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Optimization of bath mixing and steel cleanliness during steel refining through physical and mathematical modeling

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Abstract

During ladle refining process, argon gas is purged into the ladle for stirring the molten steel bath to eliminate thermal and composition gradients and to achieve inclusion flotation. Operating parameters like purging location, porous plug configuration and argon flow rate primarily affect liquid steel refining. The efficiency of ladle processing is often quantified through mixing time. To optimize the mixing time and the associated process parameters for improved bath homogenization and inclusion flotation under different operating conditions, water modeling studies using 0.2 scale perspex model and computational fluid dynamics studies using ANSYS CFX v14.5 have been carried out through fluid profile assessment and mixing time comparison. Comparative study was made between single plug, dual plug and top lance purging configurations. The studies helped in identifying the optimum argon purging rates and configurations under normal operational practices. Under abnormal operating conditions involving purging failure from either of the two porous plugs, usage of a top lance along with the single working porous plug has been investigated and found to improve mixing and inclusion flotation in the ladle equivalent to dual plug operation. The lab scale studies have been validated on plant scale through inclusion mapping and found to be in close agreement.

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Acknowledgement

The authors would like to thank secondary steelmaking operations team at JSW Steel Ltd. Vijaynagar Works for the help and support extended during this study.

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Correspondence to PRANAV KUMAR TRIPATHI.

Symbols

Symbols

CPC:

Calcined petroleum coke

LHF:

Ladle heating furnace

\(g\) :

Gravitational acceleration

\(t\) :

Time

\(T\) :

Temperature

\(C_{D}\) :

Drag coefficient

\(R_{e}\) :

Reynolds number

\(d\) :

Distance or length

\(d_{\beta }\) :

Mean particle diameter of phase \(\beta\)

\(A_{{\alpha \beta }}\) :

Interfacial area density

\(p\) :

Gauge pressure

\(P_{k}\) :

Turbulence production due to viscous forces

\(r\) :

Location vector

\(r_{\beta }\) :

Volume fraction of phase \(\beta\)

\(S_{M}\) :

Momentum source

\(S_{{M,buoy}}\) :

Momentum source due to buoyancy

\(P_{{kb}}\), \(P_{{\varepsilon b}}\):

Buoyancy production & dissipation terms (represent influence of the buoyancy forces)

\(C_{{\varepsilon 1}}\), \(C_{{\varepsilon 2}}\):

\(k - \varepsilon\) Turbulence model constants

\(u\) :

Fluctuating velocity component in turbulent flow

\(U\) :

Vector of velocity \(U_{{x,y,z}}\)

\(U_{t}\) :

Turbulent velocity scale

\(l_{t}\) :

Turbulence length scale

\(\alpha\) :

Subscript indicating phase \(\alpha\)

\(\beta\) :

Subscript indicating phase \(\beta\)

\(\rho\) :

Density

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

Reference density

\(\sigma _{k}\) :

Turbulence model constant for the \(k\) equation

\(\sigma _{\varepsilon }\) :

\(k - \varepsilon\) Turbulence model constant

\(\tau\) :

Shear stress

\(\delta\) :

Identity matrix or Kronecker Delta function

\(k\) :

Turbulence kinetic energy per unit mass

\(\varepsilon\) :

Turbulence dissipation rate

\(\mu\) :

Molecular (dynamic) viscosity

\(\mu _{t}\) :

Turbulent viscosity

\(\nabla\) :

Gradient

\(\cdot\) :

Scalar product

\(\otimes\) :

Tensor product

\(d_{b}\) :

Bubble diameter

Q :

Flowrate (Nm3/s)

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TRIPATHI, P.K., KUMAR, D.S., SARKAR, A. et al. Optimization of bath mixing and steel cleanliness during steel refining through physical and mathematical modeling. Sādhanā 46, 146 (2021). https://doi.org/10.1007/s12046-021-01663-8

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  • DOI: https://doi.org/10.1007/s12046-021-01663-8

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