一种混合软传感器方法,结合了部分最小平方回归和无气味卡尔曼波器,用于生物过程中的状态估计
Lucas Hermann1, Andreas Kremling1
1Professorship for Systems Biotechnology, School of Engineering and Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany.
Bioengineering (Basel, Switzerland)
|June 26, 2025
概括
这项研究使用工艺数据和先进过器估计了诸如L-氨 (L-phe) 这样的关键发酵变量. 将部分最小平方回归与无气味卡尔曼波器相结合,改善了生物过程中的L-phe估计.
科学领域:
- 生物技术是生物技术.
- 生物工艺工程 生物工艺工程
- 代谢工程是代谢工程.
背景情况:
- 对生物过程状态变量的实时监控对于优化和控制至关重要.
- 专用传感器的高成本阻碍了早期的工艺开发.
- 准确的状态变量估计对于高效的发酵至关重要.
研究的目的:
- 开发和评估一种方法来估计使用复合Escherichia coli生产L-phenylalanine (L-phe) 中的关键状态变量.
- 将部分最小平方回归 (PLSR) 预测集成到无气味的卡尔曼波器 (UKF) 中,以提高状态估计.
- 评估PLSR-UKF联合方法对生物质,糖醇,L-,乙酸盐和L-氨酸 (L-tyr) 的监测的性能.
主要方法:
- 在不同的诱导剂度下,使用复合大肠杆菌进行L-phe的料批发发酵.
- 使用基于在线过程数据的部分最小平方回归 (PLSR) 估计状态变量 (生物质,甘,L-,乙酸盐,L-).
- 将PLSR预测作为测量结果集成到使用粗粒度模型的无气味卡尔曼波器 (UKF) 中.
主要成果:
- 对于L-来说,PLSR的预测准确度非常好,对于糖醇,生物质和L-来说适度,对于乙酸来说差.
- 与粗粒模型 (CGM) 单独相比,UKF显著提高了L-度的估计准确性.
- 当使用联合PLSR-UKF方法时,对糖醇,生物质,L-和酸盐的估计准确性需要进一步细化.
结论:
- 联合PLSR-UKF方法为改善生物过程中实时状态变量估计提供了一个有希望的策略,特别是对于L-phe.等目标产品.
- 这种方法可以减少对昂贵的专用传感器的依赖,促进具有成本效益的流程优化.
- 需要进一步开发以提高所有状态变量的估计准确性,特别是像酸盐这样的副产品.
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