Abstract：
The use of hybrid models is extensively described in the literature to predict the process evolution in cell cultures. These models combine mechanistic and machine learning methods, allowing the prediction of complex process behavior, in the presence of many process variables, without the need to collect a large amount of data. Hybrid models cannot be directly used to predict final product critical quality attributes, or CQAs, because they are usually measured only at the end of the process, and more mechanistic knowledge is needed for many classes of CQAs. The historical models can instead predict the CQAs better; however, they cannot directly relate manipulated process parameters to final CQAs, as they require knowledge of the process evolution. In this work, we propose an innovative modeling approach based on combining a hybrid propagation model with a historical data-driven model, that is, the combined hybrid model, for simultaneous prediction of full process dynamics and CQAs. The performance of the combined hybrid model was evaluated on an industrial dataset and compared to classical black-box models, which directly relate manipulated process parameters to CQAs. The proposed combined hybrid model outperforms the black-box model by 33% on average in predicting the CQAs while requiring only around half of the data for model training to match performance. Thus, in terms of model accuracy and experimental costs, the combined hybrid model in this study provides a promising platform for process optimization applications.
摘要：
文献中广泛描述了使用混合模型来预测细胞培养中的过程演变。这些模型结合了机械和机器学习方法，允许预测复杂的过程行为，在存在许多过程变量的情况下，无需收集大量数据。混合模型不能直接用于预测最终产品关键质量属性，或CQA，因为它们通常只在过程结束时测量，许多类别的CQA需要更多的机械知识。历史模型可以更好地预测CQA；然而，它们不能直接将操纵的过程参数与最终的CQA相关联，因为他们需要过程进化的知识。在这项工作中,我们提出了一种基于混合传播模型与历史数据驱动模型相结合的创新建模方法，也就是说,组合混合模型，用于同时预测全过程动态和CQAs。在工业数据集上评估了组合混合模型的性能，并与经典黑箱模型进行了比较，直接将操纵的工艺参数与CQAs相关联。所提出的组合混合模型在预测CQA方面平均优于黑盒模型33%，同时只需要大约一半的数据用于模型训练以匹配性能。因此，在模型准确性和实验成本方面，本研究中的组合混合模型为过程优化应用提供了一个有前途的平台。
