Integrated Design and Control Under Uncertainty

不确定性下的集成设计与控制

• 批准号: 0626162
• 负责人: Andreas Linninger
• 金额: --
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0626162&HistoricalAwards=false
• 关键词: Integrated; Design; Control; Under; Uncertainty

ABSTRACTPI: Andreas A. LinningerInstitution: University of Illinois ChicagoProposal Number: 0626162Title: Integrated Design and Control Under UncertaintyThis research is aimed at the unification of design and control for high performance processes under uncertainty. It has two main objectives:. A decision-hierarchy for the integration of process design and control objectives under uncertainty. Mathematical programming formulations and solution procedures to make the integrated design and control optimization tractable.To achieve this goal, the PI proposes a decision hierarchy with three levels: dynamic modeling, design optimization and optimal quality standards. The research plan involves two main research activities:. Task-A: Mathematical Problem Decomposition for design under uncertainty. Task-B: Computational Advances for Integrated Design and ControlIntellectual Merit:Tight risk and uncertainty metrics: The integrated design and control approach accurately quantifies the dynamic system resilience under realistic operation. Rigorous detection and resolution of worst-case uncertainty and disturbance scenarios are presented.Combinatorial Complexity: A novel problem decomposition segregates the integrated design and control into two levels of optimization. The planned stochastic design optimization with embedded control reduces massively problem size and complexity. Case studies demonstrate convergence of the method with existing mathematical programming. The methodology should significantly improve the tractability of integrated design and control problems.New Stochastic Optimization Algorithms: The PI also proposes novel optimization procedures specifically tailored for discontinuous and open-ended design optimizations. A stochastic genetic program with population learning acceleration combines the robustness of genetic algorithms with the speed of gradient-based mathematical programming.Broader Impact:Manufacturers in today's global networks must deliver products that consistently meet customer quality demands worldwide. Unavoidable variability in raw materials and operating conditions and tight product specifications make robust process operation a necessity for future economic viability. This work is expected to impact modern process operation in the following ways:Systematic decisions for robust processes: Our methodology will provide analytical guidelines for addressing different types of uncertainties with integrated design and control.Integration of design and control: The globalization of supply and demand networks will require novel design methods that enable manufacturers to develop and operate processes that consistently meet customer demands at unparalleled performance levels. High fidelity models to tightly quantify uncertainty and risk will empower U.S. industry to safely operate closer to performance limits.Taming the Combinatorial Complexity: Innovative ideas to overcome the open-ended design problems are expected to impact design activities for high performance processes like fuel cells, energy integration and optimal supply management in which tight satisfaction of operational constraints are critical.The educational plan foresees the introduction of flexible design and control into the chemical engineering undergraduate curriculum, specialized undergraduate research projects (NSF REU-Site) specifically reaching out to underrepresented groups and providing synergistic opportunities for K-14 science and math teachers serving minorities in urban areas (NSF RET-Site). A dissemination plan foresees academic outlet via publications and professional channels.

摘要：Andreas A. Linninger机构：伊利诺伊大学芝加哥分校项目编号：0626162题目：不确定性下的集成设计与控制本研究旨在实现不确定性下高性能过程设计与控制的统一。 它有两个主要目标：不确定性条件下工艺设计和控制目标集成的决策层次结构。数学规划公式和解决方案的过程，使集成的设计和控制优化易于处理。为了实现这一目标，PI提出了一个决策层次结构的三个层次：动态建模，设计优化和最佳质量标准。 研究计划包括两个主要的研究活动：任务A：不确定条件下设计的数学问题分解。任务B：集成设计和控制的计算进步智力优点：严格的风险和不确定性指标：集成设计和控制方法准确地量化了实际操作下的动态系统弹性。 最坏情况下的不确定性和干扰scenaries.Combinatorial复杂性：一种新的问题分解分离的集成设计和控制到两个层次的优化。 嵌入式控制的计划随机设计优化大大减少了问题的规模和复杂性。案例研究表明收敛的方法与现有的数学规划。 新的随机优化算法：PI还提出了专门为不连续和开放式设计优化量身定制的新的优化程序。 具有种群学习加速的随机遗传程序将遗传算法的鲁棒性与基于梯度的数学规划的速度相结合。更广泛的影响：当今全球网络中的制造商必须提供始终满足全球客户质量需求的产品。 原材料和操作条件的不稳定性以及严格的产品规格使得稳健的工艺操作成为未来经济可行性的必要条件。 预计这项工作将在以下方面影响现代过程操作：稳健过程的系统决策：我们的方法将为解决集成设计和控制的不同类型的不确定性提供分析指南。设计和控制的集成：供应和需求网络的全球化将需要新的设计方法，使制造商能够开发和操作流程，以无与伦比的速度持续满足客户需求。性能水平。精确量化不确定性和风险的高保真模型将使美国工业能够安全地运营，更接近性能极限。克服开放式设计问题的创新思想有望影响燃料电池等高性能工艺的设计活动，能源整合和最佳供应管理，其中严格满足运营限制至关重要。教育计划预见到，在化学工程本科课程中引入灵活的设计和控制，专门的本科研究项目（NSF REU-Site）专门接触代表性不足的群体，并为城市地区服务于少数民族的K-14科学和数学教师提供协同机会（NSF RET-Site）。传播计划设想通过出版物和专业渠道进行学术传播。