Model FPP

Part 2

Control

Chapter 3

Modelling and control of pulverised fuel coal mills

N.W. Rees and G.Q. Fan

3.1 Introduction

There can be no doubt that the ideas of modelling and control have generally been found quite acceptable within the electric power generation industry. With the introduction of modem distributed control systems (DCSs), it is now possible to implement many of the ideas resulting from modelling and control studies, although control engi- neers generally feel much more could be done than is currently the case (Rees and Lu, 2002). The control vendors and the applied control literature now regularly describe 'modem' control systems for the industry. Particular attention has been paid to steam temperature control (Mann and Lausterer, 1992; Nakamura and Uchida, 1989), load pressure control (Maffezzoni, 1996; Waddington and Maples, 1987) and water level control (Kwatny and Maffezzoni, 1996; Peer and Leung, 1993).

An area of power plant control that has received much less attention from modelling and control specialists is the coal mills. This is in spite of the fact that it is now accepted that coal mills and their poor dynamic response are major factors in the slow load take-up rate and they are also a regular cause of plant shut-down (Maffezzoni, 1986). The reasons for this lack of interest are uncertain but relate very much to the idea that modelling mills is very difficult, if not impossible, and that mills are subject to all sorts of disturbances such as wear, choking and unknown coal properties, beyond the wit of the engineer to model. Against this, however, there is plenty of evidence that properly modelled and controlled mills can respond much better than at present; indeed it has been suggested that performance equal to that of oil-fired plant is possible (Rees, 1997).

In the rest of this chapter we take a closer look at the modelling and control of coal mills and give some ideas from our own experience, and from that of others, where the future automation of this important area may be going.

64 Thermal power plant simulation and control

3.2 Modelling of coal mills

The problem of the transient performance of coal mills has been recognised for some time. Early work by Profos (1959) on pressure and combustion control, introduced models of coal mills relating input demand to firing rate by transfer functions, consist- ing of a first-order lag and a pure transport delay. Typical values of these parameters for different types of mills were given.

Numerous studies based on these models using step response or frequency response testing have been carried out both for single-input single-output (SISO) systems and multivariable control (Bollinger and Snowden, 1983; Hougen, 1980; Neal et al., 1980). Slightly more complicated models based on overall mass bal- ancing (O'Kelly, 1997; Rees and Mee, 1973) or heat balance analysis (Dolezal and Varcop, 1970) have also been developed. Whilst these models have been beneficial it is now recognised that some aspects of particle size distribution as well as the complicated internal structure of the mill must be considered (Corti et al., 1986; Robinson, 1985). We will discuss this matter soon but first we need to look a bit more closely at the mills themselves.

3.2.1 Vertical spindle mills

There are many types of coal mills in use, with one of the most popular types being the pressurised vertical spindle bowl mill as shown in Figure 3.1. This mill is very popular because it is economical; however, it has very low coal storage so that good control is very important. In operation, raw coal enters the mill down a chute dropping on to a constant speed of rotation table or bowl. The coal then moves under centrifugal force outwards and under three passive rollers where grinding and crushing take place. The coal output then moves towards the throat of the mill where it mixes with high-speed hot primary air. The heavier coal particles are immediately returned back to the bowl for further grinding whilst the lighter particles are entrained in the air flow and carried into the separator section.

The separator contains a large amount of coal particles in suspension by the powerful air flow. In addition some of the heavier particles entrained in the primary air-coal mix lose their velocity and fall back onto the mill table as shown, for further grinding, whilst particles that are travelling fast enough enter the classifier zone. These particles are given a swirl behaviour by vanes or deflector plates. The lighter particles are drawn out of the resulting vortex as classified pf fuel for the burners, whilst the heavier particles hit the side of the classifier cone and drop back onto the mill table for further processing. As in the separator the classifier contains a significant mass of suspended coal. These masses of coal, together with the mass of coal on the mill table and the three recirculating loads, primary, secondary and tertiary, play a major role in the dynamic behaviour of the mill.

As shown in Figure 3.1 the main inputs to the mill are the raw coal and the primary air while the output is the pf flow. The size distribution of the pf flow particles or 'finers' is usually required to be less than 75 microns and cannot be measured. It is determined largely by the intemal mill behaviour and the classifier settings which

Modelling and control of pulverised fuel coal mills 65

Raw coal from feeder

0 6 ~ O

Deflector plates

Tertiary recirculating load

Secondary recirculating load

Primary recirculating load

Flash drying zone

Mill throat

Figure 3.1 Physical structure of a vertical spindle mill


are usually not varied during mill operation. The size distribution of the raw coal input is measured infrequently using mechanical sieves and the particles are mainly in the range 75 microns to 70 mm. Air flow can be measured accurately although the measurement is often noisy. There is no doubt that mill control would be much improved if particle size measurements were available, but no satisfactory measuring equipment has yet been developed. Other important variables around the mill are mill temperature, which is easily measured and controlled by hot air and cold air dampers; mill motor current, which gives some indication of mill load; and the differential pressure between the top part of the mill and the under bowl. This AP measurement is very useful in helping to understand mill recirculating load.

3.2.2 Modelling vertical spindle mills - mass balance models

A useful physical model of the mill can be developed using internal mass balances. A block diagram schematic of the mill is shown in Figure 3.2. rhrc and rhpf represent the raw coal flow entering the mill and the pulverised fuel flow leaving, rhpre, rhsr e and rhtre are the recirculating loads and rhpff, rhsff and thtff represent the entrained coal flow picked up at the throat, separator and classifier by the primary airflow rhpa. rhff = kfrhrc is the amount of fine coal in the raw feed that is blown straight out of the mill. Mpr is the mass of coal on the table, rhg the flow of coal to the grinding table, and rhgc the output of the grinding mills. Figures 3.1 and 3.2 show the key mill structure and the variables necessary to write the mass balances.

fT.., ~pf

mtre mtff ClassifierMtfl. ,~ ~

Phsff ~ r ~ _ ~ ] [ m~r~ i Separator I

I I M~, I

ii ow, Roll

Figure 3.2 Schematic of a vertical spindle mill


A detailed transient model of the mill based on Figure 3.2 has been developed by Robinson (1985). This model considers coal in 15 particle sizes with detailed physically based models developed for each box. In the bowl modelling, for example, the flow of raw coal from the chute to the grinding zone has been modelled in terms of centrifugal effects and the difference in height of the raw coal as it flows across the coal bed. Likewise, treatment of the grinding zone includes an analysis based on known communition theory and established breaking rate functions and break- age distribution functions. The entrainment of coal from the table into the separator and classifier is examined using Lagrangian particle calculations and empirically determined classification functions.

