Development and application of mould breakout prediction system with online thermal map for steel continuous casting F. He1,2, D.-F. He*2, Z.-H. Deng1,2, A.-J. Xu2 and N.-Y. Tian2 A new system to predict and prevent sticking type breakout in slab continuous casting is presented. It uses novel logic judgment model for sticker prediction. The elaborate model has four important characteristics: (1) arranging more rows of thermocouples in high density; (2) using temperature change rate and the time lag of abnormal changes for neighbouring thermocouple temperatures; (3) abandoning frequently used temperature inversion; and (4) vertical detection and horizontal detection to accurately recognise sticker propagation. The results of example simulations and field application indicate that the present model can detect sticking type breakout completely and in time keep false alarm ratios to a minimum and so achieve better performance for breakout prediction and prevention. Also, the mould breakout prevention system (MBPS) uses online thermal map of the mould for process visualisation and assisting breakout prediction. A novel method called ‘contour region filling method’ is proposed for drawing the colour maps, which includes discrete data gridding, contour line generation and contour polygon filling. Simulation experiments demonstrate that the method is feasible and effective for drawing online thermal maps. Meanwhile, field application results indicate that the present thermal map could well reflect the characteristics of temperature field of mould in normal and abnormal conditions, and is satisfactory for the process of practical operation. Keywords: Sticking type breakout, Slab continuous casting, Mould, Breakout prediction, Online thermal map
Introduction With the development of modern high efficiency continuous casting technology, two desirable characteristics are ‘high quality slab’ and ‘high casting speed’, together with the requirement to monitor mould level of mould process online. A mould breakout prediction system (MBPS) with an online mould thermal map is the main method of mould process monitoring, which is of great significance for smooth production and quality assurance of slabs. Thus, the development of a high performance and more reliable breakout prediction system is desirable. It is well known that during continuous casting, breakout is the most catastrophic incident, which not only interrupts sequential production to attain high productivity and disturbs the production schedule, but also damages the devices of the caster, and results in huge economic cost.1 Most breakouts are of the sticking type and restrict high speed continuous casting.
1
School of Metallurgical Engineering, Anhui University of Technology, Ma’anshan, 234002, China School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
*Corresponding author, email
[email protected]
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ß 2015 Institute of Materials, Minerals and Mining Published by Maney on behalf of the Institute Received 6 April 2014; accepted 10 July 2014 DOI 10.1179/1743281214Y.0000000220
To avoid the sticking type breakout, many detection methods have been developed, such as measurement of heat flux, friction force and mould plate temperature. The measurement of temperature by means of thermocouples (TCs) embedded in mould copper plates has been proven to be the most suitable and most reliable method for detecting the sticker. In actual production, it has been shown that the mould heat flux and friction measuring methods are ineffective in detecting sticking.2 Breakout prediction systems based on measuring temperature with thermocouples are used widely. Currently, the studies for these systems can be mainly divided into two types: logic judgment systems and neural network systems, according to the algorithms of breakout prediction. The logic judgment system is the basic for breakout prediction and has been applied widely in many slab and thin slab casters, such as the mould diagnostic system of Nippon Steel,3 SAPSOL breakout prevention system,4 mould thermal monitoring system of British Steel,5,6 the MoldExpert monitoring system of Siemens VAI,7 mould breakout prevention system of Danieli8 and SMS Demag breakout prediction system.9 The neural network system is seldom applied successfully in practice, though many researchers have studied the method for improving the breakout prediction accuracy,10–14 because neural networks have better
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1 Schematic diagram of slab caster with high efficiency
adaptability and fault tolerance and can solve the nonlinear problems more efficiently. However, neural networks need a lot of effective learning data. Especially in the initial running stage of the system, breakout prediction can only rely on the logic judgment system, while neural networks lack enough learning data. Also, a neural network is a ‘black box’ model, which depends on excessive use of the data and lacks technological guidance. The implementation and maintenance of neural network systems are more complex than those of logic judgment systems. The key issue of logic judgment systems is how to get higher performance and reliability. The most significant differences of various logic judgment systems for breakout prediction are the arrangement of the thermocouples and the corresponding logic algorithms. In the MoldExpert system of VAI, two or three rows of thermocouples are arranged in the mould copper plates and the system uses three independent autoadaptive algorithms to detect