Optimization of Organic Rankine Cycle’s thermal efficiency based on Grey relational analysis with Taguchi method

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 07 | July 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 400
Optimization of Organic Rankine Cycle’s thermal efficiency based on
Grey relational analysis with Taguchi method
Jayesh Choudhary 1, Purushottam Sahu2, Ghanshyam Dhanera 3
1 Research Scholar, BM College of Technology, Indore
2Professor and HEAD, BM College of Technology, Indore
3 Assistant Professor, BM College of Technology, Indore, MP
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - For the statistical analysis in this study, the
following 9 basic process parameters were chosen: working
fluid type, heat exchanger efficiency, pinch point
temperature differential in the evaporator and condenser,
superheating temperature, evaporation temperature, and
condensation temperature. The methodology employed was
based on the Organic Rankine Cycle (ORC), where 9 tests
were conducted using Taguchi's normal l9 orthogonal
array. Subsequently, multi-response optimization was
performed using principal component and grey relational
analyses.
A comprehensive statistical analysis was conducted to assess
the impact of these parameters on the efficiencies of the ORC
system. To validate the results, confirmation tests were
carried out, demonstrating good agreement between the
predicted and experimental values of the response variables
at the optimum combination of input parameters.
The optimum operating conditions for the ORC system were
identified as A1B1C3D3E3F3G1H3I3, while the worst
operating conditions were A3B3C1D1E1F1G3H1I1, both
within the range of the analyzed operating conditions. The
first law efficiencies of the system were determined to be
17.3% for the best case and 9.6% for the worst case.
This study provides a ranking of the ORC process
parameters based on their statistical impact weight,
highlighting which parameters are effective and ineffective
in relation to the system's efficiencies.
Regenerate response
Key Words: Grey relation analysis, Organic Rankine
Cycle; Thermal Efficiency; Taguchi
Method; ANOVA
1. INTRODUCTION
The Organic Rankine Cycle (ORC) is a thermodynamic
process that converts low-temperature heat sources into
electricity by using an organic working fluid. An ORC
system's thermal efficiency is influenced by a number of
factors. The Taguchi method and grey relational analysis
can be used to examine the impact of various factors on
the ORC system's thermal efficiency.
Grey relational analysis is a technique for examining the
connection between several variables and their effects on
a specific result. The Taguchi technique, on the other hand,
is a statistical strategy that enhances a system's
performance by pinpointing the important variables that
cause variability.
To analyse the relevant factors affecting the ORC's thermal
efficiency using the Taguchi method and Grey relational
analysis, you would typically.
The Taguchi method is a statistical approach that allows
for the evaluation of the significance of different factors in
relation to the objective function. This method can be
applied in experiments, theoretical analysis, and
numerical simulations to examine the effects of various
parameters on the exergy and thermal efficiency of the
Organic Rankine Cycle (ORC).
Considering the growing urgency of climate-related issues,
there is a need for further research on the ORC system's
environmental performance, its role in emission
reduction, and its integration within low-carbon energy
scenarios. When designing optimization objectives, it is
recommended that researchers consider thermodynamic,
economic, and environmental performance in conjunction.
Additionally, working fluids can also be evaluated based
on safety indicators, besides other criteria.
In recent years, the development of Organic Rankine
Cycles (ORCs) has played a significant role in the
expansion of commercial geothermal power utilization.
This technology, particularly in the context of low-
temperature geothermal energy, has opened new
possibilities for its application.
To determine the optimal working fluid pairs for ORC
systems, a multi-objective optimization approach was
employed, considering 26 working fluid options. For
simple ORC systems, a multi-objective optimization was
conducted using exergy efficiency and levelized energy
cost as the objective functions, with the aim of obtaining
the most favorable results.

The increasing importance of energy utilization and
environmental regulations has led to a greater emphasis
on the analysis of thermodynamic cycles. Accurate and
advanced thermodynamic analysis is essential for efficient
energy utilization in thermal systems. The first and second
laws of thermodynamics provide valuable insights into the
behavior of thermodynamic systems, detailing the
movement of heat and work. The first law expresses the
conservation of energy, while the second law determines
the direction of this conservation. The combined analysis
of the first and second laws of thermodynamics is the most
effective approach for evaluating the performance of
thermal systems. Figure 4.1 illustrates the components of
a basic Organic Rankine Cycle (ORC) used to convert waste
heat into useful electrical power. The basic ORC consists of
four distinct processes: Method 1-2 (pumping), Method 2-
3 (constant-pressure heat addition), Process 3-4
(expansion), and Process 4-1 (constant-pressure heat
rejection).