As a consequence of all this detail the model consists of 76 ordinary differential equations and is more of a knowledge-based model (Maffezzoni, 1996) than a control model. It is an excellent reference model and highly recommended reading but too complicated for most control studies.

A more control-oriented model has been developed by Fan (1994) and Fan and Rees (1994). This model uses the same physical structure as shown in Figure 3.2 but the processes in each box are simplified. Eleven particle sizes are assumed in the raw coal but the grinding model is much simplified over the size mass balance model (Prasher, 1981).

The following mass balances can be written by inspection:

rhg -- 1 - w0 (1 - kf)rhrc q-- thre (3.1) 1 - w l

rare = ff/pre -[- rhsre q- rhtre (3.2)

/hpf = ff/tff - thtre -[- thff (3.3)

where w0 and Wl represent the moisture in the raw coal and in the coal on the bowl. The recirculating loads in equation (3.2) can be adequately determined from

drhpre rpre dt - rhpre q- kprerhpff (3.4)

drnsre t'sre - -rhsr e -4- ksrerhsff (3.5)

dt

d/htre "t'tre - - -- rhtr e q- ktrerhtff (3.6)

dt

where kpre, ksre and ktre are the appropriate steady-state gains and the time constants rpre, rsre and rtre are due to aeroresistance and inertia to the flow with the finer particles having longer time delays.

The suspended mass of coal in the separator Msr and the classifier Mtr can be calculated from

dMsr = (1 - kpre)rnpff - rhsff (3.7)

dt

dMtr = (1 -- ksre)rhsff - rhtf f. (3.8)

dt


To complete the model we need to determine the mass of coal Mpr on the table and the entrainment flows rhpff, rhsff and rhtff. The coal mass balance on the table can be written as

dMpr -- thg -- rhpff. (3.9)

dt

However from the entrainment point of view it is the ground coal conditions at the rim (throat) that matter so that we really need to know the flow output of the grinding rolls rhgc. This can be determined in a complex way using the size mass balance concept (Prasher, 1981), but a simpler model is used here based on the idea of 'similarity' (Fan, 1994; Prasher, 1981), which results in the rolls being described by

1 drhgc -- rhg - rhgc (3.10)

R dt

where R is the size reduction rate of the raw coal particles and rhgc is defined as the flow of ground coal such that 80 per cent of the particles will pass through a 75 micron sieve. In this study R has been determined by measuring the weight of coal in each of 11 sieve sizes and feeding this information into a Matlab program for calculation (Fan, 1994).

To determine the entrainment rate of the coal by the air at the throat, in the separator and in the classifier we need to find a relationship between the air mass flow rate at the point of interest and the pick-up rate of the coal particles. The particles are picked up by the drag force and will be entrained as long as this force is greater than the gravitational force. Kunii and Levenspiel (1969) show that the entrained particles travel at the same velocity as the carrier air and from this it is straightforward to show (Fan, 1994) that

rhpff = kpr Mprrhpa (3.11)

where kpr is a shaping factor that depends on the area of the particle flow path, the area of the primary air flow path, the density of the air and the volume of the mill occupied by the fine coal particles near the classifier. Assuming that the mass of primary air passes quickly through the mill then the secondary and tertiary final flows can be expressed by similar formulas:

rhsf f = ksrMsrrhpa (3.12)

rhtf f = ktrMtrrhpa (3.13)

where all the shaping constants have the same structure as kpf but with their own local parameter values. Since a small amount of 'finer' coal enters the mill in the raw coal and gets blown straight out again as pf coal it is appropriate to add this flow to equation (3.13) so that

rhpf : kpf(Mtr + Mff)rnpa -- rhtre. (3.14)


Small portion fine coal (%)

:D Sum6

Gain9

I

,Q Mill pf flow (kg/s)

Transfer Fen3

~ Coal near classifier (kg)

Coal returned due to classification (%)

Co; ] ~ . u 2 e d due ~ ' to air velocity (%) Transfer Fen2

Gain7

Mill level (kg)

Coal returned due to mill rim (%)

dmd Raw coal Saturation Transport (kg/s)

Primar air (kg/s)

Sum aulrll Grinding zo~e~Vtable . . . . . . . . . (kg)

Figure 3.3 Vertical spindle mill- Matlab/Simulink ® simulation

Raw coal (l~g/s)

A complete Simulink simulation model of the mill based on the above mass balance is shown in Figure 3.3. It is interesting to compare the above model with the more empirical model developed by O'Kelly (1997). This model uses three particle sizes that are carried through all the calculations. The grinding model is similar to equation (3.10) except that of the two particle sizes in the ground coal the production of one is seen as proportional to the mass of raw coal on the table while the production of the other is proportional to the mass of the larger size ground coal on the table.

An interesting non-linear model is described for the entrainment of coal of the larger ground size at the bowl rim. This is expressed as

rhpff = kpr(mprmgc) n' thpa (3.15)

where nl is an experimentally fitted constant and Mgc is the mass of ground coal on the table. A non-linear function such as this allows saturation to be modelled as might occur for example in mill choking.

3.2.3 Modelling vertical spindle mills - temperature, pressure and energy issues

Whilst the mass balance model describes the pf flow quite well, it is essential for any control studies that thermodynamic and hydrodynamic effects are also considered. The mill temperature Tm measured at the mill outlet is a critical variable from a safety viewpoint, and must be controlled within narrow bands. Likewise mill differential pressure A P measured between the mill under-bowl and the separator is a critical variable since it is an indirect measure of mill recirculating load - too high a A p


indicates possible mill overload and will trip the plant. Mill wear can also be related to A P. Another useful measurement and model relates to the energy E needed to drive the mill and its coal load. The simulation equations for Tm, A P and E are outlined below.

