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Automatic Differentiation-Based Methods for Advanced Chemical Process Simulation
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Automatic Differentiation-Based Methods for Advanced Chemical Process Simulation

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JHE 264


This presentation explores two different, difficult problems: heat exchanger design incorporating pinch analysis, and worst-case uncertainty analysis for batch reactors. Surprisingly, both of these problems can be addressed with a single underlying philosophy, in which a complicated process model is broken down into simple pieces that are then treated separately. For heat exchanger design, this approach entails applying a new variant of automatic differentiation to provide enough insight to handle the Second Law of thermodynamics in a robust pinch analysis. For batch reactors, a second variant of automatic differentiation describes how uncertainty in the process model emerges in the dynamic state, which can then be harnessed by optimization methods. Ongoing projects are also discussed.


Kamil Khan is an assistant professor in the Department of Chemical Engineering at McMaster University. His research focuses on developing efficient methods for simulation, sensitivity analysis, and optimization of complex chemical process systems. Before joining McMaster, he was a Director's Postdoctoral Fellow at the Mathematics and Computer Science Division of the Argonne National Laboratory and obtained his Ph.D. in Chemical Engineering from Princeton University.