Abstract
Catalytic hydrodechlorination (HDC) is a promising approach for the sustainable valorization of chloromethane by-product streams into valuable chloromethanes. However, the design and optimization of HDC reactors remain challenging because reaction kinetics, transport phenomena, and catalyst behavior interact across both reactor and catalyst particle scales. Reliable predictive tools are therefore highly needed to support process development and scale-up. In this work, a comprehensive multiscale packed-bed reactor model is developed by coupling reactor-scale mass, energy, and momentum balances with intraparticle transport and catalyst-specific reaction kinetics identified from laboratory-scale fixed-bed experiments. Dedicated kinetic models for palladium- and iridium-based catalysts are obtained through systematic kinetic model discrimination and nonlinear parameter estimation. For the Pd catalyst, the framework is further extended by incorporating a time-dependent deactivation model calibrated against time-on-stream experiments, enabling the prediction of catalyst activity loss under operating conditions. The resulting framework reproduces the experimental behavior of both catalytic systems, accurately capturing conversion, product distribution, outlet temperature, and, for the Pd catalyst, the evolution of catalyst activity over time. The model, validated against experimental data at laboratory scale, is subsequently applied to a pilot-scale reactor, illustrating its potential as a predictive tool for reactor analysis across different operating scales and for applications relevant to process development and scale-up. Overall, the proposed methodology provides an experimentally grounded predictive framework for catalyst assessment, reactor design, and process optimization, while offering a flexible basis for reactor scale-up and further refinement as additional experimental data become available.