DEM ‐ CSTR network model for packed‐bed reactor: A particle‐resolved reduced‐order modeling approach

Abstract Packed‐bed reactor modeling faces a fundamental conflict between the fidelity of particle‐resolved computational fluid dynamics (PRCFD) and the efficiency of macroscopic models. We present a discrete element method‐continuous stirred‐tank reactor (CSTR) network framework that bridges this gap by mapping each catalyst particle to a CSTR node and particle contacts to directed edges, preserving full geometrical heterogeneity while reducing computational cost from 8 h to 3 s, a speedup of nearly five orders of magnitude. The model reproduces PRCFD residence‐time and conversion predictions within 2% relative deviation. The particle contact network exhibits the typical “small‐world” characteristics, providing a topological basis for rapid transport and local mixing. The framework further resolves the structural origins of concentration non‐uniformities and enables rapid parametric sensitivity analysis for the non‐isothermal multi‐component reaction system. This work offers a computationally efficient yet physically faithful approach for packed‐bed reactor modeling and optimization.