There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …

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 …

Membrane bioreactors (MBRs) have proven to be an extremely effective wastewater
treatment process combining ultrafiltration with biological processes to produce high-quality …

With the rise of deep learning, there has been renewed interest within the process industries
to utilize data on large-scale nonlinear sensing and control problems. We identify key …

Highlights•Mathematical models as tools to establish quantitative links between bioprocess
CPPs and KPIs.•Review of the advantages and limitations of mechanistic and statistical …

While machine learning (ML) has made significant contributions to the biopharmaceutical
field, its applications are still in the early stages in terms of providing direct support for quality …

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess
engineering tools. They provide a unique solution to combine higher experimental …

Hybrid modeling, with an appropriate blend of the mechanistic and data‐driven framework,
is increasingly being adopted in bioprocess modeling, model‐based experimental design …

In the era of sustainable development, the use of cell factories to produce various
compounds by fermentation has attracted extensive attention; however, industrial …