scholarly journals Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel

Metals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 36 ◽  
Author(s):  
Leo S. Carlsson ◽  
Peter B. Samuelsson ◽  
Pär G. Jönsson
Keyword(s):  
Stainless Steel ◽  
Statistical Modeling ◽  
Model Performance ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Modeling Framework ◽  
Statistical Tools ◽  
Input Variables

The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both the statistical modeling framework and the statistical tools used in the modeling pipeline must be selected with the aim of handling these challenges. To achieve this, a non-linear statistical modeling framework known as Artificial Neural Networks (ANN) has been used to predict the Electrical Energy (EE) consumption of an EAF producing stainless steel. The statistical tools Feature Importance (FI), Distance Correlation (dCor) and Kolmogorov–Smirnov (KS) tests are applied to investigate the most influencing input variables as well as reasons behind model performance differences when predicting the EE consumption on future heats. The performance, measured as kWh per heat, of the best model was comparable to the performance of the best model reported in the literature while requiring substantially fewer input variables.

scholarly journals Briquette smelting in electric arc furnace to recycle wastes from stainless steel production

2015 ◽  
Vol 22 (S1) ◽  
pp. 10-16 ◽  
Author(s):  
Qi-xing Yang ◽  
An-jun Xu ◽  
Peng Xue ◽  
Dong-feng He ◽  
Jian-li Li ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Steel Production ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Stainless Steel Production

scholarly journals Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 959 ◽  
Author(s):  
Leo S. Carlsson ◽  
Peter B. Samuelsson ◽  
Pär G. Jönsson
Keyword(s):  
Statistical Modeling ◽  
Statistical Models ◽  
Linear Models ◽  
Nonlinear Models ◽  
Electrical Energy ◽  
Electric Arc ◽  
Future Research ◽  
Published Data ◽  
Industrial Setting ◽  
Input Variables

Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models in the literature predicting the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF). The aim was to structure published data and to bring clarity to the subject in light of challenges and considerations that are imposed by statistical models. These include data complexity and data treatment, model validation and error reporting, choice of input variables, and model transparency with respect to process metallurgy. A majority of the models are never tested on future heats, which essentially renders the models useless in a practical industrial setting. In addition, nonlinear models outperform linear models but lack transparency with regards to which input variables are influencing the EE consumption prediction. Some input variables that heavily influence the EE consumption are rarely used in the models. The scrap composition and additive materials are two such examples. These observed shortcomings have to be correctly addressed in future research applying statistical modeling on steel processes. Lastly, the paper provides three key recommendations for future research applying statistical modeling on steel processes.

scholarly journals Modeling the Effect of Scrap on the Electrical Energy Consumption of an Electric Arc Furnace

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1044
Author(s):  
Leo S. Carlsson ◽  
Peter B. Samuelsson ◽  
Pär G. Jönsson
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Melting Time ◽  
Arc Furnace ◽  
Total Energy Consumption ◽  
Data Set ◽  
Practical Usefulness

The melting time of scrap is a factor that affects the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF) process. The EE consumption itself stands for most of the total energy consumption during the process. Three distinct representations of scrap, based partly on the apparent density and shape of scrap, were created to investigate the effect of scrap on the accuracy of a statistical model predicting the EE consumption of an EAF. Shapley Additive Explanations (SHAP) was used as a tool to investigate the effects by each scrap category on each prediction of a selected model. The scrap representation based on the shape of scrap consistently resulted in the best performing models while all models using any of the scrap representations performed better than the ones without any scrap representation. These results were consistent for all four distinct and separately used cleaning strategies on the data set governing the models. In addition, some of the main scrap categories contributed to the model prediction of EE in accordance with the expectations and experience of the plant engineers. The results provide significant evidence that a well-chosen scrap categorization is important to improve a statistical model predicting the EE and that experience on the specific EAF under study is essential to evaluate the practical usefulness of the model.

