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由Rhodosporidium toruloides使用响应表面方法和基因算法优化的人工神经网络优化醇降解和脂质生成

Sangeeta Singh¹, Biswanath Mahanty², Lohit Kumar Srinivas Gujjala¹

¹Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India.

|August 6, 2024

概括

油性酵母有效降解,并在废水中产生脂质. 使用人工神经网络 (ANN) 和实验验证的优化实现了完全的去除,并显著增加了脂质产量.

科学领域:

生物技术是生物技术.
环境科学环境科学
微生物学微生物学

背景情况:

是一种常见的工业污染物.
油性酵母为醇降解和有价值的脂质生产提供了可持续的解决方案.
优化这些双重过程对于有效的废水处理至关重要.

研究的目的:

通过*Rhodotorula toruloides* 9564^T识别影响醇降解和脂质生成的关键因素.
开发和比较用于优化这些流程的预测模型.
实验验证最佳条件,以获得最大的醇去除和脂质产量.

主要方法:

对于因子选的普莱克特-伯曼设计 (PBD).
响应表面方法的中央复合设计 (CCD).
开发二级和人工神经网络 (ANN) 模型.
多目标优化和实验验证.

主要成果:

温度,注射器大小和动都显著影响了的降解和脂质的产生.
与二次模型相比,ANN模型显示出更高的预测准确性 (R²>>0.98).
在优化条件下,可降解100%的,脂质产量增加3.35倍 (0.918g/L).

结论:

*Rhodotorula toruloides* 9564^T 是有效的同时消除和脂质生物合成.
数学建模,特别是ANN,对于优化生物修复过程非常有价值.
该研究为增强基于酵母的废水处理和化工生产提供了强大的框架.

关键词:

阿恩·加 (ANN-GA) 是一个脂质生产脂质生产醇降解降解的过程普莱克特-伯曼设计响应表面的方法响应表面方法

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