Investigating ‘greyness’ of hybrid model for bioprocess predictive modelling

Abstract

Hybrid modelling combines data-driven and mechanistic modelling, providing a cost-effective solution to modelling complex biochemical reaction kinetics when the underlying mechanisms are not fully understood. However, the question of how much kinetic information to incorporate into a hybrid bioprocess model has not been systematically addressed. Therefore, this work built three hybrid models for predicting the temperature-dependent rates of biomass growth, glucose consumption and γ-linolenic acid accumulation during fermentation of the fungus Cunninghamella echinulata. Each hybrid model incorporated different amounts of kinetic information from a pre-existing complex kinetic model, representing three levels of hybrid model ‘greyness’, then embedded a Gaussian Process (GP) to simulate the unknown kinetics inferred from experimental measurements. Although all three hybrid models fitted well, incorporating more specific kinetic information increased the risk of incorporating incorrect inductive bias that hindered rather than enhanced hybrid model performance. This observation also held when using the hybrid models to predict the temperature-shift dynamics for the upscaled 5 L bioreactor. Nonetheless, the hybrid models demonstrated much improved predictive confidence with similar predictive accuracy to the original kinetic model, demonstrating the proficiency of hybrid modelling for accelerating the construction of confident bioprocess models for robust process optimisation and real-time monitoring.