-
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International Journal of Mechanical Engineering and Technology
(IJMET)
Volume 10, Issue 10, October 2019, pp. 52-63, Article ID:
IJMET_10_10_005
Available online at
http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=10
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
APPLICATION OF TAGUCHI-GREY METHOD
TO EVALUATE THE PERFORMANCE OF
ENERGY PRODUCED BY USING TRILATERAL
FLASH CYCLES AND LOW-GRADE
RENEWABLE HEAT
Mohammed Yunus* and Mowaffq M. Oreijah
Mechanical Engineering Department, College of Engineering and
Islamic Architecture,
Umm Al-Qura University, Makkah, KSA
*[email protected]
ABSTRACT
Increased in population and automation of process have upsurge
the demands of
fossil fuels drastically. Although the solar, bio-mass,
geothermal etc. energy
renewable resources are easily available for extraction, the
contemporary topics like
very high initial costs and power developing costs have
curtailed their progress.
Improvements on current power production methods demand
implementing a strong
thermal system to draw energy from low-grade renewable heat
sources as most of the
present techniques can only extract high temperature resources
and are very
expensive. A proposed binary system having
Trilateral-Flash-Cycle (TFC) and a
reaction turbine would tackle the contemporary topics with low
operation cost and
widely utilize the available sources to generate direct power by
means of a
hydrothermal with the reduced greenhouse gas emissions. The
performance
characteristics measured were efficiency, Gross power and nozzle
flow area have
been optimized for input factors such as speed, isentropic
efficiency, binary working
fluids and turbine diameters using hybrid Taguchi based grey
relation analysis (GRA)
where experimental trials carried out with Taguchi L9 orthogonal
array. The multiple
response optimization and ranking of input parameters were
productively completed
by GRA to find the effect of each of it. Analysis of variance
produced the higher
significant factor from selected parameters with percentage of
their contribution and
was confirmed with an affirmation (verification) test for the
obtained optimal set of
factors. Thus, the hybrid GRA with Taguchi technique design
provides the optimal
design parameters affecting multi-performances of TFC
system.
Key words: Taguchi-Grey Relation; Trilateral- Flash-Cycle;
Geo-thermal energy;
Binary working Fluids; Thermal efficiency and ANOVA
-
Application of Taguchi-Grey Method to Evaluate the Performance
of Energy Produced by using
Trilateral Flash Cycles and Low-Grade Renewable Heat
http://www.iaeme.com/IJMET/index.asp 53 [email protected]
Cite this Article: Mohammed Yunus and Mowaffq M. Oreijah,
Application of
Taguchi-Grey Method to Evaluate the Performance of Energy
Produced by using
Trilateral Flash Cycles and Low-Grade Renewable Heat.
International Journal of
Mechanical Engineering and Technology 10(10), 2019, pp.
52-63.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=10
1. INTRODUCTION
Energy retrieving from the largely available clean and renewable
sources (especially from
low amount of heat) would become the significant solution for
meeting demand such as dual
(binary) power system (BPS) to convert into electricity. BPS is
successfully applied in geo,
solar-thermal energy and retrieval of waste heat from industries
and industrial wherein ORC
(Organic Rankine Cycle) is mature technique employed to retrieve
sources of geothermal
energy in various parts of the world (Oreijah et al., 2019). It
is high time to investigate other
possible techniques for improving the efficiencies of available
BPS using other thermo
principles of cycle such as Trilateral Flash Cycle (TFC). TFCs
are employed for retrieving
excess amount of heat present in the low-grade heat resources
compared to other conventional
cycles (Rankine or Carnot). According to (DiPippo et al., 2007),
the main advantages of TFC
are the employing hot fluid not vapor while entering the
expander. Therefore, there is no extra
energy is needed drawing from developed energy for phase
transformation (fluid to vapor).
As a result, the net power developed by converting into
mechanical from thermal-energy is
larger in contrast to conventional cycles. The main motive for
TFC implementation is to
reduce the irreversibility in the heat transfer process occurs
between the primary hot source
(low-grade waste heat) and the working fluid (coolants) in
binary power system. As the
source temperature increases, recovering capacity of binary
working fluid increases and in
turn thermal energy for each unit heat source at a given mass
flow rate and temperature
increases power output. As far as the efficiency of TFC remains
lesser than other conventional
cycles but in terms of overall efficiency based on full
conversion of thermal (from heat
resource) into the mechanical energy is still higher due to
utmost capability of TFC for
extracting the sensible heat of hot source in contrast to other
conservative cycles. Besides,
various scientists have reported that no appropriate expander
(or turbine) was cascaded for
TFC system to exploit the enormous quantity of the obtained
thermal energy. Presently
available screw type expander has not proved efficient in TFC
based system.
