Abstract
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.
Highlights
With an almost 100% increase in the price of iron ore over the past 5 years, the removal of phosphorus from these ores has become essential in order to maintain the persistent quality of steel [1]
Gaussian mixture model (GMM)-twin support vector machines (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 Basic Oxygen Furnace (BOF)
A decision tree twin support vector machine based on a kernel clustering (DT2 -support vector machines (SVM)-KC)
Summary
With an almost 100% increase in the price of iron ore over the past 5 years, the removal of phosphorus from these ores has become essential in order to maintain the persistent quality of steel [1]. Increased levels of phosphorus in steel can lead to cold shortness causing brittleness and poor toughness [2,3]. The process of phosphorus removal from iron ores is known as dephosphorization. In comparison to dissolved oxygen in liquid steel, iron oxide content in slag has shown a greater influence on dephosphorization for a given slag basicity and carbon content of steel. Dephosphorization has often been defined as (%P)/[%P], i.e., the ratio of slag/steel phosphorus distribution that frequently lies around the calculated equilibrium values for the metal/slag reactions involving iron oxide in slag [3,4].
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