Robust dynamic optimization in heterogeneous multiscale catalytic flow reactors using polynomial chaos expansion. (December 2017) - Chaffart, Donovan; Ricardez-Sandoval, Luis A.
Highlights: Multiscale models are implemented to simulate catalytic flow reactor behaviour. Parametric uncertainty is propagated through the reactor model using PCE. PCE and PSE are compared for uncertainty propagation in the catalytic reactor model. Dynamic optimization is performed on the reactor model under uncertainty. Abstract: This paper explores the application of optimal design and operational strategies under uncertainty to a transient multiscale catalytic flow reactor system. The catalytic reactor is modeled using a spatially-dependent multiscale model that comprises lattice-based kinetic Monte Carlo (kMC) models coupled with continuum partial differential equations (PDEs) to account for the fine-scale and the macroscale reactor behaviour, respectively. This work compares two uncertainty propagation techniques, power series expansion (PSE) and polynomial chaos expansion (PCE), to assess their performance in multiscale process systems. The analysis reveals that PCE provides accurate results at minimal computational cost for the multiscale catalytic reactor model under the conditions considered in this work. PCE is subsequently used to perform robust dynamic optimization studies on the catalytic reactor system under uncertainty. The first study determines the optimal temperature trajectories that maximize the reactor's performance under uncertainty. The second study aims to identify the optimal design and operating policies that allow the reactor, under uncertainty in the multiscale model parameters, to meet targeted performance specifications within a level of confidence. Both studies illustrate the benefits of performing dynamic optimization studies to improve performance for multiscale process systems under uncertainty.