MODEL-BASED APPROACHES TO SUPPORT THE OPTIMISATION OF MICROALGAE GROWTH IN PHOTOBIOREACTORS

Microalgae are a wide class of photosynthetic organisms that, through photosynthesis, use solar energy to fix CO2, release O2 as a by-product and produce a biomass rich in carbohydrates, proteins, lipids and bioactive compounds. Their high photosynthetic yield and ability of growing fast and in harsh environments make them a promising candidate for sustainable bio-based technologies in critical sectors such as pharmaceutics, cosmetics, human and animal nutrition, aquaculture, chemicals and fuels, and pollution prevention. Currently, a great obstacle to the actual industrialisation of microalgae technology is represented by a limited comprehension of microalgae growth processes and reliable tools for representing their response in an industrial environment. For this reason, the development of reliable mathematical models that are capable of quantitative predictions of biomass growth as a response to different cultivation conditions is of paramount importance. The aim of this Thesis is to enhance the comprehension and representation of the phenomena that affect microalgae growth in photobioreactors by employing a combination of experimental photobioreactor development and formulation and application of mathematical models. First of all, the influence of light on microalgae on the microalgae photosynthetic response has been investigated, considering different aspects. Firstly, the effect of continuous light on ultra-thin photobioreactors has been examined. Parameters of a Haldane-like models were estimated from experimental data at different optical paths. It was found that, due to the inability to account explicitly for mixing-induced light-dark cycles, Haldane-like models have limited capability in capturing experimental results at ultra-thin light paths and thus can be unsuitable to scale up experimental results from ultra-thin scales to higher scales. Then, microalgae growth under pulsed light regimes has been addressed, with both low and high light frequencies. To describe the dynamic effect of low frequencies on microalgae growth, a dynamic reformulation of the Camacho Rubio model was proposed, based on the theoretical knowledge of photosynthetic characteristic times, and its parameters were precisely estimated from High Throughput Screening data. For high frequency pulsed light cultivation, a different mathematical model accounting for photons absorption, usage and dissipation was examined. The model was modified, and its parameters were retrieved from experimental data. The modified model was able to provide meaningful physical explanations of the observed data and was further employed to guide respirometry experiments. The understanding of nutrient dynamics is also crucial to ensure the sustainability of microalgal production. Although different modelling approaches are available, their calibration is often a complex and time-consuming task. The second main contribution of the Thesis is thus concerned with the use of model-based design of experiments techniques for the precise estimation of the Droop model in continuous photobioreactors to capture the dynamic effect of nitrogen uptake on microalgae growth. Results demonstrate that the proposed methodology can help reducing the experimental effort needed to estimate the model parameters. It is also shown that the newly calibrated model can represent nitrogen uptake dynamics effectively.