218. Modifying the Work Practices of Electric Arc Furnace Tenders to Control Their Lead Exposure in a Steel Plant

2001 ◽  
Author(s):  
J. Kominsky
Keyword(s):  
Lead Exposure ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Steel Plant ◽  
Arc Furnace ◽  
Work Practices

scholarly journals Power Flow Prediction for a Steel Plant with a Dynamic Regression Model

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Boris Bizjak
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Power Flow ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Steel Plant ◽  
Flow Profile ◽  
Industrial Complex ◽  
Arc Furnace ◽  
Dynamic Regression Model

A power flow forecast it was shown for an industrial complex consisting of more than 20 different companies. The predominant consumer of electricity in the industrial complex is a steelworks company with an electric arc furnace. A steelworks with an electric arc furnace is a very specific example of an energy consumer. Other companies in the industrial complex are not connected to the steel plant technologically, but they are on the same energy connection. They have a weekly power flow profile significantly different from the steel plant. To calculate the forecast model and perform the forecast of power flows we need only two inputs of data: Historical measurements of power flows and the number of loads of the electric arc furnace in the following days. The first showed a prediction with linear regression. The next model to predict was the seasonal ARIMA model with a regressor, also called a dynamic regression model. The dynamic regression model improved the prediction by 15% compared to linear regression, according to the RMSE measure. This was followed by an improvement in the dynamic regression forecasting model by considering the seasonality 7/5 in the time series. We did this with a model with superimposed noise. With this model, we improved the forecasting by 30% to linear regression. Logically, the filter model of the prediction model also improved, gaining more Lag coefficients and losing a constant. Qualitatively, the result is a forecast of power flow for one month with prediction error MAPE 8% and measure R2 is 0.9.

Analytical Electron Microscopy Characterization of Electric Arc Furnace Dust

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.

Monitoring the environmental and energy impacts of electric arc furnace steelmaking

2016 ◽  
Vol 104 (1) ◽  
pp. 102 ◽  
Author(s):  
Valentina Colla ◽  
Filippo Cirilli ◽  
Bernd Kleimt ◽  
Inigo Unamuno ◽  
Silvia Tosato ◽  
...  
Keyword(s):  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace
Sign in / Sign up

Export Citation Format

Share Document