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 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 Briquetting of EAF Dust for its Utilisation in Metallurgical Processes

2015 ◽  
Vol 36 (2) ◽  
pp. 263-271 ◽  
Author(s):  
Aneta Magdziarz ◽  
Monika Kuźnia ◽  
Michał Bembenek ◽  
Paweł Gara ◽  
Marek Hryniewicz
Keyword(s):  
Shaft Furnace ◽  
Steel Production ◽  
Hydrated Lime ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
High Iron Content ◽  
Coke Content ◽  
Eaf Dust ◽  
To Receive

Abstract Dust generated at an electric arc furnace during steel production industry is still not a solved problem. Electric arc furnace dust (EAF) is a hazardous solid waste. Sintering of well-prepared briquetted mixtures in a shaft furnace is one of possible methods of EAFD utilisation. Simultaneously some metal oxides from exhaust gases can be separated. In this way, various metals are obtained, particularly zinc is recovered. As a result, zinc-free briquettes are received with high iron content which can be used in the steelmaking process. The purpose of the research was selecting the appropriate chemical composition of briquettes of the required strength and coke content necessary for the reduction of zinc oxide in a shaft furnace. Based on the results of the research the composition of the briquettes was selected. The best binder hydrated lime and sugar molasses and the range of proper moisture of mixture to receive briquettes of high mechanical strength were also chosen and tested. Additionally, in order to determine the thermal stability for the selected mixtures for briquetting thermal analysis was performed. A technological line of briquetting was developed to apply in a steelworks.

Processing and Recycling Experiments of Dust from Electric Arc Furnace

2011 ◽  
Vol 378-379 ◽  
pp. 719-722 ◽  
Author(s):  
Zorica Bacinschi ◽  
Cristiana Zizi Rizescu ◽  
Elena Valentina Stoian ◽  
Dan Nicolae Ungureanu ◽  
Aurora Anca Poinescu ◽  
...  
Keyword(s):  
Steel Production ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Raw Material ◽  
Arc Furnace ◽  
Leaching Test ◽  
Fine Grained ◽  
Steel Making ◽  
Fine Grained Material ◽  
The Impact

The processing and recycling experiments of dust from Electric Arc Furnace (EAF) in industrial conditions aimed at highlighting the minimizing possibility of this waste by transforming it into a by-product that can represent either a secondary raw material for steel making in EAF or to recover iron, zinc and lead (the Waltz process). Electric-arc furnace dust (EAFD) is a by-product of steel production and recycling. This fine-grained material contains high amounts of zinc and iron as well as significant amounts of potentially toxic elements such as lead, cadmium and chromium. Therefore, the treatment and stabilization of this industrial residue is necessary. Leaching test is a method of evaluating the impact of waste that is stored (soil, water table).

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.

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