Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Capture Chromatographic Step

Abstract

In process engineering, two paradigms of modeling approaches exist: the mechanistic and the data-driven approaches with the former being completely based on knowledge while the latter completely based on data. In our previous work, we highlighted the advantages of using hybrid models that explores the synergy between mechanistic and data-driven models. Here we introduce the concept of developing a series of hybrid models constituted by a progressively increasing extent of process knowledge. Thus, aligning the models on the "degrees of hybridization" axis with data-driven model being 0% hybridized and mechanistic model being 100% hybridized. In this work, the proposed concept is demonstrated for the application of a chromatographic capture step where the models are evaluated based on (i) prediction accuracy, (ii) extrapolation ability, (iii) providing process understanding, and (iv) practical application. We show the limitations of both model variant extremes. On one hand, the performance of the mechanistic model is compromised due to an excessive imposition of knowledge, thus affecting its predictive capabilities and efficiency in practical utility. On the other hand, the data-driven model inherently is not suitable for application such as multicolumn chromatography or to gain process understanding. In contrast, a series of hybrid models could be developed with better and versatile performance in term of prediction, extrapolation, process understanding, and practical utility. We show that for general process applications the different hybrid model variants and their ensembles have comparable performance. We illustrate the criteria for selection of a particular hybrid model variant based on different considerations such as complexity of training or model development, acquired understanding, and data requirement.