Simulating combined cycle gas turbine power plants in aspen hysys

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Energy Conversion and Management
journal homepage: www.elsevier.com/locate/enconman
Simulating combined cycle gas turbine power plants in Aspen HYSYS
Zuming Liu, Iftekhar A. Karimi
⁎
Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore
A R T I C L E I N F O
Keywords:
Simulation
Gas turbine
Combined cycle
Power plant
Aspen HYSYS
A B S T R A C T
Combined cycle gas turbine (CCGT) power plants are becoming increasingly important for electricity generation.
Enhancing their thermal performance is essential for mitigating carbon emissions. This paper aims to present a
methodology for simulating the off-design operation of a triple-pressure reheat CCGT plant in Aspen HYSYS. The
modeling equations that rigorously capture the full off-design characteristics of various plant components (i.e.
compressor, combustor, turbine, heat recovery steam generator, and steam turbines) are implemented in Aspen
HYSYS, and a specially tailored procedure is proposed for solving them. The modeling strategy and solution
procedure can be extended to simulate the off-design operation of any CCGT plants and are generically ap-
plicable to other process simulators (e.g. Aspen Plus, Unisim, and Pro II). To evaluate the model’s performance,
its predictions are compared with those of an equivalent model from GateCycle. The results show the predictions
of the two models (Aspen HYSYS and GateCycle) agree well. The average differences for the power outputs and
thermal efficiencies of the gas turbine, steam cycle, and CCGT plant are less than 2.0%, 1.5%, and 0.6%, re-
spectively. Besides, the differences arise primarily from the different gas enthalpy calculations. Since the model
enables easy integration with various energy systems and can be made dynamic for predicting real-time behavior
in Aspen HYSYS, it is very useful with wide applications.
1. Introduction
Energy and environment are the two major global concerns of this
century. The global warming caused by the greenhouse gas emissions is
an existential threat. CO2 is considered as the main cause, and more
than 40% of the CO2 emissions stem from the power industry [1]. As a
result, much effort is underway on producing clean, green, and efficient
electric power. Combined Cycle Gas Turbine (CCGT) power plants are
one promising solution due to their high thermal efficiencies and low
CO2 emissions [2]. Nowadays, CCGT plants are undergoing widespread
installations. Some countries like Singapore produce more than 96% of
their electric power from CCGT plants [3].
Power plants operate under off-design (especially part-load) con-
ditions during most of their lifetimes. For example, a power plant in
Nigeria produced only 64.3% of its design capacity from 2001 to 2010
[4]. The part-load operation arises from several factors. First, the power
demand is hardly steady and rarely equals the plant design capacity.
Second, a power plant is required to maintain spinning reserves (sur-
plus capacity) to guard against unforeseen peaks in demands. Third, a
power plant may often be overdesigned to buffer against demand un-
certainties. The part-load operation decreases the plant’s thermal effi-
ciency, incurring higher fuel consumption and CO2 emissions. There-
fore, strong incentives exist for studying and optimizing the part-load
operation. To this end, rigorous simulation models that accurately
capture the full details of a power plant’s part-load operations are
needed. Such simulation models provide the basis for a variety of
routine operational tasks, such as benchmarking, process control, pro-
cess optimization, condition monitoring, fault diagnosis, performance
analysis, and performance improvement.
Zhang and Cai [5] proposed some analytical formulas for com-
pressor and turbine and combined them to predict the gas turbine
performance. Aklilu and Gilani [6] adopted the normalized parameters
from [7] to describe the characteristics of compressor and turbine and
developed a simulation model in Matlab [8] to identify the plant op-
eration mode from field data. Zhang et al. [9,10] presented a simulation
program in Excel to study the off-design characteristics of combined
cycles under different design parameters. While models in Matlab or
Excel offer much freedom in model formulation and are attractive from
a cost perspective, they are not user-friendly and require much pro-
gramming and approximations. In addition to the modeling process
being tedious, complex, and error-prone, the models may suffer from
numerical and convergence issues due to the complex nonlinear itera-
tive calculations.
On the contrary, commercial software offer a nice graphical user
interface, superior reliability, and enhanced accuracy with little or no
programming. Hence, commercial software such as GateCycle [11],
https://doi.org/10.1016/j.enconman.2018.06.049
Received 14 February 2018; Received in revised form 12 June 2018; Accepted 13 June 2018
⁎
Corresponding author.
E-mail address: cheiak@nus.edu.sg (I.A. Karimi).
Energy Conversion and Management 171 (2018) 1213–1225
Available online 23 June 2018
0196-8904/ © 2018 Elsevier Ltd. All rights reserved.
T

EBSILON Professional [12], and Thermoflow [13] have been preferred
for studying power plants. Silva et al. [14] developed a thermodynamic
information system in GateCycle for detecting plant operation anoma-
lies and evaluating the performance gain from eliminating them. Lee
et al. [15] proposed an analysis tool in GateCycle for predicting the
plant generation capacity using the correction curves of gas and steam
turbines. Liu and Karimi [16] presented the necessary correlations for
simulating a CCGT plant in GateCycle and proposed a simulation-based
method for maximizing its part-load performance. Aminov et al. [17]
evaluated the fuel saving and reduction in CO2 emissions from repla-
cing a thermal power plant by a CCGT plant using EBSILON Profes-
sional. Since GateCycle, EBSILON Professional and Thermoflow are
principally designed for power plants, they offer a nice simulation ex-
perience. However, their versatility is limited in modeling other energy
systems or options (e.g. CO2 capture [18–20], Organic Rankine Cycles
(ORCs) [21–23], fuel cells [24–26], LNG terminals [27–29], air se-
paration [30–32], and absorption chillers [33–35]). For instance,
GateCycle is unable to model these energy systems. Although EBSILON
Professional and Thermoflow offer special blocks for some energy op-
tions (e.g. CO2 capture, fuel cells, air separation and absorption chil-
lers), they simulate them as black boxes. Hence, they cannot offer the
full simulation details and freedom for process modification. To avoid
these shortcomings, Nord et al. [36] and Karimi et al. [37] modeled CO2
capture process in Aspen HYSYS and Unisim respectively, while Lee
et al. [38] simulated air separation for a gasification process in Aspen
HYSYS. However, doing so requires interfacing two separate simulation
programs (e.g. Thermoflow/GateCycle and Aspen HYSYS/Unisim) with
their different architectures and properties, and complex interactions
between them are difficult to manage. Therefore, it is desirable to si-
mulate both power plants and various energy systems in one seamless
environment or platform such as a more versatile process simulator.
