Abstract:
This work contributes to the development of Hybrid Modelling for the purpose of Process Analysis
Techniques within the Life Sciences. The hybrid modelling strategy is tailored to combine mechanistic and
statistical process sensing into a hybrid abstract model. The model is developed in an abstract form such
that it operates within an adequate process timeline. Within the life sciences, uncertainties are inevitable
due to the complexity of the problems at hand, which cover multidisciplinary aspects of biology, chemistry
and physics, thus a hybrid model is thought to provide better model diversity than a pure mechanistic or
measurement- based model. From a mechanistic perspective, a model based upon different tempro-spatial
levels is developed acquiring the required level of precision to model the main process characteristics.
From a statistical perspective, different sensing data is analysed, correlated and calibrated with
multivariate statistical algorithms to sense the main process characteristics. In a hybrid model, both types
of models are combined into a high dimensional model representation (HDMR), which combines both
complex paths (statistical and modelling) into an abstracted Sobol expansion. This model is trained by
both measurements and mechanistic data as the input variables. The developed HDMR is thought to
provide a robust alternative for process monitoring within the life sciences.
