Predicting Quantitative Indices for SEN Clogging in Continuous Casting Using Long Short-term Memory Time-series Model

Abstract

The clogging of submerged entry nozzles is a critical issue during continuous casting that adversely affects final product quality and process productivity. In order to impose effective monitoring and control over the continuous casting process, a quantitative index was formulated to quantify the magnitude of SEN clogging and erosion for a production dataset consisting of ultra-low carbon, low carbon, medium carbon, and calcium treated grades. Three critical index values are defined to represent the clogging event, erosion event, and critical casting condition. Long short-term memory network was established based on the quantitative index in the past four minutes to predict that in the future 48 seconds. The networks are found to be capable of predicting the overall trend in quantitative index, with the lowest normalized root mean squared error at 0.323 for medium carbon grade, followed by that at 0.340, 0.342, and 0.453 for low carbon, calcium-treated carbon, and ultra-low carbon grades respectively. The models can also identify most of the critical casting conditions and erosion incidents for all steel grades. Operators can take corresponding actions when critical conditions are predicted by the models in order to prevent the possible occurrence of clogging. Model precision could be improved with larger production datasets that consist of multiple number of clogging and erosion events.