Automated, Scalable AI for Real-Time Monitoring of Steel Continuous Casting System

Smart steelmaking is impeded by the need for long, manual cycles of data collection, data preparation, domain analysis, model building, operationalization, maintenance, and root cause analysis. It is simply inconceivable that supervised approaches can be applied at any scale, given data quality issues that are prevalent in the industry. Furthermore, supervised approaches simply cannot surface behaviors that are not previously recorded and understood, which further limits their adoption. We have therefore looked for new solutions to achieve line scale Smart steelmaking primarily in the form of anomaly detection from SCADA and PLC data. In this paper, we demonstrate the automatability and scalability of two approaches - semi-supervised and selfsupervised (hands-free) - with examples from continuous casting and demonstrate that the performance is comparable. A linescale anomaly detection approach enables Smart real-time decisions over the whole steel mill. The hands-free approach also does not require any upfront investment in data quality or labeling as well as the need for ongoing manual model maintenance.