Deactivation of heterogeneous catalysts is a ubiquitous problem that causes loss of catalytic rate with time. This review on deactivation and regeneration of heterogeneous catalysts classifies deactivation by type (chemical, thermal, and mechanical) and by mechanism (poisoning, fouling, thermal degradation, vapor formation, vapor-solid and solid-solid reactions, and attrition/crushing). The key features and considerations for each of these deactivation types is reviewed in detail with reference to the latest literature reports in these areas. Two case studies on the deactivation mechanisms of catalysts used for cobalt Fischer-Tropsch and selective catalytic reduction are considered to provide additional depth in the topics of sintering, coking, poisoning, and fouling. Regeneration considerations and options are also briefly discussed for each deactivation mechanism.

Catalyst deactivation, the loss over time of catalytic activity and/or selectivity, is a problem of great and continuing concern in the practice of industrial catalytic processes. Costs to industry for catalyst replacement and process shutdown total tens of billions of dollars per year.

A trickle fixed-bed reactor model for the Fischer-Tropsch synthesis applicable to both cobalt and iron catalysts which accounts for gas and liquid recycle is described. A selection of kinetic models for both iron and cobalt catalysts (4 each) is included in the reactor model and their effect on model predictions is compared. While the model is 1-D and reaction rates are determined for quasi-average radial bed temperatures, a correlation is used to account for radial thermal conductivity and radial convective heat transfer. Traditional pressure drop calculations for a packed column were modified with a correlation to account for trickle-flow conditions. In addition to describing the model in detail and showing validation results, this paper presents results of varying fundamental, theoretically-based parameters (i.e. effective diffusivity, Prandtl number, friction factor, etc.). For example, the model predicts that decreasing effective diffusivity from 7.1E-09 to 2.8E-09 m2/s results in a lower maximum temperature (518 K vs. 523 K) and a longer required bed length to achieve 60% conversion of CO (8.5 m vs. 5.7 m). Using molar averages of properties to calculate the Prandtl number for the gas phase (recommended by the authors) results in average bed temperatures up to 10 K higher and reactor lengths 17-45% shorter than assuming a Prandtl number of 0.7. Using the Tallmadge equation to estimate friction losses, as recommended by the authors, results in a pressure drop 40% smaller than using the Ergun equation. Validation of the model was accomplished by matching published full-scale plant data from the SASOL Arge reactors.