(661g) Kinetic Monte Carlo Simulation of Propylene Epoxidation on Supported Gold Nanoparticles | AIChE

(661g) Kinetic Monte Carlo Simulation of Propylene Epoxidation on Supported Gold Nanoparticles


Turner, C. H. - Presenter, University of Alabama
Lu, Z., University of Alabama in Huntsville
Lei, Y., University of Alabama in Huntsville
Propylene oxide (C3H6O, PO) is an important chemical intermediate for the production of a number of commodity chemicals, including polyol, propylene glycol, and glycol ethers.1 However, current industrial methods that produce propylene oxide from propylene pose environmental risks because of the production of chlorinated or peroxycarboxylic waste.2, 3 Using supported gold-based catalysts to produce propylene oxide directly from propylene (C3H6) and molecular H2 and O2 provides an alternative, clean, and potentially more efficient route.

The direct propylene epoxidation reaction has been previously investigated by several different groups, and gold-based catalysts tend to provide high selectivity for propylene oxide. It has been found that Au supported by titanium silicalite (TS-1) is particularly effective at catalyzing the propylene epoxidation reaction. The isolated Ti active sites are necessary for obtaining a high selectivity towards propylene oxide,4-6 since propylene oxide molecules that adsorb on adjacent Ti sites lead to catalyst deactivation and the formation of unwanted byproducts. Although the selectivity is very high (>90%), even the best catalysts found to date still suffer from multiple challenges, including low propylene conversion (<10%), poor stability and inefficient usage of H2 (<50%). Therefore, significant improvement with regard to these issues is necessary, and this requires a more thorough understanding of the underlying chemical reaction network.

In this work, KMC simulations are used to model the direct epoxidation of propylene to propylene oxide on an Au/TiO2/SiO2 catalyst. Although the model is a simple two-dimensional representation of the actual catalyst surface, the basic catalyst features are preserved (Ti concentration, Au loading, Au particle sizes), and the KMC results are benchmarked against relevant experimental data. In addition, the composition in the bulk gas phase is synchronized with the dynamic reaction events occurring on the surface, and this coupling allows us to relax the typical assumption of a constant gas-phase composition. By acquiring mechanistic information from various DFT studies, and compiling this information into a KMC model, several aspects of the experimental catalytic behavior can be captured. We find close agreement between the KMC simulations and the experimental data in several aspects, but this is only achieved by considering the re-adsorption of trace amounts of the oxidant (H2O2) from the gas phase, versus merely assuming that the desorbed products are swept away in the gas stream.

A shortcoming in the KMC model is the H2 sensitivity, which is higher than in most previous experimental studies. This may be an artifact that arises from our neglect of other competing reaction channels on the catalyst surface which may serve as hydrogen sinks. Also, several of the individual event rates used in the model deserve additional scrutiny. If some of these secondary effects can be resolved, this approach can provide an excellent modeling tool for connecting the atomistic-level features of such a catalytic system to the predicted experimental behavior. In particular, bimetallic catalyst and optimally-designed supports will likely be needed for advancing the direct synthesis of propylene oxide beyond current limits, while maximizing the availability of the oxidizing species near the Ti sites is critical for accelerating the production of PO.



1. K. Weissermel, H.-J. A., Industrial Organic Chemistry. Fourth ed.; WILEY-VCH: Weinheim, Germany, 2003.

2. Monnier, J. R., Applied Catalysis a-General 2001, 221 (1-2), 73-91.

3. Nijhuis, T. A.; Makkee, M.; Moulijn, J. A.; Weckhuysen, B. M., Industrial & Engineering Chemistry Research 2006, 45 (10), 3447-3459.

4. Stangland, E. E.; Stavens, K. B.; Andres, R. P.; Delgass, W. N., Journal of Catalysis 2000, 191 (2), 332-347.

5. Nijhuis, T. A.; Huizinga, B. J.; Makkee, M.; Moulijn, J. A., Industrial & Engineering Chemistry Research 1999, 38 (3), 884-891.

6. Chen, J.; Halin, S. J. A.; Pidko, E. A.; Verhoeven, M. W. G. M.; Perez Ferrandez, D. M.; Hensen, E. J. M.; Schouten, J. C.; Nijhuis, T. A., Chemcatchem 2013, 5 (2), 467-478.