scholarly journals Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace

2020 ◽  
Vol 91 (11) ◽  
pp. 2000053 ◽  
Author(s):  
Leo Stefan Carlsson ◽  
Peter Bengt Samuelsson ◽  
Pär Göran Jönsson
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Learning Tools ◽  
Electrical Energy Consumption ◽  
Interpretable Machine Learning ◽  
Machine Learning 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.

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

scholarly journals Improvement of Energy Efficiency and Productivity in an Electric Arc Furnace through the Modification of Side-Wall Injector Systems

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1202
Author(s):  
Yonmo Sung ◽  
Sangyoun Lee ◽  
Kyungmoon Han ◽  
Jaduck Koo ◽  
Seongjae Lee ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Cost ◽  
Side Wall ◽  
Cost Savings ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Electrical Energy Consumption ◽  
Efficiency And Productivity

The energy cost of producing steel in an electric arc furnace (EAF) has a sizable influence on the prices of natural gas and electricity. Therefore, it is important to use these energies efficiently via a tailored oxy-fuel combustion burner and oxygen lance. In this study, an important modification of the side-wall injector system in the EAF at Hyundai Steel Incheon works was implemented to reduce electrical energy consumption and improve productivity. A protruding water-cooled copper jacket, including a newly designed burner, was developed to reduce the distance between the jet nozzle and the molten steel. In addition, the jet angles for the burner and lance were separately set for each scrap melting and refining mode. The modifications led to a reduction in electrical energy consumption of 5 kWh/t and an increase in productivity of approximately 3.1 t/h. Consequently, total energy cost savings of 0.3 USD/t and a corresponding annual cost savings of approximately 224,000 USD/year were achieved.

scholarly journals The Influence of Electric-Arc-Furnace Input Feeds on its Electrical Energy Consumption

2021 ◽  
Author(s):  
Vito Logar ◽  
Igor Škrjanc
Keyword(s):  
Energy Consumption ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Furnace Operation ◽  
Arc Furnace ◽  
Electrical Energy Consumption ◽  
Arc Furnaces ◽  
Starting Point ◽  
Oxygen Ratio

AbstractOperation of the electric arc furnaces (EAFs) is a subject to consider fluctuations in terms of its key performance indicators, such as the electrical energy consumption (EEC), tap-to-tap time, steel yield, and others. In this paper, a more detailed analysis of the electric arc furnace data is performed, investigating its EEC. It is well known that the EEC is affected by the weight and the type of charged scrap, the operational delays, and the tapping temperature. On the other hand, one can also deduce that the feeds, such as the carbon and the oxygen, could also affect the EEC, due to their role in redox equations and impact to the bath energy balance. Therefore, special attention is devoted to the analysis of the carbon-to-oxygen ratio during the electric arc furnace operation and the consequent influence of the oxygen availability on the EEC. Statistical analysis of more than 2500 heats of data, which were clustered according to the produced steel grade and the charged scrap mixture, has revealed that besides the beforementioned factors, fluctuations in EEC could appear also due to different amounts of added carbon and oxygen. Since the furnace operators usually rely on predefined guidelines and their own experience when actuating the furnace, a simplistic statistical approach can be used to reveal some of the weaknesses in the control routines, which can be used as a starting point to improve their actuation, leading to decreased energy consumption. Graphical Abstract

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.

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 Coke and coal as reductants in manganese ore smelting: An experiment

Mineralogia ◽  
2018 ◽  
Vol 49 (1-4) ◽  
pp. 35-45 ◽  
Author(s):  
Bambang Suharno ◽  
Fajar Nurjaman ◽  
Achmad Rifki ◽  
Robby Krisnaldo Elvin ◽  
Alif Ardiansyah Putra ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Specific Energy ◽  
Bituminous Coal ◽  
Specific Energy Consumption ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Manganese Ore ◽  
Arc Furnace ◽  
X Ray ◽  
Kg Medium

Abstract The effect of coke and bituminous coal on the reduction of medium-grade manganese ore in ferromanganese production was investigated. Charges of 30 kg medium grade manganese ore, 12 kg limestone and varied amounts of coke and coal were smelted in a Submerged Electric Arc Furnace (SAF) at temperatures of 1300°C to 1500°C. The composition of the ferromanganese and the slag were determined by X-Ray Fluorescence. It was found that using coke as a single reductant resulted in a 96% yield of ferromanganese which was higher than by using coal either as a single reductant or in a mixture of coal and coke. It was also found that using coke as a single reductant resulted in the lowest specific energy consumption. Using coal as reductant produced ferromanganese containing high sulfur and phosphorus.

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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