OPTIMIZATION OF PERFORMANCE PARAMETERS OF GAS TURBINE

USING TAGUCHI BASED GREY RELATION ANALYSIS

Saira Alam1,*, Younis Jamal1, Asad Naeem Shah1 Mustabshirha Gul2, and Asad Raza Gardazi2

1Department of Mechanical Engineering, University of Engineering and Technology Lahore-54000, Pakistan

2Department of Mechanical Engineering, UCE&T Bahauddin Zakariya University Multan- 60000, Pakistan.

Abstract: This study is mainly based on the technique of Taguchi method with grey relational

analysis (GRA) for optimizing the multiple performance input parameters of Gas turbine. Such

techniques are normally used for conversion of multiple-objectives problem into a single

objective by using Taguchi based Grey relational analysis. The influence of process parameters

such as inlet temperature, type of air inlet filters, and machine rotor rpm on power, heat rate,

specific fuel consumption (SFC) and thermal efficiency is addressed in the study. Through this

technique an optimal setting of input performance parameters was obtained as performance

index. Orthogonal array (OA) L9 (34) was selected as Design of experiment (DOE), so nine

experiments were performed in this way. Subsequently, the signal-to-noise (S/N) ratio and the

analysis of variance (ANOVA) were employed to find the best input process parameter levels,

and to analyze the effect of these parameters on power, heat rate, SFC and thermal efficiency.

The main objective was to increase the thermal efficiency and horse power, and to decrease

SFC and heat rate. During the study, air inlet temperature of 84°F, turbine speed of 14400 rpm

and cartridge filter were found to be an optimal input parametric combination at which the

desired output effects were achieved. Moreover, analysis of variance (ANOVA) based results

revealed that air inlet temperature of 72.9% was predominant parameter among the other optimal

parameters showing a dominant impact on the output parameters of Gas turbine. Finally, a

confirmatory test with optimal levels of selected input parametric combination was carried out to

validate the results, and to illustrate the effectiveness of the Taguchi based GRA optimization

method.

Keywords: Gas turbine, Optimization, Orthogonal array, Grey relational analysis, Taguchi method________________________________________________________________________________________________

*Corresponding Author’s e-mail address: saira.alam865@gmail.com

Abbreviations

Horse Power HP Grey Relational Grade

Grey Relational Coefficient

Quality Loss Function

Analysis of variance

Degree of Freedom

GRG

Heat Release HR GRC

Thermal Efficiency ɳth QLF

Specific Fuel Consumption SFC ANOVA

Design of Experiments DOE DOF

Orthogonal Array OA

Grey Relational Analysis GRA

INTRODUCTION:

Gas Turbines have broad range of applications in industry, which are generally used to

provide electrical energy and mechanical power mainly in natural gas sector for the services as

prime movers for pipeline compression, gas lifting, gathering of gas, boosting of gas, etc. [1-

2].Gas turbine used as mechanical driver is more efficient than the steam turbine [3]. The prime

objective of a Gas Turbine is to run at optimal parameters to give maximum efficiency by

utilizing the lower fuel consumption during operations in the prevailing environmental

conditions [4].

There are a lot of ways to increase gas turbine output. Some significant methods include

control of humidity, air inlet temperature and air filtration systems as well as filter type. Such

techniques have great influence on the thermal efficiency heat rate, fuel consumption and

operational availability. Hosseini et al. [5] have reported that inlet temperature, humidity and

pressure drop have dominant effect on the performance of the gas turbine owing to the air inlet

cooling system, particularly with evaporative cooling system [5].

Air quality entering the gas turbine depends upon the filtration systems, and thus erroneous

choice of filters can have catastrophic consequences. Filter-effectiveness creates less fouling and

less deterioration, which is the key to maintain the higher efficiency and power, as output power

of a gas turbine depends on inlet air temperature and cleanliness [6]. Mohapatra and Sanjay [7]

showed that some variables such as ambient temperature, compressor pressure ratio, compressor

inlet temperature, turbine inlet temperature and relative humidity have predominant effects on

performance of gas turbine plant. Moreover, commercial methods used for inlet air cooling have

also been reported to be effective in improving the efficiency of an existing gas turbine [8].

