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

University Of Connecticut, Storrs CT

Investigators

Abstract

ABSTRACT PI: Luke E. Achenie Institution: University of Connecticut Proposal Number: 0438367 Title: Collaborative Proposal: Large-Scale Optimization Strategies for Design Under Uncertainty Research: 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.