Optimisation of Biofuels Production from Microalgal Biomass

Abstract

Microalgae are positioned as a promising platform for sustainable biofuels production due to their ability to synthesise starch and lipid molecules, which can be directed towards the production of bioethanol and biobutanol via fermentation, or biodiesel via transesterification. The commercialisation of microalgal biofuels, however, is unlikely to become a reality unless large-scale algal cultivation systems can efficiently generate high-density algal cultures rich in starch and lipids. Numerous metabolic studies have revealed the ability of cells to counteract nutrient-stressed conditions by inducing starch and lipid accumulation, allowing the exploration of tailor-made biofuel-oriented cultivation strategies. Nevertheless, it has been demonstrated that those conditions that favour starch and lipid formation do not typically favour biomass growth, complicating the identification of cultivation strategies fit for biofuels production. In this research, the challenging identification of optimal cultivation strategies maximising starch and lipid formation is approached by developing a predictive kinetic model supported by experimental observations and suitable for the simulation and optimisation of algal mixotrophic growth dynamics co-limited by nitrogen and phosphorous. The model uses a compartmentalised approach in which cells are comprised of an active biomass fraction and storage molecule fractions, allowing the identification of the individual starch and lipid concentration profiles. To construct and validate the model, laboratory-scale batch experiments were carried out with the green model species Chlamydomonas reinhardtii under various acetic acid (i.e. carbon substrate), nitrogen, and phosphorous concentration regimes. The model was then built in line with experimental data and existing modelling approaches, and the associated kinetic parameters were quantified via an optimisation-based fitting methodology. The validated model was subsequently exploited as an optimisation tool by identifying the required nutrient compositions maximising starch and lipid formation. These optimised scenarios yielded significant increases in starch (+ 270 %) and lipids (+ 74 %) compared to the non-optimised strategy. The model's predictive capacity for fed-batch cultivation dynamics was additionally assessed via the evaluation of a nutrient feeding strategy consisting of intermittent pulses of acetic acid. Such a strategy was found to significantly increase biomass formation (+ 126 %) against standard batch cultivation. Finally, a case study was carried out to quantify the production of biobutanol and biodiesel within the framework of a microalgal biorefinery. Results showed biofuel yields (g fuel per g of dry algae) of 0.103 biobutanol via the ABE fermentation of microalgal starch, and 0.038 biodiesel via the transesterification of microalgal lipids. In summary, this research presents an optimisation framework combining both modelling and experimental tools which can be systematically applied for the establishment of optimal biofuel-oriented microalgal cultivation systems and additionally reaffirms the exploitative value of microalgae as a promising biorefinery platform for biofuels production.

Date of Award	1 Aug 2019
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Konstantinos Theodoropoulos (Supervisor) & Jon Pittman (Supervisor)