The Control and Prediction of End‐Point Phosphorus Content during BOF Steelmaking Process

Wang, Zhou; Xie, Feiming; Wang, Bin; Liu, Qing; Lu, Xinchun; Hu, Li-ning; Cai, Fei

doi:10.1002/srin.201300194

Cited by 41 publications

(23 citation statements)

References 6 publications

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“…Few ML techniques have been used to predict the end-point phosphorus content in the BOF steelmaking process. For example, a multi-level recursive regression model for complete end-point P content prediction was established by Wang et al in 2014 based on a large amount of production data [16]. A predictive model based on principal component analysis (PCA) with back propagation (BP) neural network has been discussed by He and Zhang in 2018 where they predicted end-point phosphorus content in BOF based on BOF metallurgical process parameters and production data [17].…”

Section: Introductionmentioning

confidence: 99%

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Phull

Egas

Barui

et al. 2019

Metals

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Ensuring the high quality of end product steel by removing phosphorus content in Basic Oxygen Furnace (BOF) is essential and otherwise leads to cold shortness. This article aims at understanding the dephosphorization process through end-point P-content in BOF steelmaking based on data-mining techniques. Dephosphorization is often quantified through the partition ratio ( l p ) which is the ratio of wt% P in slag to wt% P in steel. Instead of predicting the values of l p , the present study focuses on the classification of final steel based on slag chemistry and tapping temperature. This classification signifies different degrees (‘High’, ‘Moderate’, ‘Low’, and ‘Very Low’) to which phosphorus is removed in the BOF. Data of slag chemistry and tapping temperature collected from approximately 16,000 heats from two steel plants (Plant I and II) were assigned to four categories based on unsupervised K-means clustering method. An efficient decision tree-based twin support vector machines (TWSVM) algorithm was implemented for category classification. Decision trees were constructed using the concepts: Gaussian mixture model (GMM), mean shift (MS) and affinity propagation (AP) algorithm. The accuracy of the predicted classification was assessed using the classification rate (CR). Model validation was carried out with a five-fold cross validation technique. The fitted model was compared in terms of CR with a decision tree-based support vector machines (SVM) algorithm applied to the same data. The highest accuracy (≥97%) was observed for the GMM-TWSVM model, implying that by manipulating the slag components appropriately using the structure of the model, a greater degree of P-partition can be achieved in BOF.

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Section: Introductionmentioning

confidence: 99%

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Phull

Egas

Barui

et al. 2019

Metals

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“…By introducing the Lagrangian function and Karush-Kuhn-Tucker conditions, the dual formulations of (4) and (5) can be derived as follows:…”

Section: Nonlinear Twin Support Vectormentioning

confidence: 99%

“…Based on multivariate data analysis, the slopping prediction was proposed by Brämming et al [4]. In 2014, the multi-level recursive regression model was established for the prediction of end-point phosphorus content during BOF steelmaking process [5]. An antijamming endpoint prediction model 2 Complexity of extreme learning machine (ELM) was proposed with evolving membrane algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

End‐Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Gao

Shen

et al. 2019

Complexity

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A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.

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“…For instance, by means of combining the methods of multivariate regression analysis and multi‐level recursive completely, the multi‐level recursive regression model was established based on large amount of production data. 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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The Control and Prediction of End‐Point Phosphorus Content during BOF Steelmaking Process

Cited by 41 publications

References 6 publications

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

End‐Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

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

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