Online estimation of electric arc furnace tap temperature by using fuzzy neural networks

Fernández, José Manuel Mesa; Cabal, Valeriano Álvarez; Montequín, Vicente Rodríguez; Balsera, Joaquín Villanueva

doi:10.1016/j.engappai.2007.11.008

Cited by 48 publications

(21 citation statements)

References 15 publications

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“…When examining the literature, some papers that propose different approaches to the modeling of the mass transfer and/or thermal processes in the EAF already exist, from simplified and more complex general heat-and mass-transfer models 2,3) to a more focused analysis of different heat submodels. 4,5) The idea of the model presented in this paper was derived from the papers of Bekker 2) and MacRosty; 3) however, the model proposed by Bekker is oversimplified for the needs of our study, while the model proposed by MacRosty either describes differently or does not address some issues on the radiative and conductive heat transfer between the steel, the slag and the gas zones and the CO postcombustion, which are in our opinion also important for the overall accuracy of the model. Therefore, a more complex model, taking into account additional relations and eliminating the deficiencies of the previously mentioned models is proposed in this study.…”

Section: Introductionmentioning

confidence: 99%

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

2012

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The following paper presents an approach to the mathematical modeling of heat and mass transfer processes in a 3-phase, 80 MVA AC, electric arc furnace (EAF) and represents a continuation of our work on modeling the electric and hydraulic EAF processes. This paper represents part 1 of the complete model and addresses issues on modeling the mass, temperature and energy processes in the EAF, while part 2 of the paper focuses solely on the issues related to the thermo-chemical relations and reactions in the EAF. As is generally known, the chemical, thermal and mass processes in an EAF are related to each other and cannot be studied completely separately; therefore, the work presented in part 1 and part 2 is related to each other accordingly and should be considered as a whole. The presented sub-models were obtained in accordance with different mathematical and thermo-dynamic laws, with the parameters fitted both experimentally, using the measured operational data of an EAF during different periods of the melting process, and theoretically, using the conclusions of different studies involved in EAF modeling. In conjunction with the already presented electrical and hydraulic models of the EAF, the heat-, mass-and energy-transfer models proposed in this work represent a complete EAF model, which can be further used for the initial aims of our study, i.e., optimization of the energy consumption and development of the operator-training simulator. The presented results show high levels of similarity with both the measured operational data and the theoretical data available in different EAF studies, from which we can conclude that the presented EAF model is developed in accordance with both fundamental laws of thermodynamics and the practical aspects regarding EAF operation.

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Section: Introductionmentioning

confidence: 99%

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

2012

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“…The paper in [29] focused on modeling the tapping temperature. The energy consumption could be optimized based on the consideration of the influential parameters.…”

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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“…Gajic et al [4], for example, have developed the energy consumption model of an electric arc furnace (EAF) based on the feedforward ANNs. Temperature prediction models [5,6] for EAF were established using the neural networks. Rajesh et al [7] employed feedforward neural networks to predict the intermediate stopping temperature and end blow oxygen in the LD converter steel making process.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Wang

Zhang

et al. 2019

Energies

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The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

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Online estimation of electric arc furnace tap temperature by using fuzzy neural networks

Cited by 48 publications

References 15 publications

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

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

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

An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

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