The first and second laws of thermodynamics are applied
to each individual component to determine work output,
heat added or rejected, and component and system
irreversibility. The energy balance equation is as follows:
Where EiandEoare the energy rate in and out, Q&is the
heat transfer rate, and W&is the
Power.(Somayaji 2008)
a. Process 1-2 (Pump): The liquid is transferred from the
condenser to the evaporator at Point 1 through a pump.
The power required by the pump can be determined using
Equation (4.1), assuming adiabatic pump efficiency.
b. Process 2-3 (Evaporator): In this stage, the working
fluid passes through the evaporator, where it absorbs heat
and undergoes vaporization as it moves from the pump
outlet to the turbine inlet. The rate of heat transfer from
the energy source to the working fluid can be calculated
using a control volume that encompasses the evaporator.
c. Process 3-4 (Turbine): Vaporized fluid from Point 3
expands through the turbine to generate mechanical
energy before being directed to the condenser at Point 4.
The power output of the turbine can be determined based
on a constant volume surrounding the turbine and the
turbine's isentropic efficiency. The equation for turbine
power includes the particular entropies of the working
fluid at the condenser's inlet (s1) and exit (s4), as well as
the temperature of the low temperature reservoir (TL). In
this case, TL is considered equal to LL T = T1-ΔTL.
2. LITERATURE REVIEW
This study offers valuable insights into the effective and
ineffective process parameters in the Organic Rankine
Cycle (ORC), accompanied by a statistical ranking of their
impact weights. The working conditions have been
optimized to maximize both the first and second law
efficiencies. Therefore, this optimization study serves as a
valuable resource for researchers, enabling them to design
more efficient ORC systems while considering various
factors such as thermodynamics, economics, environment,
and safety indicators (Bademlioglu, Canbolat, and
Kaynakli, 2020).
The literature review conducted in this study focuses on
identifying papers that primarily emphasize the ORC. The
majority of the papers analyzed were published after
2015, indicating a growing interest in multi-objective
optimization (MOO) in system design. The work is
organized into six chapters, covering optimal control goals
based on thermodynamic, economic, environmental, and
safety indicators. The advantages and disadvantages of
various optimization techniques are also discussed in the
third section.
The study's main contributions are twofold: firstly, it
provides a comprehensive summary of the major
advancements in ongoing multi-objective research, and
secondly, it offers detailed recommendations for selecting
optimization targets and techniques for ORC optimization.
Additionally, the study proposes a four-level design
approach that considers aspects related to "device" such
as ORC architecture, component structure, and working
fluid molecules simultaneously. This approach is
anticipated to further enhance overall performance and
warrants further discussion in future studies (Orc et al.,
2021).

3. METHODOLOGY
Figure 1 journal dates from 2010 to 2021.(Orc et al. 2021)
Mathematical Model 3.1 Thermodynamic Analysis of ORC
This study utilizes a thermodynamic analysis of the
Organic Rankine Cycle (ORC) with a heat exchanger. The
schematic diagram of the ORC system, which serves as the
basis for the thermodynamic analysis, is presented in
Figure 1(a). Furthermore, the corresponding T-s
(temperature-entropy) diagram is depicted in Figure 1(b).
Fig. 3.1(a) diagrams of ORC with heat exchanger
Fig. 3.1(a) diagrams of ORC with heat exchanger
Fig. 3.2Schematic (b) and T-s
Table 3.1 lists the variables and their values required
in analyses.
Parameters and their levels used in calculations.
Parameters Level 1 2 3
A Working Fluid R123 R245fa R600
B PPTDevap 0 10 15
C PPTDcon 0 5 10
D Superheating Temperature, Tsh 0 5 10
E Effectiveness of the Heat
Exchanger, ɛexc
0 0.6 0.75
F Evaporator Temperature, Tevap 100 120 130
G Condenser Temperature, Tcon 30 35 40
H Turbine Efficiency, ηT 0.75 0.8 0.85
I Pump Efficiency, ηp 0.75 0.8 0.9
3.2 Taguchi Method: The proper orthogonal array
selection is crucial in Taguchi approach. The total level of
freedom (DOF), which may be determined by adding the
individual DOF of each element, determines selection.
