9Microalgal starch and lipids, carbon-based storage molecules, are useful as potential 10 biofuel feedstocks. In this work, cultivation strategies maximising starch and lipid
Background
The production of microalgal biofuels, despite their sustainable and renowned potential, is not yet cost-effective compared to current conventional fuel technologies. However, the biorefinery concept increases the prospects of microalgal biomass as an economically viable feedstock suitable for the co-production of multiple biofuels along with value-added chemicals. To integrate biofuels production within the framework of a microalgae biorefinery, it is not only necessary to exploit multi-product platforms, but also to identify optimal microalgal cultivation strategies maximising the microalgal metabolites from which biofuels are obtained: starch and lipids. Whilst nutrient limitation is widely known for increasing starch and lipid formation, this cultivation strategy can greatly reduce microalgal growth. This work presents an optimisation framework combining predictive modelling and experimental methodologies to effectively simulate and predict microalgal growth dynamics and identify optimal cultivation strategies.
Results
Microalgal cultivation strategies for maximised starch and lipid formation were successfully established by developing a multi-parametric kinetic model suitable for the prediction of mixotrophic microalgal growth dynamics co-limited by nitrogen and phosphorus. The model’s high predictive capacity was experimentally validated against various datasets obtained from laboratory-scale cultures of Chlamydomonas reinhardtii CCAP 11/32C subject to different initial nutrient regimes. The identified model-based optimal cultivation strategies were further validated experimentally and yielded significant increases in starch (+ 270%) and lipid (+ 74%) production against a non-optimised strategy.
Conclusions
The optimised microalgal cultivation scenarios for maximised starch and lipids, as identified by the kinetic model presented here, highlight the benefits of exploiting modelling frameworks as optimisation tools that facilitate the development and commercialisation of microalgae-to-fuel technologies.
Microalgal biorefineries have recently emerged as a potentially economically viable option for the co-production of value-added products and fuels, such as biodiesel (via the transesterification of lipids) and biobutanol (via the fermentation of carbohydrates). Whilst microalgal biodiesel has been studied extensively, microalgal biobutanol has received less attention due to the low product yields of the biochemical process from which biobutanol is obtained: the Acetone-Butanol-Ethanol (ABE) fermentation. In this work, we evaluate the potential of a microalgae-based biorefinery by: i) quantifying biobutanol production via ABE fermentation of microalgae (raw and hydrolysate form) using a medium optimised via surface response analysis (SRA) methodology; ii) quantifying biodiesel (fatty acid methyl esthers, FAMEs) production via transesterification of microalgae (raw, hydrolysed, and fermented form). Using SRA-optimised medium, butanol fermentation yields of 10.31% (g g -1 cdw) and 10.07% (g g -1 glucose) were attained by microalgae in raw and hydrolysate form, respectively. Meanwhile, the raw, hydrolysed, and fermented microalgae yielded up to 0.92 %, 3.82 % and 3.29 % (g g -1 cdw) biodiesel, respectively. Results highlight the importance of pretreatment methods and further support the development of microalgal biorefineries for dual biofuel production.
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