Autonomous reaction systems for chemical synthesis: dream or reality? | Prof Federico Galvanin

Abstract

Industry 4.0 is revolutionizing chemical manufacturing with digital twins and autonomous systems powered by AI and machine learning (ML) accelerating the production and delivery of new drugs and target molecules in pharmaceutical and fine chemicals industry. UCL Chemical Engineering is at the forefront of innovation in this research area, thanks to the development of cutting-edge reactor platforms that use AI/ML to optimize chemical synthesis, reduce costs, and enable real-time process optimization. These platforms operate autonomously, requiring no human intervention, due to the integration of advanced computational algorithms.  

In this inaugural lecture, Professor Federico Galvanin will explore how these autonomous systems are transforming microreactor chemistry and ML-assisted nanocrystal synthesis in drug manufacturing. Challenges and breakthroughs in autonomous platform development will be discussed, emphasizing the role of optimal experimental design and computational modelling in driving innovation and creating new opportunities in process optimization and on-demand manufacturing.

About the Professor

Federico Galvanin is Professor of Chemical Engineering at UCL. He earned his Chemical Engineering degree in 2006 and Ph.D. in 2010 from the University of Padova, Italy. After a postdoctoral position focusing on clinical test design in Padova, he joined UCL in 2014 as a Research Associate, later becoming Lecturer in 2015, Associate Professor in 2021, and Full Professor in 2024. His expertise includes model identification, model-based design of experiments (MBDoE), and integration of AI/ML in automated and autonomous systems for developing chemical, biological, and physiological models. He leads the Galvanin System Identification (GSIG) group, focused on developing systems-based methods and computational tools for the optimal experimental design with the goal of identifying predictive models with the minimum cost, time and effort in the experimentation.

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