The presence of foreign objects may cause the engine deteriorations including compressor

erosion and degradation of turbine components, fouling of compressor airfoils, and corrosion.

Hard foreign objects are removed by proper filters which may further influence engine horse

power, engine temperature and thermal efficiency. So a gas turbine is equipped with a

sophisticated complex air filter systems for the proper filtration of the contaminations of the

system. Such filters are made up of different media including paper, cellulose, fiber, membrane

and glass. Each of them offers its own filtration capacity, performance and life depending on

environments and applications [9-10].

Researchers have shown that gas turbine performance is affected by the health of the

compressor and that environmental conditions affect the output of axial air flow compressor of

gas turbine dictated by air inlet quality of compressor. Performance deterioration of a compressor

can be compensated by its washing. Both on-line and off-line washings of a compressor have

revealed pleasant effects on the gas turbine performance [11-12]. Moreover, ambient inlet air

temperature, relative humidity and types of fuels have direct impact on gas turbine plants. The

decrease in ambient inlet air temperature increases the net output and efficiency of a plant. Also,

the turbines operating on natural gas emit lesser pollutants as compared to those running on

conventional fuels [13]. Dundas [14] analyzed and optimized the gas turbine through variation of

different gas turbine cycles, involving important parameters such as pressure ratio and

temperature.

The optimization and thermodynamic study of the gas turbine have been reported by some

researchers in different modern optimization techniques to minimize the SFC at cruise

conditions, capital and maintenance costs, as well as to maximize the performance, efficiency

and specific thrust. Such optimization approaches consist of a genetic algorithm and a modular

gas turbine simulation tool keeping in view the growing demands of maximum efficiency with

reducing fuel consumption [17-21]. Although a lot of optimized techniques are available to

predict the combination of optimal parameters to improve the required output results and better

economic performance, Taguchi-based GRA is expected to be more effective and efficient for

such purpose [23-24].

Gul et al. [25] carried out multi-variable optimization technique to maximize engine torque,

brake power, heat release, and injection pressure by utilizing orthogonal array design of Taguchi-

grey relational analysis, and pointed out the influence of input parameters on particular response

characteristics. Karnwal et al. [26] have used the Taguchi based Grey Relation Analysis for

improving diesel engine brake thermal efficiency with low emissions. Similarly, multi-response

Grey-Taguchi optimization techniques have also been used in CNC turning process [27, 36],

casting process [28], machining and milling processes [23, 29-31], submerged arc welding [33]

and energy management systems [34].

Though Taguchi based methods have been vastly used in developing of industrialized

sectors [35, 37], but their applications to scrutinize the optimum parameters and thus to evaluate

performance of a gas turbine are in infancy. The purpose of this research is to reduce the specific

fuel consumption, heat rate and to increase thermal efficiency of the gas turbine. Presently, no

such technique except Grey-Taguchi method is available which may simultaneously be used to

solve and convert multiple-objective parameters into singular and optimized output parameters

of gas turbine

1. Materials and Methods

1.1 Experimental Setup:

In current study, experiments were conducted on two shafts Gas Turbine Model No: T-

4502.GT consisting of axial compressor, annular combustor liner, mixed flow two stage turbine

(GP), and mixed flow single stage gas turbine (PT).The axial flow air compressor consists of

eleven stages as shown in the Fig 1.

Figure 1. Schematic diagram of test bench

The GP and PT are not mechanically coupled to each other, GP runs independent of the PT,

and PT runs due to the exhaust of the GP. The other performance specification of the Gas

Turbine is given in the Table1.

