Efficient optimization of a multifunctional catalytic fixed-bed reactor via reduced-order modeling approach

To reduce the computational cost associated with the two-scale character of the model comprising a pellet model coupled with a bulk gas model for a non-isothermal fixed-bed catalytic reactor, a model-order reduction methodology based on Proper Orthogonal Decomposition (POD) and Galerkin projection is employed. Particularly, a novel sampling approach based on k-means clustering is proposed, and the resulting agile reduced-order model is validated against the full-order model. The proposed reduction methodology exhibits very high accuracy when applied to a generic system of two consecutive reversible chemical reactions of the first order catalyzed by two different types of active centers and integrated in a single bifunctional pellet. The results obtained indicate that it is beneficial to divide the catalytic bed into zones made of bifunctional pellets with different ratio of two types of active centers. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

A model-order reduction methodology based on Proper Orthogonal Decomposition and Galerkin projection is employed in this study, showing high accuracy in reducing computational cost. The results suggest that dividing the catalytic bed into zones made of bifunctional pellets with different ratios of active centers is beneficial.