Modeling and Validation of an Electric Arc Furnace: Part 1, Heat and Mass Transfer

Logar, Vito; Dovžan, Dejan; Škrjanc, Igor

doi:10.2355/isijinternational.52.402

Cited by 64 publications

(105 citation statements)

References 16 publications

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“…3 to 6. It can be observed that results of the proposed algorithm are comparable to constant values of arc energy distribution reported by Logar et al 18) and Ghobara 20) at comparable input conditions. The relationship between the arc length, arc current and the calculated arc power is shown in Fig.…”

Section: Resultssupporting

confidence: 73%

“…The main difference between the results appears due to constant conductive heat transfer in Makarov's work, which does not change by altering the arc lengths. Hence, the proposed model indicates slightly different percentage of radiated heat when it is compared to the result reported by Makarov et al 21) The second validation of the arc module is performed by integrating the module into a comprehensive EAF model 18,19) in order to obtain the EAF bath temperatures and compare them with the measured EAF operational data. The measurements were performed on a 105 ton EAF, with 85 MVA transformer.…”

Section: Validationmentioning

confidence: 99%

“…A literature review of the existent EAF models [8][9][10][11][12][13][14][15][16][17][18][19][20] indicates a lack of accurate arc models that could be applied for smart sensing, control or optimization of the energy flows. It is assumed that such model should present specific features that support their industrial application, including minimum input requirement, sufficient accuracy, simple mathematics and short calculation times.…”

Section: Introductionmentioning

confidence: 99%

“…The share of convective heat transfer when air is employed to form plasma in an EAF was predicted at 72.5%, which is more than the expected value. 20,21) The second group involves models, which describe all significant parts of the EAF process; [13][14][15][16][17][18][19][20] however, those models hardly consider arc energy dissipation, except Logar et al, 18,19) who assumed constant values for arc energy distribution (i.e. 20% convective heat, 75% radiation heat, 2.5% of the energy transferred to gas zone and 2.5% of the energy lost to electrodes) and Ghobara 20) who as well as Logar et al 18,19) considered constant coefficients for the heat transferred from the arc.…”

Section: Introductionmentioning

confidence: 99%

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Low Computational-complexity Model of EAF Arc-heat Distribution

et al. 2015

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The paper presents a computational model and the corresponding algorithm for estimating the arc energy distribution to conductive, convective and radiative heat transfer in an electric arc furnace (EAF). The proposed algorithm uses channel arc model (CAM) in order to compute the distribution of the arc energy through empirical equations (to approximate arc radius), ideal gas law (to approximate arc density) and results of magneto-hydro-dynamic (MHD) models (to approximate arc pressure, temperature and velocity). Results obtained using the proposed algorithm are comparable with other similar studies; however, in contrast to other arc-energy distribution models, this model requires only two input variables (arc length and arc current) in order to calculate the energy distribution. Furthermore, simple algebraic equations used in the algorithm ensure minimal computational load and consequently lead to short calculation times which are approximately one hundred thousand (100 000) times smaller than solving the MHD model equations, making the algorithm suitable for real-time applications, such as smart monitoring and model-based control. The algorithm has been validated by two different approaches. First, the simulation results have been compared to a study dealing with arc-heat distribution in plasma arc furnace; and second, the proposed arc module has been integrated into the frame of a comprehensive EAF model in order to estimate the EAF temperature levels and compare them with operational EAF measurements. Both validations show high levels of similarity with the comparing data.KEY WORDS: arc current; arc heat distribution; arc length; channel arc model; EAF.approximately 75-85% of the total energy in low to medium power furnaces and approximately 50-60% of the total energy in ultra-high power furnaces (UHP).3) Implementing an accurate arc module in a comprehensive model-based EAF control, which ensures optimal control of arc length and slag height can lead to substantial energy saving and associated cost reduction. If a total energy reduction in a 200 ton UHP EAF with approximate annual production of 675 000 tones is 50 kWh/ton, with an assumption of 25 kWh/ton being the electrical energy, with the price of 10 $Cent/kWh, total annual energy savings will be equivalent to 1.7 million $ per year.It is known that the heat generated by the arcs is dissipated into the furnace by all three mechanisms of heat transfer (convection, conduction and radiation); however, the amount of the heat, transferred by each mechanism varies according to several factors, such as arc length, arc current, slag height, stage of melting etc. Development of a computational model of the arc, which allows estimation of the arc-heat distribution should therefore include all three types of heat transfer mechanisms, which can also be used to obtain the overall energy balance of the EAF. Application of such models may provide appropriate tools for optimizing the energy flow or use of model-based control systems in the EAFs.

