An Innovative Optimization Technique on Performance Efficiency Verification in a Coal Thermal Power Plant Unit Stephen Tangwe, Michael Simon and Edson Meyer
Abstract— Coal thermal power plant, like most power plants is constructed solely for electricity generation. More explicitly, routine energy efficiency interventions are carried out in a unit of the power plant to ensure that it continues to operate at optimal performance as per the manufacturer rating. The study focused on the development and building of a multiple linear regression model for the power generated in a unit of the coal thermal power plant; with air heater temperature, main super heater steam temperature, high pressure heater temperature, condenser well temperature, and mass of coal burnt as the predictors. The model was developed using three months after outage as well as three months data after a year of the intervention from open literature. An optimization technique known as the constraint linear least square regression was applied in computing the optimal input data set corresponding to a desired response, whereby the mathematical model equation was used as the constraint equation. The benefits of the optimization technique were to enable the plant engineers to schedule a service plan on the unit. This was done with respect to the specific components of the unit observed to be under performing by judging from the final results after running the optimization. Based on the number of predictors showing a significant difference between the actual and the optimized data set, the maintenance can be termed minor or major intervention. Key terms- Desired response, Mathematical model, Optimal performance, Optimization method, Power plant, Predictors 1
INTRODUCTION
The electricity generated from coal thermal power plant can conveniently supply base load as opposed to those derived from renewable energy sources. It is significant to allude that although this method of electricity generation can guarantee the base load demand is associated with some major environmental and social impact. A conservative utilization of coal through an efficient optimization of the various component designs in the entire coal thermal plant unit can lead to substantial energy saving as well as reduction in global warming potential, environmental pollution and major reduction in the volume of water – use. The coal thermal power plant is a convectional source of electricity generation with coal as the primary fuel. It generates electricity with steam turbine as the prime mover. There is an ongoing challenge on generation of sufficient electrical energy to meet the demand in all the sectors (residential, industrial and commercial) and eventually require that very efficient coal thermal power plants be provided. There are basically two categories of coal boiler plant; namely the “drum” and the
We are very delighted to acknowledge the Fort Hare Institute of Technology and the University of Fort Hare for the financial support towards our research on the coal power plants. Stephen Tangwe; Fort Hare Institute of Technology, University of Fort Hare, P.B. X1314 Alice, South Africa, (
[email protected]).
“once through” type. The “once through” type is very cost effective and with a far better performance efficiency than the “drum” type. 1.1
BASIC OPERATION OF A COAL THERMAL POWER PLANT The coal thermal power plant operates on the principle of Rankine’s cycle with water/steam as the refrigerant. The main components involve in the coal thermal power plant are the boiler, super-heater, turbine, condenser, feed water pump and economizer [1]. The coal stored in the coal hopper is conveyed by conveyor to the pulverizer, where it is crushed and mixed with preheated air before finally blasted into the boiler. This fuel-gas mixture is ignited with the help of oil. Furthermore, with the aid of a water feed pump, demineralized water enters the boiler after being heated by the high pressure heater and the economizer. In the boiler, the desuper-heated steam, pick up latent heat at the evaporator and rise upward in the furnace, where it is further heated to super-heated steam by cascaded super-heaters. This superheated steam is finally input to the turbine. The thermal energy in the super-heated steam is extracted and converted to mechanical energy responsible to rotate the turbine blades. The steam driven turbine is coupled to an alternator (generator) and act as a prime mover. The rotation of the turbine blades forces the rotor to also rotate and cutting through the stator magnetic field lines. This results in an induce electromotive force due to the principle of electromagnetic induction. The generated alternating electricity is fed to a step up three phase transformer and the voltage is again increased. This is sent to the high voltage transmission lines via circuit breakers and bus bars. It is crucial to understand that after the extraction of thermal energy from the super-heated steam as it flows into the turbine, it becomes wet steam and flows into the condenser where the condensation process takes place. The condensate exits the condenser at low temperature and pressure. This fluid is pushed into a low and high pressure heater and the economizer by water feed pump where it becomes preheated to a high temperature and pressure and finally fed back to the boiler. The flue gases produced during the combustion of coal are ejected via the stack chimney after passing through the following heat exchangers (super heater, reheater, economizer and air heater).The fly ash is collected by the electrostatic precipitator while the bottom ash is collected by ash dump attached to the bottom of the boiler. The modern
Michael Simon; Fort Hare Institute of Technology, University of Fort Hare, P.B. X1314 Alice, South Africa, (
[email protected]). Edson Meyer; Fort Hare Institute of Technology, University of Fort Hare, P.B. X1314 Alice, South Africa, (
[email protected]).