Each factor's DOF is equal to the number of factorial
designs minus zero. Table 2 shows that there are nine
components (from A to I) and three levels (from 1 to 3),
which results in a total DOF number of 26. Fundamentally,
the DOF of the chosen orthogonal array is greater than the
entire DOF. Consequently, L27 is chosen as the Taguchi
orthogonal array in this investigation (39). The Results and
Discussion section includes a presentation of the chosen
Taguchi orthogonal array.
Table 3.3 lists the S/N ratios and thermal efficiency for the
L27 orthogonal array.(Bademlioglu et al. 2018)

Fig.3.4 Flow chart for analyzing performance
characteristics.(Sharma, Aggarwal, and Singh 2020)
Table 3.3 Thermal efficiencies and S/N ratios for the L27
orthogonal array.
3.7 Analysis of factors:
Table 3.3 shows the configuration for various input and
output parameters using the L15 orthogonal array. The SN
ratio should be calculated for each factor to assess the
effect of each factor on the responses. The effect on
deviations from mean responses was used to determine
the effect on average response and noise, revealing the
sensitivity of the experimental output to sound effects. In
this analysis, the SN ratio was chosen as the criterion of
larger is better' to optimize responses.In this study, it is
also a crucial tool to investigate how unconditional
elements affect a response. By determining the design
parameters that can minimize variation, of variation in the
responses through Anova In this study “larger the best” is
used as the optimization criteria for specific performance
.Variance of the corresponding parameter are compared
with residual variance and estimated as mean square of
the parameter to the mean square of variance to derive the
f value for f test f value is usually taken when it is greater
or equal to unity. Fisher test f test with 95% of confidence
level less than 0.05 is used to assess the significance of
individual parameter on specific performance thermal
efficiency.
4. DESIGN OF EXPERIMENT
Design of experiment is technique developed to
understand the behaviour of the mechanical system. Data
are collecting from the sets of the variable, and it can
qualitatively explain the undergoing phenomenon. Hence
it is well known that aim of any research is design the
experiment with minimum number of the experiment and
with this experiment collects maximum information as
much as possible. Every experiment focuses on the major
number of the factor which can directly affect the results
of the experiment. And such types of factor can be
detected by quantities which have maximum effect on the
experiments outcomes. Important concepts for identified
such factor is to look after the experiment performed later
or by theories.
It is the combination of DOE with optimization of process
for required results. It focuses on the effect of variation on
the performance characteristics of process. According to
Taguchi, proper parameter design should be done during
the offline phase. Taguchi performs the following steps:
determine the objectives, determine the performance
characteristics with measurement tools, determine the
factors affecting performance characteristics with their
levels and interactions if any, determine the orthogonal
array and give the factors at their levels, conduct the tests,
analyse the experimental data using the SN ratio, factor
effects, and analysis of variance to locate the significant
factors with their optimum levels. The mean to standard
deviation ratio is known as the S/N ratio. The lower the
number, the better, the higher the number, and the
nominal the better are the three subdivisions. ANOVA is
used to determine which parameter has a substantial
impact. The best mix of factors can be discovered using the
S/N ratio and ANOVA analysis..(Sharma, Aggarwal, and
Singh 2020)
PPTDevap PPTDcon
Superhea
ting
Tempera
ture, Tsh
Effectiven
ess of the
Heat
ExchE
Evaporat
or
Tempera
ture,
Tevap
Condenser
Temperature,
Tcon
Turbine
Efficiency, ηT
Pump
Efficiency,
ηp
Thermal Efficiency SNRA1
0 0 0 0 100 30 0.75 0.75 0.117 -18.6363
0 0 0 0.6 120 35 0.8 0.8 0.145 -16.7427
0 0 0 0.75 130 40 0.85 0.9 0.159 -15.9721
10 5 5 0 100 30 0.8 0.8 0.125 -18.0688
10 5 5 0.6 120 35 0.85 0.9 0.156 -16.1319
10 5 5 0.75 130 40 0.75 0.75 0.143 -16.8751
15 10 10 0 100 30 0.85 0.9 0.133 -17.523
15 10 10 0.6 120 35 0.75 0.75 0.14 -17.0899
15 10 10 0.75 130 40 0.8 0.8 0.155 -16.2046
0 5 10 0 120 40 0.75 0.8 0.116 -18.7408
0 5 10 0.6 130 30 0.8 0.9 0.16 -15.9339
0 5 10 0.75 100 35 0.85 0.75 0.13 -17.7078
10 10 0 0 120 40 0.8 0.9 0.124 -18.1596
10 10 0 0.6 130 30 0.85 0.75 0.161 -15.8797
10 10 0 0.75 100 35 0.75 0.8 0.001 -64.437
15 0 5 0 120 40 0.85 0.75 0.131 -17.6612
15 0 5 0.6 130 30 0.75 0.8 0.146 -16.7011
15 0 5 0.75 100 35 0.8 0.9 0.121 -18.3156
0 10 5 0 130 35 0.75 0.9 0.129 -17.8219
0 10 5 0.6 100 40 0.8 0.75 0.11 -19.1959
0 10 5 0.75 120 30 0.85 0.8 0.16 -15.9339
10 0 10 0 130 35 0.8 0.75 0.136 -17.3228
10 0 10 0.6 100 40 0.85 0.8 0.118 -18.5256
10 0 10 0.75 120 30 0.75 0.9 0.146 -16.6951
15 5 0 0 130 35 0.85 0.8 0.144 -16.8026
15 5 0 0.6 100 40 0.75 0.9 0.102 -19.811
15 5 0 0.75 120 30 0.8 0.75 0.146 -16.6832

Fig. 4.1 Main Effect plot for SN ratio of each parameter on
the thermal efficiency.
