Method of controlling a rotary kiln during start-up

To facilitate a smooth and quick start-up of a rotary kiln, a Fuzzy Logic Control system is proposed, the principal feature of which is to mandate control actions to decrease the specific heat consumption along a straight line from an initial high value to a steady-state value established for the kiln, and then to switch over to an existing steady-state fuzzy logic control mode. In the start-up phase, further process variables are preferably measured and monitored against reference values to mandate concurrent control actions influencing the Fuzzy Logic Control system in a weighted manner.

FIELD OF THE INVENTION 
This invention relates to a method of controlling a rotary kiln during 
start-up after a period of stoppage, and more particularly the invention 
has to do with the development of automatic procedures for facilitating a 
smooth and quick start-up. 
BACKGROUND OF THE INVENTION 
Automatic computer control of kiln systems has been a challenge for control 
engineers in the cement industry ever since the appearance of process 
computer systems in the mid-sixties. The first attempts were based on the 
application of multivariable control theory together with mathematical 
models of the kiln systems. The results were rather disappointing and the 
systems never matured to a state, where they became commercially 
available. 
Starting in the mid-seventies a different approach to computer control of 
cement kilns was pioneered and became known as the FLS-SDR/Fuzzy computer 
control system. This is described in a publication FLS-review 67, which is 
a reprint of an article entitled "Control of a cement kiln by fuzzy logic" 
by L. P. Holmblad and J.-J. Ostergaard, published by North-Holland 
Publishing Company, 1982. Briefly, based on the concept of Fuzzy Logic the 
control strategy was formulated as a set of linquistic control rules 
transferring the experience of human kiln operators to an automatic 
computer control program. The FLS-SDR/Fuzzy computer control system has 
been marketed since 1980 and has today been installed on a considerable 
number of kilns. These systems have clearly demonstrated that the Expert 
System approach embedded in the Fuzzy control concept is capable of 
efficiently controlling different kiln systems, and on-line control 
figures of 85-90% are usually achievable. 
However, up until now automatic control has been applied in more or less 
stead-state operation only after manual start-up of the kiln. The 
controller usually takes over when production has reached 70-100% of 
normal load, depending on the duration of the kiln stop; the shorter the 
stop, the earlier the controller can usually be switched on. 
SUMMARY OF THE INVENTION 
It is the object of the present invention to extend the use of automatic 
procedures to the start-up phase, and this is achieved by the features 
recited in claim 1. When using the method there defined, it has been found 
possible to switch on the automatic control means immediately after kiln 
raw material feed has been turned on at 30-35% of nominal kiln load. From 
there, the controller will automatically bring up the kiln to normal 
production level by gradually increasing in a co-ordinated way kiln feed, 
kiln speed, fuel, air to the kiln system and cooler grate speed if a 
cooler grate is included. When normal production level has been reached, 
the control is automatically switched to steady-state control mode. Like 
the control scheme for steady-state kiln operation, the start-up control 
strategy is formulated as a set of control rules using Fuzzy Logic as an 
inference method, but the rule set as well as control parameters are 
especially designed for the start-up purpose. 
FUZZY CONTROL CONCEPT 
Before proceeding to a detailed description of the start-up control 
strategy, a brief outline will be given of the basic principles of the 
SDR/Fuzzy control concept. 
In many industrial processes, the problems of limited number of measurable 
variables, of unreliable measurements, of time delays and of 
non-linearities have resulted in a low level of automation. This 
characterization certainly applies to the cement industry. Cement 
manufacturing is a process that is complex and difficult to automate and 
the control functions--especially on the supervisory and coordinative 
level--is to a great extent left to the skill and experience of human 
operators. 
Fuzzy Control, the application of Fuzzy Logic techniques to automatic 
control, offers an interesting and attractive alternative in these cases. 
For a better understanding of the concept of Fuzzy Logic Control and its 
application, reference is made to the literature listed at the end of this 
description, which includes the FLS-review 67 method above. 
The principles of fuzzy logic control, as applied to steady stage operation 
of a rotary kiln, will in the following be described by quotation of parts 
of said FLS-review with reference to FIGS. 1-5 corresponding to FIGS. 2, 
3, 6, 7 and 8 therein, viz.

