A Review of Mathematical Process Models for the Electric Arc Furnace Process

Hay, Thomas; Visuri, Ville‐Valtteri; Aula, Matti; Echterhof, Thomas

doi:10.1002/srin.202000395

Cited by 46 publications

(35 citation statements)

References 90 publications

(232 reference statements)

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“…However, if an excessive amount of RP is used in the charge mix, due to its weight, longer times will be required for the melting, hence the inhibition of the oxidation kinetic is less evident. [7,18] This situation is well described by the decrease in the RP VIU from data set A to data set B: in both of them the coefficients are higher than zero; however, due to the higher weight fraction of RP used in the data set B charge mixes, its value is lower.…”

Section: Influence Of the Charging Materials Weight Fractions On The Metallic Lossmentioning

confidence: 93%

“…Due to the fact that the type of steel produced did not require special furnace settings and remained the same for all the heats, the main parameters that could influence the yield of the process (electric energy, volume of oxygen and carbon used, power-off/ power-on times, scrap preheating, and amount of hot heel) did not vary in a significant way; moreover, they were within and comparable to the state of the ones. [8] Such situation allowed to concentrate this study on the other main variable of the EAF problem, [18] the charge mix, and its effect on the metallic losses.…”

Section: Methodsmentioning

confidence: 99%

“…The control of the metallic loss is fundamental for the competitiveness of the process because from 70% up to 80% of the transformation cost in EAF depends on the scrap price. A comprehensive review of the main mathematical process models for the EAF has been recently compiled by Hay et al [18] Due to the complexity of modeling the EAF process and the wide range of applications, a high number of approaches are present and possible.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…The increase in the negative effect on the heat behavior can be explained by applying the same reasoning used for the VIU variation of the RP. LAPS and LAMM can be considered as light and lightmedium scraps, [18] therefore they have a high oxidation tendency. By considering their S/V ratio, the longer melting time required (due to the higher amount of RP) and their amount in the data set B charge mix, it is clear that they will be highly subjected to oxidation for prolongated times.…”

Section: Influence Of the Charging Materials Weight Fractions On The Metallic Lossmentioning

confidence: 99%

See 3 more Smart Citations

Modeling of a Continuous Charging Electric Arc Furnace Metallic Loss Based on the Charge Mix

Mombelli

Dall'Osto

Mapelli

et al. 2020

steel research int.

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In the recent years, a revolution of the worldwide development policies has taken place, mainly driven by the idea of achieving the sustainable development scenario (SDS). The electric steelmaking route mainly based on the scrap recycling can be already considered as a successful example of circular economy: the possibility of recycling metallic scraps a theoretically infinite number of times allowed the increase of crude steel production by electric arc furnace (EAF) especially in the Eastern and developing countries. Therefore, the optimization of the charge mix, by empirical mathematical models, has become a fundamental practice aimed at decreasing the metallic loss, increasing the furnace performance and obtaining a proper melt chemistry. Herein, regression modeling is used to investigate the relationship between the metallic loss and the charging material. For the calibration and validation of the model, the data regarding 56 experimental heats, performed by a continuous charging EAF, are used. In particular, the model developed to foresee the metallic loss starting from a given charge mix, highlighting, furthermore, the interactions among the several charging materials.

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Section: Influence Of the Charging Materials Weight Fractions On The Metallic Lossmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Mathematical Modelingmentioning

confidence: 99%

Section: Influence Of the Charging Materials Weight Fractions On The Metallic Lossmentioning

confidence: 99%

See 2 more Smart Citations

Modeling of a Continuous Charging Electric Arc Furnace Metallic Loss Based on the Charge Mix

Mombelli

Dall'Osto

Mapelli

et al. 2020

steel research int.

View full text Add to dashboard Cite

show abstract

“…The latter approach considers the furnace based on physical or thermodynamic principles. As such, these models are usually associated with higher development cost, yet allow for use outside of the range of their training data [5]. Extensive analytic process models of the EAF have been published previously by Bekker [6] and Logar [7,8], as well as MacRosty and Swartz [9].…”

Section: Introductionmentioning

confidence: 99%

Application and Evaluation of Mathematical Models for Prediction of the Electric Energy Demand Using Plant Data of Five Industrial-Size EAFs

Reimann

Hay

Echterhof

et al. 2021

Metals

Self Cite

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The electric arc furnace (EAF) represents the most important process route for recycling of steel and the second most productive steelmaking process overall. Considering the large production quantities, the EAF process is subject to continuous optimization, and even small improvements can lead to a significant reduction in resource consumption and operating cost. A common way to investigate the furnace operation is through the application of mathematical models. In this study the applicability of three different statistical modeling approaches for prediction of the electric energy demand is investigated by using more than 21,000 heats from five industrial-size EAFs. In this context, particular consideration is given to the difference between linear and nonlinear regression models. Detailed information on the treatment of the process data is provided and the applied methods for regression are described in short, including information on the choice of hyperparameters. Subsequently, the results of the models are compared. Gaussian process regression (GPR) was found to yield the best overall accuracy; however, the benefit of applying nonlinear models varied between the investigated furnaces. In this regard, possible reasons for the inconsistent performance of the methods are discussed.

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HAZOP, FMECA, monitoring algorithm, and Bayesian network integrated approach for an exhaustive risk assessment and real‐time safety analysis: Case study

Nehal,

Mekkakia‐Mehdi,

Lounis

et al. 2024

Process Safety Progress

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Hazard studies are essential in the petrochemical industry to ensure safe operations. This article provides an in‐depth analysis of the hazards associated with a vacuum distillation unit furnace. This study aims to identify probable hazard scenarios related to furnace operation, assess the associated risks, and provide prevention and mitigation strategies. A comprehensive strategy was employed to achieve these objectives, combining two analysis methods: HAZard OPerability (HAZOP) and Failure Modes, Effects, and Criticality Analysis (FMECA). This integrated approach enables a comprehensive risk assessment to be carried out and appropriate preventive measures to be taken to maintain safe operations, including renovation work. Then, depending on the results of the two methods, it is essential to constantly evaluate equipment safety, taking into account parameters such as furnace efficiency, tube temperature, and fume temperature. Therefore, a monitoring program has been created in Python, which enables real‐time examination of the furnace's safety with these critical parameters. If safety conditions are compromised, alarms are sent to mitigate risks, particularly in case of a failure. A Bayesian model is also developed to evaluate the algorithm's results and determine renovation and failure case scenarios. This comprehensive approach improves risk assessment's reliability, precision, maintains safe and efficient industrial operations.

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A Review of Mathematical Process Models for the Electric Arc Furnace Process

Cited by 46 publications

References 90 publications

Modeling of a Continuous Charging Electric Arc Furnace Metallic Loss Based on the Charge Mix

Modeling of a Continuous Charging Electric Arc Furnace Metallic Loss Based on the Charge Mix

Application and Evaluation of Mathematical Models for Prediction of the Electric Energy Demand Using Plant Data of Five Industrial-Size EAFs

HAZOP, FMECA, monitoring algorithm, and Bayesian network integrated approach for an exhaustive risk assessment and real‐time safety analysis: Case study

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