Table 1. Performance specification of the Gas Turbine

Configuration Specifications

Fuel Gas Liquid

NGP RPM 15000 15000

NPT RPM 15500 15500

Minimum Horse Power 3902 3785

Heat Rate 9629 BTU/Hp-hr 9736 BTU/Hp-hr

Minimum compression Ratio 8.6 8.6

Compressor Mass flow 37.5 37.5

In Gas turbine both liquid as well as Gas can be used as fuel but in this study Natural Gas

was used as fuel is used at pre set temperature and pressure. The experiments were performed on

the performance testing facility of the SNGPL Multan, Pakistan. The PT was coupled with

dynamometer through flexible coupling fully equipped with the instrumentation gadgetry as well

as mechanical equipments. Dynamometer is used for the torque measurement which is then used

for the calculation of the horse power of the Gas Turbine. The load variation on the power

turbine during experimentation was done with the pressurized water controlled through pressure

control valve impinging on dynamometer. Fuel control valve was used for measuring, and thus

controlling the supply of the fuel during ramping and accelerating of the engine. For the

measurement of gas turbine speed in rpm, magnetic picks were hanged on OEM at recommended

location of MPU to send signals in terms of frequency to control the system for subsequent

calculation.

Figure 2. Schematic diagram of test bench

In Fig. 2 station 1 indicates the location of measuring the air inlet filters differential in

which there is an alarm setting mechanism through PLC, in order to observe the filter health. For

the control of the air inlet ambient temperature, air inlet cooling techniques were adopted and

thus evaporative cooling system was installed in the air inlet system. Station 2 shows the inlet of

the compressor, while station 3 is the exhaust of the axial flow compressor. The combustible

gases enter into the turbines (GP+PT) at station 4, and finally the exhaust of the gas turbine is

released at location 5. Table.3 shows the experimental activities carried out by varying the filter

type and rpm of machine which were increased and decreased continuously according to the

need for defined ambient temperature. Air inlet temperature was controlled trough evaporative

cooling system to make essential modification for hot climate, as air inlet temperature is the

primary parameter which affects the performance characteristics of the machine [38, 39].

Following instruments were used during the experimentation.

Pressure Transmitter

Thermocouples, RTD

Pressure Gauges

Lube oil Flow meters

Fuel flow meters

1.2 Grey-Taguchi Methodology

The Grey-Taguchi method categorizes the important factors which compose the best

contributions to the variation. The output parameters depend on several input factors. So Grey-

Taguchi is applied to design and perform the experiments for the assessment of output responses

without running the process at all possible combinations of given input values. The Grey-

Taguchi method determines the correlations among actual and desired experimental data and

converts numerous quality characteristics into single grey relational grade (GRG) [29]. It allows

knowing the optimal combination of parameters which may affect the performance of gas

turbine.

The optimal design parameters affecting the gas turbine performance are determined and

then are controlled easily. The no of levels of the input parameters to be varied is required to be

specified. Increase in no of levels also increases the no of experiments to be performed. Input

parameters used in this study are ambient temperature, gas turbine speed and types of air inlet

filters.

Table 2. Experimental Factor and Factor Levels

Control Variables Code

Level Output to be observed

1 2 3 HP

Temp(°F) A 84 60 56.6 ɳth

Speed(RPM) B 15000 14700 14400SFC

Filter Types C Conical Barrier Cartridge Heat Rate

The purpose to use the Taguchi method was to choose the suitable orthogonal array OA in

order to investigate the all parameter at three levels. Three factors were assigned to definite

column in the OA for analyzing the main effects. An OA was used for the DOE on the basis of

L9 (33). Table 3 shows the L9 (33) OA for the experimental work.

Table 3. Orthogonal Array L9 (33)

Run No A B C

1 1 1 1

2 1 2 2

3 1 3 3

4 2 1 2

5 2 2 3

6 2 3 1

7 3 1 3

8 3 2 1

9 3 3 2

Table 4 shows Experimental values for the four outputs and these are taken for

optimization in accordance with DOE.

