Real-time monitoring of microalgal biomass in pilot-scale photobioreactors using nephelometry
Thoré, E.S.J.; Schoeters, F.; Spit, J.; Van Miert, S. (2021). Real-time monitoring of microalgal biomass in pilot-scale photobioreactors using nephelometry. Processes 9(9): 1530. https://dx.doi.org/10.3390/pr9091530
In: Processes. MDPI AG: Basel. e-ISSN 2227-9717, meer
|   |
| Trefwoorden |
Microchloropsis gaditana (L.M.Lubián) M.W.Fawley, I.Jameson & K.P.Fawley, 2015 [WoRMS]; Porphyridium purpureum (Bory) K.M.Drew & R.Ross, 1965 [WoRMS]
Marien/Kust |
| Author keywords |
microalgae; algal cultivation; biotechnology; turbidity; cell density; NTU; automation |
| Abstract |
The increasing cultivation of microalgae in photobioreactors warrants efficient and non-invasive methods to quantify biomass density in real time. Nephelometric turbidity assessment, a method that measures light scatter by particles in suspension, was introduced already several decades ago but was only recently validated as a high-throughput tool to monitor microalgae biomass. The light scatter depends on the density of the suspended particles as well as on their physical properties, but so far there are hardly any accounts on how nephelometric assessment relates to classic methods such as dry weight and spectrophotometric measurement across a broad biomass density range for different microalgae species. Here, we monitored biomass density online and in real time during the semi-continuous cultivation of three commercial microalgae species Chloromonas typhlos, Microchloropsis gaditana and Porphyridium purpureum in pilot-scale photobioreactors, and relate nephelometric turbidity to dry weight and optical density. The results confirm a relatively strong (R2 = 0.87–0.93) and nonlinear relationship between turbidity and biomass density that differs among the three species. Overall, we demonstrate how nephelometry can be used to monitor microalgal biomass in photobioreactors, and provide the necessary means to estimate the biomass density of the studied species from turbidity data to facilitate automated biomass monitoring. |
|
