(756h) Olefin Oligomerization on Acidic Zeolites: A Mechanistic Model for Catalyst Optimization

The development of shale gas resources during the last 10 years has significantly influenced the US economy and the global gas market. In particular, the chemical activation of light paraffins derived from shale gas to form olefins and their subsequent oligomerization to heavier hydrocarbons is an attractive strategy to produce chemicals and liquid fuels that can be easily transported and processed. This work presents a microkinetic model to describe the oligomerization of propylene on BEA zeolites in prospect of catalyst optimization.

A reaction network consisting of 3705 surface reactions and 538 physisorption/desorption steps and involving 909 molecular and ionic species was automatically constructed by using a network generator [1]. The frequency factors were computed in the framework of transition state theory and the activation energies were expressed as functions of the heats of reaction in the physisorbed state, which were calculated from thermodynamic considerations [2].

The model demonstrated that increasing the stabilization enthalpy of the ionic intermediates drives the selectivity of the process toward the production of C9 species. This would result into a more efficient oligomerization process whose target is the production of gasoline blend with high concentration of heavy and highly branched olefins and, as a consequence, with high octane number. This study revealed mechanistic details of acid-catalyzed oligomerization chemistry of light olefins. The developed microkinetic model represents a powerful tool to predict the product distribution, optimize the conditions of the process and design more efficient catalysts.

References

[1] Vernuccio, S. and Broadbelt, L. J., “Discerning complex reaction networks using automated generators”. AIChE J. 65, e16663 (2019).

[2] Vernuccio, S., Bickel, E. E., Gounder, R., Broadbelt L. J., “Microkinetic model of propylene oligomerization on Brønsted acidic zeolites at low conversion”. ACS Catal. 9, pp. 8996–9008 (2019).