Subido por brian.aguirre

Boiler efficiency estimation from hydrogen content in fuel

Anuncio
Boiler Efficiency Estimation from Hydrogen Content
in Fuel
Chayalakshmi C. L.,
D. S. Jangamshetti,
Savita Sonoli,
Dept. of I. T.,
Basaveshwar Engineering College,
Bagalkot, Karnataka.
Dept. of E & EE,
Basaveshwar Engineering College,
Bagalkot, Karnataka.
Dept. of E & CE,
RYM Engineering College,
Bellary, Karnataka.
investigated, but the relationship between the boiler
efficiency and its operating condition is not established
[3, 4]. There is a necessity of quick evaluation of the boiler
efficiency to understand its operating condition and
appropriate measures can be initiated without much delay.
There are two conventional ways of evaluating the boiler
efficiency: direct and indirect method. Indirect method
involves finding various boiler losses, thereby evaluating
the boiler efficiency. But the procedure is lengthy and
complex as it involves many mathematical calculations.
Usually, maintenance personnel, calculates boiler efficiency
once in a month, using conventional method. The boiler
parameters cannot be controlled instantly, hence efficiency
cannot be regulated.
Abstract: More than 45 % of world’s electricity is generated
from thermal power plants by utilizing coal as a fuel. Boilers
and turbines are the most basic components in thermal power
plants. Efficient utilization of heat energy produced from
chemical composition of fuel assures enhanced electricity
production. Performance degradation of boiler is mainly due
to boiler losses. This paper presents innovative method to
predict boiler efficiency using DASY Lab software suite. The
prediction of boiler efficiency is based on the coefficient of
correlation between boiler efficiency, the boiler loss due to
hydrogen content of fuel and Akaike Information Criterion
(AIC). Linear regression method is used to obtain the line of
best fit. Loss due to hydrogen content in fuel has a strong
correlation with the boiler efficiency. Hence, this method uses
loss due to hydrogen content in fuel and simplifies the steps in
finding boiler efficiency. The hydrogen content of fuel,
temperature of flue gas, ambient temperature, and gross
calorific value of fuel are used for finding the efficiency of
boiler. The maximum error in predicting the boiler efficiency
is 1.82 %, which signifies the authenticity of the proposed
method. The predicted boiler efficiency is validated using data
from an industry.
TABLE I: OPERATING COST OF BOILER [5]
Sl.
No
01
02
03
04
05
06
07
Key words: Boiler loss due to hydrogen in fuel, DASY Lab,
Efficiency estimation, Linear regression, Akaike Information
Criterion
I.
Percentage
Fuel
Labour
Equipment Depreciation
Electricity
Water Treatment
Sewer
Water
75.0
10.0
9.75
2.50
2.00
1.50
0.75
This paper presents a new and innovative method of
evaluating the boiler efficiency using DASY Lab software
to overcome the difficulty of conventional method.
Conventional method consumes more time. In this new
method, boiler loss due to hydrogen content in fuel is
evaluated. The correlation between the loss due to hydrogen
content and boiler efficiency is found to be strong. AIC is
used for selecting the model for prediction of boiler
efficiency. This method is algorithmic and hence can be
implementable using software.
INTRODUCTION
Around 45 % of world’s electricity is derived from thermal
power plant by utilization of coal as fuel [1]. Electricity
demand is increasing in many countries because of increase
in the number of industries, public institutions, and
apartments in urban areas every year. Production of
electricity needs to be enhanced, to meet the increase in
demand. In thermal power plants boilers are used for steam
generation to rotate turbines. Hot water and steam are the
main sources for many processes in an industry and they
consume major portion of energy cost. Hence, the industries
are under pressure to operate the boilers with peak
efficiency due to increased cost of fuel [2], because fuel cost
is the biggest operating cost, as in TABLE I. The operating
cost of boiler is 4 times the initial investment cost. Boiler
can be operated with peak efficiency when its efficiency is
monitored and its parameters are controlled. Energy
auditing helps in identifying the areas of dissipation of
energy in boilers. Different methods are available to
understand various boiler types, their behavior and
operating conditions. The operating condition of boiler is
c
978-1-4799-8792-4/15/$31.00 2015
IEEE
Particulars
II.
