Hybrid‐EKF: Hybrid model coupled with extended Kalman filter for real‐time monitoring and control of mammalian cell culture

Abstract

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.

Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments

“…Moreover, Krippl et al (2020) could demonstrate that by using hybrid modeling, different tangential flow filtration operational modes can be described from the same training data set. More details about recent advances in using hybrid modeling for bioprocess development can be found here ( Narayanan et al, 2019 , Narayanan et al, 2020 ; Noll and Henkel, 2020 ; Bayer et al, 2020b ; Narayanan et al, 2021b ).…”

Hybrid Gaussian Process Models for continuous time series in bolus fed-batch cultures

“…Time profiles are simulated in the interval of [0, 14] days and measurements perturbed with 15% Gaussian noise. For a detailed description of the insilico data, the reader is referred to Narayanan et al (2020a). For the evaluation of the results, concentrations normalized to the maximum value of the respective states in the training set are used as inputs.…”

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

“…Importantly, given that the process dynamics within a bioprocess are highly non-linear, the extended Kalman filter can be applied to non-linear systems, thanks to piecewise linearization of the process around the time trajectories of the variables through the estimation of Jacobian matrixes [137]. Another popular version of the Kalman filter for non-linear systems is the unscented Kalman filter, which uses a Taylor series expansion to linearize the model [138]. Since the accuracy of a hybrid soft sensor is significantly impacted by the accuracy of the mechanistic model, the latter must be extensively validated to ensure it can successfully represent the process [137].…”

“…By mixing material balances with statistical models, direct links between data and physical bioprocess systems can be generated. This is because, within the hybrid model structure, the Kalman filter uses the prediction from the mechanistic model and the data gained from the data-driven model to recursively update the state estimators, thus synthesizing the information gained from both types of models [138,140]. In such a way, it is possible to imagine numerous applications where multivariate models generated from spectroscopic data or other on-line measurements and mechanistic models are used in tandem to develop models that use historical data while also describing the dynamics of the system.…”