Experimental Studies and Model Based Optimisation of Microalgal Production of Fuels and Chemicals

Abstract

Microalgae are amongst the most promising renewable feedstocks for biodiesel production. Control and optimization of the microalgae growth stage can improve the competitiveness and sustainability of microalgal-derived biodiesel industry. The main objective of this work is the development of a predictive microalgae growth model, which considers the impact of growth-associated parameters such as substrate, nitrogen, light and pH. A multi-parameter predictive microalgae growth model has been developed to describe the biomass growth and the lipid accumulation in bench-scale batch systems. Consequently, experiments have been conducted at a range of conditions to estimate the kinetic parameters of the model. The model was fitted to data from lab-scale batch experiments, using 2.1 gL−1 acetic acid and 0.378 gL−1 nitrogen under constant light illumination of 125 μEm−2s−1. The predictiveness of the model was tested by computing outputs of experiments at different conditions: 1.05 gL−1 acetic acid and 0.378 gL−1 nitrogen, under the same light illumination. The validated model can then be exploited to compute optimal operating conditions of bench-scale batch experiments