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Microalgae cultivation is strongly dependent on light availability. Sunlight is the natural energy source for algae cultivation processes, but its fluctuation during the day and the natural day-night-rhythm mean an increasing challenge for an efficient algae production. Additionally, different seasons lead to unequal photoperiods and their influence on the productivity has not been determined in detail yet. Therefore, we investigated the growth of Chlorella sorokiniana at different light intensities and illumination cycles of 24 h/0 h (light/dark), 16/8 h and 12/12 h. The light integral, more exactly the amount of photons per gram biomass and day (specific light availability) was kept constant during 24 h to ensure the comparability of the results of the different photoperiods. During the cultivation in repeated fed-batch mode, the productivity and biomass yield on light of Chlorella sorokiniana were determined to characterize the growth. During the night phase a biomass loss occurred, which was independent of the night temperature and the specific light availability on the day before, but increased with higher biomass concentration. A higher biomass yield on light during the shorter photoperiods led to an improved gross productivity compared to the continuous illumination, especially at a biomass concentration above 4 g L −1 . This compensated for the night loss; therefore, the net productivity (during one day) was independent of the photoperiod. In order to determine an optimal process window biomass yield on light and productivity have to be considered. They are diametric with respect to the specific light availability and furthermore dependent on the biomass concentration. The optimal combination of biomass yield on light and productivity could be achieved with a biomass concentration below 4 g L −1 during 24/0 illumination and with a biomass concentration below 7 g L −1 during shorter photoperiods; the specific light availability should be as high as possible. [ABSTRACT FROM AUTHOR]

Abstract An efficient microalgae outdoor cultivation is hindered by the fluctuation of the photon flux density (PFD) during one day and the natural day-night-rhythm. As the light intensity is not directly adjustable, the adjustment of biomass concentration is often used for the indirect distribution of available photons to an appropriate number of cells. For this study, repeated fed batch cultivations were conducted in 28 L flat panel airlift reactors in Stuttgart, Germany between August and October 2015. The influence of the initial biomass concentration (1.5–9 g L−1) on the productivity of Chlorella sorokiniana was investigated. Two models (basing on different underlying assumptions) were developed, which used the results of a previous laboratory study to explain the effects of different PFD courses on the productivity outdoors. Both models were validated by using the results of the outdoor cultivation. With the model based on the light yield, an adequate prediction of the productivity was achieved. This leads to an assessment of the outdoor process with paradigmatic courses of the PFD. The application of the model on recorded outdoor PFD courses proved that not only the amount of photons has an impact on the productivity, but also their distribution during the day. Longer photoperiods with lower maximum PFD values (summer) lead to a higher productivity compared to shorter photoperiods with higher maximum PFD values (spring/fall). Additionally, the biomass concentration should be at least as low as 1.5 g L−1; this leads on the one hand to an improved light yield, as there is less self-shading of the algae culture, on the other hand, there is less night biomass loss at lower biomass concentrations. Highlights • Outdoor productivity of C. sorokiniana with different initial biomass concentrations • Light yield during indoor experiments was used to model the outdoor productivity. • Validation of the model with the data of the outdoor cultivations • The light integral and the light distribution influence the productivity. • Assessment of different seasonal light scenarios on outdoor productivity [ABSTRACT FROM AUTHOR]

• 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]