Combining hybrid modelling and transfer learning to simulate fed-batch bioprocess under uncertainty

Hybrid modelling utilizes advantageous aspects of both mechanistic (white box) and data-driven (black box) modelling. Combining the physical interpretability of kinetic modelling with the power of a data-driven Artificial Neural Network (ANN) yields a hybrid (grey box) model with superior accuracy when compared to a traditional mechanistic model, while requiring less data than a purely data-driven model. This study aims to construct a hybrid model for the predictive modelling of a high-cell-density microalgal fermentation process for lutein production under uncertainty. In addition, transfer learning is combined with the hybrid model to simulate new fed-batches utilizing alternative substrates operated under a different reactor scale. By comparing with experimental data, the hybrid transfer model was found to be able to simulate the new fed-batch processes that achieve heightened cell densities and higher product quantities. Overall, this work presents a novel digital model construction strategy that can be easily adapted to general bioprocesses for model predictive control and process optimization under uncertainty.