Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Abstract

Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.

Sensor Selection and State Estimation of Continuous mAb Production Processes

“…The chosen parameters were as follows: {Q max mAb , K d,amm , ρ, µ d,max , K gln }. The 21 selected variables were estimated using MHE, which is an estimation technique in which the estimation problem is formulated as an optimization problem [23,[47][48][49]. The MHE formulation at sampling time k for the simultaneous state and parameter estimation of the augmented system is given by Equations (47a)-(47f): min X(k−N),..., X(k), ŵa (k−N),..., ŵa (k−1)…”

“…Bogaerts et al [21] also applied a nonlinear and linearized full-horizon-state observer to a bioprocess involving animal cell culture. Moving Horizon Estimation (MHE) has also been applied to bioprocesses in the work by Tebbani et al [22], Rodríguez et al [23], and Raïssi et al [24]. MHE presents the capacity to effectively handle nonlinear dynamics and incorporate constraints [25].…”

Model‐based workflow for scale‐up of process strategies developed in miniaturized bioreactor systems

“…To adapt the model parameters, the objective function (weighted sum of squared residuals [RSMD]) between the simulated and experimental data for all time points and variables was minimized using the Nelder-Mead algorithm, as commonly used for model parameter identification. 3,22,23,38 Alternative approaches for model parameter identification and adaption are the Bayesian inference method 26,39,54 as well as the adaptive experimental redesign which have been discussed in the past. [40][41][42] Furthermore, model-based Design of Experiments strategies could be used to design initial experiments and identify suitable model parameters.…”