International journal of basic and applied research
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Cosmos Impact Factor-5.86
Thermodynamic Modeling and Optimization of a Coal Fired Thermal Power
Plant using Cycle Tempo, Taguchi and ANOVA
S. S. L. Patel
Research Scholar, Dr. C. V. Raman University, Bilaspur, India
Dr. Ashok Kumar Shukla
Professor, Dr. C. V. Raman University, Bilaspur, India
Dr. G. K. Agrawal
Professor, Mechanical Engineering Department, Govt. Engineering College, Bilaspur, India
Abstract
In the present study, exergy efficiency of a 250MW coal fired thermal power plant under various
operating conditions have been evaluated by using Cycle Tempo 5. Taguchi design method is
applied to optimize the operating conditions for maximization of exergy efficiency by using three
factors namely main steam pressure, condenser pressure and reheat temperature. The operating
conditions are planned with three levels of each of selected three factors as the orthogonal array of
L9. Signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) are carried out to
investigate the effects of individual parameters.
Findings of the study indicates that condenser pressure is having most dominant effect on plant
exergy efficiency with a contribution of 78.81% followed by main steam pressure with 13.05%. Finally
the correctness of analysis is tested and verified.
Keywords: Exergy efficiency, signal-to-noise(S/N) ratio, orthogonal array, Taguchi, Analysis of
Variance (ANOVA).
1.Introduction
In India, power generation capacity has grown from 42584.72MW at the end of 6th plan to
329204.53MW at the end of 12th plan. Coal has been the major fossil fuel used in power plants, with
annual consumption of 530.4MT for power generation during the year 2014-15 [1]. A slight
improvement in efficiency of coal-fired boiler will just not conserve the huge quantiy of coal but also
help to control CO2 emission. In an attempt to quantify the losses and identify its different sources in
the plant, authors have carried out the research work earlier [2-5] and concluded that the quantity
wise energy loss in boiler may not be the matter of great concern but, it is when quality degradation
is considered as the boiler energy efficiency was found to be around 87% and exergy efficiency just
around 46% only. Further, the loss through flue gases was identified as the biggest source of energy
losses by quantity while heat exchanger section as the major exergy destruction area. Thermal power
152 Received: 8 February Revised: 17 February Accepted: 24 February
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plant technology has been evolved over years and has now reached the stage where marginal
improvement in its efficiency is a difficult task.
Exergy analysis is an effective tool in the design and analysis of energy systems as it is useful for
quantifying and locating the amount of energy losses and quality degradation both [6-8]. Concept of
energy and exergy analysis has been used by the several authors [9-11] for performance assessment
of energy conversion systems like boiler and power plants. A. Acir et al. [12] investigated the effects
of variation of dead state temperatures on the energy and exergy efficienccies in a thermal power
plant. S. Mitra and S. Sarkar [13] determined the individual influence of ambient temperature,
condenser pressure and steam temperature on exergy efficiency of a thermal power plant by using
Taguchi S/N ratio and ANOVA. They found steam temperature as the most dominant factor and next to
it was ambient temperature. E. Baysal et al. [14], and S. Mitra and S. Sarkar [15] investigated the
exergy efficiencies of a thermal power plant under various operating conditions. Three factors
namely ambient temperature, condenser pressure and steam temperature with 3 levels of each are
considered for planning operating conditions. S/N ratio analysis and analysis of variance (ANOVA)
and regression analysis were carried out to determine the effects of individual parameters. Ambient
temperature was having the most dominant effect. Finally, confirmation tests verified that the Taguchi
design is successful in optimizing operating conditions. M. Jamil et al. [16] used the Taguchi method
to obtain the optimal level of the parameters involved in the cost function. Meng-Hui Wang et al. [17]
applies an Extension Taguchi method on the optimized allocation of equipment capacity for power
generation with several sources. M. Gupta and Raj Kumar [18] using second law of thermodynamics
analyze the effect of inlet steam temperature on thermo economic and exergetic performance, and
unit product cost of turbine and optimizes the value of inlet steam temperature. Many authors [19-22]
used the Taguchi method to optimize the process parameters of turning and facing operations. S. Xiao
et al. [23] applies computational fluid dynamics (CFD), Taguchi and ANOVA technique to optimize
combustion and emissions in a diesel engine.
This study is carried out with the main objective of optimization of exergy efficiency of a coal fired
250MW thermal power plant at Korba, Chhattisgarh, India. Effects of main steam pressure, condenser
pressure and reheat temperature are considered in the analysis. Various operating conditions are
planned and investigated by using the Taguchi design method. Signal-to-noise ratio analysis is used
for the investigation of optimum operating conditions, and percent contributions of different factors
are determined by analysis of variance (ANOVA). Finally the confirmation tests are also performed.
