28
wt % (level 1 to 2) leads to an increase in the CO2absorption
gradually and increases the absorption steeply from 28 to 30 wt
%. Thus, it is concluded that the effect of lean amine
concentration on CO2absorption is not linear and it can be a
source of error for estimating Yexp. The second section of
Figure 2 describes the response graph as a function of the lean
amine circulation rate. As can be seen, such as lean amine
concentration, an increase in the circulation rate increases the
carbon dioxide concentration but with a lower slope. The
circulation rate from 220 to 240 m3h−1(level 1 to 2) has no
weighty impact on the target, but from 240 to 260 m3h−1
(level 2 to 3), the CO2absorption increases sharply. The effect
of the lean amine temperature changes on the CO2absorption
is pictured in the third section of Figure 2. Raising the lean
amine temperature causes the S/Nratio to decrease. It is
expected that temperature increase reduces gas absorption.
However, reduction of the S/N ratio in the alteration
temperature from 40 to 50 °C (level 1 to 2) is sharp and in
another temperature alteration from 50 to 60 °C is almost
constant. The effects of the last two factors are nonlinear too
(as lean concentration) and cause errors.
Estimated Results at the Optimum Condition. To
calculate the expected results at the optimum condition, the
grand average of performance, Rt
̅
, was evaluated. All factors in
this work were significant and the performance at the optimum
condition should be calculated using all of them. According to
Table 3, a high level of lean amine concentration factor, a low
level of temperature factor, and a high level of lean amine
circulation rate factor were given at the highest S/N values and
were considered as the optimal condition. The contribution of
each factor was calculated and is presented in Table 5 along
with the optimum settings and levels of the various factors.
Since the S/Nratio is used, the estimated result at the
optimum condition can be converted back to the scale of
original observation units. In this case, the expected result in
Table 1. L9 (33) Table and Experiment Results
level description
results
(CO2concentrationinsweetnaturalgas)(ppm,-
mole)
trial number amine concentration (wt %) temperature (C) circulation rate (m3h−1) sample 1 sample 2
1 25 40 220 6 8
2 25 50 240 35 41
3 25 60 260 5 8
4 28 40 240 4 4
5 28 50 260 3 4
6 28 60 220 19 18
7 30 40 260 trace (0.005) trace (0.005)
8 30 50 220 0.5 2
9 30 60 240 1 3
Table 2. S/NRatios of Trials
trials 123456789
S/Nratio −16.990 −31.623 −16.484 −12.042 −10.970 −25.347 46.020 −3.274 −6.990
Table 3. Main Effects of the Individual Factors
factor level 1 level 2 level 3 Lmax
−Lmin
concentration −21.699 −16.119 11.919 33.618
temperature 5.663 −15.289 −16.274 21.937
Rate −15.204 −16.885 6.189 23.074
Table 4. Analysis of Variance
factors DOF S·SVF S′percent P (%)
concentration 2 1947.419 973.709 17.172 1834.017 46.14
temperature 2 921.154 460.577 8.122 807.751 20.321
Rate 2 992.87 496.435 8.755 879.468 22.125
Error 2 113.402 56.701 17.172 11.414
Total 8 3978.848 100
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terms of the S/Nratio is 41.036. This is equivalent to an
average performance Yexp = 0.009, which is calculated using eqs
1and 2.
M
SD 10 S
N
()/10
=[−
]
(1)
Y
(MSD)
exp 0.
5
=(2)
Interaction Study. The difference between the exper-
imental and the estimated values can be related to the
interaction between the control factors.
24
Hence, an
interaction study was performed with the software Qualitek-
4. The intensity of the presence of interactions was measured
in terms of a numerical quantity via the angle between the two
lines of the selected factors. Table 6 shows the interacting pair
factors and their severity index (S·I). The last column indicates
the desirable levels to achieve the optimum condition. As can
be observed, temperature and the circulation rate have the
highest interaction with an S·I of 21.44%. Also, it can be found
that the interaction indices are negligible, which is predictable
because the factors are independent and could not interact
with each other.
