Response surface optimization of sedimentation efficiency for sustainable green microalgae harvesting using automated non-invasive methods
Abstract Background Microalgae 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 flocc...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | Microbial Cell Factories |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12934-025-02765-2 |
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| Summary: | Abstract Background Microalgae 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. Results Both 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 (R2 = 98.99%). Biochemical analysis revealed a higher protein content in C. sorokiniana (61.6%) and greater lipid content in M. convolutum (39.31%). Sedimentation efficiency was highest at acidic pH and low salinity, reaching 96.14% for C. sorokiniana and 88.7% for M. convolutum. Sealed vessels and smaller culture volumes further enhanced sedimentation efficiency. RSM predictive models achieved high accuracy (adjusted R2 > 99%). A novel, real-time photographic method for sedimentation assessment was introduced, offering a non-invasive, sampling-free alternative to conventional techniques. This method strongly correlated with OD-based measurements (R2 = 94.89%) and presents a scalable solution for continuous biomass monitoring. Compared to conventional centrifugation, the optimized sedimentation approach is estimated to reduce harvesting costs by 77–79%. Conclusions This 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. Graphical Abstract |
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| ISSN: | 1475-2859 |