Recent Advances in Mathematical Programming Techniques for the Optimization of Process Systems under Uncertainty

Ignacio E. Grossmann, Robert M. Apap, Bruno A. Calfa, Pablo Garcia-Herreros, Qi Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Optimization under uncertainty has been an active area of research for many years. However, its application in Process Synthesis has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust optimization vs. stochastic programming), large computational expense (often orders of magnitude larger than deterministic models), and difficulty in the interpretation of the results by non-expert users. In this paper, we describe recent advances that have addressed some of these barriers.

Original languageEnglish (US)
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages1-14
Number of pages14
DOIs
StatePublished - 2015
Externally publishedYes

Publication series

NameComputer Aided Chemical Engineering
Volume37
ISSN (Print)1570-7946

Bibliographical note

Funding Information:
The authors would like to acknowledge financial support from NSF Grant No. 1159443, Praxair, Dow Chemical and the Center for Advanced Process Decision-making.

Publisher Copyright:
© 2015 Elsevier B.V.

Keywords

  • Decision rule
  • Endogenous uncertainty
  • Exogenous uncertainty
  • Robust optimization
  • Scenario generation
  • Stochastic programming

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