This is crucial to facilitate easy integration between power plants and
these energy systems.
Aspen HYSYS [39] is a powerful process simulator with a large li-
brary of ready-made component models and in-built accurate property
packages. By connecting the various components via material and en-
ergy streams, Aspen HYSYS can simulate both the steady and dynamic
performance of complex chemical/hydrocarbon fluid-based processes
[40–44]. This enables the simulation of both power plants and asso-
ciated energy systems or options. Hence, Aspen HYSYS does not have
the aforementioned shortcomings and offers an attractive platform for
simulating power plants. However, modeling the CCGT plants under
off-design conditions in Aspen HYSYS is challenging due to its se-
quential modular nature. In Aspen HYSYS, all plant components must
be solved in a sequential rather than simultaneous manner. The highly
complex steam circuits that involve mass/energy recycle in the CCGT
plants require simultaneous solution and thus pose significant chal-
lenges to Aspen HYSYS. Furthermore, detailed compressor map and
turbine characteristics have to be used for simulating CCGT off-design
performance. This requires clever constructs and implementation in
Aspen HYSYS. Therefore, a tailored non-obvious procedure is needed
for simulating the CCGT plants under off-design conditions in Aspen
Nomenclature
Symbols
A area, m2
C swallowing capacity
c1,c2,c3 IGV angle correction factors
Fcu copper loss fraction
m mass flow rate, kg/s
L generator load
LHV lower heating value
N shaft speed, rpm
P pressure, bar
PL percent part-load
PR pressure ratio
ΔP pressure drop, kPa
Qloss heat loss, kW
R gas constant
T temperature, K
U overall heat transfer coefficient, kJ/(s m2
K)
W power, kW
Greek letters
Δα IGV angle
γ specific heat ratio
π expansion ratio
η efficiency
λ constant
κ constant
ν specific volume
φ (γ − 1)/γ
Ω combustor loading
Subscripts
a air
af air filter
c compressor
ca cooling air
cc combustion chamber
cor corrected value
d design condition
g flue gas
in inlet
map performance map
max maximum
min minimum
out outlet
st steam turbine
s steam
t turbine
Acronyms
BFD block flow diagram
CCGT combined cycle gas turbine
ECON economizer
EVAP evaporator
GT gas turbine
HP high pressure
HPP high pressure pump
HRSG heat recovery steam generator
IP intermediate pressure
IPP intermediate pressure pump
LP low pressure
LPP low pressure pump
RHT reheater
RP recirculation pump
SPHT superheater
SC steam cycle
TET Turbine exit temperature
IGVC inlet guide vane control
Z. Liu, I.A. Karimi Energy Conversion and Management 171 (2018) 1213–1225
1214

HYSYS, and to our knowledge, no study in the open literature has
presented such a procedure.
In this paper, a detailed model and a systematic procedure are
presented for simulating the off-design operation of a triple-pressure
reheat CCGT plant in Aspen HYSYS. The implementation of the rigorous
modeling equations for various plant components in Aspen HYSYS is
explained in detail. This produces an Aspen HYSYS model that captures
the full details of the CCGT plant. A tailored procedure is then proposed
for solving the Aspen HYSYS model. Finally, to evaluate the model’s
performance, its simulation results are compared with those of an
equivalent model from GateCycle.
2. Methodology
2.1. Combined cycle gas turbine (CCGT) power plant
Fig. 1 shows the schematic of a triple-pressure reheat CCGT power
plant. The plant comprises a Gas Turbine (GT), a Heat Recovery Steam
Generator (HRSG), and three Steam Turbines (STs). The GT has an Air
Compressor (AC) and a turbine running on a common shaft with a
combustor in between. The common shaft rotates at a constant speed to
deliver a fixed frequency (50 or 60 Hz) of power. The ambient air passes
through an air filter to the AC, and the combustor uses the pressurized
air from the AC to burn a gaseous fuel (e.g. natural gas) and feeds the
hot gas into the turbine, where it expands to produce power. As the
turbine blades are exposed to the hot gas from the combustor, some air
from the AC exit is supplied to keep them cool. The exhaust gas from
the turbine then goes through the HRSG, before being vented to the
ambient as a flue gas. The HRSG recovers the remaining heat from the
exhaust gas to produce steam.
The HRSG comprises three steam generation subsystems: High-
Pressure (HP), Intermediate-Pressure (IP), and Low-Pressure (LP). Each
subsystem has one feedwater pump (LPP, IPP, or HPP in Fig. 1), one or
more economizers, one evaporator, and one or more superheaters. The
feedwater from each pump gets preheated in the economizers, boiled in
the evaporator, and superheated in the superheaters. Two reheaters
(RHT1 and RHT2 in Fig. 1) are located between the HP superheaters.
Moreover, two desuperheaters (DESHT1 and DESHT2 in Fig. 1) be-
tween the HP superheaters and reheaters moderate the temperatures of
HP steam and reheat steam for safe operation by injecting water. Fur-
thermore, a recirculation pump (RP in Fig. 1) recycles some hot water
from the LP economizer exit back into its feed to prevent low-
temperature corrosion. The HP steam expands in an HP steam turbine
(HPST) and then mixes with the IP steam. The mixed steam enters the
reheaters and then expands in an IP steam turbine (IPST). The exhaust
steam from the IPST mixes with the LP steam and enters an LP steam
turbine (LPST). The three STs share a common shaft that rotates at the
same speed as the GT. After expansion, the exhaust steam from the
LPST goes to a condenser, and the condensate is pumped back via the
LPP to the LP economizer.
2.2. CCGT simulation in Aspen HYSYS
The following assumptions are made for simulating the triple-
pressure reheat CCGT plant in Aspen HYSYS.
• The CCGT plant is at steady state.
• The fuel combustion is complete in the combustor.
• There are no leaks of water or flue gas from the HRSG.
• The cooling water flow of the condenser is constant.
The modeling equations describing the full off-design characteristics
of various plant components rigorously are mainly from [16]. This
paper focuses on (1) the details and challenges of implementing them in
Aspen HYSYS, and (2) a tailored procedure for efficiently and reliably
solving the resulting Aspen HYSYS model for the CCGT plant. The Peng-
Robinson fluid package is used for air, fuel, and exhaust gas, while
ASME steam table is employed for water and steam. Fig. 2 shows our
complete Block Flow Diagram (BFD) for the CCGT plant in Aspen
HYSYS.
