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

Sandip Barui,Kinnor Chattopadhyay,Heng Li,Sankha Mukherjee

doi:10.3390/met12020268

Abstract

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.

Highlights

For plant I, data from 6097 heats are found to belong to Class 1 and 7756 to Class 2, whereas for plant II there are 1035 data points belonging to Class 1 and 2046 assigned to Class 2
Looking at the classification rates obtained from both algorithms (Figure 4), least squares twin support vector machines (LSTSVM) produced an accuracy of almost 25% greater with c = 10 for plant II data than twin support vector machines (TWSVM), while TWSVM outperformed LSTSVM by a marginal 2% for plant I data
Least squares twin support vector machines, a modified version of the twin support vector machines, which itself is an extension of ordinary support vector machines, is proposed in this paper for the binary classification of the phosphorus ‘partition’ ratio denoted by l p

Summary

Introduction

Production of high-quality steel using the basic oxygen furnace (BOF) requires a deep understanding of numerous complex chemical reactions and accurate end-point control [1]. In BOF, oxygen is blown into the liquid metal, which leads to a conversion of molten pig iron and scraps into liquid steel. A high content of phosphorous in the final product leads to poor mechanical properties such as low ductility and increased brittleness, increasing the probability of cracking during deformation and welding [2]. The process of removing phosphorous from pig iron to obtain high-quality steel in BOF is known as dephosphorization. Research has shown that for a given basicity and carbon content, the presence of iron oxide has a greater effect on dephosphorization compared to dissolved oxygen in steel [4]. The partition ratio between slag and steel quantifies the ability of phosphorous holding onto (%P)

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