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

矿业和金属行业面临重大挑战，钢铁行业也不例外。所有钢铁制造商的目标都是节能和环保的运营，同时改进和优化相关的冶金工艺，以满足严格的产品质量要求，降低成本。考虑到所有这些限制，钢铁公司在研发方面投入了大量资金，其中一个主要领域是炼钢和铸造过程的物理和数学建模。然而，钢铁制造企业拥有大量长期存储的过程数据，现在有必要将数据驱动建模和机器学习纳入到解决与过程相关的问题中。如今，现代工业和工业4.0的快速发展正在加速发现融合了基础和数据驱动概念的下一代混合模式，并构建每个单元运行的数字孪生兄弟。因此，开发钢铁流程的数字孪生兄弟对加强加拿大在当今金属行业的竞争地位至关重要。多伦多大学的申请者团队在过程冶金、物理和数学建模以及机器学习方面拥有专业知识，他们的目标是在物理模型中使用定量实验技术，并生成受控过程数据，以开发钢铁过程的初步数字孪生，并将它们与工业数据集成，以开发真正的单元操作数字孪生。在这一长期愿景下，将开发用于碱性氧炉(BOF)、连铸机(CC)、钢包冶金(LMF)和用于生产金属粉末的水雾化器(WA)的物理和数字孪生兄弟。我们小组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潜在物理，并生成受控实验数据。研究人员还将使用基于机器学习的对上述过程不同输出的预测。需要回答的一些关键问题是：(I)基本的物理和数学模型在哪里不能做出准确的预测？(Ii)数据驱动的预测是否可解释？(Iii)数字双胞胎可靠吗？(4)我们能否利用基于冶金原理的基本模型和数据驱动模型的力量来开发混合技术？短期的好处将是深入了解各种单元操作的基本物理知识，以及机器学习技术在流程优化中的适用性。长期的好处将是开发数字双胞胎和混合模型，这些模型可以作为实时优化工具，并极大地造福于钢铁行业。最后，所有的知识和发现都可以异花授粉到加拿大的其他采矿和金属行业。

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.