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Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study.

Authors :
Yeh, Yen-Cheng
Syed, Tehreem
Brinitzer, Gordon
Frick, Konstantin
Schmid-Staiger, Ulrike
Haasdonk, Bernard
Tovar, Günter E.M.
Krujatz, Felix
Mädler, Jonathan
Urbas, Leon
Source :
Bioresource Technology. Dec2023, Vol. 390, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

• Comparative study to model the growth of P. tricornutum under natural sunlight. • Machine learning models outperform traditional models. • Light history enhances growth modeling using machine learning models. • LSTM model can illuminate light acclimation effect. • Applications of the models: biomass softsensor & optimal harvest strategy. Accurate prediction of microalgae growth is crucial for understanding the impacts of light dynamics and optimizing production. Although various mathematical models have been proposed, only a few of them have been validated in outdoor cultivation. This study aims to investigate the use of machine learning algorithms in microalgae growth modeling. Outdoor cultivation data of Phaeodactylum tricornutum in flat-panel airlift photobioreactors for 50 days were used to compare the performance of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) with traditional models, namely Monod and Haldane. The results indicate that the machine learning models outperform the traditional models due to their ability to utilize light history as input. Moreover, the LSTM model shows an excellent ability to describe the light acclimation effect. Last, two potential applications of these models are demonstrated: 1) use as a biomass soft sensor and 2) development of an optimal harvest strategy for outdoor cultivation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09608524
Volume :
390
Database :
Academic Search Index
Journal :
Bioresource Technology
Publication Type :
Academic Journal
Accession number :
173415511
Full Text :
https://doi.org/10.1016/j.biortech.2023.129882