Hybrid Modeling for Endpoint Carbon Content Prediction in EAF Steelmaking

Wei, Guangsheng; Zhu, Rong; Yang, Lingzhi; Tang, Tianping

doi:10.1007/978-3-319-72131-6_19

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…A more significant proportion of hot metal [C] and more carbon powder injected will directly increase the melt pool's carbon content. [ 13 ] For Hot metals [Si], elemental silicon oxidizes more readily below 1550 °C. At this point, carbon must be oxidized after the silicon has oxidized.…”

Section: Resultsmentioning

confidence: 99%

“…Effective modeling drives the EAF Digital Twin technology, [4] optimizing EAF production time and reducing the number of measurements taken with samplers or probes, thus increasing overall efficiency. With the accumulation of production data and the development of machine learning algorithms, a variety of data-driven models have been established, [5][6][7][8][9] such as multiple support vector machines, [10] nonlinear gray bernoulli model-Markov-support vector machine, [11] T-S fuzzy System, [12] extreme learning machine-extreme learning machine [13] and artificial neural network (ANN). [14] Compared to the traditional mechanism models, the aforementioned models have achieved an improvement in accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

Lu,

Zhu,

Jiang

et al. 2024

steel research int.

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Developing the prediction model of the end‐point carbon content of the electric arc furnace (EAF) is an effective way to reduce the adjustment frequency of liquid steel composition and shorten the smelting time. Previous data‐driven models lack effective handling of the missing values in EAF production data. This may be the main reason why model accuracy is difficult to improve. This article proposes a novel modeling method based on the CatBoost algorithm with two‐stage optimization. In the preprocessing session, empirical and empirical‐cumulative‐distribution‐based outlier detection (ECOD) methods are utilized to extract input features and reject outliers. The end‐point carbon content prediction model is built based on CatBoost. The generative adversarial imputation nets (GAIN) method is used in the first optimization stage to handle the missing values. In the second optimization stage, recursive feature elimination (RFE) is used to select the final features, and whale optimization algorithm (WOA) is used to optimize the parameters of the CatBoost model. After verification with actual production data, the two‐stage optimized CatBoost model demonstrates excellent performance compared with other methods, with an R2 of 0.903, mean absolute error of 0.021, root mean squared error of 0.043, and 90.34% hit ratio within ±0.05% error range.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

Lu,

Zhu,

Jiang

et al. 2024

steel research int.

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show abstract

“…At present, Many scholars at home and abroad have studied endpoint control of steelmaking process. Wei et al established a prediction model of end-point carbon content for EAF steelmaking process based on Evolving Membrane Algorithm (EMA) of Extreme Learning Machine (ELM) [12]. Yuan et al used the principal component regression method for the submodels, and established a multi-support vector machine endpoint carbon, phosphorus content and temperature prediction model [13].…”

Section: Introductionmentioning

confidence: 99%

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

Yang

Guo

et al. 2022

Coatings

Self Cite

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In this manuscript, we consider the accuracy of end-point carbon content prediction affected by oxygen injection in multiple stages of electric arc furnace (EAF) melting process. Such a prediction would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. Principal component analysis (PCA) was used to normalize the 13 input variables affecting the endpoint carbon content. log-sigmoid and tan-sigmoid functions were used to verify the same sample, and it was found that the Mean squared error(MSE) of the model under logsig + logsig function was smaller, indicating that the model was more stable. At the same time, different hidden layer nodes were tried, and finally the structure of the model was determined as 13 × 10 × 8 × 1, and the activation function was logsig + logsig. Using historical smelting data to train and test the neural network model, the correlation coefficient (R) of the verified model is 0.7632, the model prediction is in the range of ±0.03%, the hit rate of the model is 64.5%, and the hit rate of the model is 42% in the range of ±0.02%. Combining the verification basis of the model with the metallurgical reaction principle of the EAF steelmaking process, a pretreatment method of phased input of total oxygen is proposed. The oxygen is divided into three stages, which are the oxygen consumption volume of 0–5 min, 5–30 min and more than 30 min and other variables are kept unchanged. The same neural network is used to train and verify the same data. After verification, the R of the oxygen staged model is 0.8274. The model prediction is in the range of ±0.03%, the hit rate of the model is 78.5%, and the hit rate of the model is 58% in the range of ±0.02%. Finally, an on-line carbon content prediction system based on artificial neural network model is developed and applied to actual production. Running results illustrated that the hit rate of end-point carbon content is 96.67%, 93.33% and 86.67%, respectively when the prediction errors are within ±0.05%, ±0.03% and ±0.01%, the improved neural network model can effectively predict the end-point carbon content, which provides a good basis for the carbon content at the end point of EAF steelmaking process.

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State of the art in applications of machine learning in steelmaking process modeling

Zhang,

Yang

2023

Int J Miner Metall Mater

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Hybrid Modeling for Endpoint Carbon Content Prediction in EAF Steelmaking

Cited by 4 publications

References 8 publications

A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

State of the art in applications of machine learning in steelmaking process modeling

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