REGRESSION ANALYSIS
Regression is most important statistical tool used in all
fields. Especially this method is used in business and
economics to understand the relationship between two or
more variables. In statistics, regression analysis is a method
of estimating the relationship between variables. This
process helps in understanding the variation of dependent
variable with change in independent variable. Given the
independent variable, regression analysis estimates the
dependent variable using regression function. Prediction and
1107
Authorized licensed use limited to: Universidad del Valle. Downloaded on July 31,2022 at 21:16:09 UTC from IEEE Xplore. Restrictions apply.
III.
METHOD ADOPTED FOR FINDING BOILER
EFFICIENCY
Estimation of the boiler efficiency is based on the linear
regression function which uses correlation between boiler
efficiency and the boiler loss due to hydrogen content in
coal and AIC for selection of model. Linear regression is
one of the techniques to model the relationship between
dependent and one or more explanatory variable. To draw
the best fitting regression line, the slope of the line is
necessary. Then the ratio of square of error for the
regression line and the square of error from the mean value
of y is subtracted from 1 to get the coefficient of
determination, R2. The coefficient of determination provides
information about what percentage of total variation is
described by the variation in independent variable ‘x’. If R2
value is nearer to 1, then the regression line is considered as
good fit for predicting or estimating the dependent variable
from independent variable.
A case study is carried out in an industry for collection of
data. A leading cement industry, JK Cement, Bagalkot, with
coal fired boiler which generates 110TPH super heated
steam is considered for case study. Data from various
sensors required for finding the boiler efficiency such as
temperature of flue gas, ambient temperature, carbon
content in flue gas, fuel constituents, is recorded for 36
months. Existing data from 36 months is divided into two
folds. One set of data is used for framing regression model
and another set of data is used for testing the performance
1108
model. The boiler efficiency is calculated manually using
conventional method using various losses for one year and
are listed in TABLE II, and observed that the maximum loss
is boiler loss due to hydrogen in fuel.
TABLE II:
Month Jan
L1 (%) 4.20
L2 (%) 3.58
L3 (%) 1.29
L4 (%) 0.15
L5 (%) 0.37
L6 (%) 1.00
L7 (%) 0.09
L8 (%) 0.00
BE (%) 89.32
where,
L1:
L2:
L3:
L4:
L5:
L6:
L7:
L8:
BE:
Feb
4.80
7.72
2.15
0.18
0.36
1.00
0.06
0.00
83.75
BOILER LOSSES AND BOILER EFFICIENCY FOR ONE YEAR
Mar
5.30
8.64
3.74
0.19
0.40
1.00
0.22
0.00
80.51
Apr
5.30
8.68
3.82
0.20
0.42
1.00
0.19
0.00
80.38
May
4.50
7.26
2.97
0.17
0.34
1.00
0.16
0.00
83.56
Jun
5.70
9.44
3.70
0.21
0.42
1.00
0.15
0.00
79.39
Jul
4.90
8.27
3.24
0.18
0.37
1.00
0.15
0.00
81.88
Aug
5.00
8.65
3.39
0.18
0.39
1.00
0.11
0.00
81.26
Sep
5.60
9.38
2.49
0.21
0.42
1.00
0.14
0.00
80.72
Oct
5.40
6.80
2.11
0.20
0.41
2.00
0.10
0.00
82.98
Nov
4.30
6.21
1.74
0.16
0.32
2.00
0.15
0.00
85.08
Dec
4.50
5.62
1.69
0.16
0.35
2.00
0.13
0.00
85.50
Boiler loss due to dry flue gas
Boiler loss due to hydrogen in fuel
Boiler loss due to moisture present in fuel
Boiler loss due to moisture in combustion air
Boiler loss due to partial combustion
Boiler loss due to radiation and convection
Boiler loss due to un-burnt in fly ash
Boiler loss due to un-burnt in bottom ash
Boiler Efficiency
The data of TABLE II is considered for finding the relation
between the boiler efficiency and the loss due to hydrogen
content in coal. The Figure 1 shows the graph of boiler
efficiency and boiler loss due to hydrogen in fuel. ‘X’ axis
represents the value of boiler loss due to hydrogen content
in coal and ‘Y’ axis represents the boiler efficiency. The
trend line equation and coefficient of determination R2 is
obtained for various functions by regression analysis on ‘X’,
‘Y’ data pair.
