使用机器学习模型,改进户外栽培的微藻生长建模,使用光线历史数据:一项比较研究
Yen-Cheng Yeh1, Tehreem Syed2, Gordon Brinitzer3
1Fraunhofer Institute for Interfacial Engineering and Biotechnology IGB, Nobelstraße 12, 70569 Stuttgart, Germany; Institute of Interfacial Process Engineering and Plasma Technology, University of Stuttgart, Nobelstraße 12, 70569 Stuttgart, Germany.
Bioresource technology
|October 26, 2023
概括
机器学习模型,包括长期短期记忆 (LSTM),准确地预测室外培养中的微藻生长. 这些模型利用光线历史,优化生产的传统方法.
科学领域:
- 生物技术是生物技术.
- 藻类养殖 藻类养殖
- 计算生物学 计算生物学
背景情况:
- 准确的微藻生长预测对于优化种植和了解环境影响至关重要.
- 现有的数学模型往往在现实世界户外条件下缺乏验证.
- 光的动态显著影响微藻的生产力.
研究的目的:
- 评估用于微藻生长建模的机器学习算法.
- 将长短期记忆 (LSTM) 和支持向量回归 (SVR) 与传统的Monod和Haldane模型进行比较.
- 在户外种植环境中评估模型性能.
主要方法:
- 采用了Phaeodactylum tricornutum.的50天户外栽培数据.
- 用于种植的使用的平板空运光生物反应器.
- 将LSTM和SVR模型与Monod和Haldane模型进行比较.
主要成果:
- 机器学习模型 (LSTM,SVR) 的表现明显优于传统模型.
- 将光线历史作为输入的能力是ML模型成功的关键.
- LSTM在模拟光适应效应方面表现出强大的能力.
结论:
- 机器学习在户外种植中提供了卓越的微藻生长预测.
- 作为生物质软传感器,LSTM和SVR可以有效地应用.
- 这些模型有助于制定最佳的收获策略,以提高微藻产量.
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