Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network

Wang, Hongbing; Xu, Anjun; L, Ai; Nai-yuan, Tian

doi:10.1016/s1006-706x(12)60040-5

Cited by 33 publications

(25 citation statements)

References 14 publications

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“…The predictive models in Equations (5) and (6) provide insights into the nature of the dephosphorization process. Figures 8 and 9 graphically represent the positives and negatives of slag chemistry on P-partitioning in the form of a waterfall plot.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, we must look for cost-effective or relatively inexpensive ways to achieve phosphorus partitioning in large scale. In this context, Machine Learning (ML) and Artificial Intelligence (AI) based techniques can be game-changing in the pursuit of efficient process control [5][6][7]. Most of the previous works on dephosphorization employed mathematical modelling and thermodynamics analysis as tools for understanding dephosphorization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Dephosphorization in Basic Oxygen Furnaces (BOFs) Using Data Driven Modeling Techniques

Barui

Mukherjee

Srivastava

et al. 2019

Metals

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Owing to the continuous deterioration in the quality of iron ore and scrap, there is an increasing focus on improving the Basic Oxygen Furnace (BOF) process to utilize lower grade input materials. The present paper discusses dephosphorization in BOF steelmaking from a data science perspective, which thus enables steelmakers to produce medium and low phosphorus steel grades. In the present study, data from two steel mills (Plant I and Plant II) were collected and various statistical methods were employed to analyze the data. While most operators in steel plants use spreadsheet-based techniques and linear regression to analyze data, this paper discusses on the suitability of selecting various statistical methods, and benchmarking tests to analyze such dephosphorization data sets. The data contains a wide range of operating conditions, both low and high phosphorus input loads, different slag basicity’s, different slag chemistries, and different end point temperatures, etc. The predicted phosphorus partition from various statistical models is compared against plant data and verified against previously published research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding Dephosphorization in Basic Oxygen Furnaces (BOFs) Using Data Driven Modeling Techniques

Barui

Mukherjee

Srivastava

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…The method was then used to predict the end phosphorus content in BOF steel‐making process . An integrated method that combined the weighted K‐means clustering and GMDH (Group Method of Data Handling) polynomial neural network was proposed to predict the end phosphorus content of molten steel in BOF …”

Section: Literature Reviewmentioning

confidence: 99%

An Improved CBR Model Based on Mechanistic Model Similarity for Predicting End Phosphorus Content in Dephosphorization Converter

Feng

et al. 2018

steel research int.

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There has been a lack of reliable online measuring equipment on end phosphorus content in dedicated dephosphorization converter, which leads to the low control accuracy of end phosphorus content. A Case-based Reasoning (CBR) method based on mechanistic model similarity is proposed to predict the end phosphorus content in dephosphorization converter. The correlation between the influencing factors and target as well as the similarity between influencing factors of different cases are considered comprehensively in the proposed method. Firstly, the mechanistic model of end phosphorus content in dephosphorization converter is established by using the slag phosphorus distribution ratio and Matter Conservation. Then, the similarity calculation equation of mechanistic model is designed. Lastly, the prediction result is obtained by bringing the mechanistic model of end phosphorus content into the designed similarity calculation method. CBR method based on mechanistic model similarity (CBR-MM), mechanistic model, Back Propagation Neural Network (BPNN), CBR based on Euclidean Distance similarity (CBR-ED), and CBR based on Grey Distance similarity (CBR-GD) are used to predict end phosphorus content by applying practical production data. The result shows CBR-MM has higher prediction accuracy compared to other methods.

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“…and the carbon content of molten steel [3]. ANNs are also used to estimate the amount of oxygen and coolant required in the end-blow period [3][4][5][6][7]. In recent years, the support vector machine (SVM) model has also been employed to accurately estimate the endpoint parameter [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Estimation Methods of the Endpoint Temperature in Basic Oxygen Furnace Steelmaking Process with Selection of Input Parameters

Park¹,

Kim²,

Kim³

et al. 2018

Korean J. Met. Mater.

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The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.

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Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network

Cited by 33 publications

References 14 publications

Understanding Dephosphorization in Basic Oxygen Furnaces (BOFs) Using Data Driven Modeling Techniques

Understanding Dephosphorization in Basic Oxygen Furnaces (BOFs) Using Data Driven Modeling Techniques

An Improved CBR Model Based on Mechanistic Model Similarity for Predicting End Phosphorus Content in Dephosphorization Converter

Comparative Study of Estimation Methods of the Endpoint Temperature in Basic Oxygen Furnace Steelmaking Process with Selection of Input Parameters

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