Hybrid-EKF: Hybrid model coupled with extended Kalman filter for real-time monitoring and control of mammalian cell culture
Hybrid-EKF: Hybrid model coupled with extended Kalman filter for real-time monitoring and control of mammalian cell culture
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DOI：
10.1002/bit.27437
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发表时间：
2020-06-16
影响因子：
3.8
通讯作者：
Butte, Alessandro
中科院分区：
文献类型：
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作者：
Narayanan, Harini;Behle, Lars;Butte, Alessandro
在未来十年，工业4.0和设计质量将成为生物制药的主要技术驱动力，自动化和自适应过程监控是不可避免的需求，而基于模型的解决方案是实现这些目标的关键推动因素。尽管过程数字化取得了巨大进步，但在大多数情况下，生成的数据集不足以依赖纯粹的数据驱动方法，而潜在的复杂生物过程仍未完全理解。在这方面，混合模型作为一种及时实用的解决方案出现，以协同地结合可用的过程数据和机制理解。在这项研究中，我们展示了混合ekf框架的一种新应用，即混合模型与扩展卡尔曼滤波器相结合，用于哺乳动物细胞培养过程的实时监测、控制和自动决策。我们表明，在考虑的应用程序中，与基于PLS模型的工业基准工具相比，当使用混合模型开发时，这种框架的预测监测精度至少提高了35%。此外，我们还强调了这种方法在与条件过程馈送和过程监控相关的工业应用中的优势。对于后者，对于一个工业用例，我们证明了混合ekf作为滴度软传感器的应用，与最先进的软传感器工具相比，预测精度提高了50%。
In a decade when Industry 4.0 and quality by design are major technology drivers of biopharma, automated and adaptive process monitoring and control are inevitable requirements and model-based solutions are key enablers in fulfilling these goals. Despite strong advancement in process digitalization, in most cases, the generated datasets are not sufficient for relying on purely data-driven methods, whereas the underlying complex bioprocesses are still not completely understood. In this regard, hybrid models are emerging as a timely pragmatic solution to synergistically combine available process data and mechanistic understanding. In this study, we show a novel application of the hybrid-EKF framework, that is, hybrid models coupled with an extended Kalman filter for real-time monitoring, control, and automated decision-making in mammalian cell culture processing. We show that, in the considered application, the predictive monitoring accuracy of such a framework improves by at least 35% when developed with hybrid models with respect to industrial benchmark tools based on PLS models. In addition, we also highlight the advantages of this approach in industrial applications related to conditional process feeding and process monitoring. With regard to the latter, for an industrial use case, we demonstrate that the application of hybrid-EKF as a soft sensor for titer shows a 50% improvement in prediction accuracy compared with state-of-the-art soft sensor tools.