Simulation Results. To evaluate the capability of Aspen
HYSYS in estimating the CO2absorption by an aqueous DEA
solution in the gas sweetening process, the field data were
compared with the simulation results. Thus, after running each
trial and sample analysis, the operating conditions of each trial
were fed to Aspen HYSYS and the process was stimulated by a
selection of amine packages and the Kent−Eisenberg
thermodynamic model.
The input data for the absorption column in the simulation
was as follows: the pressure at the top of the tower was 28 bar,
the pressure at the lower part was 29.75 bar, and the number of
trays was 20. For the stripping column simulation, the
pressures at the top and the lower part of the column were
1.7 and 2.2 bar, respectively. Also, the number of trays was 24,
and to converge the stripping column, the temperatures of the
condenser and reboiler were specified with the values of 60 and
125 °C, respectively.
According to the unit design basis, the lean amine loading
must be less than 0.02 mole (CO2+H
2S)/mole DEA. The
amount of lean amine demonstrates the quality operation of
the regeneration unit and increasing the amount of lean amine
loading leads to a decrease in acid gas absorption. As
mentioned earlier, after running each trial, lean amine was
analyzed, and keeping the lean amine loading below the
designed quantity leads to the operation of the reboilers of the
regeneration unit in the maximum duty. Besides, rich amine
loading was controlled by adjusting the amine circulation rate.
Figure 2. Value of S/N ratio at various levels for each control factor.
Table 5. Optimum Condition of CO2Removal by an
Aqueous DEA Solution
control factor level description level contribution
concentration 30 3 20.551
temperature 40 1 14.296
circulation rate 260 3 14.822
Table 6. Table of the Test of Interactions
interacting factor pair column
interaction S·I% optimum
level
temperature ×rate 2 ×3 21.44 [1,3]
concentration ×rate 1 ×3 17.99 [3,3]
concentration ×temperature 1 ×2 10.11 [3,1]
Table 7. Comparison between Field and Simulation Data for CO2Content and Loading
loading (mole CO2+ mole H2S)/mole DEA
CO2concentration in sweet natural gas (ppm mole) rich amine
trial No lab 1 lab 2 simulation lean amine lab simulation error %
trial 1 6 8 216 0.0134 0.4929 0.4739 −3.85
trial 2 35 41 70 0.0124 0.4011 0.4216 5.11
trial 3 5 8 30 0.0120 0.4016 0.4142 3.14
trial 4 4 4 175 0.0090 0.3602 0.4752 31.93
trial 5 3 4 48 0.0115 0.4639 0.3948 −14.90
trial 6 19 18 35 0.0103 0.3475 0.4530 30.36
trial 7 0.005 0.005 147 0.0065 0.3922 0.4139 5.53
trial 8 0.5 2 68 0.0122 0.3453 0.3992 15.61
trial 9 1 3 22 0.0110 0.2880 0.3558 23.54
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According to the basic design of the unit, the amount of rich
amine loading must be kept at 0.5 mole (CO2+H
2S)/mole
DEA. The high acid gas loading enhances steel corrosion and
consequently increases the iron sulfide content, which
amplifies foaming tendency. The experimental and estimated
CO2contents in the sweet natural gas and amine loadings are
compared in Table 7. It is found that the estimated CO2
concentration in the sweet natural gas by Aspen HYSYS is
much greater than that of field data. Hence, the Kent−
Eisenberg model in HYSYS is not an appropriate model for
this process. Consequently, simulated amine loadings have
some deviations from field data, but both of them are in the
range of design basis of the plan.
The experimental data and simulated results of H2S content
in the sweet gas are presented in Table 8. As can be seen, there
is a significant difference between the experimental and
simulated values of H2S content in the sweet natural gas,
while the operating conditions are controlled to achieve a value
of less than 4 ppm of H2S in the sweet natural gas. Also, it is
observed that regardless of the circulation rate, in the same
level of lean amine concentration, such as trials 1 to 3 or trials
4 to 6, increasing the amine temperature leads to the reduction
of the estimated CO2content and an increase in the simulated
H2S content in sweet natural gas (Figures 3−5). It seems that
at a higher temperature, the kinetic effect is stronger than
solubility decrease, and the H2S concentration in the sweet
natural gas increases monotonically with lean amine temper-
ature due to the decreasing solubility.