In the quotation to follow, the references to figures have been changed in 
accordance with the above mentioned re-numbering. 
2. FUZZY CONTROL PRINCIPLES. 
2.1 Linguistic Control Rules. 
The application of fuzzy logic techniques to control is tied together with 
the concept of linguistic control rules. A fuzzy controller consists of a 
set of control rules, each rule being a linguistic statement about control 
actions to be taken for a given process condition: 
if (condition) then (control actions). 
Statements on conditions could go like "the liter weight is high", or "the 
back end temperature is somewhat low". Likewise, statements on control 
action might for example read "make a medium reduction in coal feed rate", 
or "open slightly the exhaust gas fan damper". 
The key items in the control rules are terms like "medium reduction", "open 
slightly", "high" and "somewhat low". In linguistic approximation by fuzzy 
logic each of these terms is represented by a unique fuzzy membership 
function that for a given process condition is used to establish a value 
in the interval (0,1). Hence, the logic value of a condition, which is 
ordinary binary logic is restricted to "true" and "false" (0 or 1), in 
fuzzy logic can take any value in the interval (0,1), the logic value 
being a measure of the fulfillment of the condition for a given process 
state. 
To illustrate how fuzzy logic is applied to expressions as "if oxygen 
percentage is high . . . " and "if oxygen percentage is OK . . . ", FIGS. 
1 and 2 illustrate graphically the difference between classic 1/0 logic 
(true/false logic) and fuzzy logic. 
In classic logic, FIG. 1, a given process value in the interval concerned, 
in this case 0-5% O.sub.2, would be either LOW, OK, or HIGH. In fuzzy 
logic, FIG. 2, the terms LOW, OK, HIGH, etc. are represented as "soft" 
curves, as opposed to the block curves of classic logic, and they show a 
given process value in principle as LOW and OK and HIGH in various degrees 
between 0 and 1. The idea behind the "soft" definition curves is to 
represent as realistically as possible the gradual transition between the 
human conception of values being e.g. "LOW" and not "LOW", and to avoid 
the sudden jumps at definite values. 
2.2 A Fuzzy Controller. 
The following example serves to illustrate how a simple controller is 
formulated and handled in fuzzy logic. In the example, the surplus oxygen 
in a burning process, e.g. a cement kiln, is assumed to be controlled via 
fuel rate regulation. The air flow is constant, and when the oxygen 
percentage is too high or too low, the fuel rate is adjusted accordingly. 
It must be emphasized, however, that the problems involved are not quite 
that simple in the actual operation of a cement kiln and are only used 
here to explain the fuzzy control principle. 
FIG. 3 shows a computer program for fuzzy logic control formulated in the 
language developed at F. L. Smidth for computerized process control. There 
are three control rules in question, lines 8-10, which indicate how the 
coal feed rate is to be adjusted (DCOAL) in relation to oxygen percentage 
(02). The INPUT line defines from where the measurement of the actual 
oxygen percentage is available (point label W1W01X1 in the measurement 
data base), and specifies the constants for scaling the oxygen percentage. 
The example shows 1.6% O.sub.2 as normal value, while 0.7% and 3.0% are 
the scale limits of the low and high oxygen percentages, respectively. 
Note that the scaling interval is not necessarily symmetrical with respect 
to the normal value. The OUTPUT line defines correspondingly where the 
calculated change in fuel rate is intended (data base point label 
W1V19SP), also indicating the scale factor for control actions, i.e. the 
physical value regarded as the upper limit of an individual action, in 
this case a change of 0.5 t/h. Section 3 will deal with the programming 
language in more detail. 
FIG. 4 illustrates graphically the control rules and the calculation 
sequence at a measured oxygen percentage of 1.21%. The first rule IF LOW 
(02) THEN MNEG (DCOAL) indicates that a medium negative change in coal 
feed rate is required. The oxygen percentage 1.21% is in the chosen 
interval only LOW In the degree 0.42 which is why the MNEG action is not 
to be effected fully but dampened accordingly. The shaded MNEG area is 
thus regarded as the contribution of the first rule to the resulting 
control action which is arrived at by combining this contribution with 
those of the subsequent rules. In the next rule, IF OK(02) THEN ZERO 
(DCOAL), 1.21% oxygen is OK In the degree 0.99, so the ZERO action is 
taken with almost full weight. The MPOS action of the last rule has no 
effect as 1.21% oxygen is HIGH in the degree 0.0 1.21% oxygen is thus OK 
with a tendency toward LOW, and the fuzzy control action will be ZERO to 
MNEG. 
