Bovinille Anye Cho

Dr Bovinille Anye Cho is a Royal Society Career Development Fellow in the Department of Chemical Engineering at the University of Manchester, where he completed his PhD in January 2023 under the supervision of Dr Dongda Zhang. He completed his doctorate in three years and has published over 11 original research papers during this period. His contributions to climate change research were recognised with the 2021 Commonwealth Research Impact Award.

His research focuses on mathematical modelling, computational fluid dynamics (CFD), hybrid modelling and artificial intelligence for the understanding, design and optimisation of bioprocesses. He develops mechanistic and data-driven models, including digital twins, to improve bioreactor performance, enable predictive process scale-up and advance the sustainable production of biohydrogen, biogas and high-value biochemicals from renewable and waste-derived resources.

His current research spans photobioreactor design and optimisation, microbial growth kinetics, mixing-induced light–dark cycling and the integration of experimental data with predictive computational models. Through the integration of process systems engineering, computational modelling and data intelligence, his work aims to accelerate the development of efficient, scalable and sustainable biomanufacturing processes.

Current research projects:

Digital Twins for Sustainable Bioprocesses

Developing hybrid mechanistic and data-driven models to predict, monitor and optimise bioprocess performance, supporting the sustainable co-production of biogas and valuable biochemicals.

CFD-Based Photobioreactor Design and Scale-Up

Using computational fluid dynamics and experimental data to investigate mixing, light distribution and hydrodynamics in photobioreactors, with the aim of improving microbial growth, biohydrogen productivity and scale-up performance.

Modelling Microbial Growth and Biohydrogen Production

Developing mechanistic and hybrid kinetic models to understand how substrate utilisation, nutrient interactions and mixing-induced light–dark cycles influence microbial growth and biohydrogen production.

Data-Driven Optimisation of Bioprocesses

Integrating machine learning, surrogate modelling and optimisation techniques to accelerate bioprocess design, identify influential operating parameters and improve process performance.