Refractory condition monitoring and lifetime prognosis for Rh degasser

In the steelmaking industry, there is a demand for process optimization and predictability of the refractory based on information recorded during the production process [1–3]. The amount of recorded data has recently increased dramatically and machine learning and artificial intelligence (AI) techniques are exploited to filter out useful information for modeling the production process and the most influential parameters [4–6]. In this paper, the aim is to predict the refractory life-time based on data acquired during production. In total, 110 process parameters were preselected to build a statistical model to determine the influence on refractory wear by using machine learning techniques.