3.2.3.1 Mill t e m p e r a t u r e - Tm

A simple and useful model of mill temperature can be obtained from a global input/output energy balance. It is assumed that the mill temperature is measured in the mill pf outlet duct and that this lumped parameter Tm is the same as the mill body, coal and air mass temperature in the mill. The mill energy balance then results in

dTm MmCn---~- = qin -- qout = 0pai + qrei + C)moi -- qpao - qpfo -- Ümoo (3.16)

where the 0 terms represent the input and output heat in the primary air, raw coal and the moisture, and Mm and Cn are the mass of metal in the mill and its specific heat. By standard techniques these quantities can be written as:

C)pai = Clthpa(Tpa - Tin)

C)rci = (1 - to0)thrc(T a - Tin)C2

C)moi ----- to0rhrc(Ta - Tin)C3

qpao = C1 thairTm

0pro = C2rhpfTm

qmoo = C3tolrhrcTm + Co(wo - tol)rhrc

where Tpa and Ta are the primary air and ambient air temperature, rhair is the air flow outlet of the mill and the coefficients Ci are the appropriate specific heats. Equation (3.16) with the above substitutions represents the temperature equation used in the simulations.

It may be possible to improve the temperature model by including heat generated during grinding so that the temperature of the coal bed differs from the measured pf outlet temperature. This would require a higher-order dynamic model and much more information on coal mass and surface parameters. Early results, however, suggest that significant improvements are possible using this approach.

3.2.3.2 Mill differential pressure - A P

As the primary air flows through the mill, picking up coal from the table, a differential pressure A P is developed between the under-bowl pressure and the pressure in the separator. This pressure loss is caused by frictional losses, changes in the air flow path area due to the suspended coal, and energy lost by the air in picking up the coal. The problem is complex because there is a mixture of single-phase air flow below the bowl and two-phase coal/air flow in the separator.

Modelling and control o f pulverised fuel coal mills 71

A global model for A P can be developed from an energy balance between the air input and the mill measuring point. Details are given in Fan (1994) with the resulting equation for AP being

kree-Tds - • 2 AP = kpedh + kpadth~a + (1 + Tms) 2dmrc + krldMtr (3.17)

where dh is the distance between the mill entry and the measuring point. The parameters kpe, kpa, kre and krl are complex functions of mill air flow.

Consequently, the global model is represented by a set of constant-coefficient lumped-parameter models. These parameters can be determined off-line and stored in a look-up table relating their values to operating conditions or they can be determined adaptively on-line. This will be discussed in subsequent sections.

3.2.3.3 Energy model Large coal mills consume significant amounts of power amounting to about 500 kW at full load. In addition by observing the mill power requirements for coal pulverising, useful information about mill wear, coal hardness and other operational issues can be resolved.

If Eu is the energy required by a unit mass of coal particles to be ground from size Z l to size z2 and W is the energy required to drive an empty mill, then the energy E required by a mill charged with coal mass m is

E = mEu + W. (3.18)

Assuming Eu is given by Bond's law (Kunii and Levenspiel, 1969) then

E = mkB(Z21/2 -- z l I/2) (3.19)

where kB is a constant depending on the coal. Since z2 is determined by the mill classification settings which are fixed, and the raw coal distribution is more or less constant, the mill power consumption E is mainly a function of the amount of coal mass m on the mill grinding table. It should be noted that the mass of the mill M is constant. A similar relationship for the consumed energy is given by Corti et al. (1986).

3.3 Plant tests, results and fitting model parameters

Models of physical plant are of course only as good as how well they fit the data. Unfortunately there is little coal mill data available so that most of the few models available in the literature are qualitatively evaluated or checked against a number of simple step responses. Some frequency response testing has been performed (Neal et al., 1980), and it has been suggested by Corti et al. (1986) that data collection was being carried out by ENEL in Italy.


In this section we describe some model data fitting carried out using the model from section 3.2 when fitted to data collected from power stations in New South Wales, Australia. Unfortunately, the data are not available for general use. It was developed in a collaborative project between the University of New South Wales and Pacific Power International in a project designed to develop and test modern control concepts as applied to coal mills. In passing, it is worth mentioning the fact that the data was collected from experiments carried out on two plants, one a 500 MW plant and the other a 660 MW plant. Data logging includes the mills (six of them) and appropriate pressures, temperatures and flows from all around the boiler turbine plant.

The experiments were specially designed by the modellers, plant technical staff and operators so that model parameter estimation was possible without excessively disturbing the plant or placing too much demand on the operators. This means that step and ramp changes are made and during the experiments normal plant controls are maintained except around the mills. Three different mill control configurations were used. In the first experiment, plant power demand was ramped up and down with the normal mill mass/mass control in place so that fuel and primary air varied. In the other two experiments mill controls were removed, power demand was set constant, and a step change was applied to fuel flow with constant primary air set- point, or air flow with constant fuel set-point. Extensive experiments were carried out for five different power demands between 60 and 90 per cent MCR. Following each step the plant was allowed to settle before the next step occurred. The tests were also carried out for new mill rollers and worn mills. In addition special tests were carried out, for example, on an empty mill to determine the no-load relationship between mill A P and primary air. No particular parameter identification method was used to fit the model parameters. Rather steady-state data, transient data, data from the special tests and design data were used in a heuristic way. Since the experiments were carefully designed it was possible to fit many of the parameters to the data by simple least squares. Once the first set of parameters was determined the simulation model was run in parallel with the mill and the resulting error signal was then used to further refine parameters. It was quickly found that a large number of parameters were constant throughout plant operation, but a small set of parameters varied with load and other factors such as wear. To cope with these variations a distributed model parameter set was determined as discussed later. Although this approach might appear somewhat ad hoc it is a very effective engineering approach and an excellent way of building up knowledge and understanding of the plant for modeller and plant engineer alike.

The fitted model test results against the data are shown in Figure 3.4 where the mill power and mill Ap outputs are shown. In this test the mill PA flow was constant and the feeder speed step changed after 90 and 430 samples. The parameters used in the model were determined for 70 per cent load as the feeder speed indicates. It can be seen that the model responses are quite satisfactory. In Figure 3.5 a similar test is carried out but at 80 per cent load. However, the model has the same 70 per cent load parameters as the previous simulation. Deterioration in the results is obvious both for

75

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100 200 300 400 500 600 700 800 900

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22 , , , , , , , ,

20

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= 16

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Figure 3.4

I I I I I I I I

100 200 300 400 500 600 700 800 900

Step changes in feeder speed at 70per cent load (fixed parameter model): solid line -f ield test data; dotted line - model output. Sample time = 3 s

steady-state values and for the transient response. A similar test at 50 per cent load showed even worse transient performance.