coal thermal plant contains a cooling tower at the condenser unit that prevents the cold water from the river (pond) that is fed to the inlet of the condenser from exiting at the outlet as high temperature and pressure hot water and thereby polluting the river [2] [3]. The cooling tower ensures that the heat gained by the induced water into the condenser is extracted by air and dissipated to the environment. The schematic diagram of a typical “Benson” and “Drum type” coal power plant is shown in Fig. 1 and 2. Some advantages of the “Benson” over the “Drum” types include; i) Both the mechanical conversion and system performance
efficiency is higher in a “Benson” type in relation to a “drum type”. ii) No drum is used as water storage in a “Benson type” coal thermal power plant as it is in “once through” system. iii)“Benson” coal power plant boiler can withstand very high critical temperature and pressure as opposed to the “drum type”. iv)“Benson” coal thermal plant boiler can support extreme stress as compared to the “drum type” and hence reduces the chances of fatigue.
Fig. 1: Illustrates the schematic diagram of a typical “Drum type” coal thermal power plant’s unit
Fig. 2: Shows the schematic diagram of a typical “Benson type” coal thermal power plant’s unit
The convectional mechanical efficiency of the coal thermal power plant can be enhanced by performing regular maintenance in a bid to improve the plant efficiency. The development and building of a mathematical model for a coal thermal power plant can enable better prediction of its dynamic behaviour. The achievement of an accurate mathematical model can lead to an explicit control and optimization of the power plant. More significantly, it could be noted that the type of optimization used is strongly based on the type of mathematical model developed for the power plant. The common optimization techniques includes; constrained non-linear minimization, minimax optimization, unconstrained non-linear minimization, linear programming, non-linear curve fitting, constrained linear least squares, nonlinear least squares and quadratic programming [4]. Using the optimization technique, can help to schedule a time table for the plant maintenance by the plant engineers in a bid to sustain the efficiency
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4.1 PREDICTORS PROFILES COMPARATIVE ANALYSIS IN THE BOTH SCENARIOS The first month after the intervention and the month just after one year of the intervention predictors’ data set were used to perform the simple comparative analysis to evaluate the plant’s unit efficiency. It was observed without any loss of generalities, that in the latter case the plant’s unit achieved AHT, MST and HPT exhibits a significant decrease. Hence justified the fact that the plant’s unit is experiencing a performance degradation. The figure 3 shows the variation of the AHT over a period of 24 hour. It can be depicted that the profile for the first month after the intervention is higher and therefore contributed for the increased in the load generated. This is because the higher the AHT, the lower the moisture content of the pulverize coal fed into the boiler. Figure 4 illustrates the profiles for the MST in the both scenarios. Again the MST for the first month after the intervention is higher and hence contained a greater pressure work that is supplied to the turbine and thereby an increase in the load generated. It can be delineated from the figure 5, that the HPT during the first month of the intervention is also higher, as this allow for the possible extraction of thermal energy from the steam at the high pressure heater. This will in turn enhance the bleeding process that is taking place in the reheater and responsible for performance increase in the plant’s unit.
OBJECTIVES
Objectives of the study constitute: i) Derivation of multiple linear regression mathematical models to predict the power generated using the following predictors: The mass of coal burnt; Super-heated mean steam temperature; High pressure heater temperature; Average condenser well temperature; Air heater temperature .
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ii) To build and develop a constrained linear least squares optimization technique to evaluate the performance of a unit in a coal thermal power.
AHT (o C)
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iii)To use the optimization technique as a schedule maintenance tool to prevent the degradation of key components in the unit of a coal thermal power plant.
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METHODOLOGY
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RESULTS AND DISCUSSION
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Fig. 3: AHT profiles for the both scenarios 540 520 500
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Data for both the predictors and the response are obtained from a unit coal thermal plant using open literature for two sets of duration (firstly, for the first three months after the implementation of the energy efficiency intervention and secondly, for three months after the first one year of the intervention). The first three months data correspond to the results obtained for the after implementation scenario whereby no noticeable performance degradation is observed as the unit is operating at the manufacturer specification rating. While the last one year, three months represent the after implementation scenario whereby unit performance efficiency is showing a decrement. All the predictors and desire response were processed and analyzed. Finally, a multiple linear regression model was built and developed using the first three months predictors and output data which was in turn used as the constrained linear least square equation in the optimization technique [5].