Exergy Analysis of Steel Electric Arc Furnace

2010 ◽  
Author(s):  
Ebrahim Hajidavalloo ◽  
Hamzeh Dashti
Keyword(s):  
Energy Consumption ◽  
Flue Gas ◽  
Exergy Analysis ◽  
Steel Production ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Electrical Energy Consumption ◽  
Energy And Exergy

In this paper, energy and exergy analysis of an existing steel electric arc furnace (EAF) was performed to estimate the furnace potential for increasing the efficiency and decreasing the electrical energy consumption. The results of analysis show that the energy and exergy efficiencies of the furnace are 56.9% and 40.5%, respectively. Field data show that mass flow rate of hot flue gas is around 10.4 kg/s in average which contains 18.3% and 12.2% of total input energy and exergy, respectively. By using energy of flue gas for preheating the sponge iron, electrical energy consumption of the furnace could be reduced up to 88 GJ which means 21.2% reduction in electrical energy consumption and 13.6% increase in steel production. Also, exergy efficiency improves about 10.8% by using preheating scheme.

Process and Practice of EAF with De-P Hot Metal Charging for Melting Stainless Steel

2007 ◽  
Vol 561-565 ◽  
pp. 1063-1066
Author(s):  
Fang Yi Zhu ◽  
He Bing Chi ◽  
Xing Yuan Jiang ◽  
Wei Dong Mao
Keyword(s):  
Stainless Steel ◽  
Control Theory ◽  
Integrated Control ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Hot Metal ◽  
Factors Affecting ◽  
Physical Energy ◽  
Main Factors

The article introduce the process of Electric Arc Furnace with Dephosphorized hot metal charging for melting stainless steel in Baosteel stainless steel Branch. Based on the practice of production, The main factors affecting the process of EAF with De-P HM charging are theoretically analyzed, such as using oxygen, the material charging and making slag. The optimization of hot metal charging can advance the use of chemical and physical energy, reduce the consumption of power. The optimization of using oxygen can increase the use of chemical energy. The optimization of material charging can reduce the oxidation of Cr. Making foamy slag can advance the transformer capacity and the use of power. Based on the character of the process EAF with De-P HM charging for Melting Stainless Steel, EAF productivity increased were reached with application of integrated control theory on EAF process in Baosteel stainless steel branch.

Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

Energy ◽  
2016 ◽  
Vol 108 ◽  
pp. 132-139 ◽  
Author(s):  
Dragoljub Gajic ◽  
Ivana Savic-Gajic ◽  
Ivan Savic ◽  
Olga Georgieva ◽  
Stefano Di Gennaro
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Electrical Energy Consumption ◽  
Artificial Neural

Energy Balance and Offgas Utilization of Ilmenite Smelting in a 30 MVA Direct Current Electric Arc Furnace

2012 ◽  
Vol 512-515 ◽  
pp. 2343-2348
Author(s):  
Feng Xia Han ◽  
Ting Lei ◽  
Lin Zhou
Keyword(s):  
Energy Balance ◽  
Direct Current ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Design Flow ◽  
Arc Furnace ◽  
Combustion Heat ◽  
Heat Quantity ◽  
Safety Production

Energy balance and offgas utilization of ilmenite smelting in 30 MVA direct current electric arc furnace (DC furnace) of a Yunnan company were investigated to make sure safety production, and to save energy and reduce pollution. The total input energy in the DC furnace was 2,000 kWh per ton of ilmenite, in which 1,003.31 kWh per ton of ilmenite was used to carbothermic reduction and other energy was consumed as heatloss. Based on energy conservation, the whole system heatloss in physical production should be controlled at 1,000 kWh per ton of ilmenite. With high combustion heat, the offgas discharged from furnace after treatment could be used to ilmenite drying, anthracite drying, titanium slag drying, and casting ladle baking. The offgas design flow ranged from 5,000 to 8,000 Nm3/h with the mean of 6,500 Nm3/h. The combustion heat quantity of offgas in the plant was 82,160 MJ per hour, which could save 480,276.8 kWh of electrical energy per day, and thus lower carbon emission of the whole production line.

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