通过数据驱动的建模和合成时间序列生成来加强发酵过程监测
Hyun J Kwon1, Joseph H Shiu2, Celina K Yamakawa3
1School of Engineering, Andrews University, Berrien Springs, MI 49104, USA.
Bioengineering (Basel, Switzerland)
|August 29, 2024
概括
使用变化自编码器生成合成数据显著改善了用于乙醇发酵监测的软传感器性能. 这种方法提高了预测准确度,减少了变化,为深度学习模型节省了成本和时间.
科学领域:
- 生物技术是生物技术.
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 使用深度学习回归模型的软传感器显示出对实时发酵质量预测的前景.
- 稀疏,偏差值倾向的实验数据集阻碍了模型性能.
- 为了有效的模型训练,需要一个完全分布式的解决方案空间.
研究的目的:
- 提高软传感器模型的稳定性和预测能力.
- 解决发酵数据集中的数据稀缺性和质量问题.
- 通过合成数据生成来提高深度学习模型的性能.
主要方法:
- 使用变化自编码器 (VAE) 来生成合成数据集.
- 合成数据集与原始实验数据相结合.
- 神经网络回归模型在原始和增强数据集上进行训练,用于软传感器开发.
- 在强化乙醇发酵数据上使用R2评分来评估性能.
主要成果:
- 使用增强数据集,软传感器预测能力提高了34%.
- 通过拟议的方法,数据变异性减少了82%.
- 改进的模型表现出优异的性能,与仅在实验数据上训练的模型相比.
结论:
- 通过VAE生成合成数据有效提高软传感器的可靠性和稳定性.
- 该方法为发酵过程中的深度学习提供了显著的时间和成本节省.
- 这种方法可以适应各种发酵监测应用,推进软传感器技术.
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