Department of Process Analytics and Cereal Science, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany.
Adv Biochem Eng Biotechnol. 2021;177:95-125. doi: 10.1007/10_2020_145.
In the era of technology and digitalization, the process industries are undergoing a digital transformation. The available process models, advance sensor technologies, enhanced computational power and a broad set of data analytical techniques enable solid bases for digital transformation in the biopharmaceutical industry.Among various data analytical techniques, the Kalman filter and its non-linear extensions are powerful tools for prediction of reliable process information. The combination of the Kalman filter with a virtual representation of the bioprocess, called digital twin, can provide real-time available process information. Incorporation of such variables in process operation can provide improved control performance with enhanced productivity.In this chapter the linear discrete Kalman filter, the extended Kalman filter and the unscented Kalman filters are described and a brief overview of applications of the Kalman filter and its non-linear extensions to bioreactors are presented. Furthermore, in a case study an example of the digital twin of the backer's yeast batch cultivation process is presented. A digital twin of a bioreactor mirrors the processes of the real bioreactor. It contains the physical parts, the process model and prediction algorithm to predict the bioprocess variables. These values could be used for optimization and control of the process.
在科技和数字化时代，流程工业正在经历数字化转型。现有的过程模型、先进的传感器技术、增强的计算能力以及广泛的数据分析技术为生物制药行业的数字化转型提供了坚实的基础。在各种数据分析技术中，卡尔曼滤波器及其非线性扩展是预测可靠过程信息的强大工具。卡尔曼滤波器与生物过程的虚拟表示（称为数字孪生）相结合，可以提供实时可用的过程信息。在过程操作中加入这些变量可以提高控制性能和生产力。在本章中，介绍了线性离散卡尔曼滤波器、扩展卡尔曼滤波器和无迹卡尔曼滤波器，并简要概述了卡尔曼滤波器及其非线性扩展在生物反应器中的应用。此外，在案例研究中，介绍了backer 酵母分批培养过程的数字孪生体的示例。生物反应器的数字孪生体反映了真实生物反应器的过程。它包含物理部件、过程模型和预测算法来预测生物过程变量。这些值可用于优化和控制过程。
