Project Overview

PSA cycle optimization is computationally intensive due to nonlinear PDE dynamics. Prior PINN surrogates accelerate simulation but are not optimized for decision-making accuracy near optimal operating points. This project develops an end-to-end differentiable framework that jointly trains physics-informed surrogates and PSA cycle optimization, focusing model fidelity on regions critical to purity, recovery, and energy-efficient CO₂ capture.