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.

生物过程建模已成为预测过程未来的有用工具，目的是推断操作决策（例如转移或进料）。由于经常发生在批次之间和批次内的可变性，需要在特定时间间隔（动态参数估计）更新（重新估计）模型参数以获得可靠的预测。在存在低采样频率（例如每 24 小时）、不同的连续尺度和大的测量误差的情况下，这可能具有挑战性，如细胞培养种子序列的情况。该贡献提出了一种迭代学习工作流程，该工作流程通过使用移动视界估计 (MHE) 方法更新模型参数来生成并合并有关过程中细胞生长的知识。在工业细胞培养种子序列的三个连续培养规模 (40–2160 L) 的模型更新背景下，将这种估计技术与经典的加权最小二乘估计 (WLSE) 方法进行比较。研究了这两种技术关于上述挑战的稳健性和所需的实验数据量（估计范围）。展示了所提出的 MHE 如何通过整合先验知识来解决上述困难，即使只有两个采样点的数据可用，也优于经典的 WLSE 方法。该工作流程允许将当前过程行为充分集成到模型中，因此可以成为数字孪生的合适组件。