Table 4. Design of Experiments

Run NoTemp Speed Type of Filters

OA T1 OA Speed OA Filter Type

1 1 84 1 15000 1 Conical

2 1 84 2 14700 2 Cartridge

3 1 84 3 14400 3 Barrier

4 2 60 1 15000 2 Cartridge

5 2 60 2 14700 3 Barrier

6 2 60 3 14400 1 Conical

7 3 56.6 1 15000 3 Barrier

8 3 56.6 2 14700 1 Conical

9 3 56.6 3 14400 2 Cartridge

1.3 Normalization of Experimental Data/Statistical Analysis:-

As the main objective of current work is to increase the thermal efficiency and horse Power

keeping the heat rate as well as SFC at their minimum values, so experimental data was

normalized by using higher the better & smaller the better criteria [36].

The considered criterion was completed with respect to better quality aspect of interest

[37]. So S/N ratio for the quality characteristics (higher the better) was formulated as follows.

Whereas the assessment signs of the Power and thermal efficiency to the ith time and n is the

repeated experiment number. As SFC and heat rate are the smaller the better quality characteristics hence calculation of the Smaller the better is formulated as

Actual origin of series and series for evaluation as So and Si (l) =1, 2, 3…, t where t= No of

experiment to be considered respectively Where l=1, 2, 3…….u and no of observation is u.

If the desired objective is to maximize the outputs then the normalization of original sequence is

given as follows.

For the objective to minimize the output then “smaller the better” criterion is as follows,

Now smaller the better is as under,

Target Value= OT.

Equation (iii) depicts original series sequence.

Where is the actual series the series after data giving out max the

biggest value of & min the lesser value of

As the experimentation was performed on the basis of Gray relational analysis, the GRG for

analyzing the most effective parameter formulation of grey relational coefficient (GRC) is

related as follows:

As ∆min and ∆max are the minimum and maximum values of ∆oj ( k ) sequences. ∆oj ( k ) difference

between the original values and the estimated normalized values of data are quality loss function.

The quality loss function is used to investigate whether a certain feature is lying among a given

exact limits or not.

Then GRG is the average of GRC corresponding to the given responses. The overall multiple

response characteristic of a process depends on the calculated GRG, given as follows

1.4 Analysis of variance (ANOVA).

The ANOVA was performed to find out the numerical implication of the performance

affecting optimizing parameters. The results were examined to establish the primary effects of all

the factors, and thus are tabulated by the sum of the squared deviations from the total mean of

the OGRG. It is revealed that input parameter with larger F-value has a significant effect on

multiple output responses.

ANOVA is performed after the analysis of results to approximate the most significant input

parameters which are tabulated on the basis of mentioned formulation. F-value shows the most

significant effect of input parameter on the outputs. [32].

Where SSA is sum of square of each of factor.

SSp is sum of square of process parameters

2. Results and Discussions.

2.1 Output Response based on OA

Table 5 representing the results obtained after experimentation carried out on the basis of

DOE Orthogonal Array. Nine experiments were performed by taking three input variable with

the three different levels to tabulate the output parameters i.e. Horse Power, Heat Rate, SFC and

Thermal efficiency.

Table 5. Experimental Results

Run NoOrthogonal Array

HorsePower

Heat Rate SFC ɳth

A B C1 1 1 1 3725 9284 37.5 27.412 1 2 2 3629 9212 35.1 27.63 1 3 3 3850 9165 36.3 284 2 1 2 3501 9782 40.1 26.015 2 2 3 3436.8 9594 38.25 25.526 2 3 1 3590 9440 37.03 26.77 3 1 3 4102 9300 35.67 27.358 3 2 1 3780 9245 34.02 26.59 3 3 2 3629 9145 36.9 27.83

