Integrated Computational and Experimental Studies of Microalgal Lipids Production

  • Mesut Bekirogullari

Student thesis: Phd


Microalgal biomass and its lipids are long-term promising candidates for the production of fuels, food, nutraceuticals and other added-value products. Due to irreversible depletion of fossil fuel reserves for very large demands of transportation and escalating greenhouse gas emissions (GHGs) into the atmosphere, serious consideration has been given to microalgae-derived biodiesel production due to several outstanding characteristics inherent to microalgae. However, the current production cost of microalgal biodiesel is still too expensive to compete with conventional fuels. Although microalgal lipids have an immense potential in biotechnological applications, in order to improve the sustainability of microagal biodiesel and also to enable its economic viability, microalgal biomass and lipid productivities need to be enhanced. Metabolic modifications by genetic manipulation, mutagenesis or natural selection are approaches that have been actively evaluated to develop high productivity strains. On the other hand, a combination of kinetic modelling with growth experiments at different scales is widely utilised to optimise cultivation conditions and metabolic productivities. Optimisation of the microalgae growth media composition and environmental factors such as carbon source, nutrient, light intensity and temperature can lead to high metabolic productivities. The aim of this Thesis is the development of a novel integrated experimental and computational framework to systematically identify optimal growth conditions for biomass growth and lipid accumulation and to ultimately result to a cost-effective scaled-up process. To achieve this, experiments were initially conducted with heterotrophic growth of a well-studied chlorophyte microalgae species Chlamydomonas reinhardtii at bench scale under different acetate and nitrogen concentrations, light intensity and temperature. Based on high-fidelity experimental observations and on existing literature, a detailed kinetic model was constructed through a multi-parameter quantification methodology. The developed model was based on a multiplicative modelling approach, which assumes equal contribution of growth limiting factors: substrate (acetate), nitrogen, light intensity and temperature. The model was validated and utilized in optimisation studies to predict the optimal acetate and nitrogen concentrations, light intensity and temperature in order to achieve the highest lipid productivity possible. It was found that the lipid productivity can be increased by 50.9 % compared to a base case. Scale-up of the process offers a potential pathway to produce substantial amount of lipids for biodiesel production. Therefore, the quadruple substrate kinetic model was adapted to be applied in large-scale raceway open ponds to assess the applicability of the developed kinetic model in scaled-up applications. Experiments with photoautotrophic growth of C. reinhardtii in a 2 m3 raceway open pond were screened. The open pond model proposed in this study was a function of light intensity, temperature and nitrogen. The kinetic parameters of the model were estimated using in-house obtained experimental data performed in 2m3 raceway open pond. The model was validated and is able to predict both biomass growth and lipid accumulation with high accuracy.
Date of Award31 Dec 2017
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorKonstantinos Theodoropoulos (Supervisor) & Jon Pittman (Supervisor)


  • Raceway open pond
  • Temperature
  • Light intensity
  • Nitrogen
  • Acetate
  • Microalgal lipid
  • Dynamic kinetic modelling
  • Biofuels
  • Chlamydomonas reinhardtii
  • Cultivation optimization

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