Figure 3.6 shows the results of a more complicated test. In the first 600 samples the feeder speed was constant and the air varied. After 800 samples both feeder speed


85

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Figure 3.5 Step changes in feeder speedat 80per cent load (fixedparameter model)." solid line - f ield test data; dotted line - model output. Sample time = 3 s

and air vary th rough ra ther large changes . It can be seen tha t the data fit for a fixed

mode l for this test is no t very good and especia l ly af ter 800 samples where the error

increases . In this da ta the mil l is ' c h o k i n g ' due to the h igh coal flow and low air

flow. Such an event is no t unusua l and could cause the mil l to be shut down. This


90

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. L i = ~ , L l l l i , , ~ . ~ m . , , ~ l . m , ~ L . L O

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500 1000 15oo

Figure 3.6 Step changes in both airflow and feeder speed (fixed parameter model): solid line -field test data; dotted line - model output. Sample time = 3 s

phenomenon, however, is largely missed by our fixed parameter model . It might be

noted that in these figures we have not chosen to show the p f flow from the mill. This

is because it cannot be measured and so cannot be compared with the model . We will

discuss this in section 3.4.


LT.,

7 5 - -

70

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T r T

........................ i

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- - . . . . . . . . . . . " - t - - - - . . r . - . . . . . . . . . ~ T - . % - "

I I I I I I I I

100 200 300 400 500 600 700 800 900

Figure 3.7 Step changes in feeder speed at 70 per cent load (distributed parameter model): solid line - test data; dotted line - model output. Sample time = 3 s

The mill mode l l ing p rob lems j u s t descr ibed are due to the non- l inear i t i es in the

p lant so tha t one set o f pa r ame te r values wi th the s imple model c a n n o t g lobal ly fit

the data. This difficulty can be ove rcome by us ing a d is t r ibuted pa rame te r set mode l

where different pa rame te r s are used for d i f ferent opera t ing condi t ions as measu red

85

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Modelling and control of pulverised fuel coal mills

200 300 400 500 600 700 800

400 . . . . . . .

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Figure 3.8

800

800

I I I I I I I

100 200 300 400 500 600 700 800

Step changes in feeder speed at 80 per cent load (distributed parameter model): solid line - test data; dotted line - model output. Sample time = 3 s

by mill feeder speed and PA flow. By dividing the operating space up into regions and then forming a database of parameter sets for each region a more satisfactory model can be produced. The results of the same experiments shown in Figures 3.4- 3.6 are shown with the distributed models in Figures 3.7-3.9 and are obviously much


90

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Figure 3.9 Step changes in both airflow and feeder speed (distributed parameter model): solid line - test data; dotted line - model output. Sample time = 3 s

improved. The results of Figure 3.9 are particularly interesting since they show the

'choking' behaviour well. Figure 3.10 shows the distributed results for a 'worn mill '

over a range of loads and these are also good.

The modelling results shown in this section are very encouraging. It should

be remembered, however, that to obtain this behaviour requires a lot of plant

Modelling and control o f pulverised fuel coal mills 79

t ~

80

60

50 ~ i 0 200 400

I - - I I - - /

600 800 1000 1200 1400

350

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--- 250

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Figure3.10 Operation with a worn mill (distributed parameter model): solid line - test data; dotted line - model output. Sample time = 3 s

tes t ing and data col lect ion, wh ich mus t be done for good and w o m mil ls , for

all loads and for ch anges in o ther factors such as coal calorif ic value and mois-

ture, and thus requires a large database . In sect ion 3.5 an a l ternat ive approach will be

discussed.


3.4 Mill control

3.4.1 General issues

To understand mill control and all its issues, it is helpful to fit mill control into the broader base of unit control. The unit load controller essentially maintains the balance between thermal power in the boiler, and mechanical-electrical power developed by the turbine generator. Fundamental to this balance is the steam pressure at the inlet to the throttle valves (TVs) or turbine governor valves. There are many ways in which this can be done, but increasingly coordinated or integrated controls are used as shown in Figure 3.11.

In this figure the controlled outputs are steam pressure and MW load, and the controlled variables are fuel flow and TV position. The figure also shows an oxygen controller, since fuel gas composition is strongly linked with furnace behaviour. In operation the unit demand sets the set-point for pressure and power output, either locally (LC) or remotely (RC) from the load dispatch centre, and the control systems do the rest. With most plants now controlled by distributed control systems, it is fairly straightforward to set controller parameter values for stable operation over an acceptable load range. Variations of the controller structure are also possible, e.g. the use of derivative control in the feedforward signals.

There are a number of key issues that must be discussed in relationship to Figure 3.11. The two most important issues are the use of pulverised fuel pf feedback in the fuel control loop and the contents of the milling group box. In practice, it is

F 1 0 2 measurement [ Flue gas

aemana I gen. [ 02 set I [ I r-] [ I / point 02 controller ~

I ~-] Steam pressure Pulverised fuel ] Heat~Bc le-~r ~ measure me r ~ m e ~ sure rr e r tf___q ~ .... Isu',~ I

set-point Bo~l~t~Pl~eSrS. ~ FUce~Air . . . . L._

Gen. MW TV position ~A1 ~ measurement measurement ] nator ]M~

I i

Steam pressure output

output

Figure 3.11 Unit fuel, air and MW controllers and power plant


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Steam pressure Fuel controller controller I I, Feeder 1 Submail

+ 1 - - - - - ] + [ - ~ . 2 control system ( ~ PID ~ - ~ PIPI • 3

Pressure Fuel ~ 4 set-point demand + control ~ 5

signal [ ,~ 6

I [ + _ _ ~ Feeder speed measurement 1

Pressure ~+ ~ +1~

measurement ! 1 I HOtair I controller ~ Hot air [ damper 1

4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

Figure 3.12 Unit fuel control with air/fuel mass/mass submill control

not possible to measure the pf feedback and in addition the milling block is not just one mill but many mills - up to eight for example for a 500 MW unit. This means that depending on load, mills must be switched in and out of service. In addition, since pf flow cannot be measured, it is usual to replace this measurement by the feeder speed measurement of fuel flow. In steady-state operation this is a satisfactory thing to do, but transiently there are significant differences resulting in challenging environmental problems during load change that significantly reduce maximum load change rates. Figure 3.12 shows a more detailed description of the unit fuel control part of Figure 3.11. Note that the fuel demand is for the entire mill group and this has to be split into the fuel demand for each of the individual mills.

The above two issues of fuel flow measurement and multiple mill use are key issues in overall mill control. In addition, there are major mill problems due to the uncertainty in mill input, especially calorific value and wetness. Mill performance is also influenced by mechanical issues like mill wear, mill choking and mill fire. These operator diagnostic issues are of great importance and must be considered in the development of any useful mill control system. In the rest of the chapter we will first consider the control of a single mill in an attempt to improve how an individual mill is actually controlled. The final part of the chapter will then discuss overall mill control and the development of an intelligent operator advisory system.

3.4.2 Control studies on a single mill

As we have seen it is very important that the milling group and hence the individual mills provide the correct amount of fuel, as set by the unit demand. For safe and efficient mill and furnace operation primary and secondary air flow must also be correct. Primary air flow and temperature are significant influences in mill control as we shall now see. Secondary air flow is important in the furnace but does not affect the mill. Its control is usually fairly simple and is done by measuring the air pressure


in the hot air duct to the burners and controlling this by simple feedback to a desired set-point.

The basic idea behind the control of primary air and fuel to the mill, the submill control system, is fairly straightforward and is based on the so-called 'load line' of the mill. This load line is predetermined for a mill and shows the relationship between the air mass flow and the coal mass flow required for the mill to operate in the safe air-fuel range of 1.5-2.5. Note that it is a purely static relationship and most mills are only operated at 40-100 per cent MCR. The minimum air flow is set by the need to establish a satisfactory recirculating load in the mill. The air temperature is set by the requirements of having the coal sufficiently dry in the mill whilst at the same time not having the mill temperature too high and thus risking mill explosion. There are a number of ways of controlling the mill to meet all these requirements with the most usual being so-called mass/mass mill control. The mass/mass mill controller can be operated in either air follow mode or coal follow mode with the basic idea being shown in Figure 3.13 for the air follow mode, which is usually used since it allows fuel to move first. In this mode the overall fuel demand is compared with the fuel being produced as measured by the feeder speed, a PI fuel controller then regulates the feeder speed as required. The box RB is the rnnback controller whose purpose will be described later.

In the PA controller the feeder speed and the computed air flow are compared with the load line in the function generator (FG) and an error signal generated to drive the

Fuel [ Fuel controller demandlb ~ ~ ~ Fuel control

(From pres. I ' " I ] r signal to mills controller) S ~

~ Raw coa l j Fdr. spd.'

Fuel control ~ l J feedback signal ~ " i

oa,

L Fdr. spd. "~ I ~ pf demand J ~ l I ~

pf ~ ~

Other mill fdr. Where: * - PA flow spd. meas. computation

| - Air damper • - Sensors

PA controller

Mill temp. measurement

-O

MILL

PA flow STemp

I

Figure 3.13 Mass~mass control of mill (air follow mode)

Mill temp. , ,f. . set-point

" ~ " • Cold air

Hot air ql


hot air damper thus modulating air flow. Simultaneously the temperature control loop adjusts the cold air input so that the mill outlet temperature remains at its set-point. The mill AP measures the resistance of the mill to the primary air flow and is thus indirectly measuring the amount of coal in the recirculating load in the operating mill. Should this value rise too high then the runback controller (RB) reduces the coal feeder speed to a minimum value securing safe operation of the mill, since a high A P indicates dangerous mill operation (ICAL, 1989). The mill mass/mass controller is simple and reliable and extensively used. Its transient operation is, however, poor since it does not continuously use information about the internal coal storage in the mill, the recirculating load, which is an important factor in dynamic mill control.

The performance of the mass/mass controller can be improved using a method based on the Hardgrove grindability index (ICAL, 1989). This method is shown in Figure 3.14 where the main difference can be seen to be the use of feeder speed as a control variable working on a pressure measurement ratio, as shown, instead of the fuel demand error, which is then used to control hot primary air flow. The pressure ratio is defined as A P divided by A Pair. This pressure ratio is compared to a predetermined constant KRLD, the recirculating load derivative, and if they are equal no change in feeder speed from its normal mass/mass value is used. Any difference, however, will cause a change in feeder speed with an increase in pressure ratio indicating too high a recirculating load and vice versa.

Fuel ] Fuel controller ~ Other mill Where: * - PA flow demand x~ 1 - - 7 Fuel control [ PA flow meas. computation

(From pres~? " ~ signal to mills | I1_ Air damper controller) ~ _ _ ~ ,-Sensors

~ils._.." % w i 7 I ' PAiow[ ~ Fi~ ~°ntr°l

] K I ~ Coalfeeder ~ temp.- ~ _. x / ~ I ~ Mill temp. Fdr. spdN.. - ~ " ~ set point demand , , ] [ , , ] Mill temp. .£-"x. -

2 ~ U U : L I measurement "\ P

t- T / - O RC ~ ~ C ° l d a i r M I L L

- - " I , I .otair I

Figure 3.14 Mill control using Hardgrove index


Based on this idea the feeder speed can either be increased or decreased to take into account the transient effects of the recirculating load. It has been suggested (Fan, 1994; ICAL, 1989) that, provided all the functions required to set up the Hardgrove control loops are known, excellent performance is possible from this control system - indeed it has even been suggested that pulverised fuel mills might, when appropriately tuned, have performance almost matching oil-fired systems. The sensitivity of the controller performance to its parameter values and the cost of setting the system up properly are, however, reasons given by the industry for the low take-up rate of the system.

The results of Figure 3.15 show that for a mill operating alone under mass/mass control or Hardgrove control the performance of the Hardgrove controller is significantly faster. The reasons for this can be seen from Figure 3.15c where the Hardgrove controller has an overshoot in coal on the grinding table following a demand change. Here the airflow measurement A Pair changes instantaneously, modifying the pressure ratio, rapidly resulting in an overshoot in feeder response. The extra coal contains a percentage of fines that are immediately transported to the pf flow. The mass/mass controller by contrast does not produce this extra coal. In section 3.4.3 the performance of the mass/mass controller and the Hardgrove controller, when integrated into the coordinated control of the overall plant, is discussed.

