IMPROVING STEAM TEMPERATURE CONTROL WITH NEURAL NETWORKS
by
JACQUES FRANCOIS SMUTS
THESIS
submitted in partial fulfilment of the degree
DOCTOR INGENERIAE in MECHANICAL ENGINEERING at the RAND AFRIKAANS UNIVERSITY
SUPERVISOR: Prof. A.L. NEL
JULY 1997 Summary The thesis describes the development, installation, and testing of a neural network-based steam • temperature controller for power plant boilers. Attention is focussed on the mechanical and thermodynamic aspects of the control problem, on the modelling and control aspects of the neural network solution, and on the practical and operational aspects of its implementation. A balance between theoretical and practical considerations is strived for. Experimental data is obtained from an operational coal fired power plant.
As a starting point, the importance of good steam temperature control is motivated. The sensitivity of heated elements in boilers to changes in heat distribution is emphasized, and it is shown how various factors influence the heat distribution. The difficulties associated with steam temperature control are discussed, and an overview of developments in advanced steam temperature control on power plant boilers is given.
The suitability of neural networks for process modelling and control are explored and the error backpropagation technique is shown to be well suited to the steam temperature control problem. A series of live plant tests to obtain modelling data is described and specific attention is given to discrepancies in the results. The prOcess of selecting the ideal network topology is covered and improvements in modelling accuracy by selecting different model output schemes are shown.
The requirements for improving steam temperature control are listed and the philosophy of optimal heat distribution (OHD) control is introduced. Error backpropagation through the heat transfer model is utilized in an optimizer to calculate control actions to various fire-side elements. The scheme is implemented on a power boiler.
It is shown that the optimizer manipulates control elements as expected. Problems with fuel-to- pressure oscillations and erroneous fuel flow measurement are discussed. Due to process oscillations caused by OHD control, a reduction in control quality is evident during mill trips and capability load runbacks. Substantial improvements over normal PID control however, are evident during load ramps. ii
Opsomming
Hierdie proefskrif beskryf die ontwikkelling, installasie, en toetsing van n neurale netwerk gebaseerde stoomtemperatuurbeheerder vir kragstasieketels. Aandag word gefokus op die meganiese en termodinamiese aspekte van die beheerprobleem, op die modellerings- en beheeraspekte van die neurale netwerk oplossing, en op praktiese- en bedryfsaspekte van die implementering. Daar word gepoog om 'n balans te handhaaf tussen teoretiese en praktiese oorwegings. Eksperimentele data word verkry vanaf 'n operasionele steenkool kragstasie.
As beginpunt word die belangrikheid van goeie stoomtemperatuurbeheer gemotiveer. Verhitte elemente in stoomketels se sensitiwiteit vir veranderings in hitteoordragspatrone word beklemtoon, en daar word aangetoon hoe verskeie faktore die hittebalans beinvloed. Die moeilikhede wat gepaard gaan met stoomtemperatuurbeheer word bespreek, en 'n oorsig van ontwikkelinge in gevorderde stoomtemperatuurbeheer op kragstasieketels word gegee.
Die toepaslikheid van neurale netwerke op prosesmodellering en -beheer word ondersoek en daar word getoon dat die tegniek van fout-terugpropagering gepas is vir stoomtemperatuurbeheer. 'n Reeks toetse wat gedoen is om modelleringsdata te bekom word beskryf, en aandag word spesifiek aan teenstrydighede in die resultate geskenk. Die keuse van 'n ideale netwerkuitleg word gedek en verbeteringe in die akuraatheid van modellering deur middel van verskillende uitsetskemas word getoon.
Die vereistes vir die verbetering van stoomtemperatuurbeheer word genoem en die filosofie van optimale hitteverspreidingsbeheer (OHV beheer) word bekendgestel. Fout-terugpropagering deur die hitteoordragsmodel word gebruik in 'n optimiseerder om beheeraksies aan die vuur-kant te bereken. Die OHV algoritme word op 'n kragstasiestoomketel geimplementeer.
