Collaborative Proposal: Large-Scale Optimization Strategies for Design under Uncertainty

协作提案：不确定性下的大规模设计优化策略

• 批准号: 0438279
• 负责人: Lorenz Biegler
• 金额: $ 25.08万
• 依托单位: Carnegie-Mellon University
• 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=0438279&HistoricalAwards=false
• 关键词: Collaborative; Proposal; Large; Scale; Optimization

ABSTRACTPI: Lorenz Biegler Institution: Carnegie Mellon UniversityProposal Number: 0438279Title: 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PI：Lorenz Biegler 机构：卡内基梅隆大学提案编号：0438279 标题：协作提案：不确定性下的大规模设计优化策略研究：未知信息下的优化设计是过程系统工程中的关键任务。 该项目考虑了包含两种类型的未知信息的公式：建模信息的不确定性和过程操作的可变性。 不确定性需要对流程进行过度设计，使其能够在整个不确定性范围内运行。另一方面，可以通过有效的控制策略来减轻变异性。然而，这两个问题在大多数工程系统中都存在，并且必须在设计阶段面对。 为了解决这个问题，PI 计划扩展了不确定性下设计的两阶段公式，并为此公式推导出新的优化策略。该提案的一个关键组成部分是研究人员最近开发的新型优化算法的应用。更广泛的影响：该方法将应用于化学加工中的许多重要研究问题，包括燃料电池、碳纳米管和燃料电池的建模和优化。用于制造半导体的 CVD 反应器。此外，两项重要的外展活动包括： - 分发开发的软件，以评估工业相关流程中不确定性和可变性的影响 - 为高中生、本科生和高中教师提供教育计划，以认识不确定性和流程可变性对工业相关流程的影响工艺性能、经济性、安全性和环境影响。