Online monitoring system for microalgal bioprocesses: Model-based quantification of photosynthetic activity using pulse amplitude modulation fluorometry and rapid light curves

Microalgae biotechnology has significant potential, particularly with respect to circular bioeconomy and biorefinery concepts. To fully exploit this potential, efficient bioprocessing strategies are required in order to achieve high product yields. In this context, microalgal bioprocess monitoring is crucial to ensure an efficient microalgal cultivation and in turn achieve higher productivity. This study focuses on the design, construction, automation, experimental testing and implementation of an online monitoring system based on a pulse amplitude modulation (PAM) fluorometer using rapid light curves (RLCs) for model-based quantification of microalgal photosynthetic activity as an indicator of biomass quality. The first step was to design and construct a flow-through cell including its mounting to ensure reproducible measurements. Next, a protocol was programmed to automate the measurement procedure and allow online monitoring of microalgae cultivations in photobioreactors. The system was then tested using the microalga Microchloropsis salina at optical densities ranging from about 1 to 23 corresponding to biomass concentrations from about 0.25 to 5.00 g L −1 . At distinct concentrations within this range, RLCs were determined and mathematical models describing light limitation and inhibition of the photosynthetic system were applied to the data in order to identify RLC model parameters and to determine the most appropriate models. Finally, the system was implemented via its bypass in a photobioreactor operating in batch and turbidostatic mode, with the RLC model parameters monitored online. As a major result, the study showed that pH stress can be detected by RLC model parameters shifts. Overall, the study demonstrated the system's capability to monitor photosynthetic activity online, offering valuable applications in microalgal biotechnology and bioprocess engineering. • PAM online monitoring system using a PAM fluorometer developed and applied. • Ten models for RLC-based photosynthetic activity quantification studied. • PAM system implemented in a photobioreactor to assess microalgal photosynthesis. • pH stress detection via shifts in RLC model parameters successfully demonstrated. • Novel approach for online monitoring in microalgal bioprocess engineering provided.