A simple new approach predicting how microalgae culture systems will perform under sunlight and artificial light conditions

There are many problems associated with the methods for scaling microalgae culture systems. Mathematical models - predictive models in particular - are considered key, but most of them require specialist programming and computer science skills. Even where these skills are available, new parameters (...

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Bibliographic Details
Published in:Algal research (Amsterdam) 2024-06, Vol.80, p.103517, Article 103517
Main Authors: Pruvost, J., Rasheed, R., Samhat, K., Kazbar, A., Al Jabri, H., Dauchet, J., Cornet, J.F.
Format: Article
Language:English
Subjects:
Biotechnology
Life Sciences
Mathematics
Maximal productivity
Microalgae
Optimization
Optimization and Control
Photobioreactors
Scaling
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Summary:There are many problems associated with the methods for scaling microalgae culture systems. Mathematical models - predictive models in particular - are considered key, but most of them require specialist programming and computer science skills. Even where these skills are available, new parameters (such as radiative properties for determining the effect of light attenuation on photosynthetic growth) have to be determined with each application of the model for a new strain of microalgae, and this in itself is highly complex. Consequently, the methods used in the field are still mainly (semi-)empirical. A simplified method of determining maximal performance and the corresponding optimal operating points (i.e. biomass concentration, harvesting rate) for a given culture system, strain and culture condition (including solar) is presented here. The approach involves engineering equations, which are smartly adapted in this study to eliminate parameters that are difficult to obtain, with a few conventional small-scale experiments to determine the remaining key parameters relevant to the strain. The method is applied with two strains: Haematococcus pluvialis (in green phase and under continuous light) and a Picochlorum maculatum strain isolated from the Qatar desert (in both continuous and diurnal light cycles). A deviation of
ISSN:2211-9264
2211-9264
DOI:10.1016/j.algal.2024.103517