Future energy solutions are required not only to deliver energy for a rapid growing global population, but also to safeguard the environment, which provides the basis of life on Earth. The Earth receives 3020 ZJ yr-1 of solar energy [1], which dwarfs today’s global energy demand of 0.56 ZJ yr-1 [2] and so even at low solar-to-chemical energy conversion efficiencies solar fuel systems could theoretically supply global fuel demand. Ultimately this theoretical limit will be restricted by practical, economic and socio-political constraints but it is clear that solar energy is by far the most abundant energy source available to us and capable of driving a significant portion of a future renewable fuel industry, be it via artificial solar or solar biofuels systems. Biofuel systems convert solar energy into chemical energy in the form of biomass using photosynthesis. In the biomass the energy is stored in organic molecules including oils, proteins, starch, lignin, and cellulose. Microalgal systems can offer potential advantages such as security of supply through regionally distributed production as well as the generation of useful by-products and the utilisation of waste streams. A detailed literature review is given in chapter 1 discussing the ideas and difficulties of microalgae as an energy technology. Additional background on the requirement and availability of resources for biomass production, the characteristics of microalgae biology and strain optimisation, as well as the challenges of phototrophic mass cultivation and process management are reviewed prior to the relevant research chapter 2, 3 and 4. The optimal production of selected microalgae strains requires the optimisation of nutrients, CO2 levels, light dilution properties, pH and temperature. The development of a powerful automated high-throughput microalgal screening system is described in chapter 2. It is designed to identify improved nutrient conditions for a broad range of species within a complex multi-dimensional statistical space focusing on the optimisation of the 12 most important nutrients in photoautotrophic (light and CO2) conditions. These include macro- (N [i.e. NO3-, NH4+ & urea], P, Ca, Mg) and micro-elements (Mn, Zn, Cu, B, V, Si, Fe, Se) with the remaining ones provided in reportedly replete levels and at 1% CO2 concentrations (adjustable). In chapter 3, 100 microalgae strains (axenic) have been analysed for growth kinetics using the automated nutrient screen matrix (chapter 2). Approximately 600,000 data points were recorded and the optimum photoautotrophic conditions for maximum biomass production for each microalgae strains were statistically defined. Calcium, magnesium and zinc were identified as the most significant nutrients affecting growth at elevated CO2 concentration. The carbon source utilisation was also monitored for mixotrophic and heterotrophic conditions. The 10 best performing strains based on biomass productivity, taxonomic diversity and compositional analysis were then selected as production candidates for validation experiments in flasks. Chapter 3 (and Appendix A) also describes the collection, purification and identification of local microalgae used for the high-throughput nutrient screening. Local strain collections offer significant advantages for a developing microalgae industry by overcoming regulatory aspects (e.g. Quarantine restrictions) and intellectual property restrictions. Local isolates are also often more robust to local climate conditions compared to algal strains from culture collections. More than 100 strains (mostly Chlorophytes) have been purified to the axenic level and about 50% were successfully cryopreserved for long-term storage. The taxonomic identification was initially based on morphological classification and was refined through ribosomal DNA analysis (16S, 18S). The first step of all algae-derived products is the photosynthetic production of biomass, cultivated in ponds or specific photobioreactors (e.g. flat-panel or tubular systems). Chapter 4 compares the performance of a reference (Chlorella sorokiniana (12_A9)) and high productivity microalgae strain Chlorella sp. (11_H5) (two production candidate strains selected based on the analysis in chapter 3) in high-rate-ponds, flat-panel bioreactors (0.75 m and 1.5 m high) and tubular bioreactors (0.74 m and 1.49 m high) under subtropical field conditions. System inputs (light, nutrients, CO2), outputs (biomass, nutrient uptake, O2 and CO2) and population dynamics (micrographs and flow cytometry) were monitored to define key production parameters. The highest observed daily photosynthetic conversion efficiency (PCE) based on illuminated bioreactor surface area was 4.44% in the high flat-panel systems using C. sorokiniana (12_A9) (40.8 g.m-2.d-1, 0.23 g.L-1.d-1). The highest achieved mean PCE (PBR surface based) was 2.5% in the low tubular bioreactor with Chlorella sp. (11_H5) (mean: 24.9 g.m-2.d-1, 0.43 g.L-1.d-1). A correlation was observed for C. sorokiniana (12_A9) between PCE and illuminated surface area to volume (SA:V) ratio in terms of areal productivity. Chlorella sp. (11_H5) appeared to perform better at high light and temperatures.