Shrinivas T. [24] optimized the combined cycle system at a gas inlet temperature of 1400 oC with the
modern gas turbine blade cooling system. After validation of simulated model of the present double
pressure reheat heat recovery steam generator model they compared the exergetic losses in
combined cycle system with the plant and published data. Arunkumar et al. [25] use Taguchi method
for optimizing the parameters of 210MW steam turbine operation and found the maximum turbine
efficiency as 41.7%.
153 Received: 8 February Revised: 17 February Accepted: 24 February
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2. Plant Description and Exergy Analysis
Plant details in form of its schematic layout and thermodynamic properties at various points are
already described in author’s earlier research paper [2]. Schematic layout of boiler and its operating
parameters are given in author’s other papers [3-4].and not repeated here.
The concept, use and method of exergy analysis had also been discussed in author’s earlier papers
[4-5].
3. Methodology
The various steps involved in this work are as follows:
A. Application of Taguchi method: Taguchi method is a statistical method of experimental design
developed by Prof. G. Taguchi. This method provides a simple, efficient and systematic approach for
experimental design optimization of process parameters. A powerful tool of designing system
parameters in engineering analysis involves the following steps [26-29]:
(1) Identification of objective or response variable or output quality characeteristics to be
optimized (in the present case exergy efficiency of the thermal power plant).
(2) Identification of the process parameters or control factors (in the present case main steam
pressure, condenser pressure and reheat temperature) that may influence the objective or
response variable.
(3) Identification of levels of control factors of their possible interactions.
(4) Selection of appropriate orthogonal array (OA) and assigning the factors at their levels to the
OA.
(5) Conducting the test as described in the trials of the OA.
(6) Analyzing the experimental results using the signal-to-noise ratio (S/N) and statistical analysis
of variance (ANOVA)
(7) Determine the set of optimal design parameters.
(8) Verify the results i.e. the optimal design parameters.
The control factors and each parameters level used in the investigation are given in Table 1. The
design layout for the operating parameters using L9 orthogonal array is shown in Table 2.
Table 1: Assignment of the levels to the factors
Symbol Parameters Levels
1 2 3
MSP Main Steam 135 145 155
Pressure(bar)
CP Condenser 0.08 0.10 0.12
Pressure (bar)
RHT Reheat 520 540 560
Temperature
(0C)
154 Received: 8 February Revised: 17 February Accepted: 24 February
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Table 2: Control factors experimental set-up for L9 orthogonal array
Trial No. MSP CP RHT
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
B. Signal-to-Noise ratio (S/N) Analysis: The signal-to-noise (S/N) are the log functions of desired
output and represents the amount of variation present in the quality characteristic. Taguchi method,
unlike the commonly used standard methods, considers both the standard deviation and mean of trial
results to determine the effect and subsequently optimum condition. Generally, there are three forms
of S/N ratios i.e. the lower-the better (LB), the higher-the- better (HB) and the nominal-the-better
(NB). In the present investigation to obtain the optimal operational condition, the maximum exergy
efficiency in power plant is desired. Therefore, the HB quality characteristics of exergy efficiency are
selected which means the optimal level of the process parameter is the level with the highest S/N
ratio [26-27].
1 1
The HB quality characteristics can be expressed as: (S/N) = −10log10 ×
𝑛 𝑦𝑖2
where (S/N) is the S/N ratio (dB) for higher-the-better case and yi represents the actual exergy
efficiency (II) determined from cycle tempo simulation model with different sets of parameters as
designed under the Taguchi experiment and n is the number of repetitions in a trial [26-27]. The
calculation results of exergy efficiencies [30] and corresponding S/N ratios are given in Table 3.
Table 3: Exergy results and S/N ratios for various operating conditions in power plant
Trial MSP CP RHT II (%) S/N
ratio(dB)
1 135 0.08 520 36.239 31.184
2 135 0.10 540 35.859 31.092
3 135 0.12 560 35.423 30.986
4 145 0.08 540 36.710 31.296
5 145 0.10 560 36.308 31.200
155 Received: 8 February Revised: 17 February Accepted: 24 February
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6 145 0.12 520 35.301 30.956
7 155 0.08 560 37.144 31.398
8 155 0.10 520 36.159 31.164
9 155 0.12 540 35.707 31.055
C. Analysis of Variance (ANOVA): The Taguchi method alone cannot determine the effect of
individual parameters on performance of entire system, but it is possible with ANOVA. The ANOVA is
a statistical method [31], which is used to know the extent of influence of different control factors on
the response variable and it is employed to investigate that which design parameters significantly
affect the quality characteristics [26-27]. This analysis is carried out with 5% confidence level.
