Machine learning techniques have been successfully used to simulate and
optimise bioprocesses. This study explores the feasibility to apply
Gradient Boosting, an emerging Ensemble Learning algorithm, which
combines weak learners to generate better predictions for bioprocess
dynamic modelling and prediction. A thorough procedure was presented for
Gradient Boosting based data-driven model construction. Different case
studies were employed including fermentation and algal photo-production
processes. Given that generating a large size of experimental data for
model training is time consuming and challenging to many bioprocesses,
this work launched a first investigation on the data efficiency of
Gradient Boosting by comparing its predictive capability against the
predominantly used artificial neural networks. By carrying out a series
of experimental verifications over a broad spectrum of process operating
conditions, this study concluded that Gradient Boosting may have several
advantages in small experimental datasets and can outperform artificial
neural networks for bioprocess predictive modelling, indicating its
potential for future bioprocess digitalisation and optimisation.