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.
|Number of pages||6|
|Journal||Computer Aided Chemical Engineering|
|Publication status||Published - 10 Jun 2015|
- Stochastic programming
- chance constraint