基于动态和反复性神经网络的Bacillus licheniformis BF-002的化建模和优化
Jian Ding1, Bo Wang1, Qingyuan Liu2
1The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122 Wuxi, China.
Bioresource technology
|March 7, 2024
概括
一个新的混合模型改善了Bacillus licheniformis BF-002的培养,显著增加了超过35%的子产量. 这种方法优化了关键的生长参数,以获得更好的微生物剂质量.
科学领域:
- 微生物学 微生物学
- 生物技术是生物技术.
- 工艺工程是过程工程.
背景情况:
- 菌对预防疾病和环境修复至关重要.
- 子产量是Bacillus licheniformis微生物学的关键质量指标.
- 优化种植条件对于最大限度地提高子产量至关重要.
研究的目的:
- 为培养Bacillus licheniformis BF-002菌株开发一个先进的模型.
- 优化关键培养参数,包括营养养,pH值,温度和振动.
- 为了提高Bacillus licheniformis培养中的子数量.
主要方法:
- 开发了一个混合模型,集成动态建模和循环神经网络.
- 采用遗传算法来优化养速率,pH值,温度和动速度.
- 在优化条件下进行重复批量栽培,并与对照批量进行比较.
主要成果:
- 混合模型准确地跟踪了Bacillus licheniformis种植动态.
- 在优化条件下,碳/源消耗没有显著差异.
- 在优化的重复批次中,子数量分别增加了16.2%和35.2%.
结论:
- 混合模型优于传统的动态模型来追踪Bacillus licheniformis种植.
- 使用混合模型优化种植参数可以显著提高子产量.
- 这种方法提高了来自Bacillus licheniformis的微生物制剂的质量.
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