The experimental results show the output response, which are changing with input

parameters such as ambient temperatures. With the increase of ambient temperature air inlet

density reduces that reduces the mass flow rate and ultimately causes the reduction in horse

power and thermal efficiency [38]. When air inlet filters were replaced, the changes in inlet

pressure of the gas turbine were observed due to the different efficiencies of air inlet filters. This

loss in the input pressure reduces thermal efficiency and increases the SFC. There was loss of

inlet pressure due to selection of inefficient air filters which in turn decreased the density and air

inlet mass. The decreased mass flow rate further reduced the output power and output pressure of

axial compressor. As the exhaust pressure remained unchanged, so the gas turbine pressure ratio

decreased, and ultimately thermal efficiency was also reduced. The decrease in horse power

means the reduction in fuel flow and heat rate caused by the behavioral changes of cartridge

filter. This pressure loss has the same effect as lower barometric pressure. It reduces air inlet

mass, compressor discharge pressure, shaft horse power, and fuel flow rate. Moreover, it has a

secondary effect of making the inlet pressure lower than the exhaust pressure, thus reducing the

engine pressure ratio as well.

The lower pressure ratio causes the reduction in both horse power and thermal efficiency.

A reduction of 1% in engine inlet pressure due to barometric pressure causes exactly a 1%

reduction in output air inlet mass, shaft horse power, compressor discharge pressure, and fuel

flow. However, a reduction of 1% in engine inlet pressure due to inlet duct pressure loss causes a

1% loss of thermal efficiency and almost 2% reduction in power [Give reference please]. Inlet

pressure loss lowers the pressure (and thus the density and mass flow rate) at the air inlet. The

lower mass flow rate lowers output power and compressor discharge pressure, but the exhaust

pressure remains unchanged so the engine pressure ratio and thus thermal efficiency are lowered.

Higher air inlet temperature causes effect on power to be reduced due to low mass flow rate, and

results in increase in fuel consumption and heat rate.

2.2 Grey Taguchi Method

2.2.1 Grey relational generation

As experimental results consist of the different units/dimensions, their comparative analysis

cannot be made. So, it becomes necessary to convert them into non-dimensional values so Grey

relational generation is made [24]. Table 6 shows S/N ratio values in which first two columns

show the higher the better, while negative values of last two columns indicate the characteristics

of smaller the better for the heat rate and SFC.

Table 6. Multi Response S/N Ratio

Experiment No: Power Thermal efficiency

Heat Rate SFC

1 71.423 28.758 -79.355 -31.481

2 71.196 28.818 -79.287 -30.906

3 71.709 28.943 -79.243 -31.198

4 70.884 28.303 -79.809 -32.063

5 70.723 28.138 -79.640 -31.653

6 71.102 28.530 -79.499 -31.371

7 72.260 28.739 -79.370 -31.046

8 71.550 28.465 -79.318 -30.635

9 71.196 28.890 -79.224 -31.341

Average and variance analyses were taken to optimize the performance affecting

parameters. Analysis was performed on the basis of higher the better and smaller the better

criteria for maximizing and minimizing the output parameters such as thermal efficiency, horse

power, heat rate and specific fuel consumption. Normalization of the experimental output data in

comparable units is done through Grey relational generation. [30]

Table 7. Grey Relation Generation of Output Responses

Larger-the-better (Xi*) Smaller-the-better (Xi*)

Power Thermal Efficiency Heat Rate SFC

0.4332 0.7620 0.7817 0.4276

0.2889 0.83870 0.8948 0.8223

0.6211 1 0.9686 0.625

0.0965 0.1975 0 0

0 0 0.2951 0.3042

0.2303 0.4758 0.5368 0.5049

1 0.7379 0.7566 0.7286

0.5159 0.3951 0.8430 1

0.2889 0.9314 1 0.5263

Table 7 shows the range between 0 and 1 which means that normalization of data lies

between the same ranges, and thus the value closer to 1 indicates the better performance.

