Physical, Mathematical, and Machine Learning Modeling of Iron and Steel Processes

钢铁工艺的物理、数学和机器学习建模

• 批准号: RGPIN-2021-02615
• 负责人: Chattopadhyay, Kinnor
• 金额: $ 3.35万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758248
• 关键词: Physical; Mathematical; Machine; Learning; Modeling

The mining and metals industry is facing major challenges and the iron and steel industry is no exception. All iron and steel makers are aiming for energy efficient and environmentally friendly operations and at the same time improving and optimizing the associated metallurgical processes to meet the stringent product quality demands at reduced cost. Having all these constraints in mind, iron and steel companies have heavily invested in research and development and one of the major areas has been physical and mathematical modeling of steelmaking and casting processes. However, the iron and steel makers have access to huge amount of process data which have been stored for a long time, and now it is essential to include data driven modeling and machine learning in solving process related problems. Today, the rapid development of modern industry and industry 4.0 is accelerating the discovery of  next-generation hybrid models which combine both fundamental and data driven concepts, and the building of digital twins of each unit operation. Hence, developing digital twins for iron and steelmaking processes is critical to strengthening Canada's competitive position in today's metals industry. Having expertise in process metallurgy, physical and mathematical modeling, and machine learning, the applicant's group at the University of Toronto aims to use quantitative experimental techniques in physical models and generate controlled process data to develop preliminary digital twins of iron and steel processes, and integrate them with industrial data to develop real digital twins of unit operations. With this long-term vision, physical and digital twins for basic oxygen furnaces (BOF), Continuous Caster (CC), Ladle Metallurgy (LMF), and  a Water Atomizer (WA) for the production of metal powders, will be developed. Researchers in our group will extensively use physical and mathematical modeling to understand the complicated underlying physics behind each process, and also generate controlled experimental data. Researchers will also use machine learning based predictions of different outputs for the above mentioned processes. Some of the key questions to be answered are: (i) Where does fundamental physical and mathematical models fail to make accurate predictions? (ii) Are the data driven predictions interpretable? (iii) Are digital twins reliable? (iv) Can we develop hybrid techniques using the power of both fundamental models based on metallurgical principles and data driven models? The short term benefits will be in-depth knowledge of the underlying physics of various unit operations and the applicability of machine learning techniques for process optimization. The long-term benefits will be the development of digital twins and hybrid models which can serve as real time optimization tools and benefit the iron and steel industry immensely. Finally, all the knowledge and discovery can be  cross pollinated to other mining and metals sectors in Canada.

采矿和金属行业正面临重大挑战，铁和钢铁行业也不例外。所有的铁和钢制造商都旨在提高节能和环保的操作，同时改善和优化相关的冶金工艺，以降低成本满足严格的产品质量需求。考虑到所有这些限制，熨斗和钢铁公司都在研发上进行了大量投资，主要领域之一是对钢材制造和铸造过程的物理和数学建模。但是，铁和钢制造商可以访问很长一段时间以来存储的大量过程数据，现在至关重要的是要在解决过程中包括数据驱动的建模和机器学习。如今，现代工业和工业4.0的快速发展正在加快结合基本和数据驱动概念的下一代混合模型的发现，以及每个单元操作的数字双胞胎的构建。因此，开发用于铁和钢制过程的数字双胞胎对于增强加拿大在当今金属行业的竞争地位至关重要。多伦多大学申请人小组的过程冶金，物理和数学建模以及机器学习的专家旨在在物理模型中使用定量实验技术，并生成受控的过程数据以开发初级的铁和钢铁过程，并将其与工业数据集成在一起，以开发单位操作的真实数字孪生单元操作。有了这一长期视力，将开发用于基本氧气炉（BOF），连续施法者（CC），钢包冶金（LMF）和用于生产金属粉末的水雾化器（WA）的物理和数字双胞胎。我们小组中的研究人员将广泛使用物理和数学建模来了解每个过程背后的复杂基础物理，并生成受控的实验数据。研究人员还将为上述过程使用基于机器学习的不同输出的预测。要回答的一些关键问题是：（i）基本物理和数学模型在哪里无法进行准确的预测？ （ii）数据驱动的预测是否可以解释？ （iii）数字双胞胎可靠吗？ （iv）我们可以使用基于冶金原理和数据驱动模型的两个基本模型的力量来开发混合技术？短期收益将是对各种单元操作的基础物理学以及机器学习技术用于过程优化的适用性的深入了解。长期的好处将是数字双胞胎和混合动力模型的开发，这些模型可以充当实时优化工具，并极大地使铁和钢铁行业受益。最后，所有知识和发现都可以交叉授粉到加拿大的其他采矿和金属行业。