对神经网络模型在微生物发酵中的应用进行了全面的审查
Jia-Cong Huang1, Qi Guo1, Xu-Hong Li1
1School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, PR China.
Bioresource technology
|November 12, 2024
概括
神经网络模型加速微生物发酵的优化和扩展,以获得高性能菌株. 人工智能增强了产量预测和过程监测,提高了效率,降低了发酵成本.
科学领域:
- 生物技术和生物化学工程 生物技术和生物化学工程
- 工业微生物学 工业微生物学
- 人工智能在生物处理中的应用
背景情况:
- 高性能微生物菌株和选技术的进步为下游发酵优化和扩展带来了挑战.
- 传统的方法难以有效地优化复杂的发酵过程,以获得最大的产量和成本效益.
- 人工智能的整合为应对这些挑战提供了一个有希望的途径.
研究的目的:
- 审查神经网络模型在微生物发酵中的应用.
- 突出AI如何优化发酵过程以提高产量和降低成本.
- 通过神经网络提供关于预测,优化和监测发酵的见解.
主要方法:
- 利用神经网络模型,人工智能的子集,分析和优化发酵参数.
- 应用人工智能与微生物发酵过程的特定特征相结合.
- 审查关于神经网络应用在发酵产量预测,过程优化和监控方面的现有文献.
主要成果:
- 神经网络模型可以有效地预测发酵产量.
- 由人工智能驱动的优化加速了高性能菌株发酵过程的放大.
- 这些模型有助于监测发酵过程,以提高控制和效率.
结论:
- 神经网络模型是优化微生物发酵过程的宝贵工具.
- 人工智能集成加速了高性能菌株的开发和扩展.
- 本综述提供了人工智能在现代发酵技术中的作用的全面概述.
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