2.2.2 Evaluation of GRC and Average GRG.

A Grey relational coefficient (GRC) has the ability to build up a relation between the actual

series of sequence and series of sequence formed during the Grey relational generation of the

output normalized data. Grey relational Coefficient is calculated from quality loss function

(QLF). QLF is the direct measure of the level of variation between the original sequences and

comparable sequences [25, 29]

For the optimization there is specific target which needs to be achieved. The target is a

specified upper and lower limit, with the focal point to be the middle point. Table 8 showing the

QLF is measure of level of variation, smaller value indicating an approach towards an ideal

target with smaller loss. So Taguchi QLF gives a good way to analyze the costs associated with

variability even within the limits, and thus leads to the reduction of variability of engine output

characteristics to a specific target value.

Table.8 Taguchi QLF (∆₀ᵢ ) of each Response

PowerThermal

EfficiencyHR SFC

0.5667 0.2379 0.2182 0.5723

0.7110 0.1612 0.1051 0.1776

0.3788 0 0.0313 0.375

0.9034 0.8024 1 1

1 1 0.7048 0.6957

0.7696 0.5241 0.4631 0.4950

0 0.2620 0.2433 0.2713

0.4840 0.6048 0.1569 0

0.7110 0.06854 0 0.4736

Conversion of multi objective cases to single objective case can be made through GRC

which is further used to determine the performance affecting parameters [29]

Table 9. Grey Relation Coefficient

PowerThermal

EfficiencyHR SFC

0.4687 0.6775 0.6961 0.4662

0.4128 0.7560 0.8261 0.7378

0.5689 1 0.9409 0.5714

0.3562 0.3839 0.3333 0.3333

0.3333 0.3333 0.4149 0.4181

0.3937 0.4881 0.5191 0.5024

0 0.6560 0.6726 0.6481

0.5080 0.4525 0.7610 1

0.4128 0.8794 1 0.5135

Based on OA and Grey relational coefficient, Grey relational grade is calculated. GRG is

required to give a combination which would be acceptable for all the output parameters to be at

their optimum level. A higher GRG corresponds to a parametric combination closer to the

optimal values, and thus leads to a better performance level [25]. It indicates that the

experimental result is closer to the ideally normalized value.

Table10. OA And Grey Relational Grade

GRG Run # T1 Run # Speed Run # Filter Type

0.577 1 84 1 15000 1 Conical

0.683 184

214700

2Cartridge

0.770 184

314400

3Barrier

0.351705738 260

115500

2Cartridge

0.374951797 2 60 2 14700 3 Barrier

0.47590415 2 60

314400

1Conical

0.494230691 356.6

115000

3Barrier

0.680425666 356.6

214700

1Conical

0.701451529 356.6

314400

2Cartridge

Statically delta (difference b/w highest and Lowest value) in Grey-Taguchi method

determine the most influencing factor in which most significant factors are classified, and then

multipurpose-optimization problems are shifted into single equivalent optimization problem[25].

2.2.3 Calculation of average GRG.

The effect of input parameters on GRG at different levels is independent of the others

because the experimental design is orthogonal. An average GRG deals with the various types of

parametric combinations to find out the best one, which will give the desired output results

simultaneously. This average GRG calculated for each level of input parameter gives the most

significant level in accordance with multiple responses as summarized in Table 11.

Table 11. Average Grey Relational Grade

Levels Factors

Sr No: A B C

1 0.676 0.474 0.577

2 0.400 0.579 0.578

3 0.625 0.649 0.546

Delta 0.276 0.174 0.0323

Rank 1 2 3

Figure 3. Graph between average GRG and level of factor

Above graphical response of average GRG of every input parameter at different selected

levels shown in Fig. 3 is used for the selection of best combination of input factors. This

graphical representation gives the information about the more influence of input parameter on

the output results. As the highest point on the graph is temperature rather than speed and air inlet

filter which indicates that inlet temperature is more important and sensitive parameter and thus is

ranked at position 1 in Table 11. It has been proved practically that air inlet temperature is most

significant factor for the process parameters of Gas Turbine [22]. Consequently, the optimal

parametric combination at which the desired output results can be obtained is given as: (A1, B3,

C2 ) = 84 as a temperature, 14400 rpm as a speed, and Barrier as a filter type level.