■CONCLUSIONS
In this study, the experimental design method was applied to
formulate the experimental layout and optimize the operating
conditions of a natural gas sweetening unit to remove the
maximum CO2by an aqueous DEA solution. For this purpose,
an L9 orthogonal array Taguchi method as a statistical
experimental design was applied, and the effects of lean amine
concentration, temperature, and circulation rate in three levels
as the key control factors on CO2absorption by an aqueous
DEA solution were investigated. The experiments were carried
out in the Amak GTP at amine concentrations of 25, 28, and
30 wt %, the temperatures of 40, 50, and 60 °C, and the lean
amine circulation rates of 220, 240, and 260 m3h−1.
Furthermore, the accuracy of the Aspen HYSYS Kent−
Eisenberg thermodynamic model was evaluated and field
data and simulation results were compared with each other.
From this study, the following conclusions can be drawn:
(1) It is revealed that the lean amine concentration is the
most significant control factor on CO2absorption using
aqueous DEA solution with a contribution percent of
about 46.14%, while the lean amine circulation rate and
temperature have a contribution of 22.125 and 20.321%,
respectively.
(2) Field data indicate that the CO2content for all the trials
is less than 50 ppm mole.
(3) The ANOVA-calculated error percent is lower than 15%
(11.414%). This means that all of the key operating
parameters are considered and no significant control
factors are left out from the experimental condition.
(4) The interaction study indicates that there is no
significant interaction between the control parameters.
(5) The maximum removal of CO2from natural gas by an
aqueous DEA solution is achieved at a lean amine
concentration of 30 wt %, a temperature of 40 °C, and a
circulation rate of 260 m3h−1.
(6) The effects of concentration, temperature, and circu-
lation rate of lean amine on CO2absorption are not
linear.
(7) The estimated results from the Kent−Eisenberg
thermodynamic model in the Amine Package of Aspen
HYSYS process simulator show deviations from field
Table 8. Comparison between Field and Simulation Data
for CO2and H2S Content in the Sweet Natural Gas
CO2concentration in the sweet
natural gas (ppm mole)
H2S concentration in the
sweet natural gas
(ppm mole)
trial no lab 1 lab 2 Simulation lab 1 lab 2 simulation
trial 1 6 8 216 0 0 1.04
trial 2 35 41 70 0 0 1.63
trial 3 5 8 30 0 0 2.98
trial 4 4 4 175 1 1 0.75
trial 5 3 4 48 1 1 1.50
trial 6 19 18 35 0 0 2.56
trial 7 0.005 0.005 147 0 0 0.58
trial 8 0.5 2 68 0 0 1.90
trial 9 1 3 22 0 0 2.29
Figure 3. Lean amine temperature vs. acid gas concentration in sweet natural gas at a lean amine concentration of 25 wt %.
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data. Thus, this simulator is not a proficient simulator for
the determination of CO2concentration in sweet natural
gas in the natural gas sweetening process.
(8) Regardless of the circulation rate, in the same level of
lean amine concentration, increasing the amine temper-
ature leads to the reduction of the estimated CO2
content and increase of the simulated H2S content in
sweet natural gas.
(9) A decrease in solubility leads to an increase in the
concentration of hydrogen sulfide in the sweet natural
gas with the increasing temperature of lean amine.
(10) Simulated lean and rich amine loadings show some
deviations from field data, but both of them are in the
range of the unit design basis.
It seems that if there are no operational restrictions in
changing the parameters affecting the process, more findings
would be obtained and the results would be more general-
izable.
Definition. Process Description. The unit was designed to
reduce the H2S content in the sweet gas and sweet liquid to
below 4 and 50 ppmv, respectively. As can be observed in
Figure 5, the unit is divided into three sections: slug catchers,
gas sweetening−amine trains A and B, and liquid sweetening.
The feed gases are different sour gases from different stations
that are mixed and enter the slug catchers SC-801A/B. In this
stage, the mixture is condensed and separated. The obtained
liquid is fed to the liquid sweetening unit for sulfur removal
utilizing a stripping stream and the gas portion is fed to a gas
sweetening unit for H2S and CO2removal by using an aqueous
DEA solution. The treated gas, which contains less than 4
ppmv of H2S and 0.4% mole of CO2, is sent to the NGL 700/
800 unit and some of it is used as a stripping medium in the
liquid sweetening unit to reduce the H2S content in the treated
liquid up to less than 50 ppmv and enters the NGL 700/800
unit. The produced acid gas goes to the compression station
for further treatment.
