A review on machine learning approaches for microalgae cultivation systems

Research output: Contribution to journalReview articleContributedpeer-review



Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.


Original languageEnglish
Article number108248
JournalComputers in biology and medicine
Publication statusPublished - 10 Mar 2024

External IDs

PubMed 38493599
ORCID /0000-0001-5165-4459/work/158306168
ORCID /0000-0003-2952-4986/work/158306180
ORCID /0000-0001-7012-5966/work/158306302


Research priority areas of TU Dresden

Subject groups, research areas, subject areas according to Destatis


  • Artificial neural networks, Hybrid modeling, Machine learning, Microalgae, Biomass, Biofuels, Food