Bryson (2007) noticed the work yield is due to the energy change
in the course of heating
less than that dissipated in cooling. All Heat engines (ORC and
TFC) use this as key principle
as at present, they are most successful equipments to produce
electricity from various thermal
sources economically. Using new improved heat engines, cycles,
heat sources, working fluids
and plans for power production by employing low amount
temperature sources which leads to
redesigning cycle basic factors in BPS would improve solution
for existing technology
economically. The basic devices required are condensers,
expanders (or turbines), electrical
generators, heat exchanger, and the secondary working fluid.
(Eastop & McConkey, 1993) noticed that standard RC run by a
working fluid (WF) water
works only when the thermal source is above 1800C (SKM, 2005)
whereas for low amount
temperature thermal resources (LGTR) available below 1500C,
therefore water cannot be used
as the WF. RC can be operated employing LGTR with the help of WF
other than water
having low saturation temperature at atmospheric pressure. Such
WFs are mostly hydrocarbon
organic fluids boil at very low temperatures and few have been
effectively employed in BPS.
Therefore, ORC uses organic fluid into RC (Ian K Smith &
Marques, 1994) constitutes the
same apparatus of RC but need modification of cycle factors to
work with LGTR. In 1960, the
first industrial ORC was developed to use the LGTR (Sawyer &
Ichikawa 1980). In 1987,
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Mohammed Yunus and Mowaffq M. Oreijah
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Tabor and Bronicki studied for using 4 kW solar turbines in ORC
and modified to
commercial ORC with Italian company, Ormat (Garg, 1987).
Currently, Ormat produces
ORC based systems for geo-thermal and waste heat recovery at
high MW thermal energy
level.
TFC involves the expansion of saturated liquid inside the
expander instead of saturated
vapor but remains consisting of four processes similar to ORC as
shown in figure 1. The
processes start by compressing the WF in a saturated liquid
phase by a fluid pump. The
constant pressure WF enters the heat exchanger and heat gets
added. The saturated liquid WF
enters the Reaction Turbine with the isentropic expansion where
the temperature and pressure
of the WF decrease simultaneously. Finally, the WF gets
condensed going into the condenser
before inflowing to the pump to form a fresh cycle. The working
theory of TFC was explored
widely in 1992 (Ian K Smith, 1993; I. Smith, 1992). The TFC
offers two benefits to use as an
striking thermo principled cycle such as firstly, it corresponds
the temperature profiles,
secondly, it get operated at actual pressures to implement them
efficiently practicable for the
low-power functions (Zamfirescu & Dincer, 2008). The key
output shows that the TFC best
med the considered Kalina and ORC by an increment of 7 % under
similar operating
parameters. That was due to the ability of TFC to recover most
of the heat from the heat
source.
The efficiency of the BPS can be improved by improving the
efficiency of individual
components among several techniques to develop the thermodynamic
performance. Each
component has various control parameters and optimum value of
these factors will contribute
to improvement of various outputs of TFC based BPS. As the
trilateral shows an advantage of
increasing the heat retrieval from heat resource, yet, required
to develop an effective expander
to improve the power utilization essentially due to the pressure
difference of the WF. The
expander (outflow reaction turbine) is the major unit in the BPS
to extract the power and by
thermal efficiency will be increased gradually with design
parameters namely, diameters,
speed of rotation. WF in BPS has the vital role in achieving
efficiency and the productivity.
Some characteristics of the WF are important for selecting the
proper WF such as higher
thermal conductivity, thermodynamically stable, compatible with
a wide range of materials,
low toxicity. Another output exit nozzle area to be optimized.
In this work, multi optimization
of outputs under various control factors listed in Table 1 are
carried out using taguchi based
grey relation analysis to find optimum values of control factors
for improving the outputs.
