Prediction of Energy Consumption in an Electric Arc Furnace Using Weka

2020 ◽  
pp. 58-70
Author(s):  
Nury León-Munizaga ◽  
Maritza Aguirre-Munizaga ◽  
Katty Lagos-Ortiz ◽  
Javier Del Cioppo-Morstadt
Keyword(s):  
Energy Consumption ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace

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.

Thermodynamic analysis of solid waste and energy consumption to reduce the effects of an electric arc furnace on the environment

2019 ◽  
Vol 19 (3) ◽  
pp. 308 ◽  
Author(s):  
Yildiz Koc ◽  
Huseyin Yagli ◽  
Enver Onur Ozdes ◽  
Ertugrul Baltacioglu ◽  
Ali Koc
Keyword(s):  
Energy Consumption ◽  
Solid Waste ◽  
Thermodynamic Analysis ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace

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 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.

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 Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2142 ◽  
Author(s):  
Miha Kovačič ◽  
Klemen Stopar ◽  
Robert Vertnik ◽  
Božidar Šarler
Keyword(s):  
Energy Consumption ◽  
Linear Regression ◽  
Genetic Programming ◽  
Programming Model ◽  
Electric Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Spring Steel ◽  
Arc Furnace ◽  
Electric Energy Consumption

The electric arc furnace operation at the Štore Steel company, one of the largest flat spring steel producers in Europe, consists of charging, melting, refining the chemical composition, adjusting the temperature, and tapping. Knowledge of the consumed energy within the individual electric arc operation steps is essential. The electric energy consumption during melting and refining was analyzed including the maintenance and technological delays. In modeling the electric energy consumption, 25 parameters were considered during melting (e.g., coke, dolomite, quantity), refining and tapping (e.g., injected oxygen, carbon, and limestone quantity) that were selected from 3248 consecutively produced batches in 2018. Two approaches were employed for the data analysis: linear regression and genetic programming model. The linear regression model was used in the first randomly generated generations of each of the 100 independent developed civilizations. More accurate models were subsequently obtained during the simulated evolution. The average relative deviation of the linear regression and the genetic programming model predictions from the experimental data were 3.60% and 3.31%, respectively. Both models were subsequently validated by using data from 278 batches produced in 2019, where the maintenance and the technological delays were below 20 minutes per batch. It was possible, based on the linear regression and the genetically developed model, to calculate that the average electric energy consumption could be reduced by up to 1.04% and 1.16%, respectively, in the case of maintenance and other technological delays.

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