Daar word aangedui dat die optimiseerder die beheerelemente na verwagting verstel. Probleme met brandstof-teenoor-druk ossillasies en foutiewe brandstofmeting word bespreek. As gevolg van prosesossillasies wat veroorsaak word deur OHV beheer, vind 'n daling in beheerkwaliteit plaas gedurende meulklinke en noodgedwonge vragvennindering. Noemenswaardige verbetering bo PID beheer is egter merkbaar gedurende vragveranderinge. iii
Table of Contents
Summary
Opsomming ii
Table of Contents iii
List of Figures vi
List of Tables
List of Variables xi
1. Introduction 1 1.1 Power generation 1 1.2 A brief history of boiler control 2 1.3 The need for steam temperature regulation 5 1.4 Research hypothesis 6 1.5 Overview of thesis 6
The power plant boiler 9 2.1 Cycle description 9 2.2 Heat transfer theory 14 2.3 Steam generator design 19
Steam temperature control 30 3.1 Control elements for steam temperature regulation 30 3.2 Difficulties associated with steam temperature regulation 40 3.3 Temperature excursion study 47 3.4 Instrumentation and control configuration 55 iv
3.5 Developments in steam temperature control 61
4. Neural networks and process control 74 4.1 Description of a neural network 74 4.2 Selecting the size of a neural network 77 4.3 Training the network 78 4.4 Process modelling with neural networks 79 4.5 Process control with neural networks 81
5. Plant modelling 87 5.1 Desired model characteristics 87 5.2 Acquiring test data 89 5.3 Calculations and assumptions 98 5.4 Neural network model 120
Neural networks and steam temperature control 135 6.1 Requirements for improved steam temperature control 135 6.2 Optimal heat distribution control 139 6.3 Controller design 141 6.4 Expected results 155
Practical implementation and results 157 7.1 The PC as control platform 157 7.2 Interfacing to existing boiler controls 159 7.3 Steady state testing and optimization 165 7.4 Transient testing and optimization 167 7.5 Final results 185
Conclusion 190 8.1 Discussion 190 8.2 Return to research hypothesis 192 V
8.3 Future research 193
Bibliography 195
Appendix A. Heat distribution test programme 204
Appendix B. Variables recorded during heat distribution tests 210
Appendix C. Spreadsheet model 213
Appendix D. OHD graphic display 215
Appendix E. Selected test results 216 vi
List of Figures
1.1 South African power demand through a typical day
2.1 Carnot cycle. 9
2.2 Carnot cycle T-S diagram. 9
2.3 Rankine cycle. 10
2.4 Rankine cycle T-S diagram. 11 2.5 Superheat cycle T-S diagram 12
2.6 Reheat cycle with economizer. 13
2.7 Reheat cycle with economizer T-S diagram 13
2.8 Fire-side components of a steam generator. 19
2.9 Different firing systems indicating fuel injection angle: 20
2.10 Diagrammatic view of the water & steam path through power plant components. . 22
2.11 Typical steam temperature characteristics. 23
2.12 Heat rise in boiler elements vs. steam pressure 23
2.13 Different heat zones in a steam generator. 24
2.14 Typical location of steam generator elements 27
2.15 Layout of the Kendal boiler heat transfer elements 29 3.1 The effect of burner tilt angle on fireball elevation. 32
3.2 Effect of burner tilt angle on furnace exit temperatures. 33
3.3 Kendal superheater stages and desuperheater locations. 39
3.4 Reheater outlet temperature reacting to increased spray water flow. 42
3.5 Reheater outlet temperature response under two load conditions 44
3.6 Causes of temperature excursions at Kendal. 48
3.7 Main steam temperature deviations from setpoint caused by load variations 49
3.8 Temperature excursion caused by a mill shut down. 52
3.9 Basic temperature control loop. 55
3.10 Cascade control arrangement. 56
3.11 Feedforward control. 57 3.12 Combined feedback, feedforward and cascade control arrangement. 58
3.13 Multiple control elements with coupled control. 60
4.1 Schematic representation of a typical artificial neuron. 74 vii
4.2 Neuron transfer functions. 75 4.3 Feedforward_ neural network. 76 4.4 Backpropagation signal flow. 84 4.5 Feedforward and backpropagation modes. 85 5.1 Measurements on feed water system, economizer and evaporator. • 92 5.2 Measurements on superheater and reheater. 93 5.3 Correlation between fuel flow and total heat gain was obtained for all tests. 97 5.4 Heat shifts achieved during heat distribution tests. 98 5.5 Feed water heater. 103 5.6 Relation in pressure differential (DP) across superheater stages. 106 5.7 Variables for heat balance calculations 108 5.8 Superheater spray water flow measurement 111 5.9 Reheater spray water flow measurement. 111 5.10 Superheater spray and warmup flow. 111 5.11 Discrepancies between calculated and measured air flow ratio. 115 5.12 Correlation between fuel flow and generator load. 116 5.13 Air flow vs 02 in flue gas with fuel flow derived from generator load. 118 5.14 Normalized difference between LH and RH air flow measurements. 