FIG. 5 shows how the physical control action is determined. The area 
contributions from individual rules are collected, and the control action 
is determined as the abscissa value dividing the combined area into two 
equal parts (A1=A2). In the case in question, DCOAL=-0.105 t/h. 1.21% 
oxygen hence results in a reduction of the coal feed by 0.105 t/h. 
It will be seen that in principle all three rules influence the resultant 
control action according to the degree of fulfillment. Even though the 
control strategy only consists of three apparently rough and inaccurate 
control instructions, the fuzzy logic technique implies assessment of the 
degrees to which the conditions in question are met and then weighs up the 
pros and cons so that the resultant action becomes a "reasonable" 
compromise between the sayings of the individual rules. 
It has for a long time been recognized that control of the kiln start-up 
had to be addressed differently from control of the kiln in normal 
operation. Also, it has been argued that automatic kiln start-up perhaps 
was not so interesting as control of steady-state operation, considering 
that start-up periods hopefully would be very short compared to the 
periods of normal operation. 
However, it remains a fact that kiln start-up constitutes a period of 
considerable risk for damage to the kiln, requiring a lot of operator 
attention, and that a smooth and quick start-up may save substantially in 
terms of equipment failure and production loss. 
DESCRIPTION OF A PREFERRED EMBODIMENT 
FIG. 1 illustrates a rotary kiln of conventional type for producing cement 
clinker by heating a mixture of limestone, clay and sand components. The 
kiln is a long steel tube 101, which is mounted at a slight inclination 
from horizontal and is rotated by a kiln drive 102 at a speed of 1-2 
rev/min. The clinker production is a continuous process. A slurry of the 
raw materials is fed at 103 through a system of cyclones 104 to the upper 
end 105--or back end--of the kiln, while fuel, e.g. coal dust, is fed at 
106 to a burner at the lower--or firing--end 107 of the kiln. Due to the 
inclination and rotation of the kiln tube 101, the material is transported 
slowly through the kiln in 3-4 hours and heated in countercurrent with the 
hot combustion gases from the burner, the combustion gases being drawn 
through the kiln and the cyclones 104 by an exhaust gas fan 108 that may 
be provided with a control damper (not shown). In the cyclones 104, the 
exhaust gases flow in countercurrent to the raw materials, and the 
cyclones therefore serve as preheaters for the raw materials. 
For controlling the kiln, a so-called fuzzy controller 109 is provided, in 
which fuzzy logic rule sets and the required programs for fuzzy logic 
evaluation, as represented in FIG. 2, for fuzzy logic determination of 
control action signals, as represented in FIG. 4, and for fuzzy logic 
weighted averaging of control action signals, as represented in FIG. 6, 
are stored. Preferably, the fuzzy logic controller incorporates modes for 
both the automatic start-up phase and for steady state operation, and 
forms part of a process computer system incorporating also data 
acquisition, reporting and supervision functions. 
Input connections to the fuzzy controller 109 from measuring locations for 
process variables are shown as follows: 
201: Temperature after the preheater system. 
202: 02 after the preheater system. 
203: CO after the preheater system. 
204: Temperature in the lowermost cyclone. 
205: 02 at the kiln inlet (back-end). 
206: CO at the kiln inlet (back-end). 
207: NO at the kiln inlet (back-end). 
208: Kiln drive torque. 
209: Clinker quality (liter weight and/or free lime, usually manual 
measurement). 
Output connections from the fuzzy controller 109 to kiln controls are shown 
as follows: 
301: Kiln feed. 
302: Kiln speed. 
303: Exhaust gas fan speed. 
304: Fuel to kiln. 
In the following is outlined the basic ideas behind a kiln start-up 
strategy that was tested out on a 3000 tpdy 4 stage preheater kiln 1 of 
the type described that had previously been equipped with a fuzzy 
controller 109 capable of controlling the kiln during normal operation. 
The objectives of the kiln start-up strategy was to be able to control the 
kiln automatically from the time that feed was applied to the kiln at 103. 
