Improving global optimization methods for dynamic process models

改进动态过程模型的全局优化方法

• 批准号: RGPIN-2017-05944
• 负责人: Khan, Kamil
• 金额: $ 1.75万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638317
• 关键词: Improving; global; optimization; methods; dynamic

Many applications in engineering, chemistry, and physics involve systems with time-varying behavior. These dynamic systems include batch processes for production of high-value specialty chemicals, bioproducts, and pharmaceuticals from lower-value raw materials through complex systems of chemical reactions, automatic control policies that maintain safety and quality by preventing systems from drifting off-course, biological processes such as cell growth, and robotics. The goal of the proposed research program is to improve global optimization methods for process models of dynamic systems.

工程、化学和物理学中的许多应用都涉及具有时变行为的系统。这些动态系统包括通过复杂的化学反应系统从低价值原材料生产高价值特种化学品、生物制品和药品的批量过程，通过防止系统偏离轨道来保持安全和质量的自动控制策略，细胞生长等生物过程，以及机器人技术。提出的研究计划的目标是改进动态系统过程模型的全局优化方法。