Abstract
A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors.
Highlights
The processing of lower grade ores is a topic of particular interest, as fluctuation in raw material cost is a key challenge to sustainability in the steel industry
The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate within basic oxygen steelmaking (BOS) reactors
The objective of this study is to carry out a machine learning (ML) analysis on the data generated from the six-ton pilot BOS trials (IMPHOS data) with the purposes of (i) identifying correlations between features and (ii) based on a training data and test data developing a neural network-based regression model to predict the decarburization rate
Summary
The processing of lower grade ores is a topic of particular interest, as fluctuation in raw material cost is a key challenge to sustainability in the steel industry. Raw materials flexibility is to a great extent enabled in the basic oxygen steelmaking (BOS) process, wherein oxidizable impurities, such as phosphorus (P) and silicon (Si), are separated into a slag phase. As a result, processing parameters have to be balanced, and many different chemical reactions compete for the available oxygen. The BOS process is in general advised by static mass- and heat-balanced models, which predict, based on inputs, the resultant end-point. The input parameters include at least the quantities of hot metal, steel scrap, iron ore, fluxes and oxygen to blow, while the resultant end-point is the temperature, weight and composition of steel produced. Dynamic process modeling of the BOS process, depending on the researchers, can be based on thermodynamics [1], multizone reactions [2,3,4], empirical or Processes 2020, 8, 371; doi:10.3390/pr8030371 www.mdpi.com/journal/processes
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