In both the above control systems, no account is made for the dynamics of the primary air response and the coal response. In practice, attempts are made using lag-lead filter networks. The difference in the speed of response causes significant pollution problems during transients, because of the out-of-balance fuel-air ratio. Improved pulveriser control is usually achieved by lagging the PA flow to the load demand change whilst having the feeder speed respond immediately and including a lead feedforward signal from the PA flow measurement (Peet and Leung, 1993). Unfortunately, the lag and lead settings are strongly affected by the operating conditions of the mill, such as load, wear and moisture. It should be noted in passing, however, that the basic problem with the mass/mass control remains, namely, the output of the mills in the form o fp f flow or energy is not measured.

3.4.3 Mill control using p f flow

Many attempts have been made to develop suitable instruments for on-line measurement of pf flow (Maffezzoni, 1986) most of which have not been satisfactory. More success has been achieved by inferential methods, usually based on Kalman filter- ing using mill models, and a number of these are working on-line (Waddington and Maples, 1987). All on-line experiments or simulation studies seem to show significant improvement in the mill control provided that good estimates of the pf flow can be determined. This of course is not surprising since the pf flow is now controlled directly by feedback.

The essence of the idea is to set up a linear dynamic model of the boiler turbine and mills such that the pf flow and other important states of the mill are observable from available measurements. The model can be obtained either by linearising a dynamic model of the system (Fan, 1994), if one is available, or on a more ad hoc basis (Clarke et al., 1989). Parameters of the model can then be fitted to

0.8 e-

'~ 0.6

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~ 15 E 10 .r~

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5 100 150 200

I I I

50 100 150 200

"~ 10

~ 5

0 d 0 200

i i i

I I I

50 100 150

Figure 3.15 Simulation results for mill control following fuel change: solid line - Hardgrove; dotted line - mass~mass. Sample time = 20 s

plant data with process and measurement noise parameters being particularly important. Standard Kalman filter (KF) programs can then be run to determine the filter gains. The whole process is non-trivial, requiring skilful setting up and plant testing if robust estimates are to be available. Properly set up, however, the KF filter feedback system produces a time leading fuel estimation signal that can provide significant


Pressure measurements Fuel

estimation Kalman 14 ] filter .~

^1 ~ u - fuel control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . _x I . . . . . . . . . . . . . . . ~__ _ s!gna} . . . . . . . . . . . . . . . . . . . . .

Pressure | [ measurement l [

~ ~ PID pressure ] . ~ . ~ ~ ~ ' ] controller [ " J "[ controller [ "l ....... [ " ] G

demand ' - - ' I

~ PI power ~ o ~" controller reheater I ] r

dynamics ~ J ',

Power output Derived TV measurement position Steam/energy i

flow

Figure 3.16 Matlab/Simulink ® simulation of mill and power plant with mill control using estimated p f flow

improvement. The process is well described by Clarke et al. (1989) and Fan (1994), where particular emphasis is placed on obtaining good low-order models. Kalman filter estimators have been operating successfully in a number of power stations in the UK since the 1980s (Clarke et al., 1989; Waddington, 1994; Waddington and Maples, 1987).

To get some idea of the performance of the pf estimated controller the system of Figure 3.16 was simulated in Matlab/Simulink ® (Fan, 1994). It should be noted that the simulation contains the boiler turbine systems and the pressure and power output controllers, as well as the mill and its controllers. The results shown in Figure 3.17 show the performance of the mass/mass controller under feeder speed feedback and pf feedback for a step increase in power demand at 10 samples. From Figure 3.17a and b the throttle valve pressure and generator output responses are faster using feedback of the estimated pf due to the observed faster change in pf flow (Figure 3.17d). The responses show some oscillation but this is not serious. Even better performance of the pf feedback is shown in Figure 3.18 where a 20 per cent disturbance in the fuel input energy has occurred at 50 samples. The figure indicates that the pf controller keeps much tighter control of the generated power output and this is very significant since the stability to such unknown disturbances is very important.

In Figure 3.19 simulation results are shown of the mass/mass controller with estimated pf feedback compared with the system under Hardgrove control. Figure 3.19a shows that the power output response of the Hardgrove controller is almost twice as fast. To achieve this response, however, costly, fast actuators are needed on the


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I I

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30

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16

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Figure3.17 Plant control using feeder speed and estimated pf flow feedback: solid line - fuel estimation feedback; dotted line - feeder speed feedback. Sample time = 1 s

control feeder and precise knowledge is required about the mill, such as the relation-

ship between mill pressure and mass flow. These relationships are usually difficult to obtain and must be determined regularly for each mill, and even slight model l ing errors dramatically affect performance. By contrast a properly tuned mass/mass controller

8 8 Thermal power plant simulation and control

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Figure3.18 System response to fuel step disturbance: solid line - fuel estimation feedback; dotted line -feeder speed feedback. Sample time = 10 s

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I I I I

1 O0 200 300 400 500

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i i I

I I I

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Simulation results of system with Hardgrove and KF feedback: solid line - Hardgrove feedback; dotted line - Kalman filter feedback. Sample time = 2 s


Fuel demand ~ stimated pulverised fuel

J , ~ Kalman ~--

Dynamic Dynamic / non-linear / ,compensator[ , compensatorl F-~ estlmat°r ~

1 J. @ E s t i m a t e d ~ mill level

~ " FIlial Speal I

Feeder speed Primary air demand demand

o Feeder speed demand

- - - o Primary air demand

Figure 3.20 Mill control with estimator dynamic compensation and limits

with pf feedback performs acceptably well, is easy to set up, very robust, and does not require high-performance actuators. Hardgrove control is therefore not very often used in industry whilst mass/mass controllers with pf feedback are becoming more popular (Waddington and Maples, 1987).

The tuning of the mill control systems shown in Figures 3.11 and 3.12 has been carried out using simulation, plant knowledge and control experience. Most of the controllers are PI or PID controllers, which are part of a complex multivariable system that includes pressure control, electrical power and throttle valve control as well as local submill controllers. The procedure used is to tune the inner loops first, then the subsystem loops and finally add in feedforward compensation. The method has proved quite quick and satisfactory. The controller settings also vary with the mode of control, e.g. boiler or turbine following, so this must also be considered. The control system developed in this section is outlined in Figure 3.20. It is similar to the scheme of Maffezzoni (1986), which includes a special instrument for measuring pf flow as against the use of a soft sensor here. Note also the inclusion of limits in the control valves/dampers that are a function of mill level.

3.4.4 Advanced multivariable and predictive control

The control described in section 3.4.3 is essentially SISO control with ad hoc proce- dures used in the design. Mills, besides being highly non-linear, are also multivariable. In this section we examine the use of multivariable control using linear quadratic (LQ) and predictive control techniques.