Air inlet ambient temperature of 84°F, GT speed of 14400 rpm and use of cartridge filter

is the optimal combination at which gas turbine offers maximum efficiency and more horse

power, together with decreased heat rate and SFC.

2.3. ANOVA ANALYSIS

Table 12 shows ANOVA analysis, which is performed on statistical problem solving tool

called Minitab 16. This show the most significant factor in the form of relative percentage of

influencing factors to the variation of output results, and thus interprets the most significant input

parameter of optimal combination [32].

Table.12 ANOVA ANALYSIS

Factors Levels

DOF Sum of square

(SS)

Mean square (MSS)

F-value Contribution

(%) 1 2 3

A 0.67920 0.40085 0.625369 2 0.1307840 0.0653920 0.0326960 72.8615%

B 0.47437 0.57954 0.64922 2 0.0464891 0.0232445 0.011622 25.899%

C 0.57783 0.57884 0.54500 2 0.0022233 0.0011116 0.0005558 1.23868%

Error 2

Total 8 0.1794966 0.0897483 0.0448741 100%

From ANOVA analysis, Table 12 reveals that the factor A Air inlet temperature with

72.9% is the most significant factor for the optimization of Gas turbine, while turbine speed

contributes 25.9% to optimization. Air inlet filter shares only about 1.2% in optimization of GT,

which has obviously very limited impact on performance of turbine.

Figure 4. ANOVA analysis of optimal combination

2.4 Confirmation of Experimentation

After identifying the optimal level of input parameters the main step was to verify and

validate the responses using the obtained process parameters [40]. A1B3C2 optimal parameters

were used for the confirmation of experiment. Results obtained from the experiment are shown

in Table 13. These results show that a combination of Temperature of 84°F and speed of 14400

rpm along with cartridge filters offers sufficient power. With the increase in speed, the power

also increases because air inlet ambient temperature is lower which means air entering the axial

flow compressor is dense and has large air volume. From the ANOVA analysis it is clear that the

optimal parameter air inlet filter has very minimum impact, while the cartridge filter causes no

pressure losses, thus pressure ratio as a whole remained same leading to no significant impact on

thermal efficiency of turbine. Gas Turbine is not consuming the much fuel due to the lower

ambient temperature. It is evident that Grey-taguchi method improves multiple responses in the

Optimization of gas turbine performance parameters.

Table 13.Validation of Taguchi Analysis Via Experimentation

Control Factors

Input parameter

Experimental output

POWER Heat Rate SFC Thermal efficiency

A1 84

3990Hp 9130 BTU/hp-hr 35lbm/hp-hr 27.78B3 14400

C2 Cartridge

3. Conclusion

In this study, grey-Taguchi method was applied to optimize the performance parameters

of Gas turbine. The impact of process parameters including inlet temperature, type of air inlet

filters and rotor speed on power, heat rate, SFC and thermal efficiency was investigated.

Following are the key findings of the study:

Taguchi method can be recommended in a sense to optimize the multi objective

response variable to single objective solution in gas turbine industry.

DOE was based on L9 OA after that calculation of GRG based on GRC converted

multi objective variable into single objective response which also showed ranked

wise impact of optimal input parameters

ANOVA analysis revealed that Air inlet filters has only 1.2% impact on the

performance of Gas Turbine, which are due to the differential of air inlet filters

counts.

The experimental result showed that the most significant factor is air inlet ambient

temperature with 72.9 % influence on the optimization.

Average GRG was established to convert multi objective variable into single

objective variable and optimal combination of multi response was obtained in

terms of 84°F, 14400 rpm and cartridge filter. The said combination is expected to

achieve the optimized performance in terms of higher thermal efficiency, higher

horse power and lower heat rate SFC.

ANOVA analysis confirmed that air inlet temperature was the most significant

factor with 72.9% contribution, while speed was found to be the secondary factor

a contribution of 25. 9% to the optimization.

Experimental validation was performed through the input optimized parameter

resulted from GRG method.

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