Response surface optimization of sedimentation efficiency for sustainable green microalgae harvesting using automated non-invasive methods.

backgroundMicroalgae such as Chlorella sorokiniana and Monoraphidium convolutum are promising sources for biofuels, pharmaceuticals, nutraceuticals, and wastewater treatment. However, biomass harvesting remains a cost-intensive bottleneck. Conventional methods like centrifugation and flocculation pose challenges due to energy demands and contamination risks. Sedimentation offers a passive, eco-friendly alternative but is highly sensitive to environmental and physiological variables. This study integrates response surface methodology with a novel, non-invasive photographic imaging technique to optimize sedimentation efficiency.

resultsBoth species exhibited optimal growth in Bold Basal Medium, achieving cell densities of 29.59 and 9.5 million cells per mL, respectively. Automated cell counting strongly correlated with manual methods (R

conclusionsThis study advances sedimentation-based harvesting of C. sorokiniana and M. convolutum by integrating RSM with a novel, automated, non-invasive imaging technique for sedimentation monitoring. This approach, rarely applied in microalgae harvesting, enables real-time assessment without disturbing the culture, enhancing process control and scalability. Sedimentation efficiency was influenced by cell morphology, biochemical composition, and environmental factors such as pH, salinity, gas exchange, and culture volume. The optimized conditions not only improved harvesting precision and reproducibility but also reduced harvesting costs, highlighting the method's potential for economic and environmentally sustainable deployment in large-scale microalgae-based production systems for biofuels, bioplastics, and high-value compounds.