Innovative Low Melting Liquid Metal Model for Optimizing Argon Injection Practices during Steelmaking and Continuous Casting for Productivity and Quality Improvements

创新的低熔点液态金属模型，用于优化炼钢和连铸过程中的吹氩实践，以提高生产率和质量

• 批准号: 522412-2017
• 负责人: Chattopadhyay, Kinnor
• 金额: $ 3.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=697643
• 关键词: Innovative; Low; Melting; Liquid; Metal

Steelmakers are under constant pressure to increase productivity, and simultaneously maintain high quality of continuously cast steel slabs because of stringent quality demands imposed by their customers. However, increasing productivity has detrimental effects on slab quality, and defects and rejections have a major impact on the producers bottom-line. Controlling fluid flows in continuous casting is one of the key parameters to ensure cleaner steel and reduce defects. Physical and mathematical modeling is an essential tool to understand and optimize fluid flows in continuous casting. However, understanding bubbly and multiphase flows is not an easy task and traditional techniques like water modeling have certain limitations. The use of water models is reasonable and allows for applying a number of well-established measuring methods. However, a generalization of those results to liquid steel flows has to be considered carefully as the true values of flow parameters (Re, Pr, Gr, Ha, etc.) are difficult to meet. In many cases, e.g. for liquid metal flows with strong temperature gradients, for two-phase flows, and of course for applications of electromagnetic fields, the flow phenomena cannot be modeled correctly by means of water experiments. However, on using low melting point metals like (GaInSn alloys, Rose Metal, Field Metal etc.) these parameters are closer to the real steel/Ar system and hence the bubble dynamics and multiphase flow patterns are expected to be more realistic than water modeling. This project will deal with generating fundamental knowledge in the area of bubbly and multiphase flows in steelmaking continuous casting, and its application to improve product quality for AMDs flat products. This research program will utilize physical modeling approach to optimize argon injection during continuous casting and ladle stirring operations. At the caster, the project outcomes are expected to enable the increase of maximum casting speed of drawn & ironed (D&I) and ultra-low carbon (ULC) steel grades. The cost saving arising from improving both liquid and solid steel quality is expected to exceed $10 million/year.

由于客户提出的严格质量要求，钢铁制造商面临着提高生产率的持续压力，同时保持连铸钢坯的高质量。然而，提高生产率会对板坯质量产生不利影响，而缺陷和废品对生产商的利润产生重大影响。控制连铸中的流体流动是确保钢材清洁和减少缺陷的关键参数之一。物理和数学建模是理解和优化连铸流体流动的重要工具。然而，理解气泡流和多相流并不是一件容易的事，水建模等传统技术也有一定的局限性。水模型的使用是合理的，并且允许应用许多行之有效的测量方法。然而，必须仔细考虑将这些结果推广到钢水流动，因为流动参数（Re、Pr、Gr、Ha 等）的真实值很难满足。在许多情况下，例如对于具有强温度梯度的液态金属流、两相流，当然还有电磁场的应用，无法通过水实验来正确模拟流动现象。然而，在使用低熔点金属（例如 GaInSn 合金、Rose Metal、Field Metal 等）时，这些参数更接近真实的钢/Ar 系统，因此气泡动力学和多相流模式预计比水建模更真实。 该项目将涉及炼钢连铸中气泡和多相流领域的基础知识及其在提高 AMD 扁平材产品质量方面的应用。该研究计划将利用物理建模方法来优化连铸和钢包搅拌操作期间的氩气注入。在连铸机上，项目成果预计将提高拉拔和熨烫（D&I）和超低碳（ULC）钢种的最大铸造速度。 提高液态和固态钢质量所节省的成本预计每年将超过 1000 万美元。