Model-based Fed-batch Algal Cultivation Strategy for Enhanced Starch Production

Gonzalo Figueroa Torres, Jon Pittman, Constantinos Theodoropoulos

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


    Promoting and ensuring widespread usage of biofuels throughout the European Union are two crucial targets of the existing strategic measures tackling global warming (Eurostat, 2017). Selection of adequate biofuel feedstocks, however, remains a major challenge obstructing large-scale production of biofuels. Microalgal biomass has been recently recognized as a promising biofuel feedstock due to its ability to accumulate significant amounts of starch (a raw precursor of sugar-based fuels) and lipids, when grown under nutrient-limited conditions (Markou et al., 2012; Suganya et al., 2016). However, such strategies need to be carefully implemented since they are typically characterised by a trade-off between biomass growth and starch/lipid accumulation. We previously identified and validated a starch-enhanced batch-mode cultivation scenario by means of a kinetic model capable of predicting the nitrogen limited dynamics of mixotrophic algal growth and simultaneous starch and lipid formation (Figueroa-Torres et al., 2017). This work focuses on starch accumulation, and the validated model is exploited by establishing an optimal fed-batch cultivation strategy capable of enhancing starch formation whilst sustaining algal growth. Optimal fed-batch operating conditions were identified by means of a model-based optimisation study maximising starch concentration. The computed fed-batch starch-enhanced scenario was subsequently verified against experimental data obtained from lab-scale cultures of Chlamydomonas reinhardtii CCAP 11/32, which yielded a 37 % increase in starch concentration with respect to that obtained by standard batch operation. Results highlight the potential use of predictive kinetic model as optimisation tools for the establishment of large-scale algae-to-fuel cultivation strategies.
    Original languageEnglish
    Title of host publicationComputer Aided Chemical Engineering
    PublisherElsevier BV
    Publication statusAccepted/In press - 31 Mar 2018

    Publication series

    NameComputer Aided Chemical Engineering
    ISSN (Print)1570-7946


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