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，钢铁行业也不例外。所有钢铁制造商都致力于节能和环保运营，同时改进和优化相关的冶金工艺，以降低成本，满足严格的产品质量要求。考虑到所有这些限制，钢铁公司在研究和开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，钢铁制造商可以访问大量的过程数据，这些数据已经存储了很长一段时间，现在有必要将数据驱动建模和机器学习纳入解决过程相关问题。如今，现代工业和工业4.0的快速发展正在加速下一代混合模型的发现，这些模型联合收割机了基础和数据驱动的概念，并建立了每个单元操作的数字孪生模型。因此，为钢铁和炼钢过程开发数字孪生模型对于加强加拿大在当今金属行业的竞争地位至关重要。申请人在多伦多大学的团队拥有工艺冶金、物理和数学建模以及机器学习方面的专业知识，他们的目标是在物理模型中使用定量实验技术，并生成受控的工艺数据，以开发钢铁工艺的初步数字孪生，并将其与工业数据整合，以开发单元操作的真实的数字孪生。基于这一长期愿景，我们将开发用于氧气顶吹转炉（BOF）、连铸机（CC）、钢包冶金（LMF）和用于金属粉末生产的水雾化器（WA）的物理和数字孪生。我们团队的研究人员将广泛使用物理和数学建模来理解每个过程背后复杂的物理基础，并生成受控的实验数据。研究人员还将使用基于机器学习的不同输出预测上述过程。需要回答的一些关键问题是：（1）基本的物理和数学模型在哪些方面无法做出准确的预测？(ii)数据驱动的预测是否可解释？(iii)数字孪生子可靠吗？(iv)我们能否利用基于冶金原理的基础模型和数据驱动模型的力量开发混合技术？短期利益将是深入了解各种单元操作的基础物理学以及机器学习技术在过程优化中的适用性。长期利益将是数字孪生和混合模型的开发，这些模型可以作为真实的时间优化工具，并使钢铁行业受益匪浅。最后，所有的知识和发现都可以交叉传播到加拿大的其他采矿和金属行业。