可能的贝叶斯深度学习方法用于在线预测Fed-Batch发酵.
Tao Wang1, Jiebing You2, Xiugang Gong1
1School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China.
ACS omega
|July 24, 2023
概括
这项研究引入了贝叶斯的深度学习方法,用于预测2-keto-l-gulonic酸发酵. 该方法准确地预测产品的形成,增强微生物发酵的在线过程监测.
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:
- 微生物发酵涉及复杂的代谢和化学反应.
- 混合的细菌培养以生产2-keto-l-gulonic酸表现出非线性和时间变化的动态.
- 准确预测产品形成对于过程优化和控制至关重要.
研究的目的:
- 为微生物发酵产品形成开发一个高度准确和强大的预测模型.
- 应用概率的贝叶斯深度学习方法来解决发酵过程的复杂性.
- 通过可靠的预测,实现有效的在线流程监控.
主要方法:
- 使用贝叶斯优化深度神经网络 (BODNN) 作为核心预测模型.
- 优化了BODNN模型的结构参数.
- 采用贝叶斯混合方法对BODNN模型进行加权组合,使用后期概率进行预测.
- 基于先前预测错误评估的机密训练数据集.
主要成果:
- 取得的平均根平均平方误差为4小时前的1.51%和8小时前的2.01%的预测.
- 在95个工业发酵批次上验证了模型.
- 证明了模型能够捕捉发酵批量动态的能力.
- 已确认适用于在线流程监控应用程序.
结论:
- 提出的贝叶斯深度学习方法为微生物发酵提供了准确而强大的预测.
- 该方法有效地处理混合细菌培养的非线性和时间变化的特征.
- 这种方法为实时监控和优化工业发酵过程提供了有价值的工具.
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