In this work, a methodology for the parameter estimation of heterogeneously-catalyzed reactions is presented. A simulation-optimization framework based on a dynamic model was coupled with an identifiability analysis, in order to detect for which parameters the dynamic model is most sensitive. The implemented identifiability analysis was based on rank-revealing matrix factorizations, with singular values as criteria for parameter selection. As the dynamic equation systems describing catalytic reactions are expected to be ill-posed, a subset selection step based on identifiability analysis was included. In order to illustrate the methodology, the ODE system describing the heterogeneously-catalyzed reaction system for the production of nopol from beta-pinene and formaldehyde was used as a case study. After applying the methodology, two out of five kinetic parameters were found to be identifiable, consistent with a Langmuir Hinshelwood Hougen Watson (LHHW) mechanism that considers adsorption on catalytic sites of different nature. Confidence intervals of the estimated parameters belonging to the identifiable subset were not higher than 3% of the parameter value. The results of this work show that the proposed mechanism is capable of reproducing the dynamics of the reaction system, and are an important input for the design of a three-phase reactor for nopol production. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.