MST ( C)
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one month after intervention one year after intervention
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480 460 one month after intervention one year after intervention
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Fig. 4: MST profiles for the both scenarios.
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FMAYI = First month after a year of intervention From the table 2, it can be delineated that the scaling constants for MST and MCB increases with an increase in the power generated. These increased were in agreement with literature. It can also be depicted that these scaling constants for the MST and MCB for the FMAI are higher than the corresponding scaling values in the FMAYI scenario. Hence, using the same dataset for this set of predictors in both FMAI and FMAYI, it can be depicted by the mathematical modeling equation that the generated load in the case of FMAI will be higher than in the case of FMAYI. The determination coefficient of the FMAI and FMAYI were very excellent (0.976 and 0.968 respectively) and therefore the modeled output mimics the actual output. No forcing constant was introduced into the models, since the constrained linear least squares optimization will be used for optimizing the predictors set of values for a given output value. The both models were used to deduce the performance degradation of the plant’s unit. In this case, the input parameters of the plant’s unit for the first month after the intervention was used to perform an adjustment using the model for the FMAYI, so that the corresponding output will be compared to the first month after using the model of FMAI. The figure 6 shows the load generated profiles and confirm that the generated load decreases as time progresses.
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HPT (o C)
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Fig. 5: MST profiles for the both scenarios. The average MCB, AHT and the load generated in the both scenarios were shown in Table I It can be depicted that the load generated in the first month after the intervention is greater, although there is no significant differences in the CWT and MCB. This is also an indicator to confirm the performance degradation after the first year of the intervention.
4.2 DETERMINATION OF MULTIPLE LINEAR REGRESSION MATHEMATICAL MODELS The models developed using the ordinary least square technique for solving the multiple linear regression [6]. The accuracy of the built models were tested by ensuring that a strong linear correlation existed between the modeled desired output and the actual measured output for the FMAI and FMAYI [7]. The equation for each of the multiple linear regression model can be represented by (1). Pg ( AHT) (MST) ( HPT) (CW T) (MCB)
1
Each of the parameters and their scaling values in the mathematical model given in (1) is shown in table 2. Table II: The mathematical models of the power generated IP
SC
AHT
-0.258
-0.506
MST HPT CWT
1.730 -3.993 -0.496
-0.196 0.042 2.620
2.681
2.519
MCB
FMAI
FMAYI
Output
Pg
IP = Input parameters, SC = Scaling constants FMAI = First month after intervention
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Generated load (MW)
Table I: Key measured parameter values in both cases IP CWT MCB Output (Pg) o C ktons MW FMAI 44.20 227.60 513.12 FMAY1 43.03 218.54 463.42 FMAI = First month after intervention FMAYI = First month after a year of intervention IP = Input parameters
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400 First month after intervention model output One year after intervention model output
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Fig. 6: Comparing generated load profiles using models 4.3 OPTIMIZATION TOOL AS INDICATOR TO TEST PERFORMANCE DEGRADATION The constrained linear least squares optimization was used to predict the extent of performance degradation of the plant’s unit. The MATLAB optimization tool box was used to run the optimization and hence evaluated the performance of the unit [8]. The constrained linear least squares regression was employed for the optimization, since the multiple linear regression models were developed for the unit and gave very excellent results for the modeled output in comparison to the actual output. The multiple regression modeled equation for the FMAI was input as the constrained equation in the problem section of the optimization graphical user interface (GUI) shown in Fig. 7. The optimization were run using the input and output data set for the FMAYI. At the section for input of the predictors’ parameters, a 5 by 5 identity matrix was introduced to carter for the five attributed parameters
intended to be optimized. At the start point, select the option, let algorithm choose point. After the complete inputting of the relevant parameters, the optimization was run from the start button. Once the optimization process was terminated and objective function value was generated which must be very close or equal to zero for a better optimized result to be guaranteed. Hence, from the data set of the FMAYI, the objective function values were far greater than zero due to the performance degradation as can be depicted from the Table III. At the final point section, all the optimized values of the predictors were displayed. Table III shows some of the actual inputs and output data and the corresponding optimized values as well as their respective objective functions.
