Gonzalo M Figueroa-Torres1, Jon K Pittman2, Constantinos Theodoropoulos3. 1. Department of Chemical Engineering and Analytical Science, Biochemical and Bioprocess Engineering Group, The University of Manchester, Manchester, M13 9PL, UK. 2. Department of Earth and Environmental Sciences, The University of Manchester, Manchester, M13 9PL, UK. 3. Department of Chemical Engineering and Analytical Science, Biochemical and Bioprocess Engineering Group, The University of Manchester, Manchester, M13 9PL, UK. k.theodoropoulos@manchester.ac.uk.
Abstract
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.
BACKGROUND: The production of microalgal biofuels, despite their sustainpan>able and renpan>owned potenpan>tial, is not yet cost-effective compared to currenpan>t convenpan>tional fuel technologies. However, the biorefinpan>ery concept inpan>creases the prospects of microalgal biomass as an economically viable feedstock suitable for the co-production of multiple biofuels along with value-added chemicals. To inpan>tegrate biofuels production withinpan> 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.
Authors: Ursula Goodenough; Ian Blaby; David Casero; Sean D Gallaher; Carrie Goodson; Shannon Johnson; Jae-Hyeok Lee; Sabeeha S Merchant; Matteo Pellegrini; Robyn Roth; Jannette Rusch; Manmilan Singh; James G Umen; Taylor L Weiss; Tuya Wulan Journal: Eukaryot Cell Date: 2014-02-28