Scaling-up information from high-throughput pulsed light data to predict microalgae growth dynamics in photobioreactors

Single cell green algae (microalgae) have evolved to tap into the vast solar energy resource via photosynthesis to produce biomass, which is a feedstock for a broad array of products including foods, feeds, fuels, biomaterials and fine chemicals. Microalgae are therefore the chassis for an array of solar biotechnologies.

In this study, we reformulated a photosynthetic unit model to capture the effect of light-dark cycles induced by self-shading and mixing in scaled-up systems. We calibrated and validated our model using High-Throughput Screening (HTS) data for Chlamydomonas reinhardtii under pulsed and continuous light regimes. By reconstructing the light history of individual cells migrating through a cultivation system, we established a link between HTS data and scaled-up systems, and validated the approach using published cultivation data.

In a case study of an outdoor microalgae cultivation scenario we demonstrated how our model captures the dynamics of both incident solar irradiance and attenuated light intensity along a simulated mixing cycle. The analysis of internal model variables provided insights into the 24-hour biomass growth profile and biomass productivity of the system. Finally, we showed how the model can be exploited for the optimisation of cultivation systems, enabling the definition of optimal forecast illumination intensities, mixing cycles, inoculation densities, and light paths.

This work represents a first step in linking small-scale HTS platforms with large-scale cultivation systems (i.e. photobioreactors). This provides a foundation to accelerate and de-risk the scale-up of microalgae production systems towards industrialised algae farming and processing.