Figure 7: Graphical user interface (GUI) of the optimization tool
Table III: Inputs and outputs of actual and optimize values Run
1
2
3
Parameter AHT MST HPT CWT MCB Pg AHT MST HPT CWT MCB Pg AHT MST HPT CWT MCB Pg
Actual FMAYI 210.5 481.8 225.5 41.6 221.3 475.7 239.2 494.5 229.8 45.0 228.9 487.2 216.7 479.5 224.7 42.9 216.0 462.4
Optimized FMAYI 210.3 483.4 221.9 41.2 223.8 451.9 239.0 495.7 226.9 44.5 230.9 467.7 216.7 481.4 220.5 42.3 218.9 434.4
Objective function 21.57
14.33
29.74
It can also be delineated that base on the optimized FMAYI results, lesser coal is burnt and a higher main stream temperature in order to achieve the desire output as compare to the actual FMAYI results. Again the objective function in all the scenarios is much greater than zero. This implies the plant’s unit need to undergo maintenance. 5.
CONCLUSION
In conclusion, after energy efficiency intervention in a coal thermal power unit of a plant, there was an improvement in the heating rating performance efficiency and in this study, the efficiency increased by 3%. A reliable optimization of a unit in a plant is governed by a robust and accurate mathematical model for the unit. Optimization of a unit plant can also give a clear indication when a unit is expected to go back on outage by a simple verification of the measured data set and the optimized data set. An excellent optimization method is governed by an accurate mathematical model and also the model residual plots should demonstrate negligible outlier within the 95% confidence level. REFERENCES [1] S. Sivanagaraju, M. Balasubba Reddy and D. Srilatha, June 21, 2010: Generation and Utilization of Electrical Energy; Publisher: Pearson Education India [2] D.P Kothari and I J Nagrath, 2003: Modern power system analysis, Publisher Tata McGraw-Hill Education [3] Babcock & Wilcox Co. (2005). Steam: Its Generation and Use (41st edition ed)
[4] Gill, P.E., W. Murray, and M.H. Wright, Practical Optimization, Academic Press, London, UK, 1981.
[5] Chatterjee, S., and A. S. Hadi. "Influential Observations, High Leverage Points, and Outliers in Linear Regression." Statistical Science. Vol. 1, 1986, pp. 379–416. [6] Robnik-Sikonja, M., & Kononenko, I. (2003). Theoretical and empirical analysis of ReliefF and RReliefF Machine Learning, 53, 23–69 [7] Coleman, T.F. and Y. Li, "A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on Some of the Variables," SIAM Journal on Optimization, Vol. 6, Number 4, pp. 1040-1058, 1996. [8] Math Works Corporation, 2012 Matlab and Simulink: Math work cooperation 2012b, Version 7.12. AUTHORS BIOS AND PHOTOGRAPHS
Stephen Tangwe holds a B.Eng. (Hons) and M.Eng degree in Electrical Engineering from AIU, Honolulu, Hawaii. He is an IEE graduate student and also an IEE Power and Energy Society member. At present, he is a graduate student member in the South African Institute of Electrical Engineers and his an adhoc Eskom M&V Engineer with the UFH team. He is also an energy efficiency PhD research candidate with Fort Hare Institute of Technology and a MATLAB application Engineer. He is also a SAEE and WSSET He is a seasonal author in many peer reviewed Journals and accredited national and international. Tel: +27783076922; Email:
[email protected] .
Co-author: Michael Simon holds a PhD degree in Physics from the University of Fort Hare. He is presently the university of Fort Hare Energy Manager and Head of the Energy Efficiency Group in Fort Hare Institute of Technology. He is also a certified Eskom M&V professional and Team leader of the Eskom M & V UFH Team. He is a Photo Voltaic & an Energy Efficiency specialist. Tel:+27(0)72546721; Email:
[email protected]
Co-author: Prof Edson L. Meyer holds a PhD degree in Physics from the Nelson Mandela University, Port Elizabeth. He is presently the Director of the Fort Hare Institute of Technology. He is also a certified Eskom M&V professional and Eskom chair in the Southern region. He is a Renewable energy consultant and a seasonal author and reviewer in accredited peer reviewed Journals Tel: 078 191 7254; Email:
[email protected] Presenter: The paper is going to be presented by Stephen Tangwe
