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DTSTART;VALUE=DATE:20260226
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DTSTAMP:20260428T184906
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UID:46808-1772064000-1772236799@chem-eng.utoronto.ca
SUMMARY:Search for Assistant Professor\, Process Intensification: Gabriel Patrón
DESCRIPTION:The Department of Chemical Engineering & Applied Chemistry is pleased to host an interview for Assistant Professor\, Process Intensification. \nPlease join us in welcoming Dr. Gabriel D. Patrón for his research seminar on February 26th and his teaching seminar on February 27th\, as per the details below: \n  \nRESEARCH SEMINAR: Thursday February 26th from 11:00am – 12:00 PM \nTitle: \nFrom Data to Decisions: ML-Aided Chemical Process Optimization Under Uncertainty \nAbstract: \nChemical process systems must be optimized across multiple timescales to ensure profitability\, safety\, and sustainability. While reactor set points and control actions are determined in real time\, the capacity and schedule of electricity-integrated processes are planned over much longer horizons. In practice\, model-based optimizers are inevitably subject to uncertainty in real-world settings. \nIn this talk\, I present recent work using machine learning (ML) to represent these uncertainties and embed them within optimization frameworks. First\, I consider reactor-level real-time optimization in the presence of epistemic\, endogenous model uncertainty. Leveraging the universal approximation properties of neural networks\, gradients are learned from historical plant data to reconcile models with reality and recover plant-optimal solutions with theoretical guarantees. Second\, I discuss how quantile neural networks can learn conditional probability distributions in stochastic optimization settings with exogenous market uncertainty. Optimizing over these learned distributions explicitly hedges against electricity price uncertainty in capacity planning and process scheduling for an electrolytic hydrogen system. These results demonstrate how combining ML with optimization enables data-driven and uncertainty-aware decision-making for chemical process systems\, with direct implications for process intensification and the design and operation of future low-carbon infrastructure. \n  \nJoin us Virtually! \nMicrosoft Teams meeting \nJoin: https://teams.microsoft.com/meet/27576045026927?p=SqF8SOCHsKEF16OCE8 \nMeeting ID: 275 760 450 269 27 \nPasscode: zJ2dK2S7 \nNeed help? | System reference \nDial in by phone \n+1 647-794-1609\,\,249456086# Canada\, Toronto \nFind a local number \nPhone conference ID: 249 456 086# \nFor organizers: Meeting options | Reset dial-in PIN \n  \nTEACHING SEMINAR: Friday February 27th from 10:00 – 11:00 AM  \nTeaching Seminar Title: In the Loop: A Hands-On Introduction to PID \n  \nJoin us Virtually! \nMicrosoft Teams meeting \nJoin: https://teams.microsoft.com/meet/25186297758341?p=8RH8HwN6WmRyRbMGFD \nMeeting ID: 251 862 977 583 41 \nPasscode: 6BU9Nx2Q \nNeed help? | System reference \nDial in by phone \n+1 647-794-1609\,\,588618099# Canada\, Toronto \nFind a local number \nPhone conference ID: 588 618 099# \nFor organizers: Meeting options | Reset dial-in PIN \n  \n  \nSPEAKER BIOGRPAHY: \n\nDr. Gabriel D. Patrón is a Research Associate in stochastic optimization at Imperial College London’s Department of Computing and the Sargent Centre for Process Systems Engineering. His current work\, funded by BP\, aims to bridge modern machine learning with process engineering to develop methods for the control and optimization of chemical and energy systems. His research interests include process sustainability and intensification\, as well as interpretable AI to uncover the systemic incentives driving the green transition. Previously\, Dr. Patrón was a Postdoctoral Fellow at the University of Waterloo\, where he also completed his PhD. He holds an MSc from Imperial College London and a BASc from the University of Toronto.
URL:https://chem-eng.utoronto.ca/event/search-for-assistant-professor-process-intensification-gabriel-patron/
LOCATION:Wallberg Building\, Room WB-215\, 200 College St\, Toronto\, ON M5T 3E5\, 200 College St\,\, Toronto\, Ontario\, M5T 3E5\, Canada
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