2.2.1. Air filter
The air filter is simulated by the Control Valve module (AFT in
Fig. 2(a)) in Aspen HYSYS. The pressure drop through the air filter
( PΔ af ) is given by the following equation [11,16].
⎜ ⎟ ⎜ ⎟ ⎜ ⎟= ⎛
⎝
⎞
⎠
⎛
⎝
⎞
⎠
⎛
⎝
⎞
⎠
−
P P
m
m
T
T
P
P
Δ Δ d
d d d
1.84 1
(1)
where PΔ is the pressure drop, m is the mass flow rate, T is the tem-
perature, P is the pressure, and subscript d denotes the design condi-
tion. While this is a precise approach to model the air filter, the pressure
drop is usually quite small (< 100 Pa) even at the design condition.
Hence, the pressure drop across the air filter is set as a fixed percentage
(0.5%) of the ambient pressure. It is computed in a Spreadsheet module
Fig. 1. Schematic of a triple-pressure reheat CCGT power plant.
1215

CCGT plant – GT (a)
CCGT plant – SC (b)
CCGT plant – SC (c)
Fig. 2. Block ﬂow diagram (BFD) for the CCGT plant in Aspen HYSYS: (a) Gas turbine (GT), (b–c) Steam cycle (SC).
1216

(SS1 in Fig. 2(a)) and then exported to AFT in Fig. 2(a).
2.2.2. Air compressor (AC)
The AC operating characteristics can be described by its perfor-
mance map, which for a typical GT compressor is expressed in terms of
pressure ratio (or isentropic efficiency) versus corrected mass flow and
corrected speed (see Fig. 3). However, the compressor vendors do not
share actual performance maps except with their customers. Hence, real
compressor maps are hard to find in the open literature, and an example
performance map is shown in Fig. 3. The map relates the following
dimensionless operational variables [16,45].
⎜ ⎟= ⎛
⎝
⎞
⎠
⎛
⎝
⎜
⎞
⎠
⎟m
m T
P
m T
P
Relative corrected mass flow: /cor r
in in
in
in d in d
in d
,
, ,
,
(2a)
= − −PR PR PRRelative pressure ratio: ( 1)/( 1)r d (2b)
=η η ηRelative isentropic efficiency: /r d (2c)
=N N T N TRelative corrected speed: ( / )/( / )cor r in d in d, , (2d)
where η is the efficiency, N is the shaft speed, and =PR P P/out in. Sub-
script cor denotes the corrected value, r denotes the relative value, in
denotes the inlet, and out denotes the outlet.
The AC is simulated by the Compressor module (AirCOMP in
Fig. 2(a)) in Aspen HYSYS. However, AirCOMP can only accept oper-
ating curves expressed in terms of pressure head (or isentropic effi-
ciency) versus volumetric flow, and not the one in Fig. 3. Hence, a
special procedure is needed to overcome this limitation.
For supplying the performance map in Fig. 3 to AirCOMP, equi-
spaced parabolic lines indexed by an auxiliary coordinate called β [46]
( ≤ ≤β0.4 1.0) are introduced on the map, as shown in Fig. 4. The β
lines intersect the speed lines (Ncor r, ) and each (β, Ncor r, ) defines a un-
ique point on the compressor map. Every point (β, Ncor r, ) on the map
represents a unique triplet of PRr, mcor r, , and ηr. Each of these three is
stored as a two-dimensional look-up table in SS-1 with β and Ncor r, as
arguments. Given any =x y β N( , ) ( , )cor r, , PRr, mcor r, and ηr are obtained
from these tables via bilinear interpolations (see Fig. 5) as follows.
=
−
−
+
−
−
f x y
x x
x x
f x y
x x
x x
f x y( , ) ( , ) ( , )1
2
2 1
1 1
1
2 1
2 1
(3a)
=
−
−
+
−
−
f x y
x x
x x
f x y
x x
x x
f x y( , ) ( , ) ( , )2
2
2 1
1 2
1
2 1
2 2
(3b)
=
−
−
+
−
−
f x y
y y
y y
f x y
y y
y y
f x y( , ) ( , ) ( , )2
2 1
1
1
2 1
2
(3c)
where f denotes PRr, mcor r, or ηr, x denotes β, y denotes Ncor r, , and
(x y,1 1), (x y,1 2), (x y,2 2), and (x y,2 1) denote the closest four points that
surround (x y, ) in a table of f x y( , ).
Modern ACs have variable inlet guide vanes (IGVs) whose openings
are varied to regulate the air flow. This opening is measured by an IGV
angle αΔ (normally ≤ ≤α0 Δ 40Âº), where =αΔ 0 corresponds to the
fully open IGVs. For a given αΔ , Eqs. (4)–(6) [47] can be used to correct
the PRr, mcor r, , and ηr read from the map.
= −PR PR c α(1 Δ )r IGV r, 1 (4)
= −m m c α(1 Δ )cor r IGV cor r, , , 2 (5)
= −η η c α(1 Δ )r IGV r, 3
2
(6)
where c1, c2, and c3 are vane angle correction factors.
Then, given β and Ncor r, , the AC can be simulated in Aspen HYSYS as
follows. Compute PRr, mcor r, and ηr from Eq. (3), PRr IGV, , mcor r IGV, , and
ηr IGV, from Eqs. (4)–(6), and PR, min and η from Eq. (2) all in SS-1.
Export PR and η to AirCOMP in Fig. 2(a) and min to its inlet stream (S1).
2.2.3. Combustor
The combustor is simulated by the Conversion Reactor module
(COMB in Fig. 2(a)) in Aspen HYSYS. The fuel combustion is defined as
a set of conversion reactions with 100% conversions. Then, for a given
fuel flow (mf ), the pressure drop in and heat loss from the combustor
are computed using the following equations [16,47] in SS-1, and ex-
ported to COMB in Fig. 2(a).
⎜ ⎟=
⎡
⎣
⎢
⎛
⎝
⎞
⎠
⎛
⎝
⎜
⎞
⎠
⎟
⎤
⎦
⎥P P
m T
P
m T
P
Δ Δ /cc cc d
in in
in
in d in d
in d
,
, ,
,
2
(7)
= −Q η m LHV(1 )loss d cc d f d f, , , (8a)
=
m
P T
Ω
exp( /300)
in
in in
1.8
(8b)
⎜ ⎟= ⎛
⎝
⎞
⎠
Q Q
m
m
Ω
Ω
loss loss d
f
f d d
,
,
1.6
(8c)
where PΔ cc is the pressure drop in the combustor, LHV is the lower
heating value, Qloss is the heat loss, Ω is the combustor loading, and
subscript f denotes the fuel.