Furthermore, the optimum level of design parameters is
the level with the maximum S/N ratio. Therefore, working
fluid = R123 (A1), PPTDevap = 0°C (B1), PPTDcon = 0°C
(C1), Tsh= 10°C (D3), ɛexc= 0.75 (E3), Tevap = 130°C (F3),
Tcon= 30°C (G1), ηT= 85% (H3) and ηp= 90% (I3) are
determined as design parameter optimum values for
maximum ORC thermal efficiency. When the optimum
condition (A1B1C1D3E3F3G1H3I3) is chosen for ORC
thermodynamic analysis, thermal efficiency is calculated
as 17.3%, which is the maximum value that can be
obtained under these working conditions
Fig. 4.2 Thermal efficiency analysis by response surface
methodology
Fig. 4.3 Main Effect plot for Means of each parameter on
the thermal efficiency.
Table 4.1Taguchi Analysis: Thermal Efficiency versus
working ... p Efficiency, ηpResponse Table for Signal to
Noise Ratios
Table 4.2Taguchi Analysis: Thermal Efficiency versus
working ... p Efficiency, ηp Response Table for Response
Table for Means
Although this case (A1B1C1D3E2F3G1H3I3) is not among
the 27 cases located in Table 4, the highest thermal
efficiency is statistically obtained for this case. As seen, the
Taguchi method allows determination of the cycle’s
optimum working condition without calculating each
possibility. On the other hand, despite PPTD=0°C is ideal
condition for evaporator and condenser; it does not reflect
actual operating conditions for the heat exchangers. Since
this is a parametric study, the operating ranges were kept
as wide as possible. For this reason, the PPTD values are
initialized at 0°C. Although the condition of PPTD=0°C is
practically impossible, it theoretically gives the best
1 -17.03 -17.41 -17.4 -22.57 -17.86 -23.58 -16.89 -22.98 -17.45
2 -22.62 -22.46 -17.42 -17.41 -17.33 -17.09 -22.49 -17.4 -22.46
3 -17.64 -17.42 -22.47 -17.3 -22.09 -16.61 -17.91 -16.9 -17.37
Delta 5.59 5.05 5.07 5.26 4.76 6.97 5.59 6.07 5.09
Rank 4 8 7 5 9 1 3 2 6
Effectiven
ess of the
Heat
Exch.,
Evaporator
Temperature,
Tevap
Condenser
Temperature,
Tcon
Turbine
Efficiency,
ηT
Pump
Efficiency,
ηp
Level PPTDevap PPTDcon
Working
Fluid
Superheating
Temperature,
Tsh
1 0.1415 0.1361 0.1357 0.1222 0.1282 0.1064 0.1438 0.1155 0.1349
2 0.121 0.1233 0.1359 0.1356 0.1376 0.1404 0.1225 0.1358 0.1234
3 0.1324 0.1355 0.1234 0.1371 0.1291 0.1481 0.1286 0.1436 0.1366
Delta 0.0205 0.0128 0.0125 0.0149 0.0094 0.0417 0.0213 0.0281 0.0133
Rank 4 7 8 5 9 1 3 2 6
Effectiven
ess of the
Heat
Exch.,
Evaporator
Temperature,
Tevap
Condenser
Temperature,
Tcon
Turbine
Efficiency,
ηT
Pump
Efficiency,
ηp
Level PPTDevap PPTDcon
Working
Fluid
Superheating
Temperature,
Tsh

working condition. In addition to the optimum condition,
the worst working condition has been determined as
A3B3C3D1E1F1G3H1I1.
