Arc Furnace Steelmaking By Zhanyong Cao

Arc Furnace Steelmaking
By Zhanyong Cao, Dilifeila Dilixiati

Electric arc furnace(EAF) has a dramatically development over the past three decades which opens up entirely new option for numerical simulation. Simulation can be used to analyze physical phenomena of resistance until today to directly observe or manipulate measurements. This paper outlines the least technology in computational fluid-dynamic(CFD) to simulate a two-dimensional(2D) case on the EAF as well as an outlook on future fields of application, while being well aware that by far not all phenomena and literature can be used. This paper dose not claim a very thorough research on EAF which meanly include: process of model, calculation pressure and velocity, and user-defined scalar transport equation.
Key words: Electric arc furnace(EAF), Computational fluid-dynamic(CFD), User-defined scalar.

In the EAF, the energy required for melting is mainly introduced by electric power and converted into heat by an electric arc. According to an estimate, the proportion of electric steel is around 40 to 45% in the total world steel production. It must be noted that EAF consumes lot of electric energy and hence the cost and availability of electrical power are important issues in electric steel development. In order to increase the efficiency of these types of huge processes, trial and error methods are needed for expensive experimental research. CFD models help to achieve this understanding and improve it.

In the process of electric arc furnace steelmaking, oxy-fuel burners and oxygen lances can be used as sources. In our case, air is used for oxy-fuel burners which has a significant effect on reducing power consumption and shortening smelting time.
Model description:
The aluminum refining process is compliable which involves a variety of independent physical phenomena, such as: thermodynamics, electricals, fluid dynamics, thermal radiation, and chemical reactions. This complexity makes modeling the process more challenging. Hence, it needs the modeling concept to overcome.
The total phases and species of reduction process is quite large and complex. Considering making the base model, total five phases of modeling concept will reduce to two phases: air and solid.
Geometric model is reduced to making axisymmetric simplification with 2D simulation. As this graph, this is a 2D geometry which has a 4 meter long inlet in lower left side and its total length is 10 meter, width is 5 meter.

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Mathematical modelling:
CFD plays a significant role in engineering prediction. It complements pure experiment and pure theory and is applied to solve partial differential equations which describe the fluid flow and heat and mass transfer.
Governing equations:
Mass, momentum and energy conservations are fundamental physical principles for all fluid flows.
The following equations were solved during modeling:
Mass conservation:
The equation of continuity is expressed as:
??/?t+?(?V ? )=S_m
where S_m is the source term which is zero in this case.
Momentum conservation:
Momentum conservation is based on the Newton’s second law.
For the case natural convection, the momentum equation is written as:
(?(?V ?))/?t+??(?V ?V ? )=(?-?_? )?g-?P+??(? ?)
User-defined scalar (UDS):
transport equation One additional transport equation was used to detect mixing efficiency in the bath. For an arbitrary scalar ?_k the following transport equation is solved:
(???_k)/?t+?/(?x_i ) (?u_1 ?_k-?_k (??_k)/(?x_i ))= S_?k
where is the diffusion term, ?_k is diffusion coefficient, which was equal to 0.00001 and S_?k is the source term which was 0.
The UDS was initialized with a linear vertical profile as it was set to 0 and 1 at the bottom and top of the melt respectively,

Even though the EAF batch process is exceedingly complex to model in complete detail, numerical simulation is an excellent tool for studying separate aspects of the process. The simulation models help us predict the benefits of electromagnetic stirring and optimize the way an electromagnetic stirrer is operated in different stages of the batch process. In our examples we have highlighted how electromagnetic stirring gives a more than ten-fold increase of the melt flow velocity, compared to natural convection. As a consequence, the span between maximum and minimum temperatures in the melt can be dramatically reduced. This helps avoiding cold regions in the furnace and reduces super-heat of the melt surface. Forced convection also strongly increases the heat transfer between melt and solids, and thus accelerates melting of large pieces of scrap. A reduced super-heat of the melt surface is shown to reduce heat losses to the freeboard, but a greater gain in energy efficiency probably lies in the possibility to stop heating earlier in the batch without risk of freezing in the bottom, when the temperature distribution is even. The electromagnetic stirrer provides efficient mixing and homogenization of species concentrations. Together with the even temperature distribution this will improve efficiency and controllability of the chemical reactions in the metal-slag interface. In particular, lower surface temperatures and efficient mixing will lower the iron-oxide content of the slag and increase the yield.