Optimizing turbidostatic microalgal biomass productivity: A combined experimental and coarse-grained modelling approach

Abstract

A combined, iterative experimental and model-based approach towards the optimization of microalgal biomass productivity has been conducted based on five long-term turbidostatic experiments using a strain of the unicellular green algae Nannochloropsis granulata. A coarse-grained model has been developed to describe and predict light-limited steady-state biomass productivity and dilution rate under turbidostatic bioprocess operation. It consists of three main components: Description of the specific growth rate (Monod-based), description of the average internal light intensity (Lambert-Beer-based) and calculation of the biomass-specific photon availability rate. The model employs just three parameters: The mean specific decadic extinction coefficient(ϵ∅ = 0.749 m2 gX-1) as obtained from direct measurements during the experiments as well as the maximum specific growth rate (μmax = 0.90 d−1) and the photon half saturation constant (KS,ph = 0.058 molph gX−1 d−1) as estimated by parameter identification from the experimental data. Following the initial parameter identification based on three experiments, two further separate identifications have been carried out iteratively using the data of the respective next consecutive experiment. Before carrying out the next experiment, the so far established model was used to predict biomass productivity and dilution rate for the respective set operating point (i.e. the controlled biomass concentration). Each resulting model was validated using the predicted subsequent productivity. The achieved model prediction accuracy, expressed as mean absolute percent error (MAPE), stayed below 2% in all three cases.