119 5.15 Furnace to boiler heat transfer mapping 120 5.16 Error on test data increases after many training runs. 122 5.17 7:50:3 neural network model output errors for all tests. 127 5.18 Absolute heat transfer rate model. 132 5.19 Relative heat transfer rate model errors. 132 5.20 Corrected relative heat transfer rate model errors. 132 6.1 Model-based predictive control. 136 6.2 Adaptive adjustment concept 137 6.3 Design heat transfer rates to maintain steam temperatures. 138 6.4 Signal flow to and from the optimal heat distribution controller. 140 6.5 Predictive calculation for error in heat manger. 143 6.6 Backpropagation of errors to obtain derivatives. 144 6.7 Bias development during an optimization run. 146 viii
6.8 Heat transfer errors during an optimization run. 146 6.9 Adjusting the design heat transfer to match plant conditions. 152 6.10 Adjusting the heat transfer model to match plant conditions. 154 7.1 Interface between PC and existing boiler control system. 159 7.2 Closed loop control signal flow diagram. 161 7.3 Feedforward control signal flow diagram 162 7.4 Mill bypass damper and air flow paths. 165 7.5 Error estimation on mill fuel flow. 166 7.6 Fuel and steam flow rates during a down ramp under OHD control. 168 7.7 Burner tilt angle during load ramp, showing optimization glitch 169 7.8 Mill demands during ramp, showing biassing error. 169 7.9 Different polynomials fitted to the same three points. 170 7.10 Modelling errors with the 7:15:3 network. 171 7.11 Modelling errors with the 7:5:3 network. 171 7.12 Oscillating fuel flow during down ramp under OHD control. 172 7.13 Burner tilt action to regulate heat transfer to superheater and reheater 172 7.14 Mill biassing to regulate heat distribution 173 7.15 Boiler pressure response to fuel flow with OHD control on and off. 174 7.16 Main steam temperature decreasing during load ramp under OHD control 175 7.17 Predicted and target heat transfer rates to superheater during load ramp. 176 7.18 Discharged and absorbed heat flows. 177 7.19 Mill fuel flow response to increased air through-flow. . 178 7.20 Mill fuel flow response to increased coal input. 178 7.21 Mill fuel flow response to increased coal and air flow. 179 7.22 Fuel flow indication increasing after mill trip. 180 7.23 Correction circuit for mill fuel flow. 180 7.24 Heat discharge calculated from the adjusted fuel flow measurement 181 7.25 Air flow and 02 control. 182 7.26 Deviations in 02 measurement caused by incorrect fuel flow measurement 184 7.27 Effect on 02 on predicted heat discharge. 184 7.28 Reheat spray flow rate used by OHD control to absorb the excess heat transfer. 185 ix
7.29 Biassed mill fuel flows under OHD control compared to normal. 185 7.30 OHD tilt biassing during load ramp. 186 7.31 Heat transfer rate to superheater during down-ramp. 187 7.32 Effect of OHD control on main steam temperature. 187 x
List of Tables
3.1 Mill combinations and corresponding tilt angles 37 3.2 Results of excursion study 47 3.3 Steady state conditions before the ramp 50 3.4 Conditions during ramp. 50 3.5 Changes in heat transfer during load ramp. 51 3.6 Conditions before mill trip 53 3.7 Conditions after mill trip 54 3.8 Changes in heat transfer caused by a mill trip 54. 5.1 Elimination of mill combinations. 90 5.2 Tilt performance: setpoint = -28°, average angle = -21° 99 5.3 Tilt performance: setpoint = 0°, average angle = 1° 99 5.4 Tilt performance: setpoint = 30°, average angle = 20° 99 5.5 Superheater spray water enthalpy. 101 5.6 Turbine outlet and feed water heater inlet conditions 104 5.7 Distillate conditions 105 5.8 Feed water discharge conditions. 105 5.9 Extremes in conditions at first stage desuperheater inlet. 107 5.10 Results of networks trained with 10 individual outputs 125 5.11 Results of networks trained with 3 grouped outputs 126 5.12 Comparison of individual to grouped output heat transfer model. 127 5.13 Comparison of two output strategies. 128 5.14 Improvement in results by modelling relative heat transfer. 129 5.15 Improvement of accuracy by correcting the outputs 130 5.16 Comparison of different heat transfer model results 131 5.17 Summary of results obtained from different network sizes 131 5.18 Heat transfer rates obtained with different initializations. 133 6.1 Improvements in heat transfer after a mill trip. 155 6.2 Furnace element setup after a mill trip. 156 7.1 Accuracy of networks with various numbers of hidden neurons. 171 xi
List of Variables
a boiler tube spacing geometric relation ratio A surface area of a boiler tube
A A Actual air flow rate [kg/s] A s Stoichiometric air flow rate [kg/s]
cpg specific heat of flue gas at constant pressure [J/kg°C] cp ,,,„„, Specific heat of steam at 4 MPa & 420°C [J/kg°C] co meta,Specific heat of 1.5 % carbon steel [J/kg°C]
