Optimal Design and Synthesis of Algae Processing Network under Uncertainty Based on Return on Investment

Jian Gong, Fengqi You

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


This article addresses the robust design and synthesis of algae processing networks under feedstock and product price uncertainty. We develop a superstructure of algae processing networks for the production of biodiesel and bioproducts. Based on the superstructure, we proposed a two-stage adaptive robust mixed-integer linear fractional programming problem to maximize the return on investment (ROI) of the algae processing processes under feedstock and product price uncertainty. Due to the fractional term and multi-level optimization structure, the problem cannot be solved directly by any solver. We propose a solution strategy that integrates a parametric algorithm and a column-and-constraint-generation algorithm. Robustness of the optimal solution is controlled by the budget of uncertain parameters, and the obtained robust optimal ROIs range from 6.38% to 7.62%. The robust optimal processing route selects algae cultivation in raceway open ponds, harvesting via centrifugation, supercritical CO2 extraction, heterogeneously catalysed transesterification, directly combusting the biogas in the remnant treatment process, and poly-hydroxybutyrate upgrading process.

Original languageEnglish (US)
Title of host publication26 European Symposium on Computer Aided Process Engineering, 2016
EditorsZdravko Kravanja, Milos Bogataj
PublisherElsevier B.V.
Number of pages6
ISBN (Print)9780444634283
StatePublished - Jan 1 2016

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946


  • adaptive robust optimization
  • algae processing network
  • superstructure
  • uncertainty

ASJC Scopus subject areas

  • General Chemical Engineering
  • Computer Science Applications


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