Practical Implications of Using an Online Data-Driven Optimizer for Calcium-Treated Steels

Abstract Calcium (Ca) additions during secondary steelmaking are a well-adopted practice to transform solid oxide non-metallic inclusions (NMIs) into globular-shaped liquid oxides. The claimed hypothesis that liquid NMIs reduce SEN clogging has been proven in the past by researchers. However, the exact quantity of Ca needed to transform the physical state of NMIs during steelmaking remains uncertain. Operators in the steel plant use a consistent quantity of Ca additions for specific steel grades, but this approach does not account for the varying physical states and evolving dynamics of NMIs characteristics in each ‘heat’. To overcome this, a study was conducted to explore the impact of varying Ca additions on the transformation and behavior of NMIs in low-alloyed Ca-treated steel grades. The aim was to establish a more reliable and responsive approach to Ca treatment, potentially leading to more effective control in preventing submerged entry nozzle (SEN) clogging. The proposed methodology involved online monitoring of NMIs state coupled with controlled variations in Ca addition, deviating from fixed quantity, to observe its effects on NMIs state transformations. Through careful analysis of collected data and the implementation of a data-driven optimizer, this study reports the practical implications of using optimal amounts of Ca during secondary steelmaking. The resulting change due to dynamic calcium silicide (CaSi)-cored wire additions and their impact on SEN clogging were evaluated. The findings reveal the significant role of optimal CaSi wire additions, leading to improved steel castability and a notable 30 pct reduction in SEN clogging tendencies. The results obtained after the implementation of the data-driven optimizer ‘ ClogCalc ’ have significant implications for steel manufacturers, offering new insights into enhancing Ca treatment efficiency.