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.
Original language | English (US) |
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Article number | 371 |
Journal | Processes |
Volume | 8 |
Issue number | 3 |
DOIs | |
State | Published - Mar 1 2020 |
Externally published | Yes |
Keywords
- Artificial intelligence
- BOS reactor
- Machine learning
- Neural network
- Steelmaking
ASJC Scopus subject areas
- Bioengineering
- Chemical Engineering (miscellaneous)
- Process Chemistry and Technology