The application of small industrial cameras linked to a computer to create models for monitoring of adverse events in the steel making process is referenced. Presented models were created by analyzing video recordings that were captured in areas that could directly threaten human life. In laboratory conditions it is possible to analyze the recorded data and then already known mathematical methods are used to evaluate phenomena that are characterized in operating conditions by rapid pace and their monitoring on site is difficult and dangerous

A. Rosová

J. Futó

M. Malindžáková

V. Baranová

English

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Modelling of outbursts and ejections occurrences during steel production

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247METALURGIJA 54 (2015) 1, 247-250M. MALINDŽÁKOVÁ, A. ROSOVÁ, V. BARANOVÁ, J.FUTÓMODELLING OF OUTBURSTS AND EJECTIONS OCCURRENCES DURING STEEL PRODUCTIONReceived – Primljeno: 2014-04-16Accepted – Prihvaćeno: 2014-09-09Preliminary Note – Prethodno priopćenjeISSN 0543-5846METABK 54(1) 247-250 (2015)UDC – UDK 669.01.1 8:669.18.047:62.001.57=111M. Malindžáková, A. Rosová, J. Futó - Technical University of Košice, BERG Faculty, Košice, SlovakiaV. Baranová, VŠB – Technical University of Ostrava, Fakulta of Me-chanical Engineering, Czech RepublicThe application of small industrial cameras linked to a computer to create models for monitoring of adverse events in the steel making process is referenced. Presented models were created by analyzing video recordings that were captured in areas that could directly threaten human life. In laboratory conditions it is possible to analyze the re-corded data and then already known mathematical methods are used to evaluate phenomena that are character-ized in operating conditions by rapid pace and their monitoring on site is difficult and dangerous.Key words: steel making process, information technology, create models, ejection, outburstINTRODUCTIONCurrent new information technologies allow to cap-ture and record data of production processes even in dif-ficult operating conditions in real time. A subsequent analysis and evaluation of measurement data, directly on site or in the laboratory can bring new knowledge about these phenomena. [1-6]. One such example, de-scribed in this article is the use of small industrial cam-eras followed by the creation of computer models for analysis of ejection and outbursts in the steel making process. Videos of these phenomena could be closely analyzed in laboratory conditions and then evaluated using already known mathematical techniques. Steel-making processes are well described already; therefore this article focuses only on problem areas that bring new insight into the observed phenomena [7]. THE EJECTIONThe term Ejection in this context represents irregular splatter of smaller or larger particles of metal and slug blasting from the neck of the converter. Ejection may be caused by several factors, namely:1.  by explosive contact of reacted and unreacted part of the melt,2. due to improper converter form,3. due to poor circulation or uneven melt,4. due to resonance motion of the melt and gas,5.  due to resonance transverse waves in the converter,6.  due to shape of the workspace of the converter, when there is no consistent depth of the melt, the amount of workspace and decarburization rate.The ejection causes a decrease in the yield of steel (0,5 to 1 %), changes in the thermal balance of melt dur-ing refining, changes in the final temperature of the steel and there are increases in crust grows on the mouth of the throat of the converter. The ejection is visually identifia-ble as a phenomenon in which the converter appears in flames, and there are splashes of molten slag and metal from the throat of the converter (Figure 1) [8, 9].THE OUTBURSTIn the steel making process, oxygen is delivered into molten iron through a nozzle in the form of gas flow for decarbonisation. Contemporary authors state that the peak of the flow causes rapid decarbonisation of slug in the final stage of the process. Gas flow detaches the liq-uid which then spatters in metal droplets. This phenom-enon is called an outburst, causing operational problems that lead to a reduction in productivity and metal yield. Therefore it is desirable to reduce the number of out-Figure 1 The ejection sequence (duration 8 s)248  METALURGIJA 54 (2015) 1, 247-250M. MALINDŽÁKOVÁ et al.: MODELLING OF OUTBURSTS AND EJECTIONS OCCURRENCES DURING STEEL...bursts. Visually the outburst can be observed as flames outburst from the converter (Figure 2) [8, 9].MODELING AND DISCUSSIONBoth outburst and ejection negatively affect the ef-fectiveness of the steelmaking process [8, 9]. The data analysis was based on the assumption that these phe-nomena are „failures” that result from some variables „that are beyond our control” during steel refining and that they negatively influence the process. The whole process of steel refining has been recorded by cameras CAM1 and CAM2 (Figure 3). The observed adverse events were recorded and the necessary information was evaluated so that we can determine „the duration until the failure occurs” or „the duration of failure”. The camera placed below the converter monitored ejections, whilst the camera positioned above the converter moni-tored the outburst. The starting point for analysis was the record of 31 subsequent casting campaigns. Given the amount of data retrieved from these records, we can assume that the file provides sufficiently substantial data sample that can be evaluated using several statisti-cal methods. For both observed phenomena we record-ed two of the variables, namely:•  the time until the phenomenon started,• the duration of the phenomenon.Both these phenomena are characterized by a ran-dom variable - time. In Figure 4 are graphs showing the number of out-bursts and ejections in each casting campaign. It is im-portant, that the number of outbursts is more than 2,5 times grater than the number ejections [10, 11].TIME UNTIL THE BEGINNING OF AN OUTBURST The analyzed file contained 290 duration values from the beginning of an outburst. These values were then divided into nine classes, with class characters in the range from 25,44 up to 416,56 seconds. The out-burst is characterised by a large number of short time intervals, where the first three classes represent more than 92 % of the total value and the remaining 6 classes represent the 8 % of the value. Histogram of the prolif-eration from the beginning of an outburst is shown in Figure 5.In quantitative terms, if the frequency values for all times until the outbursts failure were the same, there would be zero variability. In the examined values, the fluctuations of duration-until-failure values for each class are visible. The variability of these values is ex-pressed by distribution of their frequencies.TIME UNTIL THE START OF AN EJECTIONA total of 129 values until the beginning of an ejection for 31 casting campaigns were divided into 8 classes, with class characters in the range from 49 to 735 seconds. Figure 2 The outburs sequence (duration 6 s)Figure 3  Deployment of cameras CAM1 and CAM2Figure 4  The number of outbursts and ejections in casting campaign249METALURGIJA 54 (2015) 1, 247-250M. MALINDŽÁKOVÁ et al.: MODELLING OF OUTBURSTS AND EJECTIONS OCCURRENCES DURING STEEL...From Figure 6 it is evident that the time interval to out-burst start varies several times. The frequency of this phenomenon is demonstrated by the large number of short time intervals, where the first three classes are rep-resented by almost 82 % of the total number of times to ejection failure and remaining five classes consisted of 18 % of the total number of times to ejection failure.DURATION OF AN OUTBURSTThe frequency for this phenomenon is mainly re-flected by a large number of short time intervals, where the first three classes represent almost 88 % of the total duration of failure for outbursts and the remaining six classes represented only 12 % of the total number of duration of failure for outbursts (Figure 7). THE DURATION OF AN EJECTIONThe duration of the ejection from 31 casting cam-paigns were divided into 8 classes, with class characters are in the range from 19,75 to 282,25 seconds. From the Figure 8 it is clear that the phenomenon is again concen-trated in the first three time classes, where the first three classes are represented by almost 91 % of the total dura-tions of the ejection failure and the remaining five classes representing merely 9 % of the total. The duration of the disorder for ejection compared with the class mark time to failure for outbursts is 1,5 times longer [12].CONCLUSIONThe processing of the two sets of data shows that these are two distinct phenomena in the frequency of their occurrence in the process, as well as in their dura-tion. Ejection process is less favorable in terms of man-ifestation and most often occurs in the interval from the end of the third minute to the end of the sixth minute. During these three minutes occurs the ejection of dura-tion from 25 seconds to about three minutes. Frequent-ly, however, it takes 1 minute. The nature of the disorder depending on the intensity could result in the disruption of the casting, and to associated losses. Outburst, unlike ejection, is manifested by much more frequent and fast-er occurrences. The most frequently seen are short out-burts intervals ranging from thirty to ninety seconds. In comparison to ejection there is much more dynamic manifestation, with much shorter duration occurring, however, more frequently.Compared with the total duration of „failure“ for outburts the value of the class character is almost com-parable. These quantitative relations between both monitored failures show a clear box and whiskers plot (Figure 9).It is clear that for the quality of the steelmaking pro-cess it is important to keep the process without ejection, since ejections affect the terms of the effectiveness of the process so significantly. In terms of production it is important to first control the phenomenon, which is less Figure 5  Histogram of the absolute frequency and frequency function of time until an outburst beginsFigure 6  Histogram of the ab solute frequency and frequency function of the time until an ejection begins Figure 7  Histogram of the abso lute frequency and frequency function of the time until an outburst beginsFigure 8  Histogram of the absolute frequency and frequency function of the time of an ejection250  METALURGIJA 54 (2015) 1, 247-250M. MALINDŽÁKOVÁ et al.: MODELLING OF OUTBURSTS AND EJECTIONS OCCURRENCES DURING STEEL...favorable, that is the ejection, which is slower and less numerous. Given the possibility of preventing or pre-dicting ejection, it is necessary to further study and ana-lyze it on the basis of experiments carried out by using indirect methods, such as acoustic or visual manifesta-tion of ejection to predict and subsequently manage it.Finally, it is important to note that the main aim of the article is mainly to describe the possibility of differ-ent models creation, from data acquired in the most de-manding conditions and does not address the forecast-ing, or the suppression of these adverse events. The de-sign and technological solutions to eliminate these phe-nomena are already known and often used.AcknowledgementThis work was supported by the Slovak Research and  Development Agency under the grants VEGA No. 1/0216/13, VEGA No. 1/0729/12, VEGA No. 1/0552/14, VEGA  No. 1/0295/14.REFERENCES[1] D. Baricová, A. Pribulová, P. Demeter, B. Buľko, A. Ro-sová: Utilizing of the metallurgical slag for production of cementless concrete mixtures, Metalurgija, 51 (2012) 4, 465-468. [2] I. Leššo, P. Horovčák, P. Flegner: Evaluation of sensor si-gnal processing methods from the aspect of information theory, AT&P Journal, 11 (2004) 4, 70-73. [3] J. Kačur, M. Durdán, M. Laciak: Utilization of the PLC as a web server for remote monitoring of the technological proces. In: ICCC’2013: 14th International Carpathian Control Confe-rence, Rytro, Poland. Piscataway: IEEE, 2013, 144-149.[4] V. Mihalič: Steel making II. Vydavateľstvo technickej a ekonomickej literatúry, Bratislava, 1983.[5] V. Baranová: The impact of Quality of Input Material for the Converter Process, Kosice, Technicka univerzita, F BERG, Diploma thesis, 2010.[6] Ľ. Floreková: Methods of statistical quality assessment - SPC. Acta Montanistica Slovaca, 1 (1998), 1-20. [7] M. Laciak, M. Durdán, J. Kačur: Utilization of indirect measurement in the annealing process of the steel coils, Acta Metallurgica Slovaca, 18 (2012) 1, 40-49.[8] J. Kačur, M. Laciak, M. Durdán: Remote monitoring and control of the UCC process. 1 elektronický optický disk (CD-ROM). In: ICCC’2011: proceedings of the 12th Inter-national Carpatian Control Conference, 2011, Velké Karlo-vice, Czech Republic. IEEE, 2011, 180-184. [9] I. Leššo, F. Krepelka, P. Horovčák, J. Terpák, M. Benková, P. Flegner,  G. Bogdanovská, D. Bednárová, M. Šujanský, M. Hudymáčová, B. Stehlíková: Research for exact me-thods of assessing the effectiveness and quality of the pro-cess of rocks disintegration by rotary drilling, Final Report for the duration of the project: No. project: 1419407/ - Košice: TU - 2009.[10] A. Rosová: Indices system design of distribution logistics, transport logistics and material flow as parts of controlling in enterprise’s logistics. Acta Montanistica Slovaca. 15 (2010) 1, 67-72. [11] B. Stehlíková, K. Kostúr, M. Jaco: Technological process distortions identification by digital cameras taken. In: ICCC ‘2005. Volume 1. Miskolc: University of Miskolc, 2005, 355-360. [12] J. Tůma, R. Wagnerová, R. Farana, L. Landryová: Funda-mentals of Automation, 1. edition. Ostrava, VŠB-TU Ostrava, 280 p.Note:  The English Language translation was done by L. Pivka, Kosice, SlovakiaFigure 9 Box and whiskers plot

The application of small industrial cameras linked to a computer to create models for monitoring of adverse events
in the steel making process is referenced. Presented models were created by analyzing video recordings that were
captured in areas that could directly threaten human life. In laboratory conditions it is possible to analyze the recorded
data and then already known mathematical methods are used to evaluate phenomena that are characterized
in operating conditions by rapid pace and their monitoring on site is difficult and dangerous.Web of Science54125024

Malindžáková, Marcela

Rosová, Andrea

Baranová, Vladena

Futó, Jozef

DSpace at VSB Technical University of Ostrava

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Modelling of outbursts and ejections occurrences during steel production

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