Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

The electric arc furnace (EAF) contributes to almost one-third of the global iron and steel industry, and its harmonic pollution has drawn attention. An accurate EAF harmonic model is essential to evaluate the harmonic pollution of EAF. In this paper, a data-driven compartmental modeling method (DCMM) is proposed for the multi-mode EAF harmonic model. The proposed DCMM considers the coupling relationship among different frequencies of harmonics to enhance the modeling accuracy, meanwhile, the dimensions of the harmonic dataset are reduced to improve computational efficiency. Furthermore, the proposed DCMM is applicable to establish a multi-mode EAF harmonic model by dividing the multi-mode EAF harmonic dataset into several clusters corresponding to the different modes of the EAF smelting process. The performance evaluation results show that the proposed DCMM is adaptive in terms of establishing the multi-mode model, even if the data volumes, number of clusters, and sample distribution change significantly. Finally, a case study of EAF harmonic data is conducted to establish a multi-mode EAF harmonic model, showing that the proposed DCMM is effective and accurate in EAF modeling.

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Soft sensor of bath temperature in an electric arc furnace based on a data-driven Takagi–Sugeno fuzzy model

Applied Soft Computing ◽

10.1016/j.asoc.2021.107949 ◽

2021 ◽

pp. 107949

Author(s):

Aljaž Blažič ◽

Igor Škrjanc ◽

Vito Logar

Keyword(s):

Fuzzy Model ◽

Bath Temperature ◽

Electric Arc Furnace ◽

Electric Arc ◽

Soft Sensor ◽

Data Driven ◽

Arc Furnace ◽

Takagi Sugeno

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Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data

Measurement and Control ◽

10.1177/0020294018771097 ◽

2018 ◽

Vol 51 (3-4) ◽

pp. 83-93 ◽

Cited By ~ 5

Author(s):

Biao Wang ◽

Zhizhong Mao

Keyword(s):

Electric Arc Furnace ◽

Electric Arc ◽

Data Driven ◽

Arc Furnace ◽

Process Data ◽

Series Of Experiments ◽

Test Points ◽

One Class Classifier ◽

Comparative Results ◽

One Class Classification

The presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imbalanced, non-stationary and noisy. First, the raw data are divided into certain number of clusters. Then, with each cluster, a one-class classifier can be trained. So with these well-trained sub-models, new test points can be investigated. Those points that are rejected by all sub-models will be labeled as outliers. With the combination of one-class classification and clustering technique, the intricate data in electric arc furnace can be processed effectively. In addition, the detector will be updated with a specific strategy to enhance its adaptiveness. A series of experiments are carried out, and comparative results have shown the effectiveness of our method.

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Data-driven CBM tool for risk-informed decision-making in an electric arc furnace

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-019-04189-w ◽

2019 ◽

Vol 105 (1-4) ◽

pp. 595-608 ◽

Cited By ~ 2

Author(s):

Luca Fumagalli ◽

Laura Cattaneo ◽

Irene Roda ◽

Marco Macchi ◽

Maurizio Rondi

Keyword(s):

Decision Making ◽

Electric Arc Furnace ◽

Electric Arc ◽

Informed Decision ◽

Data Driven ◽

Informed Decision Making ◽

Arc Furnace

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Economic model predictive control of the electric arc furnace using data-driven multi-rate models

2016 American Control Conference (ACC) ◽

10.1109/acc.2016.7525178 ◽

2016 ◽

Author(s):

Mudassir M. Rashid ◽

Prashant Mhaskary ◽

Christopher L.E. Swartz

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Economic Model ◽

Electric Arc Furnace ◽

Electric Arc ◽

Data Driven ◽

Arc Furnace ◽

Economic Model Predictive Control ◽

Using Data

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Analytical Electron Microscopy Characterization of Electric Arc Furnace Dust

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097326 ◽

1981 ◽

Vol 39 ◽

pp. 134-135

Author(s):

J. R. Porter ◽

J. I. Goldstein ◽

D. B. Williams

Keyword(s):

Electron Microscopy ◽

Analytical Electron Microscopy ◽

Electric Arc Furnace ◽

Electric Arc ◽

Dust Particles ◽

Particle Analysis ◽

Arc Furnace ◽

Analytical Electron ◽

Residual Elements ◽

Industry Practice

Alloy scrap metal is increasingly being used in electric arc furnace (EAF) steelmaking and the alloying elements are also found in the resulting dust. A comprehensive characterization program of EAF dust has been undertaken in collaboration with the steel industry and AISI. Samples have been collected from the furnaces of 28 steel companies representing the broad spectrum of industry practice. The program aims to develop an understanding of the mechanisms of formation so that procedures to recover residual elements or recycle the dust can be established. The multi-phase, multi-component dust particles are amenable to individual particle analysis using modern analytical electron microscopy (AEM) methods.Particles are ultrasonically dispersed and subsequently supported on carbon coated formvar films on berylium grids for microscopy. The specimens require careful treatment to prevent agglomeration during preparation which occurs as a result of the combined effects of the fine particle size and particle magnetism. A number of approaches to inhibit agglomeration are currently being evaluated including dispersal in easily sublimable organic solids and size fractioning by centrifugation.

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