2.2.4. Turbine
In a heavy-duty turbine, blade cooling is necessary to prevent tur-
bine blades from overheating. In this paper, the turbine blade cooling is
simulated by bleeding two air streams from the AC exit and injecting
them into the turbine inlet and exit, respectively. As shown in Fig. 2(a),
the stator cooling air mixes with the main hot gas at the turbine inlet.
The mixed gas then expands in the turbine. Finally, the rotor cooling air
mixes with the expanded gas at the turbine outlet. The stator and rotor
cooling flows can be computed by Eq. (9) [48] in SS-1. Their mixing
with the turbine inlet and outlet gases is simulated by the Mixer module
in Aspen HYSYS.
⎜ ⎟ ⎜ ⎟= ⎛
⎝
⎞
⎠
⎛
⎝
⎞
⎠
m m
P
P
T
T
ca ca d
ca
ca d
ca d
ca
,
,
,
0.5
(9)
where mca is the mass flow rate of the cooling air, and Pca and Tca are the
pressure and temperature of the cooling air.
The turbine flow characteristics can be described by the following
constant swallowing capacity [49–51].
= = =C
m T
κP
m T
κ P
C
in in
in
in d in d
d in d
d
, ,
, (10a)
⎜ ⎟= ⎛
⎝ +
⎞
⎠
+
−
κ
γ
R γ
2
1g
γ
γ
1
1
(10b)
Fig. 3. Relativized compressor map.
1217

where C is the swallowing capacity, κ is a constant, γ is the specific heat
ratio, and Rg is the gas constant. The ambient temperature and GT shaft
speed fix Ncor r, . Then, for a given mf , β that satisfies =C Cd fixes the AC
operating point on the map. Determining the correct βrequires itera-
tions, and is done by using an Adjust module (ADJ-BETA in Fig. 2(a)) in
Aspen HYSYS. In ADJ-BETA, β is selected as the adjusted variable,
Ccomputed in SS-1 is chosen as the target variable, and Cd is supplied as
the specified target value. Once a β is given, Aspen HYSYS can simulate
AirCOMP and COMB, and adjust β to achieve =C Cd. Then, the gas into
the turbine is fully known, and Aspen HYSYS can simulate the turbine
fully.
The turbine is simulated by the Expander module (TURB in
Fig. 2(a)) in Aspen HYSYS. The turbine isentropic efficiency (ηt) is es-
timated by the following semi-empirical formula [10,52].
=
−
−
⎛
⎝
⎜ − −
−
−
⎞
⎠
⎟η η
N
N
T
T
π
π
λ λ
N
N
T
T
π
π
1
1
( 1)
1
1t t d
d
in d
in
d
φ
φ
d
in d
in
d
φ
φ,
, ,
(11)
where π = P P/in out, = −φ γ γ( 1)/ , λ is a known constant, and sub-
script t denotes turbine.
For solving TURB, its outlet pressure [P (S7)] is needed. Here, P
denotes pressure, and S7 denotes the stream in Fig. 2(a). However,
P (S7) is unknown, and depends on the HRSG pressure drop ( PΔ HRSG).
PΔ HRSG is computed from Eq. (1), and simulated by a Control Valve
module (DUCT in Fig. 2(a)) before the HRSG. P (S7) needs to be iterated
such that − =P P P(S7) Δ HRSG amb. This is achieved by using an Adjust
module (ADJ-TP in Fig. 2(a)) as follows. Given a P (S7), compute ηt from
Eq. (10) in SS-1, and export its value to TURB in Fig. 2(a). Aspen HYSYS
simulates TURB and gives T (S6), where T denotes temperature. Com-
pute = −P P P(S7) Δamb HRSG
'
in SS-1. In ADJ-TP, select P (S7) as the ad-
justed variable, Pamb
'
as the target variable, and specify Pamb as the
specified target value. The complete GT simulation procedure in Aspen
HYSYS is shown in Fig. 6.
2.2.5. Heat recovery steam generator (HRSG)
The HRSG comprises a series of economizers, evaporators, and su-
perheaters. The economizers and superheaters are heat exchangers that
extract the waste heat from the exhaust gas for heating water and
steam, respectively. Hence, they are simulated by the LNG Exchanger
module in Aspen HYSYS. However, the evaporators involve change in
state along with phase equilibrium, and hence their simulation is dif-
ferent and more challenging than the economizers and superheaters.
Normally, an evaporator consists of a boiler and a steam drum. The
boiler is a heat exchanger that extracts the waste heat from the exhaust
gas to produce water/steam mixture, while the steam drum is a phase-
separator that separates water/steam mixture into saturated water and
steam. Thus, the boiler and steam drum are simulated by the LNG
Exchanger and Separator modules in Aspen HYSYS. The steam gen-
eration process in the evaporator is simulated as follows. The boiler
extracts the waste heat from the exhaust gas to generate water/steam
mixture. The water/steam mixture mixes with the subcooled water
from the economizer in the steam drum to produce saturated steam and
water. The saturated steam goes to the superheater while the saturated
water returns to the boiler. Note that the LNG Exchanger module is
essentially the same as the Heat Exchanger module here, since there are
only two streams involved in heat exchange and mass and energy bal-
ances are of our only interest. Our motivation is to just make the BFD
look cleaner (less convoluted) as shown in Fig. 2(b–c).
The LNG Exchanger module in Aspen HYSYS needs a UA value
(overall heat transfer coefficient × heat transfer area) for heat exchange
calculation. Since U is mainly affected by the exhaust gas flow under
off-design conditions, UA is computed in SS-1 as follows [53], and then
exported to each LNG Exchanger module.
⎜ ⎟= ⎛
⎝
⎞
⎠
UA UA
m
m
( )d
g
g d,
0.8
(12)
where mg is the gas mass flow rate. The water/steam pressure losses in
HRSG heat exchangers vary as follows during off-design operation [11].
⎜ ⎟= ⎛
⎝
⎞
⎠
P P
m
m
Δ Δ for waterd
d
1.98
(13)
⎜ ⎟ ⎜ ⎟= ⎛
⎝
⎞
⎠
⎛
⎝
⎞
⎠
P P
m
m
ν
ν
Δ Δ for steamd
d d
1.98
(14)
where ν is the specific volume of steam. The water/steam pressure
losses are computed in SS1, and exported to the corresponding LNG
Exchanger modules.
