Journal
AICHE JOURNAL
Volume 63, Issue 2, Pages 582-600Publisher
WILEY
DOI: 10.1002/aic.15370
Keywords
two-stage adaptive robust optimization; superstructure optimization; algae; uncertainty; mixed integer fractional programming
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Funding
- Institute for Sustainability and Energy at Northwestern University (ISEN)
- National Science Foundation (NSF) CAREER Award [CBET-1554424]
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1643244] Funding Source: National Science Foundation
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Fractional metrics, such as return on investment (ROI), are widely used for performance evaluation, but uncertainty in the real market may unfortunately diminish the results that are based on nominal parameters. This article addresses the optimal design of a large-scale processing network for producing a variety of algae-based fuels and value-added bioproducts under uncertainty. We develop by far the most comprehensive processing network with 46,704 alternative processing pathways. Based on the superstructure, a two-stage adaptive robust mixed integer fractional programming model is proposed to tackle the uncertainty and select the robust optimal processing pathway with the highest ROI. Since the proposed problem cannot be solved directly by any off-the-shelf solver, we develop an efficient tailored solution method that integrates a parametric algorithm with a column-and-constraint generation algorithm. The resulting robust optimal processing pathway selects biodiesel and poly-3-hydroxybutyrate as the final fuel and bioproduct, respectively. (c) 2016 American Institute of Chemical Engineers AIChE J, 63: 582-600, 2017
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