AI-Driven Chemical Processes: From Human-in-the-Loop to Self-Optimising Labs

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• Dr Antonio del Rio Chanona - Imperial College London
• Wednesday 29 October 2025, 13:30-14:30
• Lecture Theatre 1, Department of Chemical Engineering and Biotechnology, West Cambridge Site.

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Artificial intelligence offers a new path to chemical processes: enabling chemical systems that not only learn from data but also improve themselves over time. In this talk, I will outline how AI can close critical gaps in process development and operation, paving the way towards autonomous, self-optimising laboratories and process plants. I will focus on three complementary algorithmic approaches. The first, Bayesian optimisation, provides a framework for experimental design and optimisation. I will particularly discuss how we can use multi-fidelity and human-in-the-loop strategies for a more informed process optimisation. Second, I will discuss how large language models (LLMs) can and are being leveraged to capture human knowledge, interact with algorithms, and enable autonomous processes via symbolic reasoning and algorithmic search. Finally, I will talk about how reinforcement learning, famous for making computers “learn” and beat the best humans in the world at various tasks, unlocks new opportunities for control and real-time optimisation of complex, dynamic processes, with particular promise in bioprocess applications. Together, these advances point to a unifying vision: AI-driven chemical processes that are faster to develop, safer to operate, and inherently more sustainable. I will conclude by reflecting on the emerging paradigm of autonomous process innovation and the opportunities it creates for the chemical sciences and industry.

This talk is part of the Chemical Engineering and Biotechnology series.

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