The HP steam and reheat steam from the HRSG may exceed their
maximum allowable temperatures (THPSmax and TRHSmax) during off-
design operation. For safe operation, two desuperheaters are installed
to moderate their temperatures by injecting water. The two desu-
perheaters are simulated by two Mixer modules (DeSH1 and DeSH2 in
Fig. 2(b)) in Aspen HYSYS. Moreover, two Adjust modules (ADJ-SH1
and ADJ-SH2 in Fig. 2(b)) are employed to control the temperatures of
HP steam and reheat steam under off-design operation. In ADJ-SH1 and
ADJ-SH2, m(S26a) and m(S41a) are selected as the adjusted variables,
T (S36) and T (S51) are chosen as the target variables, and THPSmax and
Fig. 4. β lines on a relativized compressor map.
Fig. 5. Bilinear interpolation for reading the compressor map expressed in
terms of (β N, cor r, ) as coordinates.
1218

TRHSmax are supplied as the specified target values. Here, m refers to
mass flow rate, T refers to temperature, and S26a, S41a, S36, and S51
refer to the streams in Fig. 2.
The LP economizer feedwater temperature cannot fall below the
minimum allowable temperature (TFWmin) to avoid low-temperature
corrosion. Hence, some warm water from the LP economizer exit is
recycled back to its feed. An Adjust module (ADJ-RCF in Fig. 2(c)) is
employed to control the LP economizer feedwater temperature. In ADJ-
RCF, m(S65b) is selected as the adjusted variable, T (S63) is chosen as
the target variable, and TFWmin is supplied as the specified target value.
2.2.6. Water pumps
The water pumps (HPP, IPP, LPP, and RP) are simulated by the
Pump module along with the Control Valve module in Aspen HYSYS.
The pump curves for the water pumps can be either supplied as user
input or generated automatically inside the Pump module. The control
valves (HPCV, IPCV, and LPCV) serve as regulating the steam pressures
in the HRSG to match the ST operation.
2.2.7. Steam turbines (STs)
CCGT plants usually adopt sliding pressure operation for STs under
off-design conditions. This implies that the throttling valves of STs are
fully open and the steam pressures in the HRSG are regulated by water
pumps to match ST characteristics. Since valve throttling is eliminated,
sliding pressure operation produces a better plant performance than
constant pressure operation [54]. The off-design characteristics of an ST
can be described by the Stodola’s method [55,56].
−
=
−
m T
P P
m T
P P
s in
in out
s d in d
in d out d
2 2
, ,
,
2
,
2
(15)
The isentropic efficiency (ηst) of an ST is mainly affected by the
steam flow during off-design operation, and thus can be corrected as
follows [12]:
⎜ ⎟ ⎜ ⎟ ⎜ ⎟
⎜ ⎟ ⎜ ⎟
=
⎡
⎣
⎢− ⎛
⎝
⎞
⎠
+ ⎛
⎝
⎞
⎠
− ⎛
⎝
⎞
⎠
+ ⎛
⎝
⎞
⎠
+ ⎛
⎝
⎞
⎠
+
⎤
⎦
⎥
η η
m
m
m
m
m
m
m
m
m
m
0.1035 0.2357 0.1872
0.0585 0.0163 0.98
st st d
s
s d
s
s d
s
s d
s
s d
s
s d
,
,
5
,
4
,
3
,
2
, (16)
where subscript s denotes steam, and st denotes steam turbine.
The HPST, IPST, and LPST are simulated by three Expander modules
(HPT, IPT, and LPT in Fig. 2(b–c)) in Aspen HYSYS, respectively. The
isentropic efficiencies for HPST, IPST, and LPST are computed in SS-1
using Eq. (16), and exported to HPT, IPT, and LPT.
2.2.8. Condenser
The condenser is simulated by the LNG Exchanger module (CONDR
in Fig. 2(c)) in Aspen HYSYS. It condenses the water/steam mixture
from the LPST to saturated water. Thus, the vapor fraction of S60 in
Fig. 2(c) is set to 0. Because the heat transfer in the condenser is very
efficient and the cooling water flow is kept unchanged, the UA value for
the condenser is assumed constant under off-design conditions. Then,
the condenser operating pressure varies to fully condense the water/
steam mixture. An Adjust module (ADJ-CDP in Fig. 2(c)) is employed to
find the right condenser pressure. In ADJ-CDP, P (S59) is selected as the
adjusted variable, the relative UA error of the CONDR is chosen as the
target variable, and 0 is supplied as the specified target value.
Finally, the generator efficiency [57] and the power outputs for the
GT, Steam Cycle (SC), and CCGT plant are computed in SS-1 as follows.
=
+ − − +
η
L η
L η η F F L(1 )[(1 ) ]gen
gen gen d
gen gen d gen d cu cu gen
,
, ,
2
(17)
Fig. 6. GT simulation procedure in Aspen HYSYS.
1219

= −W W W η η( / )GT t c mech gen (18)
= + + − + + +W W W W η W W W W( ) ( )SC HPST ISPT LPST gen HPP IPP LPP RP
(19)
= +W W WCCGT GT SC (20)
where ηgen is the generator efficiency, ηmech is the AC mechanical effi-
ciency, =L W W/gen in in d, , where Win is the work input to the generator,
and Fcu is the copper loss fraction.
This completes the development of an Aspen HYSYS model for si-
mulating the CCGT plant under off-design conditions.
2.3. Simulation procedure
Consider simulating the CCGT plant operation for given mf and αΔ .
Aspen HYSYS simulates the GT by iterating β and P (S7) using two
Adjust modules (ADJ-BETA and ADJ-TP). The GT simulation procedure
is presented in Fig. 6. When ADJ-BETA and ADJ-TP converge, the GT is
solved and the turbine exhaust gas flow, temperature, and composition
become known. Now, the SC must be simulated for this known exhaust
gas conditions. However, simulating the SC in Aspen HYSYS is chal-
lenging due to the following factors.
(1) Aspen HYSYS is a sequential modular simulator, in which the SC
components have to be solved in a sequential manner. However, the
HP, IP, and LP steam circuits that involve mass/energy recycle in
the SC require simultaneous rather than sequential solution. This
poses significant challenges to Aspen HYSYS. For configuring the SC
components to be solved sequentially, the Recycle module in Aspen
HYSYS is needed. Nonetheless, determining how many Recycle
modules should be used and where to place them are combinato-
rially demanding and require clever thinking.
