Continuous Caster Mould Digital Twin Development for Fluid Flow Control and Sliver Defect Minimization

钢铁制造商不断面临着提高连铸机（CC）生产率的同时保持钢坯质量的挑战。这对于拉伸和熨烫（D&I）和超低碳（ULC）板来说尤其具有挑战性，因为客户对质量提出了严格的要求。这些板坯被轧成薄片（小于0.5毫米），非常容易受到内部和外部缺陷的影响。因此，提高这些牌号的铸造速度往往会对质量差的成本产生不利影响。这些缺陷是由CC模具内部凝固的钢壳捕获非金属夹杂物（NMI）（如氧化铝和模具渣）造成的，该模具通过浸入式入口喷嘴（SEN）连续接收钢液和氩气。在较高的浇注速度下，型钢内部的气泡流、紊流和不稳定性显著增加。这加剧了在液体池中循环的NMI颗粒被困住的风险。因此，连铸模具内的流动必须优化，然后控制在高铸体吞吐量条件下。连铸机数字孪生及其应用将旨在提高AMD板坯和卷板的产品质量。该研究项目还将利用物理建模方法进行流动可视化和量化，并开发各种铸造条件下的流动指数。还将开发基于降阶模型的CFD，用于预测钢中的缺陷（即模壁附近的NMI颗粒捕获）。通过CFD对NMI圈闭进行预测，可以实时控制和调整流量指标。在铸造方面，项目成果有望提高D&I和ULC钢种的最大铸造速度，减少缺陷，并开发实时缺陷预测技术。这将通过改进SEN设计，优化氩气流量和更好的过程控制技术来实现。提高钢铁质量所节省的费用预计每年将超过250万美元。拟议的研究项目与AMD的战略愿景和业务目标一致，并将为位于加拿大安大略省的AMD创造直接利益。

Steelmakers are constantly challenged to increase the productivity of their continuous casters (CC) while simultaneously maintaining steel slab quality. This is especially challenging for Drawn & Ironed (D&I) and Ultra Low Carbon (ULC) slabs because of stringent quality demands imposed by the customers. These slabs are flat rolled into thin sheets (less than 0.5 mm) and are extremely susceptible to internal and external defects. Therefore, increasing the casting speed for these grades often results in a detrimental impact on cost of poor quality. The defects are created by entrapment of non-metallic inclusions (NMI), such as alumina and mould slag, by the solidifying steel shell inside the CC mould, which continuously receives liquid steel and argon gas through a submerged entry nozzle (SEN). Bubbly steel flow turbulence and instabilities inside the mould are significantly increased at higher casting speeds. This exacerbates the risk of entrapping NMI particles circulating inside the liquid pool. Thus, the flow inside the CC mould must be optimized and then controlled at high caster throughput conditions. The continuous caster digital twin and its application will aim to improve product quality for AMD's slabs and coils. This research program will also utilize a physical modeling approach for flow visualization and quantification and development of a flow index for various casting conditions. A reduced order model-based CFD will also be developed for predicting defects (i.e. NMI particle capture near mould walls) in steel. The flow index will be controlled and adjusted in real time if NMI entrapment is predicted by CFD. At the caster, the project outcomes are expected to enable the increase of maximum casting speed of D&I and ULC steel grades, reduction in defects, and development of real time defect prediction techniques. This will be achieved by identification of improved SEN designs, optimized argon flow rates and better process control techniques. The cost saving arising from improving steel quality is expected to exceed $2.5 million/year. The proposed research project is aligned with AMD's strategic vision and business goals, and will create immediate benefits to AMD, located in Ontario, Canada.