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Section: Resultssupporting

confidence: 73%

Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low Computational-complexity Model of EAF Arc-heat Distribution

et al. 2015

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“…In times of continuously growing computational capacity, the complexity of the dynamic process simulation models has increased due to the consideration of more and more phenomena. Logar et al [2][3][4] presented a comprehensive deterministic EAF model, which is based on fundamental physical and mathematical equations. The model includes all main thermal, chemical, and mass transfer phenomena in the EAF.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of the Off-gas in an Electric Arc Furnace

Meier

Gandt

Echterhof

et al. 2017

Metall Mater Trans B

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The following paper describes an approach to process modeling and simulation of the gas phase in an electric arc furnace (EAF). The work presented represents the continuation of research by Logar, Dovzˇan, and Sˇkrjanc on modeling the heat and mass transfer and the thermochemistry in an EAF. Due to the lack of off-gas measurements, Logar et al. modeled a simplified gas phase under consideration of five gas components and simplified chemical reactions. The off-gas is one of the main continuously measurable EAF process values and the off-gas flow represents a heat loss up to 30 pct of the entire EAF energy input. Therefore, gas phase modeling offers further development opportunities for future EAF optimization. This paper presents the enhancement of the previous EAF gas phase modeling by the consideration of additional gas components and a more detailed heat and mass transfer modeling. In order to avoid the increase of simulation time due to more complex modeling, the EAF model has been newly implemented to use an efficient numerical solver for ordinary differential equations. Compared to the original model, the chemical components H 2 , H 2 O, and CH 4 are included in the gas phase and equilibrium reactions are implemented. The results show high levels of similarity between the measured operational data from an industrial scale EAF and the theoretical data from the simulation within a reasonable simulation time. In the future, the dynamic EAF model will be applicable for on-and offline optimizations, e.g., to analyze alternative input materials and mode of operations.

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Water detection framework for industrial electric arc furnaces: Boundary modelling and formulation

et al. 2023

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This paper describes the development of a boundary model for the off‐gas water vapour in an industrial steelmaking electric arc furnace (EAF). The solution addresses the mechanistic components of a complete EAF water detection framework. The boundary model has been implemented on an industrial alternating current (AC) EAF. The model specifies upper and lower limits in real‐time of the expected EAF off‐gas water vapour leaving the furnace, and it provides a valuable on‐line monitoring tool to the operator on what boundary to expect for the off‐gas water vapour in different circumstances. An essential data required for the framework is the EAF off‐gas composition. So, in this work, an off‐gas analyzer with a human machine interface (HMI) and a supervisory control and data acquisition (SCADA) system was installed in the first step. Then, in order to evaluate the developed water leak detection framework and verify the obtained results, industrial trials were designed in which a certain amount of water was intentionally added into the furnace by increasing the electrode spray water flow rate. Moreover, we have shown how the presented framework can be used to appropriately adjust the alarm setting values in control/emergency shutdown systems of industrial EAF to enhance the safety and availability of the plant.

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Modeling and Validation of an Electric Arc Furnace: Part 1, Heat and Mass Transfer

Cited by 64 publications

References 16 publications

Low Computational-complexity Model of EAF Arc-heat Distribution

Low Computational-complexity Model of EAF Arc-heat Distribution

Modeling and Simulation of the Off-gas in an Electric Arc Furnace

Water detection framework for industrial electric arc furnaces: Boundary modelling and formulation

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