Collaborative Research: Large-Scale Optimization Strategies for Design Under Uncertainty

协作研究：不确定性下的大规模设计优化策略

• 批准号: 0438367
• 负责人: Luke Achenie
• 金额: --
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-01 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0438367&HistoricalAwards=false
• 关键词: Collaborative; Research; Large; Scale; Optimization

ABSTRACTPI: Luke E. Achenie Institution: University of ConnecticutProposal Number: 0438367Title: Collaborative Proposal: Large-Scale Optimization Strategies for DesignUnder UncertaintyResearch: Optimal design under unknown information is a key task in process systems engineering. This project considers formulations that incorporate two types of unknown information, uncertainty in the modeling information and variability in the operation of the process. Uncertainty requires an overdesign of the process so that it can operate over the entire uncertainty range. On the other hand, variability can be mitigated through effective control strategies. Nevertheless, both issues are present in most engineering systems and must be confronted at the design stage. To address this problem the PIs plan extend a two-stage formulation for design under uncertainty and derive new optimization strategies for this formulation. A key component of this proposal is the application of novel optimization algorithms, recently developed by the investigators.Broader Impacts: This approach will be applied to a number of important research problems in chemical processing, including modeling and optimization for fuel cells, carbon nanotubes and CVD reactors for the manufacture of semiconductors. In addition two important outreach activities include:- Distribution of developed software to assess the influence of uncertainty and variability in industrially-relevant processes- An educational program for high school students, undergraduates and high school teachers to recognize the impact of uncertainty and process variability on process performance, economics, safety and environmental impact.

摘要：项目负责人：Luke E. Achenie机构：University of connecticut课题编号：0438367题目：协作方案：不确定性下设计人员的大规模优化策略研究：未知信息下的优化设计是过程系统工程中的一个关键问题。该项目考虑了包含两种未知信息的公式，建模信息中的不确定性和过程操作中的可变性。不确定性要求对过程进行过度设计，使其能够在整个不确定范围内运行。另一方面，可变性可以通过有效的控制策略得到缓解。然而，这两个问题在大多数工程系统中都存在，必须在设计阶段面对。为解决这一问题，提出了一种不确定设计的两阶段公式，并推导出新的优化策略。这个建议的一个关键组成部分是新的优化算法的应用，最近由研究人员开发。更广泛的影响：这种方法将应用于化学处理中的许多重要研究问题，包括燃料电池、碳纳米管和半导体制造的CVD反应器的建模和优化。此外，两项重要的外展活动包括：-分发已开发的软件，以评估工业相关过程中的不确定性和可变性的影响-为高中学生，本科生和高中教师制定教育计划，以认识不确定性和过程可变性对过程性能，经济，安全和环境影响的影响。