Rees and Mee (1973) describe a very simple study of mill control using LQ techniques. The resulting control scheme decoupled the two major control loops and added dynamic LQ designed compensators. Major studies using LQ control were


carried out in the UK in the 1980s resulting in a number of power stations adopting LQ methods on-line (Clarke et al., 1989; Waddington, 1994; Waddington and Maples, 1987). Significant improvements in mill control were shown.

In more recent times there has been some attempt made to control mills using predictive control with quite interesting results (O' Kelly, 1997; Palizban et al., 1995; Rees, 1997). In the simulation study described by O'Kelly the model used is similar to the model of section 3.2. Hard non-linearities are placed on both state and control variables with tests driving the plant over the whole non-linear operating region. A fairly simple receding horizon predictive controller forms the basis of the control and is implemented in Simulink on a 486 platform.

Figure 3.21 shows the response of the mill during start-up. In developing the responses it is assumed that the mill model used by the controller acceptable. The simulation assumes that mill wanning starts at t = 0 and at 20 minutes the feeder is started at its minimum speed. At 30 mins mill loading commences at around 10 per cent rated flow per minute until the mill reaches its operating condition. After 90 mins the results show the mill responding to fast ramp changes.

The results of Figure 3.21 show that the predictive controller has excellent set- point tracking control even though the plant has strong interactions and non-linearity, and the controls and their rate of change are bounded. In the start-up test shown, the performance of the controls is superior to the current control. Robustness studies have shown that the controller is not sensitive to quite large modelling errors and will respond well provided that the linear model response is regularly updated and the general direction of the model response is correct. The controller does not require excessive computing performance and is capable of being implemented on most modem DCSs. Advanced techniques of mill control using fuzzy logic and neural network concepts have also been tried in simulation with promising results (Cai et al., 1997; Cao and Rees, 1995).

Mill dP pf flow Control flows (kPa) (kg/s) (kg/s)

15 30

10 1

\6

0 50 100 150 Time in min

20

10

0 0 50 100 150

Time in min l -p f f low 2-Mill dP 3-Hot airflow 4-Cold airflow 5 Feeder speed 6-pff low set-point 7 Mill dP set-point

Figure3.21 Start-up of mill - input~output results


3.5 Intelligent control and operator advisory systems

In the work that has been described so far, it has been shown that it is possible to develop non-linear dynamic models of a vertical spindle mill and to use these models to better understand the mill and design improved control systems. This is especially true for normal operation of the mill where the model is a satisfactory global predictor of plant behaviour. Mills, however, are also subject to regular non-normal changes caused by such factors as roller wear, coal grindability and calorific value changes, moisture content, mill blockage and the like, and these major events which are not modelled currently require experienced operator intervention or mill shut-down. It might therefore be expected that any successful advanced mill control system would be able to handle all these conditions. This can only be done, however, by combining mill controllers with some type of knowledge-based system to take into account the critical events that have just been described. To the authors' knowledge, no such system exists for coal mills, although some expert systems have been used for power plant control (Majanne et al., 1991). In this section, we try to show what could be done by listing some work from our own experience, mainly simulation studies, but carried out in collaboration with the local power industry. The motivation for doing such work is quite strong, since it can be estimated that substantial savings can be made from such factors as fewer mill fires, fewer mill runbacks, automatic operation of the mill over a wider range, optimal mill operation, and rapid diagnoses of mill faults.

An intelligent control and advisory system (ICOAS) adds considerable expertise to the existing control system. It can be developed either as part of the existing DCS or as a stand-alone system. Its two major features are the intelligent operator advisory system and its associated alarms (IOAS), and the hierarchical supervisory control (HSC). The IOAS performs quick and early diagnostics of plant faults and possible causes and it also gives reasoning behind the alarms and recommended operator actions. A 'history' feature allows this information to be stored for future use and operator training. The HSC integrates the existing controls with plant operational knowledge and operation. It can also supply limits to controlled process variables to ensure mill stability under all operating conditions.

An important feature of ICOAS is its use as a 'soft sensor', using either the process model or a Kalman filter. These estimates can then be used to improve the IOAS and to create useful indices for features such as mill wear, the effect of excessive moisture and other operational issues not included in the dynamic models.

Included with the ICOAS is an additional advanced graphical user interface (GUI), which displays all the additional information developed above in a form compatible with existing plant graphics.

3.5.1 Knowledge-based operator support system

The ICOAS system just described can be extremely complex and there are many problems in modelling, expert control and the like, to be overcome. A prototype ICOAS


Control signals ~-] Boiler/mill I Boiler/mil~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~ . . . . . . . . . . . . . . . . . . . _~_ . . . . . . . . . outp_ut_ . . . .

! I Dataacqnisiti°n I I Measurement [ o , ~, ~, w

11 [ Pre-data processing I [ Pre-data processing ] I 'I' i , o

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A S 1~[ Knowledgebase [ [ Fault detection [ D E , @ o

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, I Data processing

T o Fault detection R

~' Inference machine (B) I ~, S I Dataprocess ng * ,._--t___., [ Data log ~' i u p

I Data log ] Process history O ~, i' NO database T

Process history database S Inference machine (C) I Control room y screen s

I Control room Control room screen screen & alarm I M E

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Figure 3.22 Structure of KBOSS

has been developed by the University of New South Wales in a collaborative project with the utility Pacific Power International (PPI). The system known as Knowledge- Based Operator Support System (KBOSS) has been installed in stand-alone mode in a small Bailey Infi90 DCS (Fan and Rees, 1997). It has been tested, for a limited number of plant faults, including mill wear, mill choking and mill optimisation on a Matlab/Simulink ® boiler, turbine and mill simulation and also partially tested on a 500 MW power plant. The essential features of KBOSS are shown in Figure 3.22.

The scope of KBOSS essentially covers the IOAS part of the ICOAS system. The KBOSS rule base has been developed to recognise 15 faults or operational conditions covered by approximately 50 rules. The rule sets have been developed to provide a range of examples or scenarios including plant faults (feeder blockage, worn mills), operational problems (mill choking, mill moisture) and supervisory control (auto mill load sharing).

