适应性控制的E. 根据氧气吸收率,养批量种植中的大肠杆菌特异生长率
Renaldas Urniezius1, Deividas Masaitis1, Donatas Levisauskas1
1Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania.
Computational and structural biotechnology journal
|January 12, 2024
概括
这项研究引入了细胞生物质特异生长率 (SGR) 的自动控制系统,用于食批量培养. 该系统使用氧气吸收率 (OUR) 测量进行自适应控制,在模拟和实验中证明可行性.
科学领域:
- 生物技术是生物技术.
- 过程控制 过程控制
- 生物反应器工程 生物反应器工程
背景情况:
- 在生物工艺中,食批量种植对于最大限度地提高产品产量至关重要.
- 控制细胞生物质特异生长率 (SGR) 对于优化料批处理过程至关重要.
- 现有的方法可能会与生物过程固有的时间变化的条件作斗争.
研究的目的:
- 开发一种自动控制系统,用于精确控制SGR的设置点.
- 使用氧气吸收率 (OUR) 测量进行实时SGR估计和反控制.
- 调整控制系统以适应动态操作条件,以提高性能.
主要方法:
- 开发一个具有适应能力的比例整合 (PI) 控制器.
- 整合基于在线OUR测量的SGR估计器.
- 通过数值模拟和实验室规模的料批生物反应器实验进行验证.
- 通过开放循环数字双 SGR 估计器和自适应 SGR 控制进行交叉验证.
主要成果:
- 开发的控制系统成功地跟踪了所需的生物质生长概况.
- 在线OUR测量使控制器能够适应不断变化的工艺条件.
- 与SGR估计器相比,数字双胞胎估计器在统计上显示的偏差较小.
- 模型预测控制表现出卓越的稳定性,在特定场景中显著优于自适应系统.
结论:
- 拟议的自动控制系统提供了一种可行的方法,用于SGR设定点控制料批培养.
- 基于OUR测量的自适应控制提高了过程稳定性和性能.
- 对先进的控制策略,如模型预测控制等进行进一步的调查是有必要的.
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