In the present complex and multi response system involving
various factors
simultaneously affecting it has to be evaluated for the
developing design by knowing which of
the parameters affect the system significantly. On the other
hand, the relationship between
various factors remains grey as information or data on them not
clear or complete or doubtful
as well as complex to achieve realistic and investigational data
from much scattered for
analysis. Various conventional statistical methods such as
factor and regression analysis are
commonly used for finding mutual effect of relationship of
dependent and independent
parameters between variables need vast data and should match the
given characteristic
distribution. It is very hard to attain the data on relationship
of parameters using such
methods. Hence, conventional multi output statistical techniques
have difficulties in giving a
realistic justification and to overcome these advantages, a new
statistical method to be used.
Taguchi based Grey relational approach (GRA) considered as
well-organized tool which
performs examination by arranging an outline for planning,
predicting, and grouping of grey
schemes. GRA offers advantages like miniature models without
characteristic distribution,
independent variables and a minimum amount of computation, with
little information besides,
being a clear-cut and precise technique for choosing factors
with unique settings [4]. Hence,
the GRA is utilized to carry out multi output optimization for
developing GRG (grey
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Application of Taguchi-Grey Method to Evaluate the Performance
of Energy Produced by using
Trilateral Flash Cycles and Low-Grade Renewable Heat
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relational grade) with various grades to arrange the rank of the
grey relationship within
independent and dependent factors. The control factors (refer
Table 1.) and the corresponding
levels of each factor are used for optimizing the TFC based BPS
generating power from low
amount of temperature resources. Experimental trials were
conducted using Taguchi L9
orthogonal array[16]. GRA involve the steps are as shown in
Figure 2.
Table 1: Levels of Process factors for performance
characteristics of TFC
Process Parameters Notation Unit Levels of Parameters
Level 1 Level 2 Level 3
Reaction Turbine diameter D M 0.4 0.6 0.8
Working fluids F - R134a R11 R113
Rotational speed of Turbine R Rpm 3500 5000 8000
Isentropic Efficiency E % 50 75 100
Figure 2. Steps followed during Taguchi based hybrid GRA
Transformation of measured values into non- dimension factors
ranging the results from
zero to one to consider the level of control parameters
magnitudes is called as normalising [7,
8]. In order to perform this sequences of data from original set
to comparable are to be
converted. This process of generation of grey relationship of
grey data uses various available
techniques. In the present work, hybrid Taguchi GRA employed to
optimize the performance
outputs of TFC based BPS generating power from low amount of
heat resources for their
multi outputs like Thermal efficiency (ηtherm), Gross power (GP)
and Exit Nozzle area (Anozzle)
of a reaction turbine to increase the retrieval capacity of
excess heat available. Multi output
characteristics depend upon the quality exceptionality of the
initial data which correspond to
the sequence of initial (or original) reference and
comparability (or normalized) using (x)
(x), y =1, 2, 3, ..… p; x = 1, 2, 3, ..., q, where p and q are
the experimentation runs and
responses of data correspondingly. Also, depending upon its data
quality, the main groupings
recognized for normalizing the initial sequence by means of
either “smaller-the-better” or
“larger-the-better” referring Eq. (1) or Eq. (2) respectively
[9]:
( )
( )
( )
( ) (1)
( )
( )
( )
( ) (2)
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Mohammed Yunus and Mowaffq M. Oreijah
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Where, ( )
( ) are the maxima and minima values of ( ). But
( ) represent normalized or compatibility and
( ) is the initial sequences of the objective value. In this
investigation, p = 9 and q = 3 are employed.
Next step is to evaluate of coefficients such as deviation and
grey relational using Eq. (3)
and Eq. (4).
( )
( ) ( ) (3)
Where, ( )
( ) ( ) are the reference (or ideal) sequence, compatible
sequence and the . The grey relational (or distinguishing)
coefficient lies in the range of 0 to 1) is evaluated using:
( ( )
( ))) ( )
(4)
Third step is to evaluate the GRGs, (
) acquired by the weighted summation of the ζ grey relational
coefficients to signify the of correlation level between primary
and
compatible sequences estimated employing Eq. (5):
(
)
(
( ) ( ( ))) (5)
Finally, the GRGs are then ranked in descending order to attain
the maximum value of it
for signifying the well-built agreement occur connecting the
ideal and the compatible
sequences. The highest GRG value indicates the optimum set of
combined control parameters
to achieve the desired outputs.
