*Article* **Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters**

**Alireza Rahnama 1,\*, Zushu Li <sup>2</sup> and Seetharaman Sridhar 1,3**


Received: 25 November 2019; Accepted: 13 March 2020; Published: 23 March 2020

**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.

**Keywords:** machine learning; artificial intelligence; neural network; BOS reactor; steelmaking