(2) The HRSG steam conditions (flow, pressure, and temperature) have
to satisfy the ST characteristics, namely Eq. (15). Thus, the HRSG
and STs must be solved jointly and special constructs are necessary
for back-pressure calculations.
The procedure in Fig. 7 is designed ingeniously to address the above
challenges. Simulating the evaporator is its key first step. Consider the
HP evaporator (HP drum and HP boiler in Fig. 2(b)). For simulating it,
Recycle modules (R3 and R9 in Fig. 2(b)) are needed for specifying
stream conditions (e.g. flow, pressure, temperature, etc.). The Recycle
module in Aspen HYSYS is a mathematical operation and has an inlet
stream and an outlet stream. For example, R9 has S30a as the inlet
stream and S30b as the outlet stream. In the Recycle module, the stream
conditions can be transferred forwards from the inlet to the outlet.
Aspen HYSYS first utilizes the outlet stream conditions as assumed
values to solve the flowsheet sequentially around the Recycle module.
Based on the differences between the inlet and outlet stream conditions,
Aspen HYSYS updates the outlet stream conditions iteratively until the
inlet stream conditions match the out stream conditions within the
tolerances specified in the Recycle module.
The simulation of the HP evaporator is performed with Recycle
modules as follows. Since the HP boiler produces steam/water mixture,
the vapor fraction of S32 can be set to any value between 0 and 1. Then,
two Recycle modules (R3 and R9) are employed for specifying P (S30b),
T (S12b), and T (S30b). Aspen HYSYS uses them to solve the HP boiler
and HP drum, as the flow, pressure, and composition of the exhaust gas
streams within the HRSG are already known. The HP boiler computes
m(S32) from the energy balance and heat transfer equations, which
enables the HP drum to calculate m(S30b) and m(S33) from mass and
energy balances and water/steam equilibrium. This means that the HP
evaporator can automatically compute its own water flow. Next,
m(S30a) is set to m(S30b)in SS-1, which fixes the water flow in the HP
circuit. The pressure losses in the HP economizers are computed by Eq.
(13) in SS-1. In the following, two Recycle modules (R2 and R10) are
used for specifying T (S11b), P (S26b), T (S26b) and m(S26a). Meanwhile,
m(S26b) is set to m(S26a) in SS-1. Aspen HYSYS solves HP SHPT1 and
Fig. 7. SC simulation procedure in Aspen HSYSY.
1220

HP SHPT2, and computes T (S12b) and T (S36), respectively. The pres-
sure losses in HP SPHT 1 and HP SPHT 2 are computed by Eq. (14) in
SS-1. If T (S12a) does not match T (S12b) within the specified tolerance
in R3, Aspen HYSYS updates T (S12b), and the HP evaporator simulation
continues again. This process repeats until T (S12a) and T (S12b) are
within the specified tolerance. If T (S36) exceeds THPSmax, ADJ-SH1
adjusts m(S26a) to prevent the HP steam from over-temperature. Now,
the HPT inlet steam conditions are fully known. However, they may not
match the HPST characteristics, namely Eq. (15). Hence, knowing the
HPT inlet flow and temperature, the HPT expected inlet pressure
[P'(S36)] is computed from Eq. (15), and P (S30a) is back calculated in
SS-1 by adding the pressure losses in HP SPHT 1 and HP SPHT2.
Moreover, the pressure iteration in R9 is activated. If P (S30a) does not
match P (S30b) within the specified tolerance in R9, Aspen HYSYS up-
dates P (S30b) until the difference between P (S30a) and P (S30b) falls
within the specified tolerance. Hence, when R9 converges, the HP
steam conditions match the HPST characteristics. This completes the
simulation of the HP circuit in a sequential manner, starting from the
HP evaporator. The same simulation logic is applied to the IP/LP cir-
cuits. Finally, a Recycle module (R6) is used for the LP economizer
water recirculation; an Adjust module (ADJ-RCF) adjusts the water
recirculation flow to control the LP economizer feedwater temperature;
an Adjust module (ADF-CDP) adjusts the condenser pressure to fully
condense the water/steam mixture from the LPST. The detailed simu-
lation procedure for the SC is presented in Fig. 7. The Recycle modules
in the BFD and their stream variables are summarized in Table 1. All the
variables are transferred forwards in the Recycle modules. Aspen
HYSYS iterates on the stream variables systematically until the Recycle
modules converge. Therefore, when all the Recycle and Adjust modules
converge, the SC is solved successfully.
Inlet guide vane control (IGVC) is usually employed for part-load
operations in CCGT plants. IGVC simultaneously manipulates mf to
achieve the desired part-load and αΔ to maintain TET at its design value
(TETd) [57,58]. Two Adjust modules are used to implement IGVC in
Aspen HYSYS. As shown in Fig. 2(a), ADJ-FF adjusts mf , and ADJ-IGV
adjusts αΔ . In ADJ-FF, m(NG) is selected as the adjusted variable,
×W W/ 100CCGT CCGT d, computed in SS-1 is chosen as the target variable,
and the desired percent part-load (PL%) is supplied as the specified
target value. In ADJ-IGV, αΔ is selected as the adjusted variable, T (S7)
is chosen as the target variable, and TETd is supplied as the specified
target value.
Now, given a part-load (PL), the Aspen HYSYS model can simulate
the triple-pressure reheat CCGT plant in Fig. 1. To converge the model
smoothly, some guidelines are proposed here. First, some simple cor-
relations for the minimum and maximum parameters in the Adjust
modules are developed, as shown in Table 2. Second, based on the
minimum and maximum parameters, the initial guess and step size for
each Adjust module are set to × +0.5 (Minimum Maximum) and
× −0.1 (Maximum Minimum), respectively. Third, the Adjust modules
should be activated progressively. For instance, ADJ-BETA, ADJ-IGV,
ADJ-TP, ADJ-CDP, and ADJ-RCF are first activated. Then, ADJ-SH1 and
ADJ-SH2 are activated one at a time. Finally, ADJ-FF is activated. In
this way, the Aspen HYSYS model converges smoothly for a given PL.
It is clear from the above details that developing and solving the
CCGT model in Aspen HYSYS require ingenious constructs and thinking
based on a full understanding of Aspen HYSYS. By giving a detailed and
explicit procedure, this paper makes CCGT simulation easy for the re-
searchers, and thus makes a significant contribution. Given the plant
design data, the model requires only one input, namely the desire part-
load (PL), and produces all the useful outputs, including but not limited
to, the power outputs and efficiencies of the GT, SC, and CCGT plant.
Moreover, it can either work stand-alone, or be easily integrated with
various energy systems (e.g. CO2 capture, ORCs, fuel cells, LNG term-
inals, air separation, and absorption chillers). Furthermore, it can be
made dynamic by Aspen HSYSY Dynamics for predicting the plant real-
time behavior. Therefore, it is very useful and has wide applications.
3. Model evaluation on an example power plant
The performance of our Aspen HYSYS model is evaluated with an
example CCGT plant. Since real operational data for CCGT plants are
not available in the open literature, an alternative way is to compare its
predictions with those of an equivalent model built in GateCycle, a
widely used commercial software in the power industry.
The following data is used for evaluation. The plant is assumed to
use IGVC for part-load operation. Table 3 shows the design parameters
of the CCGT plant and Fig. 3 presents the AC performance map.
Moreover, =c 0.011 , =c 0.012 and =c 0.00013 in Eqs. (4)–(6) [47],
=λ 2.083 in Eq. (11) [10], and =F 0.48cu in Eq. (17) are used in this
paper. Furthermore, both THPSmax and TRHSmax are assumed to be
565 °C while TFWmin is assumed to be 50 °C.
Table 4 presents the design performance of the CCGT plant in Aspen
HYSYS and GateCycle. In the following, the relative deviations (RD)
between the two simulation models (Aspen HYSYS and GateCycle)
defined by Eq. (21) in GT, SC, and CCGT performance are evaluated.
=
−
×RD (%)
HYSYS Result GateCycle Result
GateCycle Result
100
(21)
3.1. Gas turbine (GT) performance
Fig. 8 shows the relative deviations for the key operating parameters
of the AC and turbine. Clearly, nearly all are within 1.0%. Moreover,
the average deviation for all operating parameters in Fig. 8 is less than
0.5%. The minor discrepancies for the operating parameters in Fig. 8
arise from the differences in gas enthalpy calculations. For calculating
gas enthalpies, GateCycle uses the NASA method [59], in which gases
are assumed to be ideal. Aspen HYSYS uses the Peng-Robinson equa-
tion-of-state [60], which is based on real gas experimental data. The
NASA method uses two separate fourth-order (5-parameter) tempera-
ture-dependent polynomials to calculate gas enthalpies below and
above 1000 K (726.85 °C), respectively. Aspen HYSYS directly calcu-
lates gas enthalpies from the Peng-Robinson equation-of-state. Hence,
Aspen HYSYS predicts a higher (lower) gas enthalpy below (above)
K1000 than GateCycle, as shown in Fig. 9. The differences in gas en-
thalpy predictions affect the complex interactions between the AC and
turbine, represented by the matching between the compressor map
(Fig. 3) and turbine characteristics (Eq. (10)). This results in the minor
discrepancies shown in Fig. 8. Because of these minor discrepancies,
Aspen HYSYS predicts a lower GT power output and efficiency than
GateCycle, as shown in Fig. 10. Moreover, as the plant load decreases,
the differences in gas enthalpy predictions drive the GT power output
and efficiency of Aspen HYSYS farther way from those of GateCycle. As
a result, the relative deviations in the GT power output and efficiency
increase with decreasing plant load. However, their maximum devia-
tions are within 3.2%, and the average deviation is less than 2.0%.
Table 1
Stream variables for the Recycle modules in Aspen
HYSYS. All are transferred in the forwards direction.
Module Stream variable
R1 T(S10b)
R2 T(S11b)
R3 T(S12b)
R4 T(S14b)
R5 T(S17b)
R6 P(S65b) and T(S65b)
1221

3.2. Steam cycle (SC) performance
Figs. 11–13 show the relative deviations for the operating para-
meters of the HPST, IPST, and LPST. Since both Aspen HYSYS and
GateCycle use the ASME steam table for water and steam, the relative
deviations in Figs. 11–13 are primarily from their gas models. Because
Table 2
Minimum and maximum parameters for the Adjust modules in Aspen HYSYS.
Module Adjusted variable Base value Minimuma
(%) Maximuma
(%)
ADJ-BETA β 0.7 0.274PL + 70.0 0.274PL + 76.0
ADJ-FF m(NG) NG design flow 0.8352PL + 14.0 0.8352PL + 18.0
ADJ-IGV Δα 100% 0.6241PL + 35.0b
0.6241PL + 38.0b
ADJ-TP P(S7) S7 design pressure 0.0144PL + 97.0 0.0144PL + 100.0
ADJ-SH1 m(S26a) S34 design flow 0 −0.0163PL + 2.0
ADJ-SH2 m(S41a) S34 design flow 0 −0.0066PL + 1.0
ADJ-RCF m(S65ba) S65b design flow −0.355PL + 128.0 −0.355PL + 146.0
ADJ-CDP P(S59) S59 design pressure 0.422PL + 54.0 0.422PL + 60.0
a
Maximum (Minimum) value/Base value × 100.
b
IGV opening (100 − Δα).
Table 3
Design parameters of the CCGT plant.
Parameter/variable Value
Ambient condition
Pressure (kPa) 101.3
Temperature (°C) 15.0
Molar fraction 77.30% N2, 20.74% O2, 1.01% H2O,
0.03% CO2, 0.92% Ar
Fuel condition
Pressure (bar) 30.0
Temperature (°C) 10.0
Molar fraction 87.08% CH4, 7.83% C2H6, 2.94% C3H8,
1.47% N2, 0.68% CO2
Gas turbine
Inlet air flow (kg s−1
) 635.0
Inlet air pressure loss (%) 0.5
Compressor pressure ratio 15.4
Compressor isentropic efficiency (%) 88.0
Compressor mechanical efficiency (%) 99.0
Fuel flow (kg s−1
) 14.74
Combustor efficiency (%) 99.5
Combustor pressure loss (%) 3.5
Combustor exit temperature (°C) 1405.0
Turbine inlet temperature (°C) 1328.0
Turbine exhaust temperature (°C) 615.0
Heat recovery steam generator (HRSG)
HP/IP/LP steam temperatures (°C) 565.0/297.0/295.0
HP/IP/LP pinch point temperatures
(°C)
10.0/10.0/10.0
HP/IP/LP approach point
temperatures (°C)
8.0/10.0/16.4
HP SPHT 1 steam outlet temperature
(°C)
510.0
RHT 1/2 steam outlet temperature (°C) 520.0/565.0
HP ECON 1/2 water outlet
temperature (°C)
208.0/280.0
Pressure losses on gas/water/steam
sides (%)
1.5/5.0/3.0
Steam turbines (STs)
HP/IP/LP ST inlet pressure (bar) 98.8/24.0/4.0
HP/IP/LP ST isentropic efficiency (%) 87.0/91.0/89.0
Condenser
Pressure (kPa) 7.4
Cooling water temperature (°C) 25.0
Cooling water temperature rise (°C) 10.0
Generator
Generator efficiency (%) 98.5
Shaft speed (rpm) 3000
Table 4
Design performance of the CCGT plant in Aspen HYSYS and GateCycle.