Initially, the operator will have preheated the kiln to reach the proper 
temperature conditions of the system. For very short kiln stops, this 
period is virtually nil, but after longer kiln stops, e.g. with 
re-bricking or extensive repair jobs, the pre-heating period may be quite 
long up to 30 hours. 
During the automatic start-up procedure, the computer gradually increases 
fuel, speed, raw material feed and air to the kiln at 106, 102, 103 and 
108, respectively, in order to reach the normal operating levels, at which 
stage the controller automatically switches on the steady-stable control 
mode. The basic principle for the timing of increasing these values is to 
let the specific heat consumption (kcal/kg clinker) follow a straight line 
from a high initial value to a finishing value equal to a value at which 
the kiln normally operates. The initial value is determined in relation to 
the duration of the preceeding kiln stop. The actual determination of the 
stop duration will be further described below. In the present case, the 
initial value was set at 1100 kcal/kg for a "stop" of 0 minutes, up to a 
value of 1500 kcal/kg for a "stop" of 3 hours or more as illustrated by 
the graph in FIG. 7. The finishing value is continuously "remembered" by 
the system from normal operation. 
The length of the start-up period is then determined from the allowed fuel 
change/min, a control parameter set as part of the tuning of the control 
strategy for the kiln. This value together with the initial kcal/kg 
clinker determines the slope of the ramp that constitutes the driving 
force in the start-up period, as will be further explained later with 
reference to FIGS. 8a and 8b. 
However, a number of other measurements are also monitored during start-up 
to ensure stable kiln operation. These were primarily the kiln drive 
torque, at 208, O.sub.2, NO and CO after the preheater, at 205, and the 
temperatures at selected points in the preheater, foremost in the 4th 
stage cyclone of the latter at 204 and after the preheater at 201. The 
measurements are monitored against ramping reference values determined by 
measured initial values and previously "learned" final values. During 
start-up, deviations between actual measured values and the reference 
values will influence the control actions. Control actions are exercised 
on fuel to the kiln at 106, on exhaust gas fan dampers at 108, and on kiln 
speed at 102. During the entire start-up, kiln feed and kiln speed remains 
synchronized to maintain constant degree of material filling in the kiln. 
The stop-time of the kiln used to determine the starting value of the 
kcal/kg set-point ramp is determined as the time interval selected 
temperatures in the preheater have been below certain values. The kiln in 
question was considered "stopped" when the temperature in the 4th cyclone 
dropped below 815.degree. C., and "started" only when the temperature 
again got over this value. By this, it is seen that the stop-time is not 
merely the outage time of the kiln motors, but more a measure of the time 
the kiln has lacked sufficient heat input. 
The entire start-up control strategy is written in the FCL language part of 
the SDR/Fuzzy system, resulting in about 50% extra program lines than used 
in case of only normal operation control. 
Referring now to the FIGS. 8a and 8b, these are a diagrammatic illustration 
of the steps described above and are believed to be substantially 
self-explanatory. For identification purposes, the boxes illustrating the 
various steps are numbered, but the numbers do not necessarily indicate 
the succession of the steps. In fact, e.g. the values mentioned in boxes 
3, 5 and 10 may be more or less permanently stored, and the stoppage time 
mentioned in box 1 will be available from the general survey system of the 
kiln. 
After the stoppage time has been determined in box 1, the initial value of 
the specific heat consumption, SHCinit, is determined in box 2 by means of 
the graph in FIG. 7. Thus, for a stoppage time of 2 hours, SHCinit will be 
set at the value marked in FIG. 7. The function illustrated by the graph 
will have to be established once and for all (subject of course to 
adjustment) for a particular kiln from experience, testing and 
verification. 
In box 4, the slope of SHC against time is now determined from the maximum 
increase of fuel per minute, illustrated as being stored in box 3. 
It is thereafter possible in box 6 to calculate the envisaged end time t2 
of the start-up period, viz. the time when the loping SHC graph, starting 
from the value SHCinit at the time t1 of switching on the fuzzy logic 
controller (from box 8), will assume the steady-state value SHCsteady, 
illustrated as being stored in box 5. It will be realized that SHCsteady 
is of course known from normal operation of the kiln. 
