Separation process optimization under uncertainty by chance constraint programming with recourse

Li Sun, Huajie Zhang

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In this paper, the methodology of chance constraints programming with resource is proposed for the separation process optimization under uncertainty. In this approach, uncertain factors are classified into two types: the first type of uncertainties is compensated for by introducing a penalty term to the optimization objective, and the other uncertainties are expressed by chance constraints at certain confidence levels in the optimization model. The solution strategy is developed by a sequence transform hybrid algorithm involving both Monte Carlo integration and improved Benders decomposition strategies with sequential quadratic programming. 1-hexene separation process is optimized as a case study to illustrate the feasibility of the proposed strategy.
Original languageEnglish
Pages (from-to)797-802
Number of pages6
JournalComputer Aided Chemical Engineering
Publication statusPublished - 10 Jun 2015


  • Stochastic programming
  • uncertainty
  • chance constraint
  • recourse

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