In a groundbreaking advance for sustainable energy, researchers have optimized Spirulina algae processing to maximize bioethanol output using microwave-assisted acid hydrolysis. The team used response surface methodology (RSM) and artificial neural networks (ANN) to fine-tune extraction and fermentation parameters.
The study identified optimal conditions—300 W microwave power, 3% H₂SO₄ concentration, and 5 minutes of treatment—producing a maximum reducing sugar yield of 3.8 mg/mL. The subsequent fermentation achieved 1.824 g/L ethanol at 30 °C, 5 g/L inoculum concentration, and 28.5 hours, validated by high-performance liquid chromatography.
Statistical analyses revealed that the RSM model offered superior predictive performance (R² = 99.87%) compared to ANN, confirming the method’s robustness for industrial applications. The authors emphasize that microwave-assisted hydrolysis reduces energy consumption and accelerates sugar conversion compared to traditional heating methods, making it a sustainable and cost-effective pathway for renewable fuel production.
These findings highlight Spirulina’s potential as a third-generation biofuel feedstock and reinforce the viability of integrating microwave-assisted techniques with computational optimization for scalable bioethanol generation.
Reference
S., K. M., V., S., Vaibhav, N. H., Sinha, S., Manian, R., Geca, M. J., Ranjitha, J., & Kasianantham, N. (2025). Modeling and optimization of acid hydrolysis for spirulina-based ethanol production by response surface methodology and neural network techniques. Folia Microbiologica. https://doi.org/10.1007/s12223-025-01363-4
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