90
% Boiler Efficiency
forecasting are the two major advantages of regression
function. The two methods of regression are: parametric and
non-parametric. In parametric regression, the function of
regression is defined in terms of unknown parameter which
is estimated from the data. Linear regression and least
square regression are the types of parametric regression.
Non-parametric regression allows the regression function to
lie in set of functions, whose dimension may be infinite [6].
To judge the goodness of fit, the coefficient of
determination (R2) is used in a linear regression model.
Coefficient of determination (R2) represents the closeness of
the data to the line of best fit. R2 is the square of the
correlation coefficient, which measures the strength of
linear relationship between variables. The range is from -1
to +1. If coefficient of correlation is positive and nearer to 1,
then there exists a strong linear relationship between the
variables such that as x increases, y also increases. If
variables have a strong negative linear correlation, it is
nearer to -1. If there is no correlation or weaker correlation
between the variables then it is nearer to 0 [7].
Relative quality of a statistical model for a given set of
data is measured with Akaike information criterion (AIC).
AIC estimates the quality of each model, given a collection
of models for the data, relative to each of the other models.
AIC provides a model selection means. AIC is framed on
information theory: it offers a relative estimate of the
information lost when a given model is used to represent the
process that generates the data. In doing so, it deals with the
trade-off between the goodness of fit of the model and the
complexity of the model [8].
88
y = -1.559x + 94.61
R² = 0.927
86
84
82
80
78
0
2
4
6
8
% Boiler loss due to hydrogen in fuel
10
Fig. 1: Boiler efficiecny vs loss due to hydrogen in fuel
TABLE III shows the trend line equation for various
functions such as linear, polynomial, logarthmic,
exponential and power. From TABLE III, it is clear that the
coefficient of determination is maximum for linear function
and polynomial function with second order. If the number of
variables are increased then the coefficient of determination
value also increases. Hence, the coefficient of determination
is not sufficient to judge the selection of model. The various
models are tested for their quality, for predicting the boiler
efficeincy, with AIC. The software program is developed in
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Authorized licensed use limited to: Universidad del Valle. Downloaded on July 31,2022 at 21:16:09 UTC from IEEE Xplore. Restrictions apply.
DASY Lab to predict the value of boiler efficiecny based on
the model selected from AIC.
DASY Lab software suite along with data acquisition
card is best suited for acquiring the data from various
sensors and analysis. A data acquisition card with 8 digital
input/output and 16 differential or 32 single ended analog
channels is interfaced with the PC with DASY Lab
software. Initially, based on the values of flue gas
temperature, ambient temperature, and hydrogen content of
fuel, the software calculates the boielr loss due to hydrogen
in fuel L2 [9].
L2 =
9 * H 2 [584 + C p (T f − Ta )]
GCV
*100
(1)
where,
H2
Cp
Tf
Ta
GCV
= Hydrogen content in fuel
= Specific heat of flue gas in Kcal/kg oC
= Temperature of flue gas in oC
= Ambient temperature in oC
= Gross Calorific Value of fuel
TABLE IV:
BOILER LOSSES AND PREDICTED BOILER EFFICIENCY USING
VARIOUS MODELS
%
Boiler
loss
(L2)
Calculated
Boiler
Efficiency
5.09
6.03
6.66
3.6
4.4
8.01
7.97
8.29
8.65
9.23
9.05
8.54
8.92
8.28
8.16
8.16
3.6
85.777
84.928
85.287
87.406
87.056
83.592
82.941
82.874
81.844
79.698
79.330
80.750
80.676
81.792
82.411
82.411
87.406
TABLE III: EQUATIONS FOR VARIOUS FUNCTION WITH COEFFECIENT OF
Predicted Boiler Efficiency
Linear
model
Expo.
model
Power
model
Poly.