2. EXPERIMENTAL PROCEDURES
Experiments were initiated by decreasing the flow rate of the WF
(either R1341, R11 or
R113) in a reaction turbine operated using TFC and WF flow rate
is controlled by feed pump
to increase it till it reaches the sensible heat. Before
entering the turbine, WF remains in the
saturated liquid phase. The experiments were carried out with
certain factors at different
levels values listed in Table 1. Hot source temperature of low
amount heat renewable energy
sources (solar or geo thermal) lies in the range of 125 to 75 ºC
with constant flow rate of 0.56
L/sec was maintained during simulation for all set of
experiments. Whereas on the cooling
section of the condenser, the temperature range of 19 to 26 ºC
with a constant flow rate of
0.52L/sec were maintained. Necessary measuring devices were used
in the BPS test rig to
measure the required responses (or thermal characteristics) and
using data accumulator or
acquisition system, temperatures and flow rate at all accessible
points of cool and hot water
were collected. Moreover, the electric DC generator was coupled
to an electronic load device
(having voltmeter and ammeter) capable of varying the magnitude
of electric generator load
to record the voltage and the current as the output (power).
Accordingly, the developed power
is noted down accurately from experimentation. Using digital
strain display, the value of the
output force is recorded as it can be changed into the torque of
the turbine. Different control
parameters having numerical values at three levels with the
interaction effect were selected as
shown in Table 1. In multi-response optimization, some quality
characteristic loss is
anticipated in comparison to an optimization of a
single-response but always the total quality
improves depending on appropriate method of choosing factors and
their levels that affect
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these quality characteristics for improving performance outputs
of TFC based BPS. The
output characteristics optimized were GP, ηtherm and Anozzle.
The Taguchi process is employed
for the carrying out of the experiment’s plan as per orthogonal
array (OA) available for four
factors at three levels.
Four columns and nine rows of L9 OA is used in the present study
to carry out the
experiments for collecting responses of TFC based BPS (refer
Table 2).
3. MULTIPLE OUTPUT OPTIMIZATION OF CONTROL FACTORS BY
GRA
The estimation of performance based output characteristics is
judged using greatest values of
GP, TE and NA of BPS. The “higher-the-better” approach for
responses is applied to perform
normalization of GRA process by using Eq. (2) [yunus et al.,
2015]. Taguchi method was
used for optimization of the multiple-response of performance
characteristics to recommend
the field of higher heat withdrawal application. The
pre-processed data of responses are listed
in Table 3. The coefficient of grey relation (refer Table 4) of
multiple responses estimated
employing using Eq. (4) with the help of coefficients of
deviation attained from Eq. (3). All
the input factors considered for equal weight setting at a value
of 0.5 were employed to
estimate the GRG from Eq. (5). GRG of each experiment is
presented in Table 4 [yunus et al.,
2016]. Thorough investigations of data from Table 4 and Figure
3, signifies that experiment
No. 3 is the best response from the set of all trials as it has
large value of GRG, it means
comparable and initial sequences are demonstrating a well-built
relationship with each other
[yunus et al., 2017]. GRA finds the most important factor
depending on the theory; a
combination of the different levels of input factors afford the
largest mean output and is the
optimum factors set for improving heat retrieval effect of TFC
based BPS.
Table 2. Experimental trials and data processing of output
characteristics of TFC based BPS
TFC performance
Factors
Isen
trop
ic E
ffic
ien
cy
Experimental Results Normalized Data
Ru
n n
o.
