bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Abstract Microalgae-driven nutrient recovery represents a promising technology to reduce effluent phosphorus while simultaneously generating biomass that can be valorized offset treatment costs. As full-scale processes come online, system parameters including composition must carefully monitored optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged characterize microalgal community in near real-time at municipal wastewater plant (WWTP) Wisconsin, USA, population morphotype dynamics were examined identify relationships between water chemistry, composition, performance. Two FIM technologies, FlowCam ARTiMiS, evaluated as monitoring tools. ARTiMiS provided more accurate estimate of total biomass, estimates derived from particle area proxy for biovolume yielded better approximations than counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent convolutional neural network (CNN) classifiers proved significantly when compared feature table-based deep (DNN) models. Across two-year study period, Scenedesmus spp. appeared most important removal, which negatively associated with elevated temperatures nitrite/nitrate concentrations. Chlorella Monoraphidium also played an role For both , smaller morphological types often high performance, whereas larger morphotypes implied stress response correlating poor rates. These results demonstrate the potential critical high-resolution characterization industrial processes. Graphical

Language: Английский