Many computational difficulties in dealing with chemical process models often result from spatially distributed states as well as nonlinear correlations (e.g., for transport coefficients or reaction kinetics). Surrogate models with sufficient accuracy represent one remedy to this problem. Featuring a lower number of states, model order reduction (MOR) generates considerably less complex models and leads to faster model evaluations. Especially for nonlinear systems, snapshot-based MOR techniques are considered to be one of the most promising methods. In this study, we apply proper orthogonal decomposition together with the discrete empirical interpolation method (POD-DEIM) to a dynamic, two-dimensional reactor model for catalytic carbon dioxide methanation. Motivated by renewable energy integration, we consider this reactor in two different dynamic scenarios: Disturbed continuous operation and start-up. It can be shown that the reduced order model (ROM) is accurate and, furthermore, the solution of the FOM is accelerated at least by one order of magnitude. (C) 2017 Elsevier Ltd. All rights reserved.