Performance Aspen HYSYS GateCycle
GT power (MW) 253.2 257.2
GT efficiency (%) 36.17 36.78
SC power (MW) 139.8 137.8
SC efficiency (%) 30.73 30.33
Plant net power (MW) 393.0 395.0
Plant efficiency (%) 56.14 56.49
Fig. 8. Relative deviations for the operating parameters of the AC (a) and
turbine (b).
1222

the SC operates below 1000 K, Aspen HYSYS predicts a higher gas
enthalpy than GateCycle. Hence, Aspen HYSYS predicts higher steam
flows and higher ST power outputs. The higher steam flows lead to
higher ST isentropic efficiencies according to Eq. (16). This enables the
HPST and IPST to expand to lower temperatures in Aspen HYSYS than
GateCycle. Since the LPST usually expands to two-phase (water/steam)
region, both Aspen HYSYS and GateCycle predict the same LPST outlet
temperature. On the other hand, HP steam and reheat steam exceed
their maximum allowable temperatures under IGVC; hence, desu-
perheaters are activated to prevent them from over-temperature. This is
why both HPST and IPST inlet temperatures from Aspen HYSYS and
GateCycle are the same. The steam pressure profiles from Aspen HYSYS
and GateCycle are jointly determined by the HRSG and STs. Hence,
their variations are dependent on the profiles of stream flows and
temperatures. Clearly, in Figs. 11–13, the relative deviations in steam
pressures and temperatures for HPST, IPST, and LPST are all less than
0.6%, and the relative deviations in steam flows and power outputs are
within 2.4%. Moreover, the relative deviations in the SC power output
and efficiency range between 1.2 and 2.0% as shown in Fig. 10, and the
average deviation is less than 1.5%.
3.3. CCGT performance
Fig. 10 shows the relative deviations for the plant power output and
efficiency. Since the GT dominates the plant performance, Aspen
HYSYS predicts a relatively lower power output and efficiency than
GateCycle. The relative deviations in the plant power output and effi-
ciency are less than 0.6% for 100–40% loads. The reason is that Aspen
HYSYS predicts a higher SC power output, which compensates its lower
GT power output. However, the two simulation models are comparable
in terms of their simulation results. Our comparison is useful for any-
body wanting to use Aspen HYSYS instead of GateCycle, and vice versa.
4. Conclusions
In this paper, a detailed Aspen HYSYS model was presented for si-
mulating the off-design operation of a triple-pressure reheat CCGT
plant. The challenges of implementing the rigorous modeling equations
Fig. 9. Gas enthalpy difference between Aspen HYSYS and GateCycle.
Fig. 10. Relative deviations for the power outputs and efficiencies of the GT,
SC, and CCGT plant.
Fig. 11. Relative deviations for the HPST operating parameters.
Fig. 12. Relative deviations for the IPST operating parameters.
Fig. 13. Relative deviations for the LPST operating parameters.
1223

for various plant components were addressed, and a tailored procedure
was proposed for their solution. The Aspen HYSYS model captures the
full off-design details of the CCGT plant including compressor map,
turbine characteristics, and flow-dependent variables, such as pressure
drops and heat transfer coefficients. To our knowledge, this is the first
fully-detailed Aspen HYSYS model in the open literature for a CCGT
plant during off-design operation. The modeling strategy and solution
procedure presented in this paper can be extended to simulate any
CCGT plants in Aspen HYSYS. Moreover, they are generically applicable
to other commercial process simulators, such as Aspen Plus, Unisim,
and Pro II. The presence of mass/energy recycle in a CCGT plant poses
significant challenges to such sequential modular simulators; hence, the
specially tailored and ingenious simulation procedure is the major
contribution of this paper.
Using the data from an example CCGT plant, it was shown that the
predictions from the Aspen HYSYS model and an equivalent GateCycle
model are acceptably comparable. The relative deviations for the most
operating parameters of the GT and SC were within 1.0% and 0.6%,
respectively. Specifically, the average deviations in the power outputs
and thermal efficiencies of the GT, SC, and CCGT plant were less than
2.0%, 1.5%, and 0.6%, respectively. A thorough study was also done in
this paper to analyze the key causes for these various deviations. It was
found that the different procedures for computing gas enthalpies are the
main factor.
As the world moves to integrate CCGT plants within wider and di-
verse energy systems to conserve fuels and reduce CO2 emissions, more
general purpose simulators such as Aspen HYSYS versus stand-alone
specialized simulators such as GateCycle are becoming essential. In this
context, Aspen HYSYS offers several important advantages over
GateCycle such as wider and more versatile physical property packages,
easy integration with other energy systems or options (e.g. CO2 capture,
ORCs, fuel cells, LNG terminals, air separation, and absorption chillers),
dynamic simulation, and real-time optimization.
Acknowledgement
Zuming Liu acknowledges ACTSYS Process Management
Consultancy Company, Singapore for hosting his industrial internship
under a ring-fenced Graduate Research Scholarship from the National
University of Singapore. The authors thank Mr Norman Lee, MD of
ACTSYS for inspiring them to work on GT modeling. They further thank
Mr Norman Lee, Dr Yu Liu, and Mr Weiping Zhang of ACTSYS for
several enlightening discussions and preliminary information on the GT
operation in a CCGT plant. They acknowledge the support from the
National University of Singapore via a seed grant R261-508-001-646/
733 for CENGas (Center of Excellence for Natural Gas). They also ac-
knowledge the use of Aspen HYSYS and GateCycle under academic li-
censes.
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