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.
