Adaptive, hybrid modeling and optimization for design and control of startup processes

用于设计和控制启动过程的自适应混合建模和优化

• 批准号: 508697-2017
• 负责人: Mhaskar, Prashant
• 金额: $ 3.06万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=719301
• 关键词: Adaptive; hybrid; modeling; optimization; design

Almost all production processes in Canada (and worldwide) go through transients: shutdowns and startups resulting from scheduled maintenance or unforeseen events. Current startup practices rely on recipes or a series of pre-decided sequence of actions. Such policies, while feasible, are far from optimal, and significant environmental and financial gains can be made by applying modeling, optimization and feedback control principles to these processes. While the notions of feedback and control have been universally adopted for continuous operation, they have not been widely utilized for the purpose of handling startups due to two primary reasons. The first is the difficulty in developing good models that could be then used for optimization and control, and the other being the unique nature of optimization problems that naturally arise in this context (due to the requirement of minimizing for instance the startup time). The proposed research will focus on both these aspects and present adaptive data driven hybrid modeling, optimization and control approaches with applications to startup of a hydrogen and air separations unit in collaboration with the industrial partner (Praxair Inc). Owing to the ubiquitous presence of these transient operations in all productions processes across Canada, the research results will benefit all manufacturing industries in Canada.

加拿大(和世界各地)的几乎所有生产流程都经历了短暂的过程：由于计划维护或不可预见的事件而导致的停工和启动。目前的创业实践依赖于食谱或一系列预先确定的行动序列。这样的政策虽然可行，但远不是最优的，通过将建模、优化和反馈控制原则应用于这些过程，可以获得显著的环境和财务收益。虽然反馈和控制的概念已经被普遍用于持续运营，但由于两个主要原因，它们并没有被广泛用于处理初创企业的目的。第一个是开发可用于优化和控制的好模型的困难，另一个是在这种情况下自然出现的优化问题的独特性质(由于要求最小化启动时间)。拟议的研究将集中在这两个方面，并提出自适应数据驱动的混合建模、优化和控制方法，并与工业合作伙伴(Praxair Inc.)合作，应用于氢气和空分装置的启动。由于这些瞬变操作在加拿大各地的所有生产过程中无处不在，因此研究成果将使加拿大所有制造业受益。