通过响应表面方法和神经网络技术,模拟和优化以螺旋藻为基础的乙醇生产的酸水解
Kavitha M S1, Shenbagamuthuraman V2, Nallanthighal Harsh Vaibhav3
1CO2 Research and Green Technologies Center, Vellore Institute of Technology, Vellore-632014, Tamilnadu, India.
Folia microbiologica
|October 16, 2025
概括
这项研究优化了使用微波辅助酸水解和响应表面方法 (RSM) 来从螺旋藻中生产生物乙醇. 最佳条件产生了最大的降解糖和1.824g/L乙醇,RSM的表现优于人工神经网络.
科学领域:
- 生物技术是生物技术.
- 可再生能源可再生能源是可再生能源.
- 使用藻类生物质利用
背景情况:
- 螺旋藻为生物乙醇生产提供了可持续的原料.
- 高效提取糖和优化发酵对于经济可行性至关重要.
研究的目的:
- 为了优化微波辅助的酸水解,从螺旋藻中提取总减少糖.
- 通过响应表面方法 (RSM) 和人工神经网络 (ANN) 来优化生物乙醇发酵条件.
主要方法:
- 微波辅助酸水解与参数:微波功率,硫酸度和时间.
- 响应表面方法 (RSM) 用于优化水解和发酵.
- 人工神经网络 (ANN) 用于发酵的比较建模.
主要成果:
- 在优化水解条件下达到3.8mg/ml的最大降解糖度.
- 最高乙醇度为1.824g/L,在30°C,5g/L注射液和28.5小时发酵时获得.
- RSM模型显示高显著性 (水解R2=99.87%,发酵R2=97.42%) 和优于ANN的性能.
结论:
- 优化的微波辅助酸水解对于从螺旋藻中提取糖是有效的.
- RSM是优化生物乙醇发酵参数的可靠和有效工具.
- 螺旋藻是有效生产生物乙醇的有希望的原料.
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