Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes

Abstract

In this work, we aim to introduce the concept of the degree of hybridization for cell culture process modeling. We propose that a family of hybrid models can be created with varying fractions of process knowledge explicitly encoded in the model, defined as the degree of hybridization, with the two extremes being fully data-driven (0%) and fully mechanistic (100%) models. Subsequently, the aim is to compare the different models based on different metrics: model accuracy, the experimental effort for model development, extrapolation capability, the capability of generating new process understanding, and ease of utilization in practice, and to demonstrate that this could provide an additional degree of freedom for model selection. We could quantitatively demonstrate that for the cell culture process, either extreme has limitations. The major drawback of the data-driven model is the poor performance at low data availability as well as poor extrapolation capability, inability to provide process understanding, and subsequently inefficient practical application. On the other hand, the mechanistic model has poor accuracy due to the addition of excessive knowledge that then biases the models. Moving from data-driven to mechanistic models, the performance of the models improves progressively, as long as the knowledge added is not too biased. We show that the choice of the hybrid models to be used is based on the goal of model development. For instance, hybrid models including mass balances on each species show better performance in transferring models across different modes of operation. On the other hand, models with a higher degree of hybridization allow for more process interpretation possibilities. For modeling accuracy, amount of training data, extrapolation, and practical applications, the Hybrid Rate (HR) model is found to have the optimal degree of hybridization. This is likely due to the compromise between adding process knowledge and increasing the model parameters achieved by the HR model. The HR model features the incorporation of mass balance and channels the data-driven modeling to cell-specific rates, and thus these two pieces of information appear to be the most crucial ones. Finally, we believe that the concept will be instrumental in progressively developing and testing hypotheses about complex processes such as cell cultures.