A novel estimation of electrical and cooling losses in electric arc furnaces

Trejo, Eder; Martell, Fernando; Micheloud, O.; Teng, Lidong; Llamas, Armando; Montesinos‐Castellanos, Alejandro

doi:10.1016/j.energy.2012.03.024

Cited by 27 publications

(13 citation statements)

References 10 publications

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“…The theory of radiation in enclosures is commonly used in the design of combustion chambers [84,85], furnaces [86][87][88][89][90], ovens [91], solar collectors [92][93][94][95], thermo-photovoltaics [96], microchannels [97], micro heat exchangers [98] and fuel cells [99][100][101]. All those domains will welcome new methodologies that will make the design easier and more straight-forward.…”

Section: Literature Reviewmentioning

confidence: 99%

New analytical methodologies for radiative heat transfer in enclosures based on matrix formalism and network analogy

Baracu

Costinaş

Ghiaus

et al. 2016

Applied Thermal Engineering

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Section: Literature Reviewmentioning

confidence: 99%

New analytical methodologies for radiative heat transfer in enclosures based on matrix formalism and network analogy

Baracu

Costinaş

Ghiaus

et al. 2016

Applied Thermal Engineering

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“…A thorough review of CFD models for the EAF process was presented by Odenthal et al [2]. Another commonly used modeling framework is by applying linear-or nonlinear programming with physicochemical-and process-based constraints to the mass-and energy balance equations of the EAF [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The approach solves the mass and energy balance equations for every time step from beginning to the end of the process.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling

Carlsson

Samuelsson

Jönsson

2019

Metals

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Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models in the literature predicting the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF). The aim was to structure published data and to bring clarity to the subject in light of challenges and considerations that are imposed by statistical models. These include data complexity and data treatment, model validation and error reporting, choice of input variables, and model transparency with respect to process metallurgy. A majority of the models are never tested on future heats, which essentially renders the models useless in a practical industrial setting. In addition, nonlinear models outperform linear models but lack transparency with regards to which input variables are influencing the EE consumption prediction. Some input variables that heavily influence the EE consumption are rarely used in the models. The scrap composition and additive materials are two such examples. These observed shortcomings have to be correctly addressed in future research applying statistical modeling on steel processes. Lastly, the paper provides three key recommendations for future research applying statistical modeling on steel processes.

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“…Modeling is a valuable tool because it enables process engineers to evaluate proposed changes without interfering with the process, which may be more or less harmful to the steel plant supply chain. These modeling approaches can be, for example, physicochemical models using established relationships such as the mass-and energy balance equations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Another approach is through Computational Fluid Dynamics (CFD) modeling [20].…”

Section: Introductionmentioning

confidence: 99%

Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel

Carlsson

Samuelsson

Jönsson

2019

Metals

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The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both the statistical modeling framework and the statistical tools used in the modeling pipeline must be selected with the aim of handling these challenges. To achieve this, a non-linear statistical modeling framework known as Artificial Neural Networks (ANN) has been used to predict the Electrical Energy (EE) consumption of an EAF producing stainless steel. The statistical tools Feature Importance (FI), Distance Correlation (dCor) and Kolmogorov-Smirnov (KS) tests are applied to investigate the most influencing input variables as well as reasons behind model performance differences when predicting the EE consumption on future heats. The performance, measured as kWh per heat, of the best model was comparable to the performance of the best model reported in the literature while requiring substantially fewer input variables.

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A novel estimation of electrical and cooling losses in electric arc furnaces

Cited by 27 publications

References 10 publications

New analytical methodologies for radiative heat transfer in enclosures based on matrix formalism and network analogy

New analytical methodologies for radiative heat transfer in enclosures based on matrix formalism and network analogy

Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling

Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel

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