Magnetic field-based arc stability sensor for electric arc furnaces

During the last decades the strategy to define the optimal Electric Arc Furnaces (EAF) electrical operational parameters has been constantly evolving. Foaming slag practice is currently used to allow high power factors that ensures higher energy efficiency. However, this performance depends on strict electric arc stability control. Control strategies for these are normally defined for alternating current furnaces (AC EAF) and are based on intrusive and highly expensive systems. In this work we analyze the variation of the magnetic field vector around the direct current EAF (DC EAF) and its relationship with arc stability. We propose a cheap stability control system with no installation or integration requirements and thus, easily implementable to both AC and DC EAFs. To this end we have built a non-intrusive and low-cost 3-axis Hall-effect sensor that can be mounted neighboring the furnace’s electrical bars. The sensor allows acquiring the magnetic field magnitude and orientation that provides a newly defined arc stability factor metric. This proposed Arc Stability Index has been compared with three different alternative well established and more expensive measurement methodologies obtaining with similar results. The proposed index serves as a closed loop signal to the electrical regulation for controlling the arc voltage, ensuring the most convenient arc length that guaranties non-instabilities. The new system was developed and industrially validated at two different DC EAF’s in ArcelorMittal demonstrating an improvement of 6.7 kWh per Liquid steel ton during the evaluated period and a time reduction of 1.1 min per heat over the current standard procedure. Additional validation tests were also carried out also in ArcelorMittal AC EAF proving the capability of this technology for both AC and DC of furnaces.Partial financial support of this work by the Basque Govern-ment (Hazitek AURRERAB ZE-2017/00009 and FASIN ZE-2016/0016 Projects) is gratefully acknowledged

A Fundamental Wave Amplitude Prediction Algorithm Based on Fuzzy Neural Network for Harmonic Elimination of Electric Arc Furnace Current

Electric arc furnace (EAF) causes the harmonics to impact on the supply network greatly and harmonic elimination is a very important research work for the power quality associated with EAF. In the paper, a fundamental wave amplitude prediction algorithm based on fuzzy neural network for harmonic elimination of EAF current is proposed. The proposed algorithm uses the learning ability of the neural network to refine Takagi-Sugeno type fuzzy rules and the inputs are the average of the current measured value in different time intervals. To verify the effectiveness of the proposed algorithm, some experiments are performed to compare the proposed algorithm with the back-propagation neural networks, and the field data collected at an EAF are used in the experiments. Moreover, the measured amplitudes of fundamental waves of field data are obtained by the sliding-window-based discrete Fourier transform on the field data. The experiments results show that the proposed algorithm has higher precision. The real curves also verify that the amplitude of fundamental wave current could be predicted accurately and the harmonic elimination of EAF would be realized based on the proposed algorithm

Estudo de escórias de refino primário de aços com vistas a redução do consumo energético em fornos elétricos a arco

As indústrias siderúrgicas almejam aliar a máxima produtividade com um menor custo, neste contexto, no processo de produção de aço em uma aciaria elétrica uma série de fatores necessita de um melhor entendimento para alcançar as metas exigidas (máxima produtividade e menor custo). Um destes fatores que tem efeito direto sobre os custos é a escória espumante no refino primário, que além de favorecer o rendimento metálico, melhora a eficiência energética do FEA. O objetivo geral do presente trabalho é estudar as escórias de refino primário visando a redução do consumo energético, avaliando a espumação e os diferentes parâmetros que a afetam, utilizando a termodinâmica computacional (software FactSage 6.4) e dados industriais. A metodologia desenvolvida buscou especificamente: a) avaliar a saturação de MgO para o sistema CaO-SiO2-FeO-Al2O3-MgO através da termodinâmica computacional; e para os dados industriais: b) encontrar relações entre a composição química da escória e o consumo de energia elétrica; c) utilizar os diagramas de saturação isotérmicos (ISD’s) para avaliar o consumo de energia elétrica, avaliando a posição das corridas analisadas; d) encontrar relações entre os harmônicos de tensão e o consumo de energia elétrica; e) encontrar relações entre os harmônicos de tensão e a composição química da escória. Com relação à análise dos dados obtidos, pode-se concluir que: 1) a análise de saturação de MgO realizada no programa FactSage 6.4 apresentou os resultados esperados com relação à variação de composição química, a comparação entre os diagramas ternários e ISD’s; 2) o menor gasto de energia elétrica foi encontrado para as seguintes faixas de composição química: FeO entre 27,5 e 30% em massa, basicidade binária entre 3,0 e 3,2, MgO entre 5,5 e 6,5% em massa; 3) os ISD’s podem ser utilizados para a análise do consumo energético; 4) o teor de FeO de menor desvio padrão dos harmônicos foi de 29%; 5) quanto maior a variação dos THDv’s (distorção harmonica total de tensão), maior o gasto energético.Steelmaking industries aim to combine maximum productivity with a lower cost, in this context, in the steel production process in an electric steel plant a number of factors needs a better understanding to achieve the required goals (maximum productivity and lower cost). One of these factors that have a direct effect on costs is the foamy slag in the primary refining, which in addition to promoting the metal yield improves the energy efficiency of FEA. The overall objective of this work is to study the primary refining slag aimed at reducing energy consumption, evaluating the foaming and the different parameters affecting it, using computational thermodynamics (FactSage 6.4 software) and industrial data. The methodology consisted of: a) evaluate the MgO saturation for the CaO-SiO2-FeO-Al2O3-MgO system by computational thermodynamics; and with industrial data: b) find relationships between the chemical composition of the slag and the consumption of electricity; c) use the isothermal saturation diagrams (ISD) to assess the energy consumption, evaluating the position of the analyzed heat; d) find relationships between the voltage harmonics and power consumption; e) find relationships between the voltage harmonics and the chemical composition of the slag. Regarding the analysis of the data, it can be concluded that: 1) the MgO saturation analysis in FactSage 6.4 program presented the results expected in relation to the variation in chemical composition, the comparison between the ternary diagrams and ISD's; 2) the lowest power consumption was found for the following chemical composition ranges: FeO between 27.5 and 30%, binary basicity between 3.0 and 3.2, MgO 5,5 e 6,5%; 3) ISD's can be used for the analysis of energy consumption; 4) the FeO content of less harmonic standard deviation was 29%; 5) the greater the variation of THD's largest energy expenditure

Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking

In recent years, improving the sustainability of the steel industry and reducing its CO2 emissions has become a global focus. To achieve this goal, further process optimization in terms of energy and resource efficiency and the development of new processes and process routes are necessary. Modeling and simulation have established themselves as invaluable sources of information for otherwise unknown process parameters and as an alternative to plant trials that involves lower costs, risks, and time. Models also open up new possibilities for model-based control of metallurgical processes. This Special Issue focuses on recent advances in the modeling and simulation of unit processes in iron and steelmaking. It includes reviews on the fundamentals of modeling and simulation of metallurgical processes, as well as contributions from the areas of iron reduction/ironmaking, steelmaking via the primary and secondary route, and continuous casting

Industrial time series modelling by means of the neo-fuzzy neuron

Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the Neo-Fuzzy Neuron is configured and trained according by means of the auxiliary signal, past instants and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modelled. The obtained results indicate the suitability of the Neo-Fuzzy Neuron method for industrial process modelling.Postprint (published version

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Avaliação do potencial de reciclo do resíduo refratário MgO-C no processo de refino primário de uma aciaria elétrica

O desenvolvimento tecnológico de forma sustentável é um dos maiores desafios na indústria, em especial na aciaria devido à alta geração de resíduo na produção do aço. Utilizar o resíduo gerado como fonte de matéria-prima reduz o impacto ambiental, ao mesmo tempo é possível se obter ganhos com um custo de produção mais baixo. Este trabalho buscou avaliar uma prática de reúso para o resíduo de refratário magnésia carbono, de forno elétrico a arco e de panelas de aciaria, aplicado no processo de refino primário do aço como substituto da cal dolomítica. Resultados na literatura já haviam mostrado a eficiência da prática de reutilizar-se resíduos de refratários com carbono na formação da escória espumante, no entanto, em condições diferentes de qualidade de resíduo e práticas operacionais do presente trabalho. Neste, os resultados desenvolvidos na aciaria mostram uma forma simples de beneficiamento do resíduo, em uma planta composta por um britador de mandíbulas e um conjunto de peneiras vibratórias. O material é adicionado diretamente na carga fria do forno e a sua avaliação é feita através da composição química da escória e parâmetros elétricos no forno. O método de reciclagem mostrou bom potencial, com resultados relevantes de economia de matéria-prima, e deve ser testado em uma escala maior, frente a complexidade do processo.Technological development in a sustainable way is one the biggest industry challenges, especially in meltshop due to the high generation of waste in steel production. Using the waste generated as a source of raw material reduces the ecological impact combined with a lower cost of production. This work goal to evaluate a recycling practice for waste of magnesia carbon refractory from electric arc furnace and ladle, applied in the primary refining of steel as alternative for dolomite. Other studies have already shown efficiency practice in the formation of slag foaming, however, in different conditions of waste quality and operational practices. The results of the present work show a simple way of beneficiation the waste in a plant composed of a jaw crusher and vibrating screens. The waste is added directly into scrap charge and its evaluation is done through the chemical composition of slag and electrical parameters in furnace. The recycling method showed a good potential and should be tested in larger scale, according to the complexity of the process