Simulation of EAF refining stage

Elkoumy, Mohamed Mahmoud; El-Anwar, Mohamed I.; Fathy, Ayman; Megahed, G. M.; El-Mahallawi, I.; Ahmed, H.

doi:10.1016/j.asej.2017.10.002

Cited by 15 publications

(22 citation statements)

References 29 publications

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“…The EAF drawing is illustrated in Figs. 1-a 34) and 1-b. The parameters used to build the melt geometry are summarized in Table 1.…”

Section: Operating Conditionsmentioning

confidence: 99%

“…At the end of the refining stage, the temperature and chemistry were measured during waiting and arcing time to investigate the effect of forced convection compared to natural convection on steel melt homogeneity. 34) The temperature measurements made at the end of the refining stage before tapping "for natural convection condition" showed 1 630-1 650°C, and showed a variation only within a range from 28 to 92°C, at the receiving stage in the ladle refining furnace (LRF). 34) The temperature measurements made at the end of the refining stage before tapping "for forced convection condition" showed 1 630-1 650°C and varied within a range of 66 to 74°C at the receiving stage in the LRF.…”

Section: Operating Conditionsmentioning

confidence: 99%

“…34) The temperature measurements made at the end of the refining stage before tapping "for natural convection condition" showed 1 630-1 650°C, and showed a variation only within a range from 28 to 92°C, at the receiving stage in the ladle refining furnace (LRF). 34) The temperature measurements made at the end of the refining stage before tapping "for forced convection condition" showed 1 630-1 650°C and varied within a range of 66 to 74°C at the receiving stage in the LRF. 34) The chemistry measurements made at the end of the refining stage before tapping "for natural convection condition" varied from 0.03-0.04% C, and varied from 0.0095 till 0.0117% C at the receiving stage natural convection computational model are illustrated in Fig.…”

Section: Operating Conditionsmentioning

confidence: 99%

“…The refining conditions include different slag thickness combined with different steel melt temperature and chemistry. This study is based on previous study done by Elkoumy et al 34) for simulation of EAF refining stage.…”

Section: Introductionmentioning

confidence: 99%

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Computational Simulation Model for Metallurgical Effects during EAF Refining Stage: Waiting and Arcing Time

Elkoumy¹,

El-Anwar

Fathy³

et al. 2018

ISIJ International

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3D computational fluid dynamics models using Fluent were developed to investigate the steel melt flow during waiting and arcing time. Both models were transient that analyzed 60 seconds to investigate the flow characteristics considering variation in steel melt thermo-physical parameters and operating conditions. The velocity of melt movement was high enough to make a turbulent flow (solved with realize k-ε turbulence model). It was found that the steel melt flow velocity increases by a combined effect of the steel melt temperature and composition, and slag pressure. The slag pressure increases by a double effect of slag density and height, and the steel melt fluid flow velocity changes with the slag pressure. The effect of the slag thickness is more significant than the effect of thermo-physical properties of steel melt. Although, the maximum steel melt velocity "during arcing time" may be as large as 0.67 m/s located at steel met outlet, the melt exhibits completely dead zones with minimum flow velocity distribution especially at the bottom and circumference areas. This indicates the importance of combined stirring and large reaction rates to achieve a complete homogeneous melt especially at bottom and circumference areas.

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“…The EAF drawing is illustrated in Figs. 1-a 34) and 1-b. The parameters used to build the melt geometry are summarized in Table 1.…”

Section: Operating Conditionsmentioning

confidence: 99%

Section: Operating Conditionsmentioning

confidence: 99%

Section: Operating Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computational Simulation Model for Metallurgical Effects during EAF Refining Stage: Waiting and Arcing Time

Elkoumy¹,

El-Anwar

Fathy³

et al. 2018

ISIJ International

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“…The data collected covered the whole range of mainly carbon content: 0.02-0.20% C at high temperature of 1550-1700 C. The aim of this study is to develop a detailed model simulating the industrial EAF refining stage that can be used as a guide by steelmakers to implement different optimization and control strategies. This study is the third in a series of studies on the EAF refining stage, where the first two focused on the steel melt microscale computational analysis, [45,46] and this study focuses on steel melt macro analysis based on real-life measurements.…”

Section: Introductionmentioning

confidence: 99%

Empirical Model for Predicting Process Parameters during Electric Arc Furnace Refining Stage Based on Real Measurements

Elkoumy

Fathy

Megahed

et al. 2019

steel research int.

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The present study aims at building a road map‐model for the steel melt compositional variations during the electric arc furnace (EAF) refining stage. The model is based on real measurements and compared to thermodynamic predictions. The studied parameters are steel melt carbon content and temperature. Herein, high‐temperature investigations (1550–1700 °C) with different chemistry ranges (mainly carbon content, 0.02–0.20% C) are carried out during the refining stage operation. The work develops a detailed empirical model to simulate the industrial EAF refining stage, which can be used to implement different optimization and control strategies for the EAF refining process. Furthermore, the study investigates the refining stage metallurgical phenomenon and the different operating conditions used to produce different steel grades over a wide range of carbon content; from ultra‐low carbon steel “0.02% C” till medium carbon steel grades “0.20% C.” Moreover, the work controls the steel melt tapping parameters for best cost achievement of steel melt yield and electrical energy consumption using simple measurements. These measurements are fitted into simple regression equations and contour plots for best refining stage control of the desired steel grade to be produced.

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