4.2 GRA-BASED TAGUCHI DESIGN
An L15 orthogonal array is used in the current analysis.
Multi-attribute decisions can be obtained using the grey
relational analysis technique. The experimental results for
thermal efficiency and machining force in GRA are
normalised to a number between 0 and 1. The information
for thermal efficiency is expressed as maximum values for
all objective functions. The higher is better. Thermal
efficiency is maximum and stated as if the response is to
be maximum, then higher the better qualities is meant for
normalisation to scale it into an acceptable range using the
following formula variation.
)
1
(
)
(
min
)
(
max
)
(
)
(
)
( 















k
x
k
x
k
x
k
x
k
y
i
i
i
i
i
11(1
Where
)
(k
yi indicates the normalization value of grey
relational generation, max
)
(k
xi and min
)
(k
xi express
the maximum and minimum value of
)
(k
xi , respectively.
Lastly,
)
(k
xi represents the optimum value.
Table 4.6 Normalized results, grey relational coefficients,
grey relational grade and order.
Table 4.7 Mean S/N ratios and weight factor for the first
law efficiency.
Fig. 5.4 Residual plot for Grey Relation Grade
Fig. 5.5 Interaction Plot for thermal efficiency
Fig. 5.6 Surface Plot for thermal efficiency.
Case
ηI Normalized
Deviatin
sequence
Grey
Relation
Coefficient
Grey
Relation
Grade RANK
1 0.117 0.7270 0.2730 0.6469 0.3234 23
2 0.1455 0.9051 0.0949 0.8404 0.4202 10
3 0.159 0.9894 0.0106 0.9792 0.4896 4
4 0.1249 0.7764 0.2236 0.6910 0.3455 19
5 0.1561 0.9713 0.0287 0.9457 0.4728 5
6 0.1433 0.8913 0.1087 0.8214 0.4107 12
7 0.133 0.8270 0.1730 0.7429 0.3715 15
8 0.1398 0.8695 0.1305 0.7930 0.3965 13
9 0.1548 0.9631 0.0369 0.9314 0.4657 6
10 0.1156 0.7183 0.2817 0.6396 0.3198 24
11 0.1597 0.9938 0.0062 0.9877 0.4938 2
12 0.1302 0.8095 0.1905 0.7241 0.3621 17
13 0.1236 0.7683 0.2317 0.6833 0.3417 20
14 0.1607 1.0000 0.0000 1.0000 0.5000 1
15 0.0006 0.0000 1.0000 0.3333 0.1667 27
16 0.1309 0.8139 0.1861 0.7287 0.3644 16
17 0.1462 0.9094 0.0906 0.8466 0.4233 9
18 0.1214 0.7545 0.2455 0.6707 0.3354 21
19 0.1285 0.7989 0.2011 0.7131 0.3566 18
20 0.1097 0.6814 0.3186 0.6108 0.3054 25
21 0.1597 0.9938 0.0062 0.9877 0.4938 2
22 0.1361 0.8463 0.1537 0.7649 0.3825 14
23 0.1185 0.7364 0.2636 0.6548 0.3274 22
24 0.1463 0.9101 0.0899 0.8475 0.4238 8
25 0.1445 0.8988 0.1012 0.8317 0.4158 11
26 0.1022 0.6346 0.3654 0.5778 0.2889 26
27 0.1465 0.9113 0.0887 0.8493 0.4247 7
MAX 0.1607 1.0000 1.0000
MIN 0.0006 0 0
PPTDevap 0
PPTDcon 0
Superheating Temperature, Tsh 10
Effectiveness of the Heat ExchE 0.75
Evaporator Temperature, Tevap 130
Condenser Temperature, Tcon 30
Turbine Efficiency, ηT 0.8
Pump Efficiency, ηp 0.9
Working Fluid R123
Hold Values
0.165
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0.167
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Fig. 5.6 Contour Plot for thermal efficiency
Table: 4.8 ANOVA table for grey relational grade
Additionally, Grey relation analysis method is
implemented as an optimization technique for maximizing
the first law efficiencies simultaneously. Evaporator
temperature, turbine efficiency, heat exchanger
effectiveness, and condenser temperature are derived as
the primary process parameters from the Grey relation
analysis, and the impact ratios of these parameters are
estimated as 48.90.37%, 17.43%, 10.80%, and 12.32%,
respectively. The optimum and worst operating conditions
for ORC are identified as A1B1C3D3E3F3G1H3I3 and
A3B3C1D1E1F1G3H1I1, respectively, after taking into
account several performance characteristics. The optimal
condition presents the highest first law efficiencies. Within
the range of analysed operating conditions, the system's
first law efficiencies are found to be 17.3% for the best
case. The first are found to be 9.6% for the worst
condition.