model
86.6747
85.2092
84.2271
88.9976
87.7504
82.1224
82.1848
81.6859
81.1247
80.2204
80.5011
81.2961
80.7037
81.7015
81.8886
81.8886
88.9976
90.5037
89.6856
89.1223
91.8916
91.1594
87.9273
87.9625
87.6814
87.3663
86.8611
87.0176
87.4625
87.1301
87.6902
87.7955
87.7955
91.8916
87.2058
85.5952
84.6647
90.5923
88.6144
82.9631
83.0088
82.6501
82.2645
81.6793
81.8565
82.3804
81.9869
82.6611
82.7939
82.7939
90.5923
88.7547
88.1078
87.7626
90.1060
89.3308
87.2655
87.2756
87.2034
87.1442
87.0979
87.1058
87.1598
87.1151
87.2053
87.2305
87.2305
90.1060
START
DETERMINATION
Sl.
No
01
02
03
04
05
Function
Linear
Exponential
Logarithmic
Polynomial
with order 2
Power
Trend Line Equation
Y= -1.559 x + 94.61
Y= 95.26e-0.01x
Y= - 9.76 ln (x) + 102.3
Y= -0.009 x2- 1.689 x +
95.02
Y= 104.3 x-0.11
R2
Value
0.927
0.924
0.911
0.927
Enter H2 content,
GCV of fuel
Read flue gas temperature &
ambient temperature from
temperature sensors
0.903
The boiler efficieny is predicted based on the trendline
equation considering ‘X’ value as percentage boiler loss
due to hydrogen content in fuel (% L2) and ‘Y’ value as
percentage boiler efficiency. The estimated boiler efficiency
value is compared with the industry data for its validity.
The values of percentage boiler loss due to hydrogen in
fuel, calculated boiler efficiecny and predicted boiler
efficiency using various models are listed in TABLE IV.
The flow chart for implemention of the system is shown in
Fig. 2. Finally the percentage of error is found using
equation 2, which is also listed in TABLE V.
Measured value ~ Actual value
*100
% Error =
Actual value
Calculate the boiler loss due
to hydrogen content in fuel
Predict the boiler efficiency using
Linear model equation
END
Fig. 2: Flow Chart of the proposed system
(2)
The error value is maximum for the month, where the point
of boiler efficiency deviates more from the best fit line and
the error value is minimum for the month, where the point
of boiler efficiency deviates little or no deviation from the
best fit line. TABLE V shows that the percentage error and
square of error is minimum for linear model and is
maximum for exponential model. Hence, only coefficient of
determination alone is not sufficient to judge the suitable
model for prediction. Akaike Information Criterion which is
one of the model selection methods is considered for the
selction of model.
AIC is adopted for selection of appropriate model for
prediction of boiler efficiency. In order to calculate AIC for
various models, sum of squares of error (SSE) for each
model is required. The values of squares of error and their
sum is listed in TABLE V. AIC is calculated using equation
3.
AIC = N ln (SSE/N) + 2 K
(3)
where,
N
K
SSE
= Number of observations
= Number of independent valiable + 1
= Sum of square of error
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Authorized licensed use limited to: Universidad del Valle. Downloaded on July 31,2022 at 21:16:09 UTC from IEEE Xplore. Restrictions apply.
1109
AIC is calculated for each model and listed in TABLE V
as last row. The lowest value of AIC is for linear model.
The model with best fit has the lower AIC value. This is
considered for prediction of boiler efficiency from the loss
due to hydrogen content in fuel. Figure 3 shows the
percentage error in prediction of boiler efficiency using
various models.
