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 language||English (US)|
|Title of host publication||Computer Aided Chemical Engineering|
|Number of pages||14|
|State||Published - 2015|
|Name||Computer Aided Chemical Engineering|
Bibliographical noteFunding 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.
© 2015 Elsevier B.V.
- Decision rule
- Endogenous uncertainty
- Exogenous uncertainty
- Robust optimization
- Scenario generation
- Stochastic programming