CONCLUSION
1. The investigation of grey relation method is
implemented as an optimization technique for
maximizing the first law efficiencies
simultaneously. Evaporator temperature, turbine
efficiency, heat exchanger effectiveness, and
condenser temperature are derived as the
primary process parameters from the Grey
relation analysis, and the impact ratios of these
parameters are estimated as 48.90.37%, 17.43%,
10.80%, and 12.32%, respectively.
2. The optimum and worst operating conditions for
ORC are identified as A1B1C3D3E3F3G1H3I3 and
A3B3C1D1E1F1G3H1I1, respectively, within the
range of analysed operating conditions, the
system's first law efficiencies determined to be
17.3% for the best case. The first observed to be
9.6% for the worst condition. This study lists the
ORC process parameters that are effective and
ineffective as well as their statistical impact
weight rankings.
3. the optimum level of design parameters is the
level with the maximum S/N ratio. Therefore,
working fluid = R123 (A1), PPTDevap = 0°C (B1),
PPTDcon = 0°C (C1), Tsh= 10°C (D3), ɛexc= 0.75
(E3), Tevap = 130°C (F3), Tcon= 30°C (G1), ηT=
85% (H3) and ηp= 90% (I3) are determined as
design parameter optimum values for maximum
ORC thermal efficiency. When the optimum
condition (A1B1C1D3E3F3G1H3I3) is chosen for
ORC thermodynamic analysis, thermal efficiency
is calculated as 17.3%, which is the maximum
value that can be obtained under these working
conditions
4. The value for the R –sq is 91.51% which is good
agreement between the input and the output
relationship. It shows that there is strong
relationship between the input and the output
variables. Now the value of the R-Sq (adj) =
92.04%hence the data are well fitted for the new
sets of the variables
5. Statistically, F-test decides whether the
parameters are significantly different. A larger F
value shows the greater impact on the Evaporator
Temperature, Tevap performance characteristics
[15]. Larger F values are observed for Evaporator
Temperature (P=0.00) (51.67 %)
Reference:
1. Abdellah, Youcef et al. 2021. “Towards
Improvement of Waste Heat Recovery Systems: A
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https://doi.org/10.1016/j.ijft.2021.100100.
2. Annuar, Arbainah Shamsul et al. 2021. “Jo Ur l P
Re.” Carbohydrate Polymers: 118159.
https://doi.org/10.1016/j.carbpol.2021.118159.
3. Authors, For. 2017. “Grey Systems : Theory and
Application Article Information :”
4. Bademlioglu, A H, A S Canbolat, and O Kaynakli.
2020. “Multi-Objective Optimization of
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5. Bademlioglu, A H, A S Canbolat, N
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https://doi.org/10.1016/j.applthermaleng.2018.0
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6. Ehyaei, E Ghasemian M A. 2017. “Evaluation and
Optimization of Organic Rankine Cycle ( ORC )
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7. Gimelli, A, A Luongo, and M Muccillo. 2016.
“EFFICIENCY AND COST OPTIMIZATION OF A
REGENERATIVE ORGANIC RANKINE CYCLE
POWER PLANT THROUGH THE MULTI-
OBJECTIVE APPROACH.” Applied Thermal
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http://dx.doi.org/10.1016/j.applthermaleng.2016
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8. Goyal, Ashwni, Ahmad Faizan Sherwani, and
Deepak Tiwari. 2019. “Environmental Effects
Optimization of Cyclic Parameters for ORC System
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Energy Sources, Part A: Recovery, Utilization, and
Environmental Effects 0(00): 1–14.
https://doi.org/10.1080/15567036.2019.163344
3.
9. Heberle, Florian, and Dieter Brüggemann. 2010.
“Exergy Based Fl Uid Selection for a Geothermal
Organic Rankine Cycle for Combined Heat and
Power Generation.” 30: 1326–32.
10. Le, Van Long, Abdelhamid Kheiri, Michel Feidt,
and Sandrine Pelloux-prayer. 2014.
“Thermodynamic and Economic Optimizations of
a Waste Heat to Power Plant Driven by a
Subcritical ORC ( Organic Rankine Cycle ) Using
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