Evolutionary data driven modeling and tri-objective optimization for noisy BOF steel making data

Mahanta, Bashista Kumar; Gupta, Prakash; Mohanty, Itishree; Roy, Tapas Kumar; Chakraborti, Nirupam

doi:10.1016/j.dche.2023.100094

Cited by 4 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 23 ] Mahanta et al used an evolutionary deep‐neural‐network model to predict the end point of converters based on time–series data (oxygen flow rate, lance position, gas composition, and flow rate). [ 24 ] Bae et al used equal‐width binning (144 s) to divide the oxygen lance position data into 10 bins, and the mean, standard deviation, maximum, minimum, and integral of each lance sequence were included as new features in the regression calculations. Results showed that the model with time–series data improved the prediction performance.…”

Section: Introductionmentioning

confidence: 99%

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

Kang,

Zhao,

et al. 2024

steel research int.

View full text Add to dashboard Cite

In this article, main process data, notably time–series data such as lance position patterns, are analyzed during converter steelmaking, and methodologies in data processing and transforming are proposed. In this study, utilizing both the transformed key time–series and primary static process data, the influence of various process parameters on the end‐point parameters of converter steelmaking is analyzed. Furthermore, it establishes predictive models for the end‐point content of carbon (C) and phosphorus (P), as well as the end‐point temperature. In the findings, it is indicated that the end‐point carbon content and temperature are primarily influenced by the oxygen flow pattern, lime addition pattern, and key smelting parameters. The end‐point phosphorus content is mainly affected by the oxygen flow pattern, limestone addition pattern, and dolomite addition pattern. Regarding the prediction of end‐point carbon and phosphorus content, and end‐point temperature, compared to seven sub‐models, the hybrid model demonstrates an average accuracy improvement of 37.88%, 25.03%, and 31.51%, respectively, and the end‐point hit rate improves by 18.77%, 19.59%, and 20.41%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

Kang,

Zhao,

et al. 2024

steel research int.

View full text Add to dashboard Cite

show abstract

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

Ghalati,

Zhang,

El‐Fallah

et al. 2023

Materials Genome Engineering Advances

View full text Add to dashboard Cite

Basic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, Machine Learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions.

show abstract