FIELD OF THE INVENTION
The present invention generally relates to a continuous casting process for steel
manufacturing. More particularly, the invention relates to an Advance Alarming
System for longitudinal facial cracks at corner, predicting, prior to formation, of
cracks that lead to breakouts in thin slab continuous casting.
BACKGROUND OF THE INVENTION
The steel making chain of processes initiates at the mineral extraction and
refinement stages, passes through coke and sinter making, then the making of
liquid iron in the blast furnace followed by its transformation to liquid steel
through chemical processing. The next major step is the continuous
transformation of liquid steel to solid, followed by thermo-mechanical processing
of discrete solid slabs (or blooms/billets) into thin sheets of different properties.
Among all these steps the continuous transformation of liquid steel to solid is
most critical both from the perspective of product quality as well as process
stability and throughput as any disruption, delay or slackness in this stage will
immediately impact both upstream liquid as well as downstream solid processing.
The mentioned continuous transformation is called continuous casting on which
this invention is focused. More information on continuous casting may be found
in [1, 2].
One of the typical and most undesirable disruptions of the continuous casting
process is by the occurrence of "breakouts" in the mold. These occur when the
thin solid shell that develops all around the perimeter of the continuously cast
strand and thickens with progression of cooling, breaks for some reason with
resultant emptying out of liquid steel onto the downstream machinery and
equipment effectively disrupting the casting process for a few hours. There are
many causes of breakouts, primarily stickers, cracks and improper shell
formation. Their relative frequency depends on the specific nature of the casting
process.
Conventional slab casters (also called thick slab casters) come with slab sectional
thickness of around 200-250 mm and cast at speeds of between 1.2-2.2 meters
per minute. In modern thin slab casters (figure 1 provides a schematic of the
complete thin slab casting line of the Direct Sheet Plant (DSP) at Ijmuiden,
Netherlands, where this work is executed) these vary between 75-95 mm and 4-
6 m/min. Breakouts in the former are predominantly of the "sticker" type where
formative thin shell sticks to the mold wall and the surrounding region is torn
apart due to tension. In the latter type of casters breakouts are predominantly
caused by cracks and other shell formative defects like "lumb" where shell is
weak at the narrow faces, bulges and breaks as ferro-static pressure increases.
Considering the seriousness of disruption in the steel making chain caused by
breakouts, attempts have been made since early days of conventional casting to
develop methods to detect formation of breakout-like conditions and take
consequent action to prevent the breakout. For this purpose, the mold walls are
embedded with arrays of horizontally layered thermocouples (TC), each TC
reflecting the instantaneous variation in temperature of strand shell closest to its
sensing point. "Stickers" are accompanied by a typical rise and then fall of
temperature as the "tear" passes the point, and then a corresponding rise and
fall in the TC placed immediately below. This typical pattern with occasional
(sometimes severe) variations is detected by some patternrecognition software
that alarms the formation of a tear which will quickly lead to a breakout - the
alarm is followed by immediate reduction in casting speed allowing the fault
(tear) to heal and thus prevent the sticker breakout.
While the TC based breakout detection system works well in conventional
casters, they do not adequately serve the purpose in thin slab casters for the
following two reasons:
1. unlike conventional casters, breakouts of sticker type constitute only a minor
proportion of all breakouts, while the other types of breakout more typical of
thin casters are not accompanied with neat temperature-time patterns as
seen in stickers
2. due to higher casting speeds, the residence time of a fault in the mold, after
its detection by TC, is only around 10 secs. The speed reduction takes effect
in around 6 secs, leaving very little time for actual healing even after
detection. Thus even if a fault were detected by TC in the mold, there is no
guarantee that the resultant breakout can be prevented.
These two factors make conventional TC-based breakout detection a poor
method for thin slab casters, and alternatives need to be found.
The second factor above makes it imperative that the fault be detected even
before it has formed in the mold, because once it forms there will be little time
left to save the situation. This eliminates the possibility of using TC for direct
one-to-one mappings between temperature-time patterns and crystallized faults.
Furthermore, the detection can only be made by recognizing some typical
emergent pattern prior to fault formation in a few among the hundreds of online
parameters that characterize continuous casting. This is on the expectation that
a fault formation is preceded by, or in other words the root cause of a fault lies
in, a certain specific combinatorial pattern acquired by some among the
hundreds of parameters characterizing casting. This specific combinatorial
pattern has to be recognized in process real time by the new method. Over and
above, it is imperative that the recognition be accurate in the sense that nearly
all genuine faults be captured, while practically no false alarms are raised. And
the alarming has to be in sufficient advance to allow definitive operator response
for mitigation, which implies at least three minutes in advance of a breakout.
An analogy will illustrate the challenge faced by the inventors. This is like the
requirement that prior to a global economic recession as in 2008, an algorithm
should be able to analyze automatically hundreds of global socio-economic-
political indicators, and predict sufficiently in advance that a recession is about to
occur even before it has started, so that definitive action can be taken to avert
the recession. Moreover, the prediction has to be correct (no true negatives) and
should not raise false alarms (no false positives) as either will lead to severe
consequences! The recognition of the specific combinatorial pattern in process
real time that leads to a Longitudinal Facial Crack at Corner type of fault is
performed by an Artificial Neural Network (ANN). Further verification of
prediction to filter out possibilities of error is performed by using some novel
modeling techniques explained below.
Examples of prior work on thermocouple based breakout detection systems may
be seen in IPA 420/KOL/04, while examples of prior work based on multi-
parametric analysis are seen in US Patent 6,564,119 and US 2004/0172153. In
the latter statistical techniques are used to evaluate the difference or 'distance'
between a set of running parameters of casting and a corresponding set of ideal
parameters extracted from best conditions. The distance provides real time
monitoring and when it exceeds some threshold an alarm is raised. In this
approach an ideal condition is not taken into account as that itself is a function of
other casting characteristics, including experimentation with new process
conditions. Instead running conditions of casting are taken as baseline and
significant deteriorations as predicted by the ANN are considered as digressions
which may lead to breakout. While the ANN output can be viewed as a
monitoring parameter, severe digressions are alarmed for immediate responsive
action. More details follow.
OBJECTS OF THE INVENTION
It is therefore an object of the invention to propose a system for advance
prediction of faults in thin slab cluster to eliminate rupture of formative solid shell
in the mold.
Another object of the invention to propose a system for advance prediction of
faults in thin slab cluster to eliminate rupture of formative solid shell in the mold,
which is enabled to predict the fault- occurrences sufficiently in advance so as to
successfully implement responsive action to mitigate the faults.
A further object of the invention to propose a system for advance prediction of
faults in thin slab cluster to eliminate rupture of formative solid shell in the mold,
which transmits an abnormality-index data in real time including generation of
alarm signal to mitigate any impending fault.
SUMMARY OF THE INVENTION
The continuous casting process is one of the most important links in the steel
making chain as it represents transition in steel processing from liquid to solid
state, and due to its inherent technical challenges impacting on stability,
productivity and quality. One of these technical challenges relates to "breakouts"
where the formative solid shell encapsulating liquid ruptures in the mold leading
to drainage of the internal liquid and consequent blockage of equipment and
process. In thick (conventional) casters 'stickers', i.e. shell sticking to mold, are
the predominant cause of breakouts and almost all such casters come equipped
with 'Breakout Detection Systems' (BDS) that tap signals from an array of mold-
embedded thermocouples and process them in real time to recognize specific
patterns that precede sticker breakouts. On recognition, an alarm is generated
with consequent reduction in casting speed and mitigation of fault. In thin slab
casters conventional BDS functionality is hamstrung by the fact that 'stickers'
constitute only a minor proportion of all breakouts and casting speeds are so
high that the time between sensing of a fault by the thermocouples and its
mitigation by speed reduction is sometimes insufficient for complete elimination
and may still result in breakout. This method fulfills the need for an advance
prediction system for faults in thin slab casters that are attuned specifically to
types of faults characteristic of such casters and furthermore, prediction is
sufficiently in advance to enable successful mitigation by responsive action. In
particular, this system attends to breakouts of type 'Longitudinal Facial Crack at
Corner'. It feeds online multiple casting process parameters into an Artificial
Neural Network (ANN) which delivers in real time a value of 'Abnormality Index'
(AI). This AI is further tracked to recognize sudden & continuous increase which
is indicative of sharp deterioration of casting and an impeding fault whence an
alarm is generated followed by mitigating action. The system has been confirmed
to work with high accuracy, i.e. capturing most faults amid low false positives.
The system functionality is split sequentially into two parts. First, an offline
synthesized subset of the total set of process parameters is fed as input into the
ANN which generates a dimensionless number that represents the instantaneous
quality of casting. This number varies from 0 to 1 where the former denotes very
good casting and the latter a breakout. Hence the number can be termed as an
instantaneous 'Abnormality Index', i.e. AI, as it represents the degree of
abnormality of casting. This instantaneous AI can fluctuate within short time
spans but remains within a band; hence it is more appropriate to deal with the
short-time moving average of this AI, termed MAI. Second, the MAI is
transformed into an alarming index using certain modeling techniques that utilize
the mid-term historical variation of this MAI in the current casting sequence. The
alarming index is usually 0 but whenever it reaches 1 an alarm is raised and
action taken.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
Fig. 1. Schematic of complete thin slab casting process as at Direct Sheet Plant.
Fig. 2. Basic Layer and Neuronal structure of an Artificial Neural Network.
Fig. 3. Flow of computation in an Artificial Neural Network.
Fig. 4. Flowchart detailing the flow of logic in the advance alarming system.
Fig. 5. High Level Architecture of the Advance Alarming System.
DESCRIPTION OF THE PREFERRED EMBODIMENT
This invention provides real time analysis of the combination of online
parameters that accompany continuous casting, particularly thin slab continuous
casting, and consequent decision making in real time on whether a fault
condition is impending which can be mitigated by taking appropriate action.
The above functionality is split sequentially into two parts. First, an offline-
synthesized subset of the total set of process parameters is fed as input into an
Artificial Neural Network (ANN).
An Artificial Neural Network, from a mathematical perspective, is a 'Universal
Approximator'. This implies that, given a mathematical function of any degree of
complexity, the ANN can emulate this to any desired degree of accuracy [Ref].
Here by "emulate" is implied that, given a set of input values to both the ANN
and the mathematical function, the output from the ANN will be sufficiently close
to that provided by the closed-form transformation.
The utility of an ANN arises when there is some amount of data available from a
process or system that intrinsically encapsulates the functionality of that process,
and an observer would like to capture this functionality. An ANN precisely serves
that purpose. This data is used to 'train' the ANN so that it learns this
functionality, and when a new input vector is provided to the ANN it emulates
the process to generate representative output. The core of the training process is
the iterative readjustment of the intra-neuronal weights so that the final output
generated by the ANN for a given input vector comes as close as possible to the
actual process output for the same input vector, and this holds true for each of
the training data records.
Structurally an ANN is made up of at least three layers of neurons, where the
first is the input layer, the last is the output layer, and the rest are the hidden
layers. This is shown in figs. 2 & 3. Each neuron in a layer is connected, in
principle, to all neurons in its neighboring layer/s. The neuron first sums up all
inputs from its preceding layer neurons multiplied by the connecting weight
between the two neurons, and then provides a nonlinear transform to the sum.
This becomes its output which now serves as input to the neurons of the next
layer. The summing up relationship is

and the nonlinear transform is of type

in the above relations wik represents the weight of the connection between
neurons k in the previous layer and i in the current, and the nonlinear function
shown is the sigmoid function as an example. The outputs {y} from the first or
input layer are simply the process input values, and the outputs from the output
layer are the final process outputs corresponding to the given inputs. As is seen
clearly from fig. 3, the flow of information is from left to right across the layers.
The ANN in this invention takes the process parameters of continuous casting as
inputs, and generates as output an abstract or 'conceptual' quantity - the
Abnormality Index. In a sense it monitors the health of casting. The Abnormality
Index is termed "abstract or conceptual" as it is not a physical quantity that can
be measured. There does not exist any process output data like "abnormality of
casting" on which the ANN can be trained. Instead, the data available is binary -
either 0 implying good casting or a 1 implying breakout. Hence by the principles
of training, the ANN is also expected to generate a binary output - either a 0 or
a 1.
The problem is that the quality of the casting process is not at all binary
classifiable, in terms of either perfect or a breakout. Most of the time it is in
between, and much closer to 0 than to 1. And as mentioned there is no available
measure of the actual state of the casting process, which can be used to train
the ANN. That which is available from the process is a 0 or a 1. More
importantly, the process shifts from state 0 to state 1 continuously and almost
monotonously in the few minutes preceding a breakout, and the function of this
invention is to capture this shift in the process.
The invention captures this shift by using the third element of information on the
process, apart from a 0 or a 1. This is the fact that the shift from 0 to 1 is near
continuous and near monotonous. So the output from the ANN, which is also
either close to 0 or close to 1 reflecting its training master (real process) output,
is continuously tracked to identify periods of continuous and monotonous
increase, and then conclusion drawn if indeed a situation has been identified as
that which prevails just a few minutes before a breakout.
For achieving the above certain pre-requisites are necessary. First, instead of
using instantaneous AI, the moving-average AI or MAI is used. Second, the
continuous casting process is accompanied by more than three hundred online
process parameters. The ANN will be unable to handle that sort of input
numbers; furthermore all calculations need to be in process real time. Hence,
through careful analysis that combines core domain knowledge on the
continuous casting process with statistical and heuristic methods, the number of
inputs actually provided to the ANN is cut down to simply 5 and these are listed
hereunder:
Input 1: difference between average wide face and narrow north face heat fluxes
Input 2: difference between average wide face and narrow north face heat fluxes
Input 3: average of narrow face heat fluxes
Input 4: average friction from two cylinders
Input 5: casting speed.
To conclude on whether the current conditions qualify for raising alarm, the most
important factor is a steep rise in MAI. For identifying such a steep rise, first step
is to create a baseline value that represents good casting in the current
sequence. This is termed as NICC or 'Normality Index of Current Casting'. It is
extracted by taking a long-term average of the value of MAI. Next the running
MAI is compared against the NICC, and checked if a threshold value of the ratio
of these two quantities has been crossed. If the NICC is very close to zero (as
can happen very often) this threshold ratio is crossed very easily. To prevent this
occurrence a minimum alarming threshold of MAI is also defined. If both these
conditions are met a potential alarming situation is created.
A potential alarm is converted to an actual alarm after a few more checks. First,
there is a 'reaction period' after an earlier alarm has been raised and mitigating
action has been taken, and the situation is still to stabilize. An alarm is not raised
within this 'shadow window' of a previous alarm. Second, a check for continuity
and monotony is made, i.e. the 'potential alarm' is allowed to continue for at
least ten seconds (the 'threat threshold') before the actual alarm is raised.
There is an alternate situation where the NICC is running at high levels.
Imposing the 'threshold ratio' on this high NICC implies pushing the requirement
on MAI to be very high, which would lead to missing genuine alarms. This
usually occurs when the sequence has started shortly and the time window for
calculating NICC is small. For eliminating this possibility an upper threshold,
called 'NICC threshold' is imposed on NICC - if that value is crossed, NICC is set
to that value.
The algorithm for alarm generation is stated below. This is further explained in
the flowchart of fig. 4.
Steps of alarm generation algorithm:
1.0 The ANN runs on a processor that receives the field inputs in real time
and generates an AI every 1 sec
2.0 The value used for analysis is not the instantaneously predicted AI but the
moving average taken over a period of 10 seconds termed as MAI
3.0 At every instant there is associated a Normal Index of Current Casting
(NICC) which can be an average value of AI of past one hour of casting or
(if casting is recently started) a value from lookup tables based on casting
product.
4.0 The AI output is compared against the NICC and if it is some threshold
ratio (or 'Rise Factor') more than NICC it is taken as digression into bad
casting
5.0 The software on processor then looks at the values of different input
variables and uses a system of logic to decide the choice of the remedial
action
6.0 The remedial action is flashed on HMI screens for operators to execute.
Explanations:
for #3: The NICC is nothing but the baseline AI predicted by the ANN over
periods of good casting. In the 168 hours of the week, it may be assumed that
approximately 160 hours of good casting is performed. In other words, 95% of
the time casting is "good". However, the value of AI can drift based on specific
casting conditions. Hence, the average AI of last one hour of casting can be
considered as the reference baseline Abnormality Index of good casting -
called Normal Index of Current Casting or NICC.
for #4: as explained earlier, when casting (as an operation) quality deteriorates
to a level where a LFC corner breakout becomes likely, the AI (moving window
average over 10 secs) starts moving steeply upwards while the NICC (moving
window average over 3600 secs) remains practically unchanged. Then the ratio
of AI to NICC, which would normally be close to 1, also shoots up sharply. If the
ratio crosses the 'Rise Factor', it is taken indicative of a breakout if an additional
criterion is also met. This is simply that the AI should be above a
threshold value called the 'MAI Threshold'.
The architecture of the Advance Alarming System incorporating the above
algorithm is illustrated in fig. 5.
16
Patent References
Other References
1. World Steel University website, continuous casting link:
http://www.steeluniversitv.org/content/html/ena/default.asD?catid=27&pa
qeid=2081271519. valid Mar 23, 2011.
2. B.G. Thomas, "Modelling of the Continuous Casting of Steel -Past, Present
and Future", Metallurgical and Materials Transactions B, Vol 33B, No. 6,
Dec 2002, pp. 795-812.
3. Kurt Hornik: Approximation Capabilities of Multilayer Feedforward
Networks. Neural Networks, vol. 4, 1991.
WE CLAIM
1. A system in a continuous casting process of steel manufacturing for
advance prediction of faults in a thin slab cluster to eliminate rupture of
formative solid shell, the process comprising:
- passing of molten steel through a water-cooled near-vertically-aligned
lubricated mold of rectangular crosssection about a meter long to emerge
in the form of a continuous strand consisting of a solid shell encapsulating
liquid material, the strand being further cooled to complete solidification
using water sprays even as the orientation is changed to horizontal using
rollers before being finally cut into discrete slabs, the formative shell being
susceptible to tear, crack, or break either in the mold or early in the spray
cooling zone leading to outflow of inner molten steel and its quick
solidification on downstream machinery, the system comprising:
- at least one input module receiving field inputs in real time and generating
processed variables;
an artificial neural network (ANN) running on a processor with a prediction
module to receive processed variables in real time, and periodically
generating data representing abnormality index (AI) based on analysis of
moving average index (MAI) value of the input variables, and comparison
of one of normal index of current casting (NICC) data and a value from
look-up tables on casting product, the processor determines a possible set
of remedial actions by utilizing a logic rule, and outputs the processed
data;
- a post-processing module operably connected to the prediction module
receiving the processed data for display including generating warning or
alarm for operators to execute, and arching, the system is further
configured to:
predict a breakout even before the formation of the incipient crack that
would ultimately magnify into the breakout and thus warn operating
personnel well in advance to take mitigating action that will prevent the
crack from crystallizing further;
use real time multi-parametric analysis by passing selected parameters
through the Artificial Neural network (ANN) that generates a value of
Abnormality Index (AI) being a reflection of the deviation from normal
process conditions;
- recognize the deviation from normal conditions that can potentially lead to
a breakout in process real time with high fidelity wherein the short-term-
time or 'moving' average of the AI is compared against the long-term-time
average that represents normal casting and a sharp increase in the
moving average data against the long-term-time average is taken as a
measure of the deterioration of the real process of the type that can
culminate in a breakout.

The invention relates to the continuous casting process that is one of the most
important links in the steel making chain as it represents transition in steel
processing from liquid to solid state, and due to its inherent technical challenges
impacting on stability, productivity & quality. One of these challenges relates to
"breakouts" where the formative solid shell encapsulating liquid ruptures in the
mold leading to drainage of the internal liquid and consequent blockage of
equipment and process. In thick (conventional) casters 'stickers', i.e. shell
sticking to mold, are the predominant cause of breakouts and almost all such
casters come equipped with 'Breakout Detection Systems' (BDS) that tap signals
from an array of mold-embedded thermocouples and process them in real time
to recognize specific patterns that precede sticker breakouts. On recognition an
alarm is generated with consequent reduction in casting speed and mitigation of
fault. In thin slab casters conventional BDS functionality is hamstrung by the fact
that 'stickers' constitute only a minor proportion of all breakouts and casting
speeds are so high that the time between sensing of a fault by thermocouples
and its mitigation by speed reduction is sometimes insufficient for complete cure
and may still result in breakout. This method fulfills the need for an advance
prediction system for faults in thin slab casters that are attuned specifically to
types of faults characteristic of such casters and furthermore, prediction is
sufficiently in advance to enable successful mitigation by responsive action. In

particular, this system attends to breakouts of type 'Longitudinal Facial Crack at
Corner'. It feeds online multiple casting process parameters into an Artificial
Neural Network (ANN) which delivers in real time a value of 'Abnormality Index'
(AI). This AI is further tracked to recognize sudden & continuous increase which
is indicative of sharp deterioration of casting and an impeding fault whence an
alarm is generated followed by mitigating action. The system has been confirmed
to work with high accuracy, i.e. capturing most faults amid low false positives.