Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty |
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| Affiliation: | 1. CNRS & LIX, Ecole Polytechnique, 91128 Palaiseau CEDEX, France;2. DEI, University of Bologna, viale Risorgimento 2, 40136 Bologna, Italy;3. DICAM, University of Bologna, viale Risorgimento 2, 40136 Bologna, Italy;1. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA;2. Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA;3. School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA;4. Hyundai Oilbank Company Ltd., Seoul, Korea |
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| Abstract: | Optimization under uncertainty has been an active area of research for many years. However, its application in Process Systems Engineering 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/chance constrained optimization vs. stochastic programming), large computational expense (often orders of magnitude larger than deterministic models), and difficulty of interpretation of the results by non-expert users. In this paper, we describe recent advances that have addressed some of these barriers for mostly linear models. |
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| Keywords: | Decision rule Robust optimization Stochastic programming Exogenous uncertainty Endogenous uncertainty Scenario generation |
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