Rea
ctio
n
Turb
ine
Dia
met
er
Work
ing F
luid
s
Rota
tional
Spee
d o
f R
T
Outp
ut
pow
er
(KW
)
Ther
mal
Eff
icie
ncy
(%
)
Nozz
le E
xit
area
(mm
2)
Outp
ut
pow
er
Ther
mal
Eff
icie
ncy
Nozz
le E
xit
are
a
Ideal
Sequence
1 1 1
1 0.4 R13
4a
3000 50 339.7 3.4 44.77813
7
0 0 0.3004
2 0.4 R11 5000 75 800.5 8 40.22282 0.5677 0.5672 0.245
3 0.4 R11
3
8000 100 1151.4 11.5
1
70.91513
4
1 1 0.6203
4 0.6 R13
4a
5000 100 939.4 9.39 26.38628
4
0.73882 0.7386 0.0753
5 0.6 R11 8000 50 677.6 6.78 26.4006 0.4163 0.4168 0.0755
6 0.6 R11
3
3000 75 740.8 7.41 101.9404 0.494 0.4945 1
7 0.8 R13
4a
8000 75 924.5 9.25 20.2307 0.721 0.7213 0
8 0.8 R11 3000 100 1083.5 10.8
4
32.9899 0.9163 0.92 0.1562
9 0.8 R11
3
5000 50 641.9 6.42 74.4596 0.3723 0.3724 0.6637
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Table 3. Coefficients of deviation and grey relational of output
characteristics of TFC based BPS
Run No. Deviation Coefficient Grey Relational Coefficient
GP ηtherm Anozzle GP ηtherm Anozzle
Ideal Sequence 1 1 1 1 1
1 1 1 0.6996 0.33333 0.3333 0.4168
2 0.4323 0.4328 0.755 0.5363 0.536 0.3984
3 0 0 0.3797 1 1 0.5684
4 0.2612 0.2614 0.9247 0.6586 0.6567 0.351
5 0.5837 0.5832 0.9245 0.4614 0.4616 0.351
6 0.506 0.5055 0 0.497 0.4972 1
7 0.279 0.2787 1 0.642 0.6421 0.3333
8 0.0837 0.08 0.8438 0.8566 0.8621 0.3721
9 0.6277 0.6276 0.3363 0.4434 0.44342 0.53
Table 4. Grey relational grades and their order
Run No. Grey Grade Order
1 0.3611 1
2 0.4902 4
3 0.8561 9
4 0.5554 6
5 0.4247 2
6 0.6647 7
7 0.5390 5
8 0.6969 8
9 0.4723 3
From greater average of GRG, optimum parameter level has been
found as D1F3R3E3,
i.e., Diameter of 0.4m, working fluid of isopentane, speed of
8000 rpm and isentropic
efficiency of 100%. Output tables were produced employing the
Taguchi method to estimate
the average GRG for every level of input factor for performance
characteristics of TFC based
BPS (refer Table 6).
Table 5. Mean grey relational grade at each level
Input Parameters Grey Relational Grade
Max.-Min. Rank Level 1 Level 2 Level 3
Reaction Turbine Diameter 0.5691 0.5483 0.5694 0.0211 4
Working fluids 0.5373 0.6644 0.4852 0.1792 2
Rotational speed of RT 0.5742 0.5060 0.6066 0.1006 3
Isentropic Efficiency 0.4194 0.5646 0.7028 0.2834 1
Total mean grey relational grade = 0.5623
The highest GRG values shown in Table 5 for a set of input
factors are D1-F2-R3-E3. It
represents the optimum set of TFC input parameters for the
multi-outputs of performance
measurements during assessment of extraction of high heat.
D1-F2-R3-E3 combination
indicates the Rotor diameter of 0.4m, the working fluid type of
isopentane (R113), at a speed
of 8000 rpm and isentropic efficiency of 100%. The main
influence of averages of GRG vs
levels is plotted in Figure 3. The dotted lines stand for the
total GRG average.
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Figure 3. Plot of total average of GRG vs. input parameters
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Mohammed Yunus and Mowaffq M. Oreijah
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0.080.040.00-0.04-0.08
99
90
50
10
1
Residual
Pe
rce
nt
1.00.80.60.4
0.04
0.02
0.00
-0.02
-0.04
Fitted Value
Re
sid
ua
l0.040.020.00-0.02-0.04
4
3
2
1
0
Residual
Fre
qu
en
cy
16151413121110987654321
0.04
0.02
0.00
-0.02
-0.04
Observation OrderR
esid
ua
l
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Grey Relational Grade
Figure 4. Residual plots of GRG
4. ANOVA (ANALYSIS OF VARIANCE) APPLIED FOR GRG
Analysis of Variance has been used to find out the ranking or
grading of GRG at 95%
probability interval for examining the importance level of input
parameters on multiple
response of TFC using Minitab® 19 statistical software [yunus et
al., 2016]. A total sum of
squared deviations (SST) can be calculated using Eq. (6):
∑(yj ym)
j
(6)
where, N = number of trials in OA, yj = average of GRG for
jth
trial and ym = total average
of GRG. Average square (MS) is achieved by using Eq. (7) as
follows
(7)
The most influential input factor on the multiple responses is
having a large value of F (or
small value of P). The % contribution of an input parameter can
be estimated using an Eq. (8)
and corresponding residual plot of GRG is shown in Figure 4
[15].
(8)
The ANOVA GRG results for of BPS have been determined (refer
Table 6) and shows the
isentropic efficiency is the only significant control factor
multi-outputs considered
simultaneously during high extraction of heat. The %
contribution of input factor for
isentropic efficiency in the multi-response performance is
25.96.
