Modeling, Simulation, and Validation with Measurements of a Heat Recovery Hot Gas Cooling Line for Electric Arc Furnaces

Keplinger, Thomas; Haider, Markus; Steinparzer, Thomas; Trunner, Paul; Patrejko, Andreas; Haselgrübler, Manfred

doi:10.1002/srin.201800009

Cited by 9 publications

(13 citation statements)

References 13 publications

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“…Several previous articles have presented estimates of the energy sources and energy sinks during the EAF process [14][15][16][17][18]. These reported values have been compiled in Table 1 and presents a guidance regarding which input variables that need to be considered when predicting the EE consumption.…”

Section: Parameters Governing the Ee Consumptionmentioning

confidence: 99%

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

2020

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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.

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Section: Parameters Governing the Ee Consumptionmentioning

confidence: 99%

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

2020

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“…Sankey diagrams are commonly used to present estimates for each of the ingoing and outgoing energy terms. Numerous such diagrams are presented in the literature, where the EAF energy balance is studied based on measurements from the process [11,[26][27][28][29]. Using these reported data, Table 2 has been computed.…”

Section: Energy Dynamicsmentioning

confidence: 99%

“…Ingoing and outgoing energy in the EAF process as percentages of energy sources and energy sinks, respectively. Values are computed from [11,[26][27][28][29].…”

Section: Energy Dynamicsmentioning

confidence: 99%

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

Carlsson

Samuelsson

Jönsson

2019

Metals

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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.

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“…Each of the energy factors are related to physical and chemical entities (see Table 1). Calculations of ingoing and outgoing energy in the EAF process have previously been calculated [9,25,[44][45][46]. This is compiled in Table 2.…”

Section: Energy Balance Equationmentioning

confidence: 99%

“…Ingoing and outgoing energy in the EAF process as percentages of energy sources and energy sinks, respectively. Values are computed from a synthesis of reported values [9,25,[44][45][46].…”

Section: Energy Balance Equationmentioning

confidence: 99%

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

Carlsson

Samuelsson

Jönsson

2019

Metals

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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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Modeling, Simulation, and Validation with Measurements of a Heat Recovery Hot Gas Cooling Line for Electric Arc Furnaces

Cited by 9 publications

References 13 publications

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

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

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

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

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