COIF Concentration of 0 2 in flue gas [%] D dimension of boiler tube surface parallel to gas flow [m] es heat discharge error to evaporator [W] e, heat discharge error to superheater [W] e, heat discharge error to reheater [W] f nonlinear mapping function
fe neural network mapping of evaporator neural network mapping of superheater neural network mapping of reheater
fed design heat transfer curve of evaporator
fsd design heat transfer curve of superheater
frd design heat transfer curve of reheater ha extraction steam enthalpy [J/kg]
/ad distillate (condensed extracted steam) enthalpy [J/kg] lift feed water enthalpy at heater inlet [J/kg] feed water enthalpy at heater outlet [J/kg] h, steam enthalpy at desuperheater inlet [J/kg] ho steam enthalpy at desuperheater outlet [J/kg] h,4„ outlet enthalpy of reheat steam [J/kg] hsp, spray water enthalpy [J/kg] kaa, convection heat transfer coefficient [W/m2 °C] Ica thermal conductivity of ash [W/m°C] kg thermal conductivity of flue gas [W/m°C] k thermal conductivity of a boiler tube [W/m°C] xii
mez extraction steam mass flow rate [kg/s] mf feed water mass flow rate [kg/s] m, steam mass flow rate at desuperheater inlet [kg/s] ink steam leakage rate [kg/s] moo main steam flow rate [kg/s] mo steam mass flow rate at desuperheater outlet [kg/s] desuperheater spray water flow rate [kg/s] Mass of reheater tubing and header material [kg] Mnewo Mass of steam inside reheater tubing & headers [kg] L length of a boiler tube [m] Q quantity of heat [J] P, output of a neuron output of a neuron q heat transfer rate [NV] 9er excess heat transfer [W]
qf total furnace heat discharge [NV] qn actual heat discharge to evaporator qaa actual heat discharge to superheater qn actual heat discharge to reheater [NV] qed design heat discharge to evaporator [W] qsd design heat discharge to superheater [W] q,d design heat discharge to reheater [w] qn predicted heat discharge to evaporator qn predicted heat discharge to superheater [W] qrp predicted heat discharge to reheater [NV] T., heat transfer rate through conduction gram, heat transfer rate through convection [W] grad heat transfer rate through radiation [NW] vector of actual heat transfer rates [W] g a vector of modelled heat transfer rates [W] a,.a vector of corrected modelled heat transfer rates ra outer radius of ash layer [m] r, inner radius of boiler tube [m] output of a neuron r„, scalar sum of relative heat transfer rates ro outer radius of boiler tube [m] vector of modelled relative heat transfer ratios [W/W] 12c film conductance [why? °C] s entropy T temperature [°C] TJ temperature of fluid inside boiler tube [°C] Tg temperature of combustion gas [°C] T,,,„„, Average steam temperature (assumed) [°C] T surface temperature of a boiler tube [°C] Too temperature of a free gas stream [°C] vector of furnace conditions affecting heat transfer rate Vg linear velocity of gas stream [m/s] tr weight (gain) of a neural network connection weight (gain) of a neural network connection w weight (gain) of a neural network connection
WT work produced by turbine [J] We work consumed by compressor [J] input to a neuron or neural network y output of a neuron ae gain factor on the evaporator heat transfer error gain factor on the superheater heat transfer error ar gain factor on the reheater heat transfer error
Pg density of flue gas [kg/m3] boiler thermal efficiency
Pg viscosity of flue gas [kg/ms] a Stefan-Boltzmann constant = 5.669 x 104 [whn2K4] Emissivity of a non black body
1
1. Introduction
1.1 Power generation The world today consumes vast amounts of energy as nations strive to satisfy much more than only the basic human needs of food, shelter and clothing. Virtually the entire environment of a westerner is in some way dependent on adequate supplies of energy. Over the period from 1950 to 1990, annual world electrical power production and consumption rose from slightly less than one trillion kilowatt hours (1.0 * 10' 2 kWh) to more than 11.5 trillion kWh [1].
In South Africa, access to electricity is considered one of the rights of every resident. Eskom, the national power company, with an installed capacity of 38 497 MW, expands its services to new customers at a rate of 300 000 connections per year [2]. This contributed to an average growth in electricity sales of 3.6% over the past five years [2], but also contributed to a growth in the peak electricity demand, with a new winter maximum demand of 27 967 MW recorded on 24 August 1996 [3]. The average power demand during a 24 hour period in South Africa is shown in Figure 1.1. During an average day in the winter, the peak load demand is 50% higher than the base load demand. This demand variation requires many of the power stations to perform large load changes daily.
26
' 24
2 22 E a) 1,3_ 20
pi 18
16 0 3 6 9 12 15 18 21 24 Hour of the day — Summer Winter
Figure 1.1 South African power demand through a typical day. [2] 2
In 1950 rougly two-thirds of the electricity came from thermal (steam-generating) sources and about one-third from hydroelectric sources. In 1990 thermal sources still produced about two-thirds of the power, but hydro power had declined to just under 20 per cent and nuclear energy accounted for about 15 per cent of the total [1]. Of all the fossil fuels used for steam generation in power plants today, coal accounts for most of the energy [4]. At an annual production rate of about 3.5 billion metric tons worldwide, serious depletion of coal resources will take around 185 years [5]. Therefore, it may be said that coal-fired power stations will be one of the prime sources of electrical power for many years to come.
Compared to its beginning, the generation of electricity has become a very complicated business.
High energy costs demand that as much electricity as possible be generated from the fuel . consumed. Higher availability of equipment is needed to stem rising operating and maintenance costs. Protection of both personnel and equipment must be achieved, and unscheduled shutdowns must be kept to a minimum. While obviously instrumentation and control systems cannot satisfy such concerns by themselves, the above demands have resulted in a substantially increased requirement for sophisticated instrumentation and automatic control systems. In this context, modern power plants are among the most highly automated and centrally controlled and monitored production facilities in the world.
1.2 A brief history of boiler control The earliest known boiler control application was that of a float valve regulator for boiler water level control [6]. This device was described in a British patent by James Brindley in 1758. Mother float valve regulator of considerable originality was independently invented in 1765 in Russia by Ivan Polzunov. In a British patent of 1784 Sutton Wood documented some improvements to the float valve regulator. James Watt and Matthew Boulton of Boulton & Watt Co. adopted the float valve regulator as a standard attachment to their boilers somewhere between 1784 and 1791 [6].
A discussion on control system development will probably not be complete without reference to the steam engine governor. The origins of this device lie in the lift-tenter mechanism which was used to control the gap between the grinding-stones in both wind and water mills. Boulton. 3 described the lift-tenter in a letter (dated May 28, 1788) to Watt, who realized it could be adapted to govern the speed of the rotary steam engine. The first design was produced in November 1788, and a governor was first used early in 1789 [7].
Steam pressure control was first patented in 1799 by Matthew Murray who regulated the furnace draught inversely to steam pressure [6]. His device used the force of steam pressure acting against a weighted piston to drive a damper in the flue gas duct. In 1803 Boulton & Watt used steam pressure to alter the height of water in a column, which, in turn, changed the position of a flue gas damper via a float and chain system [6].
From that time in the early 1800's, while there were some improvements in the hardware used, the application concepts in boiler control did not advance much until the early 20th century [8].
During the early part of this century power stations used only a few absolutely necessary instruments for measuring pressure, vacuum, speed, voltage and current. As additional types of instrumentation became commercially available, more equipment was used to provide data for control and operation of power plant which was consequently growing in complexity [8].
From the 1930's onward, considerable thought was given to automatic control equipment and to the development of automatic controllers for boiler plant operation [9]. Progress was slow at first, because there was much debate about the real need for such equipment, but improvements in instrumentation since the Second World War gave an impetus to the acceptance of automatic control systems. By approximately 1950, boiler control developed into integrated systems for feed water control, combustion control, and steam temperature control [9].
On the plant side, economic considerations have demanded larger and more complex generating units. Correspondingly, the instrumentation requirements have had to keep in step with this development by the provision of more sophisticated automatic control. In the period 1950 to
1970 the development of boiler control was primarily hardware-oriented where many improvements to pneumatic and electronic controllers were made. This further development of controllers, mechanisms, electronics, and relays led to the design of equipment for complete automatic boiler control, and subsequently to schemes for automatic start-up, loading, running 4
and shutting-down of large complicated boiler-turbine units [9].
Historically, meters, gauges, and lights displayed equipment status to the operator, while recorders made a permanent record of plant performance. Remotely operated air cylinders and electric motors served as actuators and gave plant operators the capability of responding quickly and efficiently to changing plant requirements. From 1970 onwards, the development of microprocessors has sparked a beneficial transition to the greater precision of digital control. computer monitors have replaced the panel-board instrumentation, to provide the operator with past and present process information through sophisticated microprocessor-based distributed control hardware [10].
