Machine learning for biochemical engineering: A review

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 dimensional, nonlinear and stochastic domains common to biochemical engineering. We review the use of machine learning within biochemical engineering over the last 20 years. The most prevalent machine learning methods are demystified, and their impact across individual biochemical engineering subfields is outlined. In doing so we provide insights into the true benefits of each technique, and obstacles for their wider deployment. Finally, core challenges into the application of machine learning in biochemical engineering are thoroughly discussed, and further insight into adoption of innovative hybrid modelling and transfer learning strategies for development of new digital biotechnologies is provided.

# Microbial Metabolic Engineering and Bioproduction # Biochemistry, Genetics and Molecular Biology # Machine Learning in Materials Science # Materials Science # Machine Learning in Bioinformatics

Max Mowbray, Thomas Savage, Chufan Wu, et al. (2021). "Machine learning for biochemical engineering: A review". Biochemical Engineering Journal. https://doi.org/10.1016/j.bej.2021.108054