stickers. The system is applied in many slab casters in China where false alarm ratios are high, even more than 50%, though the sticker detection ratios are 100%.15,16 In the mould breakout prevention system of Danieli, six or more rows of thermocouples are arranged in the mould copper plates. The system’s logic algorithm relies on the rate of temperature changes of thermocouples in the row near the meniscus and temperature inversion characteristic. Through many field applications, it is revealed that there are misspredictions and rather late alarm phenomena for some atypical temperature variation patterns of sticking, as for example, the temperature inversion does not appear on any of the thermocouples, or small difference in temperature inversion cannot meet the predefined threshold. The breakout prediction system of SMS Demag that uses three rows of thermocouples in mould and simple logic algorithm17 also has a high false alarm ratio. A false alarm results in reducing the casting speed or stopping the caster, which could affect the quality of
slabs. More and frequent false alarms disrupt the normal and stable production pace, and cannot meet the production requirements of high efficiency continuous casting. Thus, sticking type breakouts must be detected completely and false alarm ratios must be kept to a minimum. Three or more rows of thermocouples are more reliable at detecting stickers with less chance for false alarms, although the analysis algorithms are more complex, and more thermocouples require more maintenance.18 For breakout detection systems using more rows of thermocouples, the design of the logic algorithm is very crucial, which determines the performance and reliability of the system. Meanwhile, more rows of thermocouples are advantageous for better visualisation of temperature distribution on the mould and monitoring of local heat transfer behaviour in the whole mould. In this research, based on mould temperature change characteristics of sticking type breakout processes, a reliable mould breakout prediction system based on more rows of thermocouples has been developed and used in the slab continuous casting process. Two important features of this system are the breakout prediction model and online thermal map of mould. To detect potential sticking type breakouts completely and quickly, and keep the number of false alarms to a minimum, a novel logic judgment model is proposed to improve the performance of breakout prediction. As for the online thermal map of mould, a novel method called ‘contour region filling method’ is used to draw an accurate dynamic temperature nephogram of the mould efficiently, which could well reflect the characteristics of the temperature field of the mould in normal and abnormal conditions.
Description of slab continuous casting process The study was carried out on the slab caster in steel plant H in China. Figure 1 shows a schematic of the
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2 Schematic diagram of sticking type breakout
equipment installed on the caster. The machine is equipped with many key technologies, such as ladle tundish slag detection, mould liquid level automatic control, dynamic control of secondary water, dynamic soft reduction, etc. The basic parameters are detailed in Table 1. The steel grades cast on the slab caster are various and complex, such as ULC, LC, MC, peritectic, peritectic-HSLA, MC-HSLA, pipe grades, etc. Breakout incidents on the two slab casters from March to September in 2012 were investigated, as shown in Table 2. The sticking type breakout accounted for 72?22% of all breakout accidents, and was the main breakout type. Sticking type breakout is difficult to avoid completely just by improving casting technology such as improving the performance of mould powder, elaborate operation and controlling mould level fluctuations, especially in high speed casting. A mould breakout prediction system is an effective and main method to prevent the sticking type breakout fully. Then a brief introduction to the sticking type breakout theory is described below. The formation of a sticker breakout starts with localised sticking followed by tearing of the shell and the tear propagating down the mould,2,19 as shown in Fig. 2. As the tear and associated hot spot moves downward, the thermocouples embedded in mould copper plates show a temperature change as shown in Fig. 2. During normal casting conditions, the temperature change with time at each measuring point in the mould is stable and has no large fluctuations. Many quality defects could be predicted by identifying their own abnormal temperature variation patterns from normal temperature profile waves. The key issue for sticker breakout prediction is to recognise typical Table 1 Basic parameters of caster
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Parameters
Value
Unit
Machine type Number of strands Slab section size Casting speed Machine arc radius Machine length Mould type Mould length Capacity of ladle Capacity of tundish
Vertical bending 2 (230/250)6(900–2150) 0.80–2.03 9.5 39.4 Combined straight mould 900 260 80
… … mm2 m min21 m m … mm ton ton
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temperature patterns of stickers. The mould breakout prediction system developed by this study is implemented within the control system, as shown in Fig. 1. The main task of the system is to recognise a sticker quickly and take appropriate and effective countermeasures to prevent the breakout, such as reducing the casting speed automatically, so that the ruptured shell will be healed.
Mould breakout prediction system System architecture and implementation The software structure of the MBPS is the Client/Server (C/S) structure. The system program is developed with Delphi (Object Pascal) language, which can run on any Windows operating system. As shown in Fig. 3, the system consists of a data acquisition unit, MBPS server, breakout prediction model, offline unit, execution unit and MBPS client. Quick and reliable data collection is the basis for MBPS. In the study, the data acquisition unit is comprised of data measurement, data transmission and upper computer. The main task of data measurement is to measure the object’s target information by sensors and to convert the analogue signals of the sensors into digital signals. The data transmission part is to transmit data to the remote upper computer by the industrial PROFIBUS-DP network. The tasks of upper computer are to collect all required data from the process database of the two-level automation system and the PLC. DB links are established to connect two Oracle databases, namely, process database and the database of MBPS, and using synonyms access the data. The communication between Siemens PLC-400 and upper computer is realised through OPC (Object Linking and Embedding for Process Control) technology of the configuration software WinCC. Table 2 Investigation on breakout accidents of two slab casters Items
Value
Total casting heats 11 468 Total number of breakouts 18 Number of sticking type breakouts 13 Number of breakouts in start of cast 2 Number of breakouts caused by scum entrapment 1 Number of breakouts caused by cracks 2 Occurrence rate of sticking type breakout/% 72.22
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execution unit is to action breakout prevention according to the results of the breakout prediction model, as for example, when a sticker alarm is received, sound light alarm will be given and instruction of reducing the casting speed will be sent to speed controller. The offline unit can be used to review the historical data and analyse the abnormal events of the casting process. The MBPS client HMI is to visualise these measured data and the results of model, such as real time casting conditions, online thermal map, etc. The main monitoring interface of the MBPS client is shown in Fig. 4.
Mould instrumentation
3 Mould breakout prediction system architecture
The MBPS server is used for data storage, data management and data exchange. Primarily, the server collects field measured data, sends this to the breakout prediction model and then receives alarm information and sends this to the execution unit. In addition, the server compresses and then stores the measured data and the alarm records for historical playback and offline analysis. The breakout prediction model receives field real time data and identifies the breakout symptom by novel logic algorithm, and then sends the alarm information to the MBPS server. The task of the
Figure 5 shows the arrangement of thermocouples embedded in mould copper plates. Seven rows of 12 columns, a total of 84 thermocouples, are embedded in each broad face. Four rows of two columns, a total of eight thermocouples, are embedded in each narrow face. In total, 184 thermocouples are installed in the four copper plates. These thermocouples are marked according to Fig. 5, as for example, 7G denotes the thermocouple in the seventh row of G column. The measured temperatures depend on the type of the thermocouple and embedding depth in mould copper plate. In this study, high precision K type thermocouples were installed in mould copper plates. The welded joint of the NiCr and a Ni thermocouple wire forms the thermocouple. Two twisted and shielded wires are connected to the signal conditioning devices and therefore are influenced less by external fields such as electromagnetic brakes and stirrers. A good contact of
4 Main monitoring interface of MBPS
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5 Arrangement of thermocouples embedded in mould copper plates (mm)
the thermocouple to the copper plate is ensured by spring loading. The thickness of each mould copper plate is 41 mm and the embedded depth of the thermocouples is 20?5 mm. The accuracy and reliability of temperature measurement are the precondition and guarantee for breakout prediction.
Mould breakout prediction model Novel logic judgment model for sticking type breakout prediction The design of the mould breakout prediction model is critical as this is the core of the MBPS. A novel logic
6 Flow chart for new logic judgment model
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judgment model for detecting the stickers is proposed, based on the analysis of historical data of the stickers, such as detailed mould temperature history, casting speed and mould level. Establishment of model
From the analysis of historical data, mould temperature change of the sticking process reflects three important characteristics. The first characteristic is that when the tear line is passing a thermocouple, the change of its temperature with time shows a significant rise followed by a significant fall, and, the rise and fall have a certain temperature change rate and duration.
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7 Logic judgment checking for new model
The second characteristic is the time lag of abnormal changes for neighbouring thermocouple temperatures, due to two-dimensional propagation of the sticker, namely, perpendicular (x) and parallel (y) to the casting direction. This is the most important characteristic of identifying propagation behaviour of the stickers and is used widely. In many studies, only horizontal detection or vertical detection for the sticker propagation is taken into consideration. So the horizontal and vertical detections are considered simultaneously in the present model, which is more effective for identifying the sticker propagation behaviour. The third characteristic is the temperature inversion or intersection, namely, there is a crossing between the temperature curves of the upper and lower thermocouples in the same column during the propagation process of a sticker. However, the crossing phenomenon does not appear on any of the thermocouples systematically. Sometimes they do cross, but the crossing time is rather later or there is only a small difference in temperature inversion between them. These cases can result in miss-detection or late detection. In this research, the characteristic is not considered in the logic model of identifying the stickers. Relying on the first and the second characteristics above, a new logic judgment model based on more rows of thermocouples is established, as shown in Figs. 6 and 7. In the figures, TC(i,j) represents the thermocouple in the ith row of j column and b is the angle of tear line. For this present model, the logic rules are listed in detail, as follows. Data pre-processing rule In the harsh environment of continuous casting, the lager temperature fluctuations and malfunction of the thermocouples are usually caused by many factors, such as water invasion, fault of A/D converters and poor contact. Data pre-processing is to reduce the effect of bad data on the breakout prediction model, as shown in equation (1) Tmax §T§Tmin
(1)
where T is the temperature of the thermocouple; Tmin and Tmax are the predefined minimum and maximum temperatures respectively. When a thermocouple cannot meet equation (1), the thermocouple will not participate in the calculation of the model. Temperature rise checking rule The temperature change rate of each thermocouple could be calculated by equation (2). Based on the
temperature change rate curves of Fig. 7, the temperature rise checking rule is established as shown in equation (3). When equation (3) is met, the check thermocouple is marked as having an abnormal state of temperature rise dT T ði,j,tnow Þ{T i,j,tprev ~ h~ (2) dt tnow {tprev min max min hmax up §h§hup and tup §tup §tup
(3)
where h is the temperature change rate, and in normal condition, h is approximately equal to 0uC s21; t is the time; T(i,j,tnow) and T(i,j,tprev) are the temperature of the thermocouple in the ith row of j column now and previous respectively; (tnow2tprev) is the period of time for calculating the temperature change rate; hmax up and hmin are the predefined maximum and minimum up temperature rising rates respectively; tup is the duration max of temperature rising rate in the range [hmin up ,hup ], and, max min tup and tup are the predefined maximum and minimum of the duration respectively. Checking rule of propagation speed of sticker in vertical (y) direction amax Vc §Vy ~
Dy §amin Vc ty
(4)
where Vy is the propagation speed of sticker in vertical (y) direction, namely, the descending speed of hot spot; Dy is the longitudinal spacing between adjacent thermocouples, and it is 111 mm in the study; ty is the propagation time of sticker in y direction between adjacent thermocouples, and can be estimated by the starting time when h exceeds hmin up , as shown in Fig. 7; Vc is the casting speed; amax and amin are the predefined maximum and minimum of the ratio of Vy to Vc. Temperature fall checking rule min min hmax down §h§hdown and tdown §tdown
(5)
min where hmax down and hdown are the predefined maximum and minimum temperature falling rates respectively; tdown is the duration of temperature falling rate in the range max min [hmin down , hdown ], and, tdown are the predefined minimum duration. When equation (5) is met, the check thermocouple is marked as having an abnormal state of temperature fall.
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Logic of vertical detection and horizontal detection Vertical detection in a column is to recognise whether the sticker propagates parallel (y) to the casting direction, while vertical detection in the adjacent columns is to recognise the sticker propagates on both sides of the sticking point, namely, horizontal detection. The strategy and the arrangement of more rows of thermocouples in high density can tolerate one or more faulty TCs, which is beneficial to reducing the impact of thermocouple failures on breakout prediction performance. The vertical detection and horizontal detection take as the following steps: (i) when abnormal temperature rise of a thermocouple such as TC3 in Fig. 7 is detected by equation (3), vertical detection in the column is carried on from current time A to past time C, which is to detect whether abnormal temperature rise appears on other thermocouples (such as TC1 and TC2) above the thermocouple of the column. Here, the interval from time A to time C is the predefined check interval of abnormality (ii) it is assumed here that for TC1 and TC2, the abnormal temperature rise is detected. Then, based on the abnormal temperature rise of the three thermocouples (TC1, TC2 and TC3), the propagation speed of the sticker in the column is checked by equation (4) (iii) if equation (4) is met, the temperature fall checking is carried on the thermocouple (TC1) of the first abnormal temperature rise, from time B to time A. Here, the time B is the starting time when h exceeds hmin up for TC1. And if the abnormal temperature fall for TC1 is detected by equation (5), the number of abnormal thermocouples in the column can be obtained, which is namely the number of thermocouples of abnormal temperature rise, and marked as M (iv) along with the vertical detection in the column, the vertical detection in the adjacent column is carried out in the same way, as shown in Fig. 7. The number of abnormal thermocouples in the adjacent column is marked as N. If M§2 and N§2, the total number of abnormal thermocouples in the column and its adjacent column is
MzN, otherwise 0. The logic of the breakout alarm are presented as follows: If MzN§abnormal thermocouples count threshold of sticking alarm, a sticking alarm is released on TC3. Otherwise, If MzN§Abnormal thermocouples count threshold of sticking warning, a sticking warning is released on TC3. Here, the breakout alarm state of the model is divided into two levels: sticking warning and sticking alarm. When a sticking alarm is released, a red light is displayed. And when a sticking warning is released, a yellow light is displayed. Logic on which breakout alarm is suppressed In some particular and unstable casting conditions, it is reasonable to suppress a breakout alarm of the model for avoiding undesired false alarms. If any one of the following five conditions is true, a breakout alarm of the model is suppressed. (i) during start cast, the cast length is less than 3 m (ii) mould level exceeds the predefined range between the minimum and maximum within the last 120 s (iii) the casting speed is less than 0?5 m min21 within the last 120 s (iv) the casting speed change rate exceeds 0?006 m min21 s21 within the last 120 s (v) within the following 60 s after a breakout alarm. Simulation of model
Based on the historical data of stickers or sticking breakout incidents, the simulation experiments of new model were carried out. In this paper, a simulation example is presented here to demonstrate the novel logic judgment model developed in the previous section in detail and examine the performance of the model for detecting the sticking breakout. In addition, the model parameters are fine tuning again and again through alarm analysis, as shown in Table 3. This particular example is a sticking-type breakout incident, which occurred in 19 July 2012. The alarm record of the model for the example is ‘2012-07-19 00:52:55, sticking alarm on the loose side for TC at row 4 column F’. The alarm analysis process of the model is presented as follows.
Table 3 Basic parameters of model
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Parameters
Value
Unit
Minimum temperature Maximum temperature Minimum temperature rising rate Maximum temperature rising rate Maximum temperature falling rate Minimum temperature falling rate Interval for calculating temperature change rate Minimum duration for abnormal temperature rise Maximum duration for abnormal temperature rise Minimum duration for abnormal temperature fall Range of the ratio of Vy/Vc Check interval of abnormality Abnormal thermocouples count threshold of sticking alarm Abnormal thermocouples count threshold of sticking warning Minimum mould level Maximum mould level
50 200 0.18 2.20 20.17 22.00 5 3 25 5 [0.38, 1.50] 30 6 3 80 120
uC uC uC s21 uC s21 uC s21 uC s21 s s s s … s … – mm mm
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a variation of temperature with time for TCs at column E of loose side; b variation of relative temperature gradient with time for TCs at column E of loose side; c variation of temperature with time for TCs at column F of loose side; d variation of relative temperature gradient with time for TCs at column F of loose side; e variation of temperature with time for TCs at column G of loose side; f variation of relative temperature gradient with time for TCs at column G of loose side 8 Alarm analysis of new model (continued on page 202)
As shown in Fig. 8, during a period of time before and after the alarm, the curves of temperature with time and relative temperature change rate with time in the alarm
column and adjacent column are checked and analysed. From Fig 8a and b, according to the logic rules of new model, the number of abnormal thermocouples in
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8 Continued
column E is 0 within 30 s before the alarm. This is because when temperature fall checking is carried out on the thermocouple (2E) of the first abnormal temperature rise in column E, from time B to the alarm time, the temperature falling rate of the thermocouple cannot reach the predefined maximum temperature falling rate (20?17uC s21). In the same way, from Fig. 8c and d, the
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number of abnormal thermocouples in column F is 4 within 30 s before alarm. The abnormal thermocouples are 1F, 2F, 3F and 4F, which can meet all logic rules of new model, namely, the column has the characteristic of sticker propagation, and the abnormality has spread to 4F. Also, from Fig. 8e and f, the number of abnormal thermocouples in column G is 2 within 30 s before
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a mode 1; b mode 2 9 Casting speed control strategies for breakout prevention
alarm. The abnormal thermocouples are 1G and 2G. Though the temperature rising rate of 3G exceeds the predefined limit (0?18uC s21), the duration of abnormal temperature rise does not reach the predefined value (3 s). Through above analysis, the total number of abnormal thermocouples in column F and its adjacent columns is 6, which has reached abnormal thermocouples count threshold of sticking alarm. At the same time, from Fig. 8, the alarm time of new model is 21 s earlier than the breakout time. If reducing the casting speed is taken at the alarm time, there will be enough time to prevent the breakout incident. In addition, from Fig. 8a, c and e, the temperature inversion is not typical of that of a sticking breakout. Only the first row of thermocouples has the phenomenon with some thermocouples in the same column, and the crossing time is rather later so, the temperature inversion is not very reliable for recognising the stickers.
Field application results The new model was integrated into the MBPS and used on the slab casters of steel plant H in China. In practical
application, the breakout prediction model, in addition to recognising the stickers as early as possible, the control strategy of casting speed for preventing the breakout after an alarm is not to be ignored. Based on the characteristic of more rows of thermocouples, different casting speed control strategies were designed according to the location of the alarmed thermocouple. In this study, two control modes of casting speed are adopted for the recovery of the stickers, as shown in Fig. 9. When the alarmed thermocouple is in the fourth row or above, the casting speed control mode 1 can be used to prevent the breakout. Otherwise, breakout prevention must use the casting speed control mode 2, namely, cast stop. The novel logic judgment model, which can predict the stickers accurately and quickly, combined with effective casting speed control, can prevent sticker breakouts completely. After using the proposed technology above, the actual application effect of the model in September and October 2012 is shown in Table 4. Here, ratio of detection5correct alarm times/(missprediction timeszcorrect alarm times); accuracy ratio of
Table 4 Running results of new model Parameters
September
October
Total
Total number of heats Missprediction times Correct alarm times False alarm times Ratio of detection Accuracy ratio of prediction Ratio of false alarm Frequency of alarm/times/heat Frequency of false alarm/times/heat
1681 0 9 2 100.00% 81.82% 18.18% 0.65% 0.12%
1569 0 9 3 100.00% 75.00% 25.00% 0.76% 0.19%
3250 0 18 5 100.00% 78.26% 21.74% 0.71% 0.15%
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a main flow; b nephogram drawing of triangular element 10 Program flow chart of contour region filling method
prediction5correct alarm times/(missprediction timeszcorrect alarm timeszfalse alarm times); ratio of false alarm5false alarm times/(false alarm timeszcorrect alarm times). Frequency of alarm5(false alarm timeszcorrect alarm times)/total number of heats; Frequency of false alarm5false alarm times/total number of heats. Table 4 shows that for the new model, the ratio of detection is 100% and frequency of false alarm is 0?15% times/heat. The sticking type breakouts have been prevented completely and false alarm times are kept at a relatively low level. Therefore, it is obvious that the novel logic judgment model is effective and reliable for sticker prediction.
Online thermal map of mould An online thermal map of the mould is an effective tool for realising the online visualisation of mould process and assisting breakout prediction technology. It can help the operators to intuitively observe the casting process and help the researchers to do in-depth study of
metallurgical behaviours in the mould, such as developing new steel grades and using new casting powder, etc. Also, the thermal map is beneficial for guiding and improving on-site operation and process control. As for practical applications of online thermal maps of the mould in continuous casting, there have been many studies,9,18,20–22 such as monitoring of a sticking type breakout, cold spot, hot spot, surface depressions or longitudinal cracks, working state of the SEN and analysing the melting and lubrication conditions of the casting powder, and the effect of mould taper change. However, the drawing method of the online thermal map of mould is seldom reported, which is not just to draw temperature nephogram by simple interpolation algorithm, and must also take real time performance, accuracy and image quality into account. The detailed definition of a mould thermal map is that by using measured temperatures with a certain number of thermocouples embedded in mould copper plates and a special algorithm, the temperature field of the mould copper plates can be obtained and displayed on a
Table 5 Actual temperature data of thermocouples on loose side of mould copper plate (uC)
1 2 3 4 5 6 7
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A
B
C
D
E
F
G
H
I
J
K
L
38.7 38.6 38.5 38.3 38.5 37.7 37.8
47.1 44.7 43.8 43.4 43.1 43.1 45.6
156.3 168.2 136.0 150.9 99.5 105.8 122.8
145.9 99.8 120.8 132.7 112.1 111.4 126.7
148.4 131.3 119.9 126.6 110.7 105.4 130.6
167.0 143.6 114.8 113.8 114.6 103.4 116.6
99.9 143.6 121.9 121.5 129.5 113.1 123.6
146.8 140.9 115.2 119.8 123.8 104.9 101.9
154.4 142.4 119.8 123.0 124.9 108.5 116.4
46.5 147.5 136.0 105.8 135.4 110.9 117.0
46.3 44.7 44.0 43.0 43.0 43.1 47.2
38.6 38.0 38.3 38.0 38.4 38.5 38.3
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13 Thermal map in normal state (slab width: 1500 mm)
11 Mapping between colour and temperature
computer monitor screen. This thermal map is a dynamic two-dimensional temperature nephogram, and not static. Therefore, it not only requires high efficiency of drawing, but also can ensure the accuracy and image quality, and reflect the characteristics of the actual temperature field. In view of the above characteristics and improving its practical application effect, a contour region filling method has been proposed to draw intuitive online thermal map of mould quickly and efficiently.
Novel method for drawing online thermal map Contour region filling method
The basis of this method is to first draw contour lines and then to fill the corresponding colour for the region Table 6 Schemes and results of the experiments Parameters
between adjacent lines; the key step is the drawing of contour lines. As for the drawing method of contour lines, there are grid sequence and contour sequence methods.23 The grid sequence method is widely used in practice, but the method has some problems, such as complex tracing and ambiguity of the contour line, which results in difficulty of program implementation, and thus, affect the efficiency of drawing. In this study, for generation of the contour line, a method combined with grid sequence method and cell subdivision method24 is adopted. Based on the idea of grid sequence method, the contour lines of each rectangle element are drawn in turn, according to the order of rectangular grid cells. Here the difference is that by using cell subdivision method, contour extraction of the rectangular element is converted into contour extraction of a triangular element, which avoids complex tracing and ambiguity of contour line, and is efficient, accurate and simple. The main process of drawing the nephogram by the contour region filling method includes discrete data
Experiment results
Scheme
m
n
DT
Drawing time/ms
Accuracy
Image quality
1 2 3 4 5 6 7
1 2 3 2 2 2 2
1 2 3 2 2 2 2
0.5 0.5 0.5 0.1 0.3 0.8 1.0
110 188 312 890 328 140 109
No OK OK OK OK No No
No OK OK OK OK No No
14 Thermal map 1500 mm)
for
slab
with
cracks
(slab
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12 Temperature nephogram for loose side of mould (scheme 2)
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a 1?25 m min21; b 0?9 m min21 15 Thermal maps for different casting speeds (slab width: 1600 mm)
gridding, contour line generation and contour polygon filling. The detailed program flow of this method is shown in Fig. 10. Here, in the rectangular grid composed of four adjacent measuring points, the temperature of the grid nodes can be calculated by inverse distance weighted interpolation method,25 as shown in equation (6) T~
n n X Ti X 1 = p p d d i~1 i i~1 i
(6)
where n is the number of scatter points in the rectangle and here is 4; Ti is the temperature of each scatter point, i51, 2, 3, and 4; di is the distance from the scatter point to the interpolation point. p is the power parameter, and here is 1. In this method, there are three important parameters, namely, m, n and DT. Their setting is very important, which will directly affect the precision of contour, speed of drawing and image quality. Colour–temperature mapping
The mapping relationship between colour and temperature is obtained by linear interpolation method of Lagrange, as show in Fig. 11. Here, the RGB colour model is used. Temperature range [Tmin, Tmax] is divided equally into five temperature intervals, which correspond to five colour intervals. Tmin, Tmax and the colour mode can be set according to actual demand. If a temperature (T) is given and T1,T,T2, the colour relative to T can be calculated by equation (7) Colour~
T{T1 ðColour2{Colour1ÞzColour1 (7) T2 {T1
Simulation experiments
In the same computer operating environment (namely, operating system: Windows XP SP3, CPU: Intel Core2 Duo 2?33 GHz, 2 GB of memory), simulation experiments of the contour region filling method proposed above were carried out for selecting optimum parameters of drawing the thermal map. Take for example, drawing the temperature nephogram for the loose side of mould by this method. Experimental data are actual measured temperature of the thermocouples on the loose side of the mould copper plate, as shown in Table 5. Schemes and results of the experiments are shown in Table 6. For online thermal map, real time performance (namely drawing time) is the key, and accuracy and image quality can also meet the requirements. From Table 6, the optimum is scheme 2. The temperature nephogram for scheme 2 is shown in Fig. 12. The size of the nephogram displayed on the screen is determined by
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reducing the real size of mould in accordance with certain proportion. Not only can the optimum scheme ensure accuracy and image quality, but also its drawing time is only 188 ms. The acquisition cycle of actual temperature data is 1000 ms. It is obvious that the contour region filling method is feasible and effective for drawing online thermal map of mould.
Field application results The online thermal map based on the contour region filling method was integrated into the MBPS and applied in slab continuous casting process. Owing to the online thermal map, field operators can monitor the inner life of mould visually and get valuable online thermal information, which can help them to take corrective action in time for avoiding imminent abnormal events. As for actual applications, the examples about thermal maps in normal and abnormal conditions are presented and analysed. Figure 13 is a typical thermal map for the broad face of the mould in normal condition. In this case, the colour near the meniscus is relatively bright due to high temperature, and also stable with time, while in the middle and lower parts, the colour map gradually becomes dark with the decreasing of longitudinal temperature. Figure 14 shows a thermal map with characteristics of cracks. Owing to low temperature, the colour in the cold spot and surface depression is darker than the surrounding colour. This may be caused by incorrect taper, local poor heat removal, large liquid level fluctuation or poor melting of casting powder. Figure 15 reveals thermal maps for different casting speeds. With decreasing of casting speed, the whole colour map becomes dark. This is because decrease of casting speed results in thickening of slab shell and slowing of heat transfer. Figure 16 shows a typical thermal map for a stickingtype breakout process. When a sticker occurs near the meniscus, a hot spot with bright colour comes from the top, as shown in Fig. 16b. Then with the descending of the hot spot, a ‘V’ shape bright zone is visualised and expands downward, as shown in Fig. 16c–f, which symbolises formation and development of the tear. When the hot spot reaches the mould exit, a breakout will happen, as shown in Fig. 16h. Through the above analysis, the thermal maps visually display the formation and propagation behaviour of a sticker, which can help the operators to determine and avoid the potential breakout. At the same time, by comparing Fig. 16 with Fig. 13, it shows that the width of the colour map varies in accordance with the casting slab width. In addition, when there is bad measured temperature data, the thermal map will interpolate the temperature with the
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a 6 : 17 : 31; b 6 : 17 : 41; c 6 : 17 : 51; d 6 : 18 : 01; e 6 : 18 : 11; f 6 : 18 : 16; g 6 : 18 : 26; h 6 : 18 : 31 16 Thermal maps for sticking type breakout process (slab width: 2000 mm)
temperatures of adjacent measuring points for improving the display effect. Through above analysis, in practical application, online thermal map based on the contour region filling method could well reflect the characteristics of temperature field of mould in normal and abnormal conditions and is satisfied for the process of practical production.
Conclusions A high performance and reliable breakout prediction system is very important for ensuring smooth production and high quality slabs, and is beneficial for improve the online monitoring level of mould processes and the basis of realising high speed continuous casting. For this purpose, a new mould breakout prediction system with online thermal map was developed for slab continuous casting. This paper describes in detail the architecture and implementation of the system. For the system, the most important technologies are the mould breakout prediction model and the online thermal map. To reduce the ratio of false alarms, a novel logic judgment model is proposed for predicting the stickers. The model is based on more rows of thermocouples, and considers temperature change rate and propagation behaviour of stickers, which abandons the frequently
used the characteristic of temperature inversion model. The logic of the model is described in detail and analysed quantitatively through a simulation experiment using sticking type breakout history. By applying the proposed model in a steelmaking plant, field application results show that the detection ratio of stickers is 100% and the frequency of false alarm is 0?15% times/heat. It can be seen that the present model can achieve better performance for breakout prediction and prevention. To realise visualisation of the mould process and assist breakout prediction technology, a contour region filling method has been proposed to draw intuitive online thermal maps of the mould quickly and efficiently. In the method, discrete data gridding, contour line generating and contour polygon filling are used to draw nephogram. As for generation of contour lines, a method combining grid sequence method and cell subdivision method is adopted, which avoids the past problems about tracing and ambiguity of contour line, and is efficient, accurate and simple. An online thermal map of the mould based on the method is applied in actual production process. The results show that the thermal map could well reflect the characteristics of temperature field of mould in normal and abnormal conditions, and is satisfied for the process of practical operation.
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Acknowledgements This research is supported by Fundamental Research Funds for the Central Universities of China (No. FRFBR-10-027B).
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