High biomass production is essential for a viable microalgal biofuel industry. Optimising nutrient supply is a key factor for high efficiency microalgal biomass productivity. The aim of this thesis is therefore to utilise advanced robotics and computational tools to develop a comprehensive, automated approach to optimise nutrient supply for a wide variety of microalgal species and to employ it to define the complex elemental interplay in terms of biomass yield. For this purpose, a two-stage, high-throughput, miniaturised nutrient screen system was developed. It comprised a full-factorial Screen 1 for optimisation of nitrogen and phosphate, and an incomplete factorial Box-Behnken design Screen 2 to optimise calcium, magnesium, iron, copper, boron, manganese, zinc, molybdate, selenium, vanadium and silicon. Conducting a full-factorial screen for all of these variables at three concentration levels would require 310 individual experiments. In contrast, this screen can sample this statistical space much more efficiently (240 samples per species) opening up new investigations that were until now not technically possible due to the time and labour involved. The miniaturised system was based on a platform of eighteen 96 microwell plates (1728 conditions) which were incubated at controlled light, CO2 and temperature using a modified Tecan Freedom Evo 150 robot.
Initial nutrient formulations for both screens were based on average concentration values derived from a literature search analysis of 11 freshwater microalgal media. The system generated reliable maximum growth rate data based on Cronbach’s Alpha statistical analysis (repeatability test) and data reproducibility assessments. Growth rate data was also comparable to conventional flask and bioreactor systems.
The first round of experimentation based on the literature (Screen 1.1), tested the nutrient requirements of 8 microalgal strains. Screen 2 successfully identified the fact that two key nutrients (calcium and magnesium) were limiting. Based on this, Screen 1.1 was improved using higher calcium and magnesium levels and yielded Screen 1.2. The improved medium significantly improved maximum growth rates towards the positive TAP (mixotrophic) control.
Having designed the nutrient screen (Chapter 3), tested and improved it (Chapter 4), Screen 1.2 was used to investigate the capacity of using the screen to monitor nutrient uptake (Chapter 5). This was conducted within a program of work focused on water desalination. In this experiment, Na+ and Cl- were monitored and demonstrated the potential of this approach not only for nutrient optimisation but for its application for challenging practical problems such as water desalination.
This novel and validated system established an automated nutrient optimisation platform which has subsequently been used (post this PhD) to analyse 100 species, 35,000 conditions and over 600,000 time resolved data points. It therefore provides a powerful new method for detailed analysis of nutrient provision for optimised microalgae production.