通过Aureobasidium pullulans使用多目标优化策略与直角实验设计合人工神经网络和遗传算法的高效生产pullulan
Shiwei Chen1, Tingbin Zhao2, Miaoxin Li1
1Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science and Technology, Ministry of Education, Tianjin 300457, China; Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, School of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China.
International journal of biological macromolecules
|September 17, 2024
概括
这项研究使用混合方法优化了pullulan的生产,实现了更高的产量和分子量,同时降低了成本. 综合方法显著提高了Aureobasidium pullulans的发酵效率.
科学领域:
- 生物技术是生物技术.
- 微生物发酵 微生物发酵
- 生物聚合物生产 生物聚合物生产
背景情况:
- 高效的普鲁兰生产对于各种工业应用至关重要.
- 传统的优化方法在发酵过程中经常面临多目标挑战.
研究的目的:
- 开发一种混合优化策略,以提高普鲁兰的产量和分子量.
- 整合正交实验设计 (OED),反向传播人工神经网络 (BP-ANN) 和遗传算法 (NSGA-II).
主要方法:
- 使用马尔托德克斯特林作为Aureobasidium pullulans发酵的碳来源.
- 使用OED来识别影响pullulan生产和分子重量的关键因素.
- 开发了一个BP-ANN模型来预测生产和分子量,与NSGA-II集成,用于多目标优化.
主要成果:
- 据OED显示,MgSO4·7H2O和pH分别对生产和分子量产生显著影响.
- 对于这两个参数,BP-ANN模型显示了高精度 (适合性>0.980).
- 混合BP-ANN-NSGA-II方法实现了6.89%的生产增加,368.97%的分子量增加和42.49%的成本降低.
结论:
- 混合优化策略有效地平衡了pullulan生产,分子量和成本.
- 多目标优化显著优于普鲁兰发酵的单目标方法.
- 这种综合方法为改善生物聚合物发酵过程提供了一个强大的框架.
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