As power plant control became increasingly more complex, the number of measurement signals from the plant, and control signals to the plant has increased too. Currently around 2 000 analog signals and 6 000 binary signals are being installed on a new boiler-turbine unit. There is a gradual movement towards the use of microprocessor-based "intelligent" instrumentation, where, in addition to measuring one or more process variables, self-diagnostics, time stamping, some administrative functions, linearization and even control are also performed by the measuring devices [11]. These instruments are linked to the control system via a two-wire digital bus which conforms to one of a few industrial field bus standards [12].
Today, virtually all control functions are performed digitally by microprocessor-based; programmable controllers. Traditionally, binary control would be done via a programmable logic controller (PLC) while analog control would be done via a distributed control syslem (DCS), but nowadays this distinction is not as clear, and most PLCs and DCSs can do both binary and analog processing [13]. Control algorithms with increased flexibility are becoming available to provide on-line gain scheduling, nonlinear control, instrumentation and actuator linearization, automatic tuning, and many other features [14].
Progress is also being made on advanced control philosophies in many directions. A good example of this is steam temperature control which is one of the most difficult processes to control in steam generating plant. Many different control strategies have been proposed for, and 5
were tested on the steam temperature control loop. This thesis will discuss the various areas of
progress on advanced steam temperature control at a later stage. It will also introduce a new
control philosophy, discuss its advantages and disadvantages and document results obtained on a live 686 MW power plant boiler.
The modelling, practical work and experimentation discussed here was done on Unit 3 at Kendal Power Station, located near Witbank in South Africa. The station comprises six identical boiler-
turbo-generator units, each rated for 686 MW continuous operation. The peak generating
capacity of the station is 4320 MW (6 * 720 MW peak), which rates it as one of the largest coal
fired power stations in the world. •
1.3 The need for steam temperature regulation
In any modem thermal power station, it is of the greatest importance to keep very close control over the steam temperature and temperature gradients, for the following reasons:
Since the expansion of turbine components is directly related to the temperature of steam,
strict requirements on the regulation of steam temperature are imposed by the small
clearances between stationary and moving turbine parts [15].
To maximize the time-to-rupture of boiler components by limiting excessive creep due to
high temperatures [16]. Creep is the time dependent deformation of a material subjected
to stress lower than its yield stress. The creep rate of steel increases with temperature
[17].
To maintain safety margins. A drastic reduction in the yield strength and tensile strength
of steels occurs at temperatures above 540-560°C, depending on the composition of the
steel [17] & [18].
Close matching of steam temperatures to metal temperatures are necessary, especially during start-up and shut-down to prevent distortion on turbine casings [19].
Steam temperature gradients must be kept within tolerances to prevent excessive stress
in the thick-walled components [20]. Repeated temperature transients of an excessive
nature cause thermal fatigue of boiler components.
0 Because the efficiency of the steam cycle is dependent (amongst others) on steam temperature [21], it is beneficial to operate with temperatures as close to the upper limits 6
as possible.
The list above is probably not exhaustive, but it does point out the importance of good steam temperature control on power plant.
1.4 Research hypothesis The following hypotheses underline the work undertaken in this thesis. The heat transfer rate from the firing system to the evaporator, superheater and reheater in a power plant boiler can be modelled by using a neural network trained on real plant test data. Such a neural network model can be used to predict the effect that firing system disturbances will have on the heat transfer rates before the steam temperature is affected significantly by these disturbances. Adjustments to the firing system for minimizing the errors between actual and design heat rates can be obtained by iteratively backpropagating the errors through the neural network. In this way, the effect of firing system disturbances on steam temperature can be largely neutralized.
1.5 Overview of thesis Chapter 2 describes the power plant thermodynamic cycle and defines the various mechanisms of heat transfer between fuel and boiler tubes. It also describes how heat transfer changes with varying boiler load and boiler conditions. The placement and surface area of boiler components and the sensitivity of heated elements to changes in heat distribution patterns are discussed.
Chapter 3 deals with various methods of, and control elements for, steam temperature control. Three main classes of steam temperature control elements are discussed. The effect on steam temperature regulation of long process time lags, variations in process parameters, and process disturbances are presented. The results of a study into the origin . of temperature excursions at Kendal power station are documented. The instrumentation 7 and control configurations applied in practice are discussed and an overview of documented developments in advanced steam temperature control on power plant boilers are made.
Chapter 4 discusses the suitability of applying neural networks to process modelling and control. The artificial neural network, and aspects related to the topology and training of networks, are discussed. Arguments are presented for applying neural networks to the modelling of existing processes. Various neural network controller designs are described, and the error backpropagation technique is shown to be well suited to the steam temperature control problem.
