Process analytical technology as <scp>key‐enabler</scp> for digital twins in continuous biomanufacturing
A. Schmidt, Heribert Helgers, Lara Julia Lohmann, Florian Lukas Vetter, Alex Juckers, Mourad Mouellef, Steffen Zobel‐Roos, Jochen Strube
Abstract
Abstract Over the last few years rapid progress has been made in adopting well‐known process modeling techniques from chemicals to biologics manufacturing. The main challenge has been analytical methods as engineers need quantitative data for their workflow. Industrialization 4.0, Internet of Things, artificial intelligence and machine learning activities up to big data analysis have taken their share in solving fundamental problems like component‐ or at least group‐specific evaluation of spectroscopic data. Besides, concerning inline analytics methods included in process analytical technology concepts the key technology has been the generation of decisive validated digital twins based on process models. This review aims to summarize the methodology to achieve a holistic understanding of process models, control and optimization by means of digital twins using the example of recent work published in this field. © 2021 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).