In box 7, a ramping reference function of SHC against time from t1 to t2 is 
established. This function is illustrated by the graph in FIG. 9, which is 
of course in fact the same as that which was used for the determination in 
box 6. 
Quite similarly, ramping reference functions for other selected process 
variables SPV are established in box 11 from measured values at the time 
t1 (box 9) and stored steady-state values (box 10) at the time t2. 
The actual instantaneous values of SHC and SPV are measured in box 13, and 
both these and the ramping reference functions in boxes 7 and 11 are 
sampled by recurring pulses from box 12. 
In box 14, the difference between the actual instantaneous value and the 
instantaneous reference value of SHC and each SPV is determined. This 
step, like all previous steps, is executed by conventional mathematical 
calculation, such as will now be explained with reference to FIG. 9. 
FIG. 9 shows that at the starting time t1 of the automatic start-up phase, 
the reference value of SHC is SHCinit, and that at the end of that phase, 
the reference value of SHC is SHCsteady. Let us consider the situation 
where one of the recurrent sampling pulses is supplied from box 12 at the 
time t. A simple proportionality calculation will show that at the time t, 
the reference value of SHC is 
##EQU1## 
If at the same time t, the value of SHC measured in box 13 is SHCmeas, the 
determination of the difference in box 14 is: 
EQU SHCdiff=SHCmeas=SHCref, 
as also illustrated in FIG. 9. 
The further step in box 14 is the fuzzy logic evaluation of SHCdiff. This 
is the step illustrated in prior art FIG. 2, with the difference, however, 
that here it is applied to a mathematically calculated difference value, 
and not to a directly measured value. It must be determined, once and for 
all, from expert knowledge and experience, which values of SHCdiff are to 
be considered HIGH, SMALL POSITIVE (SPOS), SMALL NEGATIVE (SNEG), LOW, 
etc. The operation illustrated in FIG. 2 will then determine to which 
degree (between 0 and 1) a given value of SHCdiff fulfills the various 
terms. 
In box 15, the evaluation results from box 14 are to be applied to fuzzy 
logic rule sets. These, again, have to be established from expert 
knowledge and read into the computer for permanent storage therein. A rule 
set relating to coal adjustment during start-up from SHCreference ramp 
might read: 
IF HIGH (SHCdiff) THEN SNEG (DCOAL) 
IF SPOS (SHCdiff) THEN ZERO (DCOAL) 
IF ZERO (SHCdiff) THEN ZPOS (DCOAL) 
IF SNEG (SHCdiff) THEN SPOS (DCOAL) 
IF LOW (SHCdiff) THEN MPOS (DCOAL) 
where DCOAL means change of coal feed per minute, ZPOS=zero 
positive=slightly positive, MPOS=medium positive, and the other 
expressions have been explained earlier (or are self-evident). 
There will be a considerable number of fuzzy logic rule sets some of which 
may involve one or two SPVdiff's, or SHCdiff and one SPVdiff and may 
mandate commands to one or two kiln controls. 
In box 16, the resulting commands mandated by the rule sets in box 15 are 
to be applied to a fuzzy logic weighted averager. This is the operation 
illustrated in prior art FIG. 4. The operation is obviously performed 
separately for mandated commands to separate kiln controls. 
In box 17, the resulting command signals are applied to the respective kiln 
controls, and then in box 18, the next sampling pulse is awaited. 
Generally speaking, the electric and electronic equipment, circuits and 
storage facilities used for carrying out steps 1-20 must not necessarily 
be specially provided for the starting-up phase, but may in part be 
implemented in that used for steady state fuzzy control or general survey. 
While the invention has been described above with reference to a particular 
type of a conventional rotary kiln system for use in the cement industry, 
it is to be understood that the invention is equally applicable to other 
types of rotary kiln systems for use in the cement industry and analogous 
industries where a rotary kiln forms the main processing component. 
Moreover, it is observed that the concept of Fuzzy Logic Control is to be 
construed in its broadest sense so as to include any form of control 
system, irrespective of its practical modality, which is capable, on the 
basis of pre-set data and relationships established empirically or by 
experience, and of measured values of a plurality of process variables, to 
mandate concurrent control actions and to weigh these against one another 
such as to arrive at a concerted control similar to that which would be 
exercised by an experienced operator.