Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study
Abstract
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.
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Algal digital twin has advanced momentum in optimizing and enhancing algal cultivation for biomass production system design, and developing smart systems for algal cultivation for biomass production.
“ADT has advanced momentum in optimizing and enhancing ACB system design, and developing smart systems for ACB [25,26].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [26]
The LSTM model demonstrates remarkable proficiency in capturing the effects of light acclimation on microalgal growth.
“The LSTM model demonstrates remarkable proficiency in capturing the effects of light acclimation on microalgal growth.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [26]
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A review on machine learning approaches for microalgae cultivation systems
Syed, T., Krujatz, F., Ihadjadene, Y., Mühlstädt, G., Hamedi, H., Mädler, J., & Urbas, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108248 · Cited by 92 · see citing works
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- Authors
- Yeh, Y.-C., Syed, T., Brinitzer, G., Frick, K., Schmid-Staiger, U., Haasdonk, B., Tovar, G. E. M., Krujatz, F., Mädler, J., & Urbas, L.
- Affiliations
- University of Stuttgart (DE)
- Institute of Automation (DE)
- Fraunhofer Institute for Interfacial Engineering and Biotechnology (DE)
- Technische Universität Dresden (DE)
- Corresponding author
- Yeh, Yen-Cheng
- ORCID
- Venue
- Bioresource Technology
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 390
- Pages
- 129882
- ISSN
- 0960-8524 (ISSN-L)
- OpenAlex
- W4387911354
- Cited by
- 56
- References
- 50
- Citations by year
- 2026: 24 · 2025: 22 · 2024: 10
- FWCI
- 2.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Bioresource Technology (publishedVersion)
- PubMed (publishedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Stuttgart Center for Simulation Science, Universität Stuttgart
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- Phaeodactylum tricornutum, open-field cultivation, history of light, light acclimation, airlift photobioreactor, long short-term memory, support vector regression, Monod kinetics, Haldane model
- MeSH terms
- Culture Media, Diatoms, Biomass, Microalgae, Photobioreactors
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