Improvement of electrical arc furnace operation with an appropriate model

Labar, Hocine; Djeghader, Yacine; Kamel, Bounaya; Samira, Kelaiaia Mounia

doi:10.1016/j.energy.2009.03.003

Cited by 42 publications

(17 citation statements)

References 10 publications

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“…Köhle's work based on statistical regression was compared to a neural network and fuzzy logic based model for predicting final steel temperature, with the latter providing improved results [23]. Several efforts have focused on EAF modeling for electrical energy efficiency and control [24][25][26][27][28][29].…”

Section: Past Modeling Of Steel Product Manufacturingmentioning

confidence: 99%

Development and Application of Models for Steelmaking and Casting Environmental Performance

Haapala

Catalina

Johnson

et al. 2012

Journal of Manufacturing Science and Engineering

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Growing interest in sustainability is driving manufacturers to improve the environmental performance of their products and processes. The production of steel and steel products consumes materials and energy resources, and creates wastes and emissions. Industry leaders and policy makers have identified ironisteel and metal casting as areas of concern from an environmental perspective. By evaluating steel product manufacturing processes commonly employed in the heavy equipment industry, environmental impacts can be mitigated during product and process design. A process modeling approach that is focused on improving the environmental peiformance of steel product manufacturing is developed and demonstrated. The process models focus on part production employing electric arc furnace (EAF) steelmaking and sand casting with chemical binders, and relate process energy and material inputs and outputs to product and process design characteristics. The models are based on scientific principles, as well as empirical data reported in the literature. Models of the two processes are applied to assess the production of a representative ground engaging tool (GET) component. It is found that EAF electricity use can be reduced by more than 30% and process-related CO2 emissions by nearly 20% over initial settings. Replacing the polyurethane nobake sand mold binder with a low nitrogen furan binder is predicted to reduce casting emissions by more than 50%, and sulfur dioxide emissions by over 90%. Thus, the models are capable of estimating changes in environmental performance due to modifications in material type, part geometry, and process parameters. This process modeling approach demonstrates improvements in environmental performance for the production of a GET component, and can be extended to assess and compare other steel alloys and components.

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Section: Past Modeling Of Steel Product Manufacturingmentioning

confidence: 99%

Development and Application of Models for Steelmaking and Casting Environmental Performance

Haapala

Catalina

Johnson

et al. 2012

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The melting process in the EAF as the most energy-intensive step in the production of stainless steel has been investigated in a number of studies using various approaches with the goal to better understand the effect of various process parameters and thus further decrease the specific electrical energy consumption. For example, an optimal operating strategy can be determined taking into account different melting stages and electrodes position [6]. Understanding the influence of both gas burners [7] and direct reduced iron [8] on energy efficiency is also very important.…”

Section: Introductionmentioning

confidence: 99%

Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

Gajić

Georgieva

et al. 2016

Energy

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“…A scheme of the EAF is presented in Figure 1 The main EAF operation steps are as follows [1][2][3]: charging and melting, refining (oxidizing of the melt), chemical composition and temperature adjusting, and tapping (discharging of the furnace). With respect to energy consumption, the contemporary research has mainly focused on the total (electric and chemical) consumed energy [2,[4][5][6] and individual (electric or chemical) consumed energy [7,8] including other aspects of EAF operation such as transformer optimization [1,[9][10][11], molten steel residue [12,13], scrap type [14,15], scrap management [14,16,17], electrode regulation [18][19][20], oxygen injectors [13,[21][22][23], and slag cover [24].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study

et al. 2019

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The electric arc furnace operation at the Štore Steel company, one of the largest flat spring steel producers in Europe, consists of charging, melting, refining the chemical composition, adjusting the temperature, and tapping. Knowledge of the consumed energy within the individual electric arc operation steps is essential. The electric energy consumption during melting and refining was analyzed including the maintenance and technological delays. In modeling the electric energy consumption, 25 parameters were considered during melting (e.g., coke, dolomite, quantity), refining and tapping (e.g., injected oxygen, carbon, and limestone quantity) that were selected from 3248 consecutively produced batches in 2018. Two approaches were employed for the data analysis: linear regression and genetic programming model. The linear regression model was used in the first randomly generated generations of each of the 100 independent developed civilizations. More accurate models were subsequently obtained during the simulated evolution. The average relative deviation of the linear regression and the genetic programming model predictions from the experimental data were 3.60% and 3.31%, respectively. Both models were subsequently validated by using data from 278 batches produced in 2019, where the maintenance and the technological delays were below 20 minutes per batch. It was possible, based on the linear regression and the genetically developed model, to calculate that the average electric energy consumption could be reduced by up to 1.04% and 1.16%, respectively, in the case of maintenance and other technological delays.

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Improvement of electrical arc furnace operation with an appropriate model

Cited by 42 publications

References 10 publications

Development and Application of Models for Steelmaking and Casting Environmental Performance

Development and Application of Models for Steelmaking and Casting Environmental Performance

Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study

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