TABLE V: PERCENATGE ERROR USING VARIOUS MODELS
Linear
Model
1.05
0.33
1.24
1.82
0.80
1.76
0.91
1.43
0.88
0.66
1.48
0.68
0.03
0.11
0.63
0.63
1.82
SSE
AIC
Sq. of
Error
1.095
0.109
1.544
3.315
0.636
3.090
0.831
2.055
0.772
0.429
2.179
0.457
0.001
0.012
0.401
0.401
3.315
20.650
7.31
Expo
5.544
5.601
4.497
5.132
4.713
5.186
6.054
5.800
6.747
8.987
9.690
8.312
8.000
7.211
6.533
6.533
5.132
Sq. of
Error
30.73
31.38
20.22
26.33
22.21
26.89
36.65
33.65
45.52
80.78
93.90
69.10
64.01
52.00
42.68
42.68
26.33
745.14
68.27
12
Power
1.66
0.785
0.729
3.645
1.790
0.752
0.081
0.270
0.513
2.486
3.184
2.019
1.624
1.062
0.464
0.464
3.645
Sq. of
Error
2.774
0617
0.532
13.28
3.204
0.566
0.006
0.073
0.263
6.180
10.14
4.076
2.640
1.129
0.215
0.215
13.28
59.21
25.21
Poly
3.47
3.74
2.90
3.09
2.61
4.39
5.22
5.22
6.47
9.28
9.80
7.98
7.98
6.61
5.84
5.84
3.08
Linear
Exponential
Power
Polynomial
Sq. of
Error
12.1
14.0
8.43
9.54
6.83
19.31
27.31
27.29
41.94
86.21
96.08
63.01
63.70
43.80
34.20
34.20
9.54
597.5
64.51
10
% Error
8
6
is used to identify the better fit. With linear function, the
minimum error is 0.03 % and the maximum error obtained
is 1.82 %. Very few parameters are involved for calculation.
Software is developed with DASY Lab software.
ACKNOWLEDGEMENT
The first author would like to thank the authorities of J K
Cement, Muddapur, for their valuable suggestions and
comments on the paper.
REFERENCES
[1] Rehman Saidur, “Energy Savings and Energy Reductions in Industrial
Boilers,” Thermal Science, Vol. 15, No. 3, pp. 705-719, 2011.
[2] Miroslav Kljajic, Dusan Gvozdenac, Srdjan Vukmirovi, “Use of Neural
Networks for modeling and predicting boiler’s operating performance,”
Energy(Elsevier journal), pp. 304-311, 2012.
[3] Gvozdenac D, Kljajic M., “Technical and economical assessments of
the energy efficiency of boilers improvement in the province of
Vojvodina,” International Conference on Engineering and Environment,
Thailand: ICEE, 2007.
[4] Kljajic M, Petrovic J, Gvozdenac D. , “Review of boiler’s operating
performance in different energy sectors in the province of Vojvodina,”
International Conference of Engineering Technologies, Serbia: ICET;
2009.
[5] James McDonald, “Saving Boiler Fuel”, CSTR – September 2005.
[6]Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining,
“Introduction to Linear Regression Analysis,” 5th Edition, Wiley
Publication.
[7]C.L. Cheng , Shalabh, G. Garg, “Coefficient of determination for
multiple measurement models,” Elsevier Journal of Multivariate Analysis,
Vol. 126, pp. 137-152, April 2014.
[8] W.D. Penny, “Comparing Dynamic Causal Models using AIC, BIC and
Free Energy,” Elsevier Journal on Neuro Image 59 (2012) 319–330.
[9] Chayalakshmi C.L., D. S. Jangamshetti, Savita Sonoli, “Design and
Development of an ARM platform based Embedded System for
Measurement of Boiler Efficiency,” 2013 IEEE Symposium on Industrial
Electronics & Applications (ISIEA2013), 978-1-4799-1124-0/13,
September 22-25, 2013, Kuching, Malaysia.
4
2
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17
Months
Fig.3: Percentage error in prediction of boiler efficeincy using various
models
CONCLUSION
This paper presents a new method of estimating and
evaluating the boiler efficieny. Simple and efficient method
is proposed that removes the drawback of conventional
method. Human efforts in finding boiler efficiency using
conventional method is eliminated. Linear regression
function is adopted for estimating boiler efficiency from
hydrogen content of fuel. Trend line is developed for
various models such as linear, exponential, power, and
polynomial with order 2. The value of coefficient of
determination is same for both Linear model and
Polynomial Model. Hence, coefficient of determination is
not sufficient alone to judge the better fit. One of the model
selection measures, Akaike’s Information Criterion (AIC),
1110
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Authorized licensed use limited to: Universidad del Valle. Downloaded on July 31,2022 at 21:16:09 UTC from IEEE Xplore. Restrictions apply.
Descargar