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

Self Cite

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.

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

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“…The performances of our proposed models (TWSVM and LSTSVM) were compared against the logistic regression-based classifier and 25 well-established data-driven regression equations (adapted for classification) discussed by Barui et al LSTSVM outperforms all classification models considered for comparison for the plant II data [25]. On the contrary, the logistic regression model performs best for the plant I data.…”

Section: Algorithm Performancementioning

confidence: 99%

“…Besides the ML algorithms used in recent years, there have been many regression models (based on thermodynamic or empirical concepts) that predicts the l p values from other chemical compositions of slag. In 2019, Barui et al selected 25 existing regression models and tested their performances against their own data-driven linear regression model using RMSE and R 2 values [25]. In this article, we compared our classification models (TWSVM and LSTSVM) against those 25 regression models that were applied to plant I and plant II data for classification.…”

mentioning

confidence: 99%

Least Squares Twin Support Vector Machines to Classify End-Point Phosphorus Content in BOF Steelmaking

Barui

Mukherjee

et al. 2022

Self Cite

End-point phosphorus content in steel in a basic oxygen furnace (BOF) acts as an indicator of the quality of manufactured steel. An undesirable amount of phosphorus is removed from the steel by the process of dephosphorization. The degree of phosphorus removal is captured numerically by the ‘partition ratio’, given by the ratio of %wt phosphorus in slag and %wt phosphorus in steel. Due to the presence of multitudes of process variables, often, it is challenging to predict the partition ratio based on operating conditions. Herein, a robust data-driven classification technique of least squares twin support vector machines (LSTSVM) is applied to classify the ‘partition ratio’ to two categories (‘High’ and ‘Low’) steels indicating a greater or lesser degree of phosphorus removal, respectively. LSTSVM is a simpler, more robust, and faster alternative to the twin support vector machines (TWSVM) with respect to non-parallel hyperplanes-based binary classifications. The relationship between the ‘partition ratio’ and the chemical composition of slag and tapping temperatures is studied based on approximately 16,000 heats from two BOF plants. In our case, a relatively higher model accuracy is achieved, and LSTSVM performed 1.5–167 times faster than other applied algorithms.

“…Figure 9 shows a schematic of k-fold cross verification. The k-fold cross-validation is a no-repeat sampling technique in which each sample has only one chance to be included in the test set during each calculation [45]. In this study, the dataset was randomly divided into five equal parts using a 5-fold cross-validation method.…”

Section: Model Improvement By Pcamentioning

confidence: 99%

Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

Zou

Zhang

Han

et al. 2021

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability.