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

钢铁制造商一直面临着在提高连铸机（CC）生产率的同时保持钢坯质量的挑战。由于客户对质量的严格要求，这对于拉深和熨平（D&I）和超低碳（ULC）板坯尤其具有挑战性。这些板坯被平轧成薄板（小于0.5 mm），极易出现内部和外部缺陷。因此，提高这些等级的铸造速度通常会对质量差的成本产生不利影响。这些缺陷是由非金属夹杂物（NMI）（如氧化铝和结晶器熔渣）被凝固的钢壳夹在连铸结晶器内而产生的，连铸结晶器通过浸入式水口（SEN）连续接收液态钢和氩气。在更高的铸造速度下，结晶器内的气泡钢流湍流和不稳定性显著增加。这加剧了截留在液体池内循环的NMI颗粒的风险。因此，必须优化CC结晶器内的流动，然后在高连铸机产量条件下对其进行控制。连铸机数字孪生及其应用将旨在提高AMD板坯和钢卷的产品质量。该研究计划还将利用物理建模方法进行流动可视化和量化，并为各种铸造条件开发流动指数。还将开发基于降阶模型的计算流体动力学，用于预测钢中的缺陷（即结晶器壁附近的NMI颗粒捕获）。通过计算流体力学预测NMI的包埋，可以真实的实时控制和调节流动指数。在连铸机上，该项目的成果有望提高D&I和ULC钢种的最大铸造速度，减少缺陷，并开发真实的时间缺陷预测技术。 这将通过确定改进的SEN设计、优化的氩气流速和更好的工艺控制技术来实现。由于提高钢材质量而节省的成本预计将超过250万美元/年。拟议的研究项目符合AMD的战略愿景和业务目标，并将为位于加拿大安大略的AMD带来直接利益。

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.