Chapter 5 focusses on the creation and testing of a boiler heat distribution model. The desired characteristics of a heat distribution model for a power plant boiler are listed. The design and execution of a series of live plant tests for acquisition of modelling data are described. Processing the data and calculating the heat transfer rates to the boiler components are described, assumptions are motivated, and the calculation of any unmeasured variables are explained. Specific attention is given to discrepancies in the results. The task of selecting the ideal network topology is described and comparative results are given. Different model output schemes are introduced.
Chapter 6 deals with the design of a neural network based heat distribution controller.
The requirements for improving steam temperature control are listed and it is shown that neural networks lend themselves very well to these requirements. The philosophy of optimal heat distribution (OHD) control is introduced. It is shown how the error backpropagation technique can be applied to calculate optimal control actions.
Chapter 7 describes the implementation and testing of the OHD controller. The development of the software programme and hardware interface is described and intricacies are pointed out. Problems with mill production rates and process noise are addressed. Transient tests are described, and problems experienced with process gain changes, oscillations, and erroneous fuel flow measurements are explained. Final results 8 with OF-ID control are compared to normal P1D control and improvements, and drawbacks, are discussed. 9
2. The power plant boiler
2.1 Cycle description
2.1.1 Carnot cycle In 1824, Sadi Carnot, a French engineer, published a small, moderately technical book, Reflections on the Motive Power of Fire' [22]. With this, Camot made three important contributions: the concept of reversibility, the concept of a cycle, and the specification of a heat engine producing maximum work when operating cyclically between two heat reservoirs each at a fixed temperature. The importance of the Carnot Cycle here is that it forms the basis of the water-steam cycle in power generation.
Figure 2.1 Carnot cycle.
Camot cycles consist of two reversible isothermal and two reversible iserifropic processes (Figure 2.1). A high temperature heat source and low temperature heat sink are placed in contact with the Carnot device to accomplish the required isothermal heat addition
.Q, (a- b) and rejection Q2 (c-d) respectively. The reversible adiabatic process involves expansion that produces work output Wr (b-c) and compression that requires work input We (d-a). The state changes experienced by the working fluid are shown in the temperature-entropy diagram of Figure 2.2.
Translated to English from: Reflexions sur In puissance motrice du feu.
10
T
S Figure 2.2 Carnot cycle T-S diagram.
The classic Camot cycle is such, that no other can have a better efficiency than the Camot value between the specified temperature limits [21]. Other cycles may equal it, but none can exceed it. Practical attempts to attain the Carnot cycle encounter irreversibilities in the form of finite temperature differences during the heat transfer processes and fluid friction during work transfer processes. Moreover, as all of the process fluid has not yet condensed at state d, the compression process (d-a), is difficult to perform on this two- phase mixture. Compressing the gaseous state also consumes large quantities of energy. Consequently, other cycles appear more attractive as practical models.
2.1.2 Rankine Cycle The cornerstone of the modem steam power plant is a modification of the Camot cycle proposed by W.J.M. Rankine [23], a Scottish engineering professor of thermodynamics and applied mechanics. The elements comprising the Rankine cycle are the same as those appearing in Figure 2.1 with the following exceptions: the condensation process accompanying the heat rejection process continues until the saturated liquid state is reached and a simple liquid pump replaces the two-phase compressor. 11
Figure 2.3 Rankine cycle.
Figure 2.3 shows the component layout of the Rankine cycle with a boiler as high temperature heat source, a condenser as low temperature heat sink and a liquid pump replacing the two-phase compressor. The temperature-entropy diagram of the Rankine cycle (Figure 2.4) illustrates the state changes for the Rankine cycle. With the exception that compression terminates at boiling pressure (state a), rather than the boiling temperature (state a), the cycle resembles a Carnot cycle. The lower pressure at state a, compared to a', greatly reduces the work of compression between d-a.
Figure 2.4 Rankine cycle T-S diagram. 12
This Rankine cycle eliminates the two-phase vapour compression process, reduces compression work to a negligible amount, and makes the Rankine cycle less sensitive than the Carnot cycle to the irreversibilities bound to occur in an actual plant. As a result, when compared with a Carnot cycle operating between the same temperature limits and with realistic component efficiencies, the Rankine cycle has a larger net work output per unit mass of fluid circulated, smaller size and lower cost of equipment.
2.1.3 Superheat cycle The turbine in an unmodified Rankine cycle receives dry, saturated vapour from the boiler. Therefore, part of the vapour condenses as it expands and cools through the turbine. In superheat cycles, the vapour is heated above the dry-saturation point, before being fed to the turbine. The use of superheat offers a simple way to improve the thermal efficiency of the basic Rankine cycle and reduce vapour moisture content to acceptable levels in the low-pressure stages of the turbine [21].
Figure 2.5 Superheat cycle T-S diagram.
2.1.4 Reheat cycle Even with the continued increase of steam temperatures and pressures to achieve better cycle efficiency, in some situations attainable superheat temperatures are insufficient to prevent excessive moisture from forming in the low-pressure turbine stages. The solution to this problem is to interrupt the expansion process, remove the vapour for reheating at constant pressure, and return it to the turbine for continued expansion to condenser 13 pressure (Figure 2.6). The thermodynamic cycle using this modification of the Rankine cycle is called the reheat cycle. Reheating may be carried out in a section of the same boiler supplying primary steam, in a separately fired heat exchanger, or in a steam-to-steam heat exchanger. Most present-day utility units combine superheater and reheater in the same boiler [4].
Figure 2.6 Reheat cycle with economizer.
For large installations, reheat makes possible an improvement of approximately 5 percent in thermal efficiency and substantially reduces the heat rejected to the condenser cooling water [24]. The operating characteristics and economics of modern plants justify the installation of only one stage of reheat except for units operating at supercritical pressure. One further addition to the Rankine cycle for increasing efficiency Was that of the economizer. This element raises the temperature of feed water by utilizing the low temperature heat after the flue gas had been cooled by evaporator, superheater and reheater (Figure 2.7).
14
T Superheater Evaporator 12\ eheater
Economizer Allik-4 1
Feed pump Turbines
Condenser
Figure 2.7 Reheat cycle with economizer T-S diagram.
2.1.5 Regenerative Rankine cycle Refinements in component design soon brought power plants based on the Rankine cycle to their peak thermal efficiencies, with further increases realized by superheating and reheating the steam as described above. Efficiencies were further boosted by increasing the temperature of the steam supplied to the turbine and by reducing the sink (condenser) temperature. Currently, all of these are employed with still another modification, being regeneration.
The regenerative cycle reduces irreversibility by bleeding hot, partially expanded steam from the turbine(s) and using it to heat the compressed water fed to the boiler. In this way it increases the overall cycle efficiency. Apart from increasing cycle efficiency, regeneration impacts the process in two ways: it changes the temperature of the boiler feed water and it reduces the steam flow through the reheater. These two issues will be discussed in more detail later in Chapter 5.
2.2 Heat transfer theory During the combustion process inside a furnace, enormous quantities of chemical energy is converted to heat and discharged into the furnace space. Most of this heat is transferred to the boiler tubes and working fluid while a small percentage is lost to atmosphere through the hot flue gas. Heat transfer takes place through three individual mechanisms: conduction, convection and radiation. In a power plant boiler, heat is transferred simultaneously by all three mechanisms. 15
The mechanisms of heat transfer will be discussed here to point out the factors influencing heat transfer between the burning fuel and the working fluid. For the purpose of this thesis it is not necessary to do an in-depth analysis of heat transfer. However, it is important to emphasize the differences in the physical mechanisms of heat transfer and to discuss the main factors influencing it
2.2.1 Conductive heat transfer
Conduction takes place by elastic molecular impact, molecular vibration and in metals by
electronic movement. In comparison to heat transfer through convection and radiation,
heat transfer by means of conduction through the flue gas to the boiler surfaces is
negligibly small [25]. However, heat conduction theory does play a role at the boiler tube
surface where the heat has to pass through the metal tube wall or through a covering layer
of ash or slag.
The equation for heat conduction through multi-layer cylindrical walls [26] can be written
to apply to heat conduction through a boiler tube covered with ash:
2n-L(Tg - Tf) qcond In(r olr ,) In(r jr 0) (2.1) Ict Ica
where:
q gond heat transfer rate through boiler tube and ash [W] thermal conductivity of the boiler tube metal [W/cm°C]
ka = thermal conductivity of ash [W/ m°C]
Tg = temperature of combustion gas [°C] = 7} temperature of fluid inside boiler tube [°C] L = length of the boiler tube [m] r, = inner radius of boiler tube [m] ro = outer radius of boiler tube [m]
ro = outer radius of ash layer [m]
The thermal conductivity lc, of steel ranges between 20 and 50 W/ m°C depending on its 16
temperature and composition [26]. Much lower is the thermal conductivity of ash and
slag, both being below 1.0 W/ m°C [26]. Therefore, if an ash layer forms on a boiler tube, it significantly reduces, and quickly dominates, the heat transfer rate into the tube.
Due to this reduction in heat transfer, modern furnaces have high pressure sootblowers installed to periodically blow the contaminants from the heat transfer surfaces.
2.2.2 Convective heat transfer
Convection in a power plant boiler involves transportation and exchange of heat due to
-flue gas motion and is governed by the laws of aerodynamics and fluid dynamics.
Convective heat transfer is described by Newton's law of convection [26]: