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Bubble Characterization in a Continuous Casting Mold: Comparison and Identification of Image Processing Techniques

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Abstract

Gas injection is a common practice in numerous metallurgical vessels, and continuous casting mold is one of the examples of such vessels. Argon gas injection into the submerged entry nozzle contributes to increasing the sequence length. However, in some unfavorable conditions, these bubbles become responsible for the occurrence of steel defects such as blister, sliver, and mold slag exposure, etc. Bubbles size distribution in the mold is one of the indicators of cast steel slab quality. Previous studies on estimating the bubble size distribution in mold lack in providing the challenges of automatic image processing (IP) techniques employed to measure the size of the bubbles. On the other hand, bubble size measurement studies performed on other reactors such as bubble columns, etc., are programming intensive and need in-depth knowledge of coding. Some automatic IP techniques also exist which facilitate a user-friendly environment and quick estimation of bubble characteristics. Even though these IP techniques are easy to use, an assessment of their applicability with respect to experiments performed in a particular study is required. Therefore, in this study, details of imaging and automatic IP techniques are discussed through a comprehensive comparative analysis. Three different IP techniques were examined for measuring the bubble sizes, and the performances of these techniques were compared with respect to a newly developed manual benchmark technique. One of the identified techniques, Ellipse Split, was applied to estimate the bubble size distribution at different gas fractions for validation.

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Abbreviations

F g :

Gas fraction in liquid

\(Fr^{\prime}_{m}\) :

Modified Froude number for model

\(Fr^{\prime}_{p}\) :

Modified Froude number for prototype

ρ Air :

Density of air at 25 °C

v Air :

Velocity of air at 25 °C

ρ steel :

Density of liquid steel at 1550 °C

ρ water :

Density of water at 25 °C

ρ Ar :

Density of Ar at 1550 °C

v Ar :

Velocity of Ar at 1550 °C

Q Ar,1550 °C :

Flow rate of Ar at 1550 °C

Q Ar,25 °C :

Flow rate of Ar at 25 °C

Q Air,25 °C :

Flow rate of air at 25 °C

Q m,l :

Liquid flow rate of model

Q p,l :

Liquid flow rate of prototype

L p :

Characteristic length of prototype

L m :

Characteristic length of model

T Ar,1550 °C :

Temperature of Ar at 1550 °C

T Ar,25 °C :

Temperature of Ar at 25 °C

λ :

Ratio of characteristic lengths (scale factor)

d B :

Diameter of the bubbles

d i :

Diameter of ith bubble

R CHT :

Radius of circle covering a binary object by Circle Hough Transformation

A proj :

Area of projection of bubbles

b :

Ratio of major to minor diameters

d 32 :

Sauter mean diameter of the bubbles

\({\rho }_{g}\) :

Gas density

U :

Average liquid velocity

D :

Nozzle diameter

\({\mu }_{L}\) :

Viscosity of liquid

D f :

Far distances for acceptable sharpness

D n :

Near distances for acceptable sharpness

F :

Focal length

N :

Aperture F-number

C :

Circle of confusion (mm)

S :

Focus distance or distance to subject (mm)

H :

Hyperfocal distance (mm)

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Acknowledgments

The authors thank the Natural Sciences and Engineering Research Council of Canada (NSERC), ArcelorMittal, and the University of Toronto Dean’s Catalyst Professorship for funding this research.

Conflict of Interest

Authors declare no conflict of interest in publishing this manuscript.

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Authors and Affiliations

  1. Process Metallurgy Research Lab, Department of Materials Science and Engineering, University of Toronto, 184 College Street, Toronto, ON, M5S 3E4, Canada

    Amiy Srivastava, Ali Asgarian & Kinnor Chattopadhyay

  2. ArcelorMittal Global Research and Development, Hamilton, ON, L8H 3N8, Canada

    Joydeep Sengupta

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  1. Amiy Srivastava
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  2. Ali Asgarian
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  4. Kinnor Chattopadhyay
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Appendix

Appendix

A. Bubble Population Independence Study for Different Image Processing Techniques

Sauter mean diameters from the four image processing techniques, with increasing number of bubbles, were analyzed for 1.7 and 7.9 pct Fg. This study is known as the bubble population independence study. The purpose of this study is to obtain a value of d32 which should remain approximately constant (within 0.5 pct error) on introducing further new bubbles to the observations. From d32 analysis for both 1.7 and 7.9 pct Fg, shown in Figures A1(a) and (b), respectively, it could be seen that d32 values observed from W + AP and ES are close to those obtained via benchmark technique. However, d32 values by CHT significantly deviate from the benchmark.

Fig. A1
figure 20

Sauter mean diameters calculated using four image processing techniques at (a) 1.7 pct Fg, (b) 7.9 pct Fg

Full size image

A corresponding error analysis was performed in which pct error in d32 was calculated with increasing number of bubbles until it reduced to < 0.5 pct. Figures A2(a) and (b) represent the plots of errors in d32 measurement for 1.7 and 7.9 pct Fg, respectively. Errors for 1.7 and 7.9 pct Fg reach below 0.5 pct at approximately 8000 and 2500 bubbles, respectively. Values of d32 at this low error were used to compare the image processing techniques with respect to the benchmark.

Fig. A2
figure 21

pctError in measuring d32 for (a) 1.7 pct Fg, (b) 7.9 pct Fg

Full size image

B. Bubble Population Independence at Different Pct F g

Bubble population study was performed at low to high pct Fg, as shown in Figure B1. Value of d32 becomes independent of bubble population within the range of 8000 to 10,000 bubbles.

Fig. B1
figure 22

Bubble population independence study from low to high pct Fg

Full size image

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Srivastava, A., Asgarian, A., Sengupta, J. et al. Bubble Characterization in a Continuous Casting Mold: Comparison and Identification of Image Processing Techniques. Metall Mater Trans B 53, 2438–2457 (2022). https://doi.org/10.1007/s11663-022-02541-2

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