Microalgae have a broad spectrum of potential applications, such as for human consumption, aquaculture feed, nutraceuticals and pharmaceuticals. The utilisation of microalgae offers a sustainable approach due to their ability to fixate CO2 and uptake inorganic nitrogen and phosphorous sources from wastewater.
The freshwater microalgae Haematococcus pluvialis is of explicit industrial interest due to its high content of astaxanthin. Astaxanthin is a secondary carotenoid with a bright red colour and high potential as a food supplement due to strong antioxidant, anti-inflammatory and antitumoral properties.
Astaxanthin is meant to protect the cell from various stress conditions. The most common stress factors H. pluvialis faces are intense illumination, salt stress and nutrient deprivation. Given that the growth under stress conditions is very limited, a two-stage production is often applied where the cells are grown under favourable conditions in their green motile stage and afterwards exposed to one or several stress conditions.
Translating the behaviour of cells with respect to growth and product accumulation into a mathematical model can give valuable insights and help to improve dynamic processes. The most important input parameters for microalgae growth models are light intensity, nutrient concentration, temperature, CO2 availability and, in the case of a mixotrophic system, the concentration of additional carbon (e.g. glucose, acetate).
In general, there are many different ways to approach a microalgae growth model. A model can simulate the behaviour at a molecular level or focus on an entire population. The latter requires some simplifying assumptions and makes use of empirical correlations (e.g. Monod kinetics). However, it requires less computational power and can give accurate results if calibrated and validated correctly.
Building a reliable model for optimisation and decision support requires not only appropriate mathematical expressions but also accurate data. This data can often not be obtained by a single experiment, especially when the model should account for several input parameters. A thoughtful selection of experiments and accurate work in data acquisition is therefore key to success.
In conclusion, modelling approaches can result in optimised and automatized processes. This can guide us towards a world where microalgae play an important role in sustainable production processes (e.g. astaxanthin from H. pluvialis). However, a thoughtful plan and accurate collection of data is required in order to succeed.