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在微生物生物表面活性剂生物工艺中整合人工智能的进展和挑战
Vaibhav Kadam1, Sheetal Kusal2, Shruti Patil3
1Symbiosis Centre for Waste Resource Management, Symbiosis International (Deemed University), Lavale, Pune, India.
AMB Express
|November 27, 2025
概括
人工智能 (AI) 通过优化生物过程来增强微生物生物表面活性剂的生产. 尽管存在数据挑战,但人工神经网络和混合模型等人工智能技术对可持续化学和气候行动充满希望.
科学领域:
- 生物技术是生物技术.
- 可持续化学 可持续化学
- 微生物工程 微生物工程
背景情况:
- 微生物生物表面活性剂提供可持续的替代品,但面临生物过程复杂性.
- 由于生产挑战,工业应用有限.
研究的目的:
- 评估当前的生物表面活性剂研究趋势.
- 专注于基于人工智能 (AI) 的优化,以提高生物过程的性能.
- 确定定性和定量AI战略.
主要方法:
- 系统的文献审查.
- 图书统计分析. 图书统计分析.
- 基于人工智能的优化技术审查.
主要成果:
- 人工神经网络与响应表面方法相结合,被广泛用于优化.
- 混合人工智能技术在利用联合优势方面显示出重大前景.
- 人工智能集成为研究进步和创新提供了双重视角.
结论:
- 人工智能,特别是混合模型,可以显著推进生物表面活性剂研究和工业可扩展性.
- 解决诸如有限的数据集和模型可转移性等挑战至关重要.
- 开放式数据和先进的人工智能模型是未来发展的关键.
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