Recently, unicellular microalgae have attracted considerable interest due to their ability to utilize CO2 in the photosynthetic process and produce various valuable compounds such as biofuels, bioplastics, pigments, and vitamins. Microalgae are a promising source for a sustainable replacement for fossil fuels in transportation and power generation. Their efficient use solar energy utilization and capacity
Depending on the strain, microalgae are rich in protein, carbohydrates, lipids and offer highly valuable components such as astaxanthin or β‐carotene. Moreover, microalgae take up CO2 and can grow solely on sunlight with high areal productivities. Currently, microalgae biomass is mainly produced for high‐value applications related to human consumption, including nutraceuticals and pharmaceuticals. However, microalgae
In the realm of sustainable solutions, microalgae stand out as versatile organisms with the potential to revolutionize multiple industries. From biofuels to pharmaceuticals, these microscopic powerhouses hold the promise of eco-friendly innovation. However, unlocking their full potential depends on understanding their environment, particularly the influence of temperature. Temperature modeling, a powerful tool in this endeavor,
Microalgae are photosynthetic microorganisms that typically live in freshwater, soil or marine habitats, but also more extreme habitats, such as salt, sulfur-rich lakes or on snow surfaces. They are becoming increasingly important in the bioeconomy and biotechnology sectors as an attractive, sustainable source of value-added products, owing to their enormous potential for the production of
Model-based design of experiments (MBDoE) for parameter precision can greatly accelerate the development of predictive mechanistic models. A particular focus has been on sequential MBDoE, where (possibly dynamic) experiments are designed one-at-a-time using gradient-based techniques. For nonlinear models, the resulting optimisation problems are typically nonconvex, and thus prone to converging to local optima. Numerical failure
