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 hours), different consecutive scales and
large measurement errors, as in the case of cell culture seed trains. In
this contribution, two estimation techniques, which differ in terms of
their objective functions, were investigated regarding robustness
concerning the aforementioned challenges and the required amount of
experimental data (estimation horizon). A common weighted least squares
estimation (WLSE) and a moving horizon estimation (MHE), which takes
prior knowledge about model parameters into account, were applied for
re-estimation of model parameters over three consecutive cultivation
scales (40 - 2,160 L) of an industrial cell culture seed train. It is
shown how the proposed MHE can deal with the aforementioned difficulties
by integration of prior knowledge, even if only data at two sampling
points are available, outperforming the classical WLSE approach. A
workflow illustrating the required steps is presented.