Process Chemistry. The acid gas absorption is not a
physical process only. Only one fraction of H2S ionizes in
water to hydrogen ions and sulfide ions
25
HS HO (HO) (HS)
22 3
+↔ +
−
(3)
DEA is a weak base and ionizes in water to form amine ions
and hydroxyl ions
25
RNH HO (RNH) (OH)
2222
+↔ +
−
(4)
where R indicates the ethanol group CH2CH2OH. While H2S
dissolves into a solution containing amine ions, a weakly
bonded salt of the acid and the base is produced as below, and
then, the sulfide ion is absorbed by the amine solution.
25
(
RNH) (HS) RNHSH
22 22
+↔
+−
(5)
Figure 4. Lean amine temperature vs. acid gas concentration in sweet natural gas at a lean amine concentration of 28 wt %.
Figure 5. Lean amine temperature vs. acid gas concentration in sweet natural gas at a lean amine concentration of 30 wt %.
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Based on reaction 5, the reaction of salt formation is not
complete. As the arrows indicate, an equilibrium level of H2S
remains in the hydrocarbon flow. Thus, the overall reaction
can be summarized as follows
25
RNH HS RNHS
H
2222
+↔ (6)
Operating variables are set to raise forward reaction 6 in the
absorption step and enhance the reverse reaction in the
regeneration step. The CO2absorption is achieved according
to the following reactions
25
CO HO HCO
22 2
3
+↔
(7)
2
RNH HCO (RNH)CO
223222
3
+↔ (8)
2
RNH 2HCO (RNH)HCO
22322
3
+↔ (9)
The CO2absorption is slower than the H2S absorption
because reaction 7 is carried out slowly and occurs first. The
rate of all absorption reactions is enhanced at high pressures
and low temperatures, and also, high H2S and CO2contents
shift the equilibrium reactions toward the right side. The amine
regeneration is performed at low pressures and high temper-
atures and shifts the equilibrium of the mentioned reactions to
the left side. On the other hand, the low H2S and CO2partial
pressures of the generated stripping vapor in the reboiler lead
to a high driving force for the H2S and CO2mass transfer.
Operating Conditions and Control. The absorption of
H2S/CO2into the aqueous amine solution is increased by five
factors:
26,27
low temperature, low acid gas loading, high amine
concentration, high H2S/CO2partial pressures in the feed
stream, and intimate contact. In general, the fourth and fifth
factors are not operating variables and are fixed by the unit
design criteria and choosing equipment in the absorbers’
design. Furthermore, low feed rates may, however, cause poor
tray efficiency and thus somewhat a poor H2S/CO2removal in
comparison with achievable at or near design flow rates. In
general, decreasing the temperature of the lean amine solution
causes an increase in H2S/CO2removal. Besides, the lean
amine temperature must be maintained at 10 °C higher than
the temperature of the gas feed stream to avoid any possible
condensation of the hydrocarbon vapors. The lean amine is
cooled typically by air to about 60 °C.
It should be noted that the acceptable acid gas removal
efficiency depends on good aqueous amine solution regener-
ation and restricting the H2S/CO2loading in the rich amine to
favor the forward direction of reaction 6.TheH
2S/CO2
loading of the aqueous amine solution is controlled by
adjusting the amine circulation rate.
■EXPERIMENTAL PROCEDURE AND SAMPLING
As mentioned earlier, this study was undertaken on the GTP of
Amak. To measure accurate and reliable data, all flow
instruments of sour gas, sweet gas, lean amine, and rich
amine were calibrated. After calibration, operating variables,
including lean amine concentration, temperature, and circu-
lation rate of the trial, were set and each trial was run. When a
steady state was achieved, flow sampling was performed. The
CO2content of two obtained samples from sweet gas was
measured, and the analysis of two samples of sour gas as a feed
was performed to simulate a process with field data. The
carbon dioxide and hydrogen sulfide contents in the gas
samples were analyzed by the gas chromatography method.
For this purpose, a calibrated Agilent 6890 series gas
chromatograph equipped with a DB-1 capillary column was
Figure 6. Feed source of GTP.