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Table 6. Analysis of Variance Results on GRG
Source DF Adj SS Adj MS
F-
Value
P-
Value
Reaction Turbine Diameter 2 0.000882 0.000441 0.00 0.996
Working Fluids 2 0.050981 0.025491 0.61 0.493
Rotational speed of RT 2 0.015835 0.007918 25.96 0.015
Isentropic Efficiency 2 0.120527 0.060263 5.49 0.099
Error 0 0 0
Total 8 0.188225
Regression equation
GRG = 0.5623 + 0.006867 *D1 - 0.01400*D2+ 0.007133*D3 -
0.02500*F1+ 0.1021*F2 –
0.07710*F3 + 0.01197*R1 - 0.05630*R2+ 0.04433*R3 - 0.1429*E1+
0.002367*E2 + 0.1405*E3
5. VERIFICATION OF RESULTS BY VALIDATING TEST
The optimal set of input factors as D1-F3-R3-E2 for maximizing
GP, ηtherm and Anozzle are
optimized using hybrid Taguchi and Grey Relational Approach. A
confirmation test was
carried out by using an D1-F1-R1-E1 optimal setting. The results
of the verification test were
1151.4 KW for GP, ηthermal of 11.51% and Anozzle of 90.9mm2.The
confirmation test result is
observed better than the experiments carried out (refer Table
3). Subsequent to finding the
optimal combination of TFC control factors and the most
influential factor, the concluding
stage to verify the feasibility of proposed combined Taguchi
based grey method by
conducting some confirmation tests. The optimal grey relational
grade, гopt, is calculated as
[15]:
∑ ( )
(9)
where, is the total GRG average, , is the GRG average of the of
ith
parameter of
optimum levels, and c is the quantity of most influential as
regards to TFC based BPS
parameters[16]. In all outputs such as GP, ηthermal and Anozzle
for an optimum set of control
parameters were sufficiently higher as compared to those of
initial setting of factors (having
0.80). Three supplementary trials were conducted at various
levels of optimum parameter set
and the mean of these is considered in the verification test.
The forecast GRG values and the
result of the validation test are provided in Table 7.
Performance based outputs such as GP
has been enhanced from 1100 to 1150 KW, ηthermal is from 11.5 to
12% and Anozzle is
improved from 85 to 90 mm2.
Table 7: TFC based BPPS performance results using primary and
optimum input factors
Initial Process
Parameters
Optimal process parameters
Prediction Experiment
Level D1-F1-R1-E1 D1-F3-R3-E3 D1-F3-R3-E3
Gross Power 1100 - 1151.4
Thermal Efficiency 12 - 11.51
Nozzle Exit Area 85.008 88.075 90.915134
Grey Relational
Grade 0.80 - 0.8561
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6. CONCLUSION
BPS using low amount of heat energy available in of geo-thermal
energy are employed
proficiently for extracting least possible available heat
derived from secondary WF. Response
characteristics experiments such as Thermal efficiency (ηtherm),
Gross power (GP) and Exit
Nozzle area (Anozzle) were carried out using TFC and reaction
turbine with various WF. The
multi-responses were collected under L9 OA combination of
control factors using Taguchi
design approach. The assessment of GRG enumerates the general
performance of input
factors of TFC based BPS for developing power from low amount of
heat sources. The
highest GRG value was obtained when the diameter of turbine is
0.4m, WF of isopentane,
Speed of 8000 rpm and isentropic efficiency of 100% for
performance characteristics and
these could be recommended levels of TFC based BPS control
factors for maximizing
responses measured. GRA Ranking determined by employing ANOVA
method discovered
that the speed of RT is the most significant control factor
which affects the multi-output
characteristics. The contribution of speed is relatively higher
about 25.46% when compared
with the other input factors. The optimal set of input factors
upon a verification tests revealed
the GRG enhancement is 0.8561 contrasts to primary combination
values of input factors
about 0.80 shows substantial improvement in the performances of
TFC such as gross power,
thermal efficiency and nozzle exit area have been validated.
This investigation reduced time
as well as cost including production cost to increase the
quality. The optimization of
combined multiple responses are significantly simplified with
repeatability and
possibility.Hence, the hybrid GRA and Taguchi design used for
multiple-output optimization
with set of optimal input factors helps in accomplishing
substantial improvement of grey
relation.
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