Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms
able to learn autonomously, directly from the input data. Over the last decade, ML …

The field of machine learning is comprised of techniques, which have proven powerful
approaches to knowledge discovery and construction of 'digital twins' in the highly …

Real‐time monitoring of bioprocesses by the integration of analytics at critical unit
operations is one of the paramount necessities for quality by design manufacturing and real …

Advanced model‐based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …

Abstract Model‐based online optimization has not been widely applied to bioprocesses due
to the challenges of modeling complex biological behaviors, low‐quality industrial …

Hydrothermal liquefaction (HTL) has potential for converting abundant wet organic wastes
into renewable fuels. Because HTL consists of a complex reaction network, deterministic …

Kinetic models offer incomparable insight on cellular mechanisms controlling protein
glycosylation. However, their ability to reproduce site-specific glycoform distributions …

Predictive modeling 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 …

Highlights•Data analytics has increasing significantly in recent years in the biopharma
sector.•No clear trend observed between algorithm utilisation and data size.•PLS was found …

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes
to maintain the reliability and cost‐effectiveness of its products. Such optimization demands …