Rule development has been achieved by surveying the operational literature, talk- ing with plant experts, carrying out mill tests and reading training and maintenance literature. Direct experience of faults has also been included. To extend the work to cover all major faults, a more formal knowledge-base development process is needed, as described by AI-Dabbagh et al. (1993) and Parker (2003). The system described


in Figure 3.22 has been developed using a reference model combined with a fuzzy logic/pure rule base inference mechanism. For normal process behaviour the model matches the plant behaviour and no advice needs to be presented to the operator. However, when there is a system mismatch, the knowledge base is searched and appropriate advice given to the operator for action. The searching mechanism used in the knowledge base is multilayered with a branch tree structure. This means that the complete knowledge base need not be searched for all situations, thus increasing search efficiency and reducing the computational burden.

A key feature of the KBOSS system is the existence of a good reference model. In section 3.3 it has been shown that this requires a large database and this entails extensive plant experimentation. Furthermore, each time the worn mill rolls are replaced (approximately 6,000-10,000 hrs) a completely new model of the mill must be established. To avoid this problem, KBOSS uses a special adaptive control system that continuously computes local dynamic models. Snapshots of these models are then stored in the database as the reference models. These snapshots will only need to be changed when future plant behaviour indicates significant differences between the database reference model and the latest dynamic model. A point of significant interest is that the adaptive model can be used very effectively by any advanced model-based controller such as those described in section 3.4.4.

The above system seems to work quite well in its limited task for both simulation and plant studies as the two examples in the next section indicate.

3.5.1.1 Mill runback and KBOSS control

The mill runback controller described in section 3.4 is essentially a switch which detects high mill load defined by a specified value of high mill AP (4.5 kPa). Whilst it is certainly the case that the mill should be shut down to minimum load if the high AP is due to mill overload, high AP can be caused by other factors which do not require such action. Since shutting down to minimum load is an operational loss and under certain circumstances can result in mill instability, there is a considerable incentive to be sure that runback is absolutely necessary.

In Figure 3.23, the high mill A P has been caused not by load, but by high moisture content in the raw coal and reduced mill grindability. This can be determined by KBOSS using not just a mill AP measurement but also mill power, mill level and other soft sensed mill conditions, together with a set of rules. Once this possibility has been recognised, the operating condition can be alleviated without running the plant down.

Figure 3.23 shows that when using the runback controller, the plant is run back to base load when 4.5 kPa is reached. However using KBOSS stops AP rising above 4.5 kPa so that the mill can continue running.

3.5.1.2 Optimal grinding control

Mills consume large amounts of power so it makes sense to try to optimise the coal grinding process. Experiments show that there is a best depth for the most efficient coal grinding and that this depth can be related to mill power and the soft sensed mill


3O

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~ 2o

~ 15 E

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--~ lO

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500

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0 I I I I I

d 0 100 200 300 400 500 600

Figure3.23 Simulation results for mill controlled by expert system: solid line - KBOSS; dotted line - runback. Sample time = 10 s

9 6 Thermal power plant simulation and control

500

~ ' 4 0 0

300

200

100

F

I i i i i

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3O

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_

i I I i I

lO0 200 300 400 500 600

i i i i i

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Simulation results f o r mill with optimised grinding solid line - KBOSS; dotted line - mass~mass. Sample time = lOs


level through a set of knowledge-based rules. The results of Figure 3.24 show that an expert system can hold the mill power in a tight optimum range (350-400 kW) as shown in Figure 3.24a. Note that in Figure 3.24d the mill control keeps AP just below its critical value.

3.6 Conclusions

In this chapter we have looked at some problems associated with the control of vertical spindle coal mills. It is now well recognised that all the various types of coal mills associated with power plant have difficult control problems and often restrict plant take-up of load or cause plant shut-down. The chapter shows that, contrary to conventional wisdom, it is possible to develop fairly simple models of coal mills which can be used to obtain better performance. The chapter develops a vertical spindle mill model to better understand mill control. This can be done by estimating pf flow from the mill and by evaluating the internal mill recirculating loads. This information can also be used as part of an intelligent control system to improve operator performance and the analysis of mill alarms. The results given in the chapter are largely the outcome of simulation studies. Limited studies have however been carried out on a 500 MW plant in a collaborative project with PPI. These studies also show promising results indicating that mill control is a fruitful area for research and development.

3.7 Acknowledgements

The authors wish to acknowledge the support given to this project by the former Australian Electrical Research Board and by Pacific Power International. The latter support was made possible by Mr Don Parker of PPI whose knowledge and enthusiasm were a great help. Dr Michael Cheng must also be thanked for his work on the simulation and site tests towards the end of the project.

3.8 References

AL-DABBAGH, M., CHEN, D., MOORTHY, S., and ACHORN, E.: 'The development of an intelligent alarm processor - an alternative approach'. Proceedings of the Australian Universities Power Engineering Conference, Wollongong, Australia, 1993, pp. 361-367

BOLLINGER, K. E., and SNOWDEN, H. R.: 'The experimental determination of coal mills', IEEE Transactions on Power Apparatus and Systems, 1983, 102, (6), pp. 1473-1477

CAI, J., LI, Z., and WANG, P.: 'Fuzzy control of a ball mill for the pulverizing system of a thermal power plant'. Proceedings of the IFAC/CIGRE Symposium on ControlofPowerSystems and Power Plants, Beijing, China, 1997, pp. 214-218


CAO, S. G., and REES, N. W.: 'Fuzzy logic control of vertical spindle mills'. Pro- ceedings of the IFAC Symposium on Control of Power Plants and Power Systems, Cancun, Mexico, 1995, pp. 49-55

CLARKE, R., WADDINGTON, J., and WALLACE, J. N.: 'The application of Kalman filters to load/pressure control of coal fired boilers', lEE Colloquium, 1989, Digest No. 1989/27 pp. 2/1-2/6

CORTI, L., DEMARCO, A., RADICE, A., and ZIZZO, A.: 'Control and modelling of coal mills'. Proceedings of the IFAC Symposium on Power Plants, Pittsburgh, 1986, pp. 92-97

DOLEZAL, R., and VARCOP, L.: 'Process dynamics' (Barking Elsevier, New York, 1970)

FAN, G. Q.: 'Modeling and control of vertical spindle mills in power plant'. ME thesis, University of New South Wales, 1994

FAN, G. Q., and REES, N. W.: 'Modelling of vertical spindle mills in power plant'. Proceedings of the Electrical Engineering Congress, Sydney, 1994, 1, pp. 235-240

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