LAUSR

Predictive modelling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, a subdomain of machine learning that serves to transfer knowledge from a generalised model to a more domain-specific model, provides a promising solution. Whilst transfer learning has been used in natural language processing, image analysis, and chemical engineering fault detection, its application within biochemical engineering has not been systematically explored. In this study, we demonstrated the benefits of transfer learning when applied to predict dynamic behaviours of new biochemical processes. Two different case studies were presented to investigate the accuracy, reliability, and advantage of this innovative modelling approach. We thoroughly discussed the different transfer learning strategies and the effects of topology on transfer learning, comparing the performance of the transfer learning models against benchmark kinetic and data-driven models. Furthermore, strong connections between the underlying process mechanism and the transfer learning model's optimal structure were highlighted, suggesting the interpretability of transfer learning to enable more accurate prediction than a naive data-driven modelling approach. Therefore, this research shows a novel approach to effectively combining data from different resources for bioprocess simulation. This article is protected by copyright. All rights reserved.