Abstract
Catalyst deactivation significantly affects reactor performance, process efficiency, and economic viability in chemical processes. The precise estimation of kinetic and deactivation parameters in transient tubular reactors is essential but remains challenging due to strong parameter correlations, nonlinear dynamics, and limited prior knowledge of parameter values. Model-based Design of Experiments techniques for improving parameter precision (MBDoE-PP) has been shown to enhance parameter identifiability by optimally designing informative transient experiments even when catalyst deactivation occurs. However, MBDoE-PP is highly sensitive to parameter misspecification and can lead to suboptimal or infeasible solutions under model uncertainty, in which is the typical case in reaction systems exhibiting catalyst deactivation. In this work, a robust MBDoE framework for parameter precision (RMBDoE-PP) is proposed to explicitly account for processmodel parameter mismatch (PMPM) during the experimental design stage. In the framework, stochastic sampling is used to evaluate metrics of the Fisher Information Matrix (FIM) and the experimental design is carried out considering a worst-case scenario, where the globally least informative parameter corresponding to the minimum information content across the uncertainty domain is identified. The performance of MBDoE-PP and RMBDoE-PP is assessed in the sequential closed-loop experimental design framework under varying levels of PMPM. Results demonstrate that RMBDoE-PP consistently yields tighter confidence intervals and more accurate parameter estimates with fewer experimental runs, confirming its superior robustness for deactivation kinetics.