MINITAB software is used to carry out the ANOVA calculation and the results are shown in Table 4.
The result shows that the condenser pressure is the most dominating factor with the percent
contribution of 78.81% followed by main steam pressure with 13.05% contribution.
Table 4: Result of ANOVA for exergy efficiency
Source DoF Adj SS Adj MS F-Value P-Value %
contribution
MSP 2 0.37016 0.18508 814.92 0.001 13.05%
CP 2 2.23595 1.11798 4922.59 0 78.81%
RHT 2 0.23052 0.11526 507.51 0.002 8.13%
Error 2 0.00045 0.00023 0.02%
Total 8 2.83708
D. Determination of Optimal Factor Levels: The mean response and the mean S/N ratio for the
MSP, CP and the RHT are as shown in Fig 1. The optimum condition corresponds to parameter levels
are shown as the peak point in Fig 1. The values are given in Table 5 based on the S/N ratio. In
addition, the optimal combination of levels for the criteria of the highest response and highest S/N
ratio are determined as A3B1C3 for the exergy efficiencies as presented in Table 6. In other words,
the optimum operation conditions for the best exergy efficiencies and system performance in power
plant are main steam pressure at level 3, the condenser pressure at level 1 and the reheat steam
temperature at level 3.
156 Received: 8 February Revised: 17 February Accepted: 24 February
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Table 5: Response Table for mean S/N ratio
Level MSP CP RHT Overall Mean S/N Ratio
1 31.090 31.290 31.100 31.147
2 31.150 31.150 31.150
3 31.210 31.000 31.190
Delta 0.120 0.290 0.090
Rank 2 1 3
Figure 1: Mean S/N ratio vs process parameter
E. Confirmation test: Once the optimum level of process parameters are decided, the final step is to
predict and verify the improvement in performance in terms of the response variable by using the
optimum level of process parameters. The predicted S/N ratio can be determined by [32]:
𝑜
𝜂𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 = 𝜂𝑚 + 𝜂𝑖 − 𝜂𝑚
𝑖=1
Where m is the total mean of the S/N ratio, I is the mean S/N ratio at the optimum level, and o is the
number of parameters considered. Improvement in the S/N ratio is as shown in Table 6.
157 Received: 8 February Revised: 17 February Accepted: 24 February
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Table 6: Result of confirmation test for exergy efficiency
Levels Starting Optimal process parameters
process
Prediction Actual
parameters
A1B2C2 A3B1C3 A3B1C3
Exergy efficiency, % 35.859 37.129 37.144
S/N ratio 31.092 31.394 31.398
Improvement of S/N 0.306
ratio
Prediction error 0.004
F. Validity checking of the whole analysis: (a) From Table 6 it is clear that a higher F-value has
larger effect (in terms of % contribution) on response variable i.e., exergy efficiency. Hence the
analysis and thereby, contributing effect of control factors are justified by F-test.
(b) ‘Hypothesis’ tests:
Our Null Hypothesis (H0): Control factors are not significant in respect of response factor i.e., exergy
efficiency.
Alternative Hypothesis (HA): Control factors are significant in respect to response factor. The decision
rule for accepting the null hypothesis or rejecting is that:
At a α level of confidence, reject H0 if P > α, and do not reject H0 if P < α
Where, for a 95 % confidence level, α = 1 – 0.95 = 0.05.
In the present work, from Table 4, it is observed that p-values of all three parameters are less than
0.05 i.e. these factors are realy very significant in respect of exergy efficiency but the degree of their
influence is different.
Conclusion
In this present investigation:
Using Taguchi S/N ratio and ANOVA the individual influence of control factors on response factor
(here exergy efficiency) of a thermal power plant, is determined. It is found that the condenser
pressure is having most dominant effect on plant exergy efficiency with a contribution of 78.81%,
followed by main steam pressure with 13.05%.
The optimum operating conditions in terms of specific levels of the control variables for the best
exergy efficiency of a thermal power plant are identified. For example, in present analysis main
steam pressure at level 3, the condenser pressure at level 1 and the reheat steam temperature at
level 3
The ANOVA calculations are carried out considering 95% confidence level and confirmation test
varifies the optimum level of process factors selected and finally the validity of the whole
analysis and results are verified by F-statistics values and ‘hypothesis test’.
158 Received: 8 February Revised: 17 February Accepted: 24 February
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