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applied. The following chromatographic conditions were set to
provide accurate results:
28
manual splitless injection, an inlet
temperature of 105 °C, a total gas flow rate of 30 mL min−1,a
column gas flow rate of 2 mL min−1, and a detector outlet
temperature of 200 °C. Helium was used as a carrier gas.
28
The
H2S+CO
2loading of lean amine must be less than 0.02 and it
was measured for two samples after running each trial. It
should be noted that the reboiler in the regeneration unit of
GTP operated at the maximum duty to achieve the minimum
amount of acid gas loading for lean amine and the acid gas
loading of rich amine shall not exceed 0.5 mole H2S+CO
2per
mole of amine and it was checked for two samples of rich
amine.
■STEADY-STATE SIMULATION AND OPTIMIZATION
The capability of the Aspen HYSYS and Kent−Eisenberg
thermodynamic model in the CO2absorption estimation by an
aqueous DEA solution in the Amak gas sweetening process was
evaluated, and the field data were compared with the
simulation results, as mentioned earlier.
The Taguchi method and the software Qualitek-4 were
applied to optimize the operating variables of the CO2removal
process from sour gas including lean amine concentration,
temperature, and circulation rate. The levels of the selected
parameters are listed in Table 9. In conclusion, an L9
orthogonal array for three-level factors was selected as the
experimental layout to design the trials and determine the
effects of various parameters on the CO2removal process yield
using the aqueous DEA solution in GTP. To evaluate and
analyze the results of the Taguchi design, CO2concentration in
the sweet gas was considered as the main target value with the
quality characteristic of “Smaller is better”. Whereas the
experiments included multiple samples per trial condition, the
S/Nratios of the experimental results were used to compute
the main effects of the individual factors. In general, the aim of
any experiment is always to determine the highest possible S/
Nratio for the result. A high S/Nratio implies that the signal is
much higher than the random effects of the noise factors. After
calculating the S/Nratio for each experiment, the average S/N
value is calculated for each factor and level. Determining the
differences between the S/Nratio values at the high and low
levels of a factor presents the main effects of the single factor.
To evaluate the quality characteristics and visual presentation,
the average effects of the factors are graphed on an appropriate
scale. Therefore, the S/Nratio for each factor and the average
S/Nratio versus levels are also plotted to study the trend of the
influence of the factors. The Taguchi design can also predict
the optimal condition and performance by ANOVA analysis.
■AUTHOR INFORMATION
Corresponding Authors
Nasrin Salimi Darani −Department of Gas Engineering,
Ahwaz Faculty of Petroleum Engineering, Petroleum
University of Technology (PUT), Ahwaz 7118361991, Iran;
Email: salimi.nasrin99@gmail.com
Amir H. Mohammadi −Discipline of Chemical Engineering,
School of Engineering, University of KwaZulu-Natal, Durban
4041, South Africa; orcid.org/0000-0002-2947-1135;
Email: amir_h_mohammadi@yahoo.com
Authors
Reza Mosayebi Behbahani −Department of Gas Engineering,
Ahwaz Faculty of Petroleum Engineering, Petroleum
University of Technology (PUT), Ahwaz 7118361991, Iran
Yasaman Shahebrahimi −Department of Chemical
Engineering, Faculty of Engineering, Arak University, Arak
38156879, Iran; orcid.org/0000-0003-4772-0200
Afshin Asadi −Department of Chemical Engineering, Faculty
of Engineering, Arak University, Arak 38156879, Iran;
Shintech Ethane Cracker Plant, Plaquemine 70764,
Louisiana, United States
Complete contact information is available at:
https://pubs.acs.org/10.1021/acsomega.1c00744
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
The authors thank the National Iranian South Oil Company
(NISOC) for supporting this research study.
■NOMENCLATURE
DEA, diethanolamine
DOF, degree of freedom
DGA, diglycol amine
F, F-ratio
GTP, gas treating unit
MEA, monoethanolamine
MDEA, methyldiethanolamine
MEG, mono ethylene glycol
QC, quality characteristic
R, ethanol group
S′, pure sum
S·I, severity index
S/N, signal-to-noise
S·S, sum of squares
V, variance
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