Optimal processing pathway selection for microalgae-based biorefinery under uncertainty

•Identification of optimal microalgae processing pathways under uncertainty.•Techno-economic uncertainties in the dataset are considered.•The optimization problem is formulated as a stochastic MINLP model.•Optimal processing pathways are determined under different objective functions.•Optimal struct...

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Vydáno v:Computers & chemical engineering Ročník 82; s. 362 - 373
Hlavní autoři: Rizwan, Muhammad, Zaman, Muhammad, Lee, Jay H., Gani, Rafiqul
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 02.11.2015
Témata:
Biofuels
Chemical engineering
Decision-making under uncertainty
Mathematical models
Microalgal biorefinery
Nonlinear programming
Optimization
Pathways
Refining
Stochastic mixed integer nonlinear programming (sMINLP)
Superstructures
Uncertainty
Uncertainty analysis
Stochastic mixed integer nonlinear programming (sMINLP)
Microalgal biorefinery
Biofuels
Uncertainty analysis
Decision-making under uncertainty
ISSN:0098-1354, 1873-4375
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Shrnutí:•Identification of optimal microalgae processing pathways under uncertainty.•Techno-economic uncertainties in the dataset are considered.•The optimization problem is formulated as a stochastic MINLP model.•Optimal processing pathways are determined under different objective functions.•Optimal structures are compared with respect to different objective functions used. We propose a systematic framework for the selection of optimal processing pathways for a microalgae-based biorefinery under techno-economic uncertainty. The proposed framework promotes robust decision making by taking into account the uncertainties that arise due to inconsistencies among and shortage in the available technical information. A stochastic mixed integer nonlinear programming (sMINLP) problem is formulated for determining the optimal biorefinery configurations based on a superstructure model where parameter uncertainties are modeled and included as sampled scenarios. The solution to the sMINLP problem determines the processing technologies, material flows, and product portfolio that are optimal with respect to all the sampled scenarios. The developed framework is implemented and tested on a specific case study. The optimal processing pathways selected with and without the accounting of uncertainty are compared with respect to different objectives.
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ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2015.08.002