Intrinsic reaction and deactivation kinetics of Methanol-to-Propylene process (MTP) over an industrial ZSM-5 catalyst

Abstract

The lack of accurate recognition of the reaction kinetics and the catalyst deactivation are challenges in commercializing the methanol-to-propylene process (MTP). Accordingly, this research aims to develop reliable intrinsic kinetic models for MTP reactions and catalyst deactivation on an industrial ZSM-5 catalyst. An efficient reaction network was developed based on a combination of hydrocarbon pool and dual-cycle mechanisms considering individual pathways for producing olefins, paraffins, and aromatics. Six deactivating models were investigated based on the possible coke precursors of aromatics, olefins, and oxygenates. Since the deactivation rate of the catalyst at normal operating conditions is slow, the “acceleration deactivation” technique was employed to reduce the time and cost of deactivating experiments. The proposed kinetic models considered the combined effect of water on reducing the rate of progress of reactions and catalyst deactivation. The experiments were performed in a fixed-bed reactor under conditions relevant to industrial operations leading to a full conversion of oxygenates as follows: temperature of 733–763 K, feed WHSV of 5–14 h⁻¹, and feed methanol content of 50–93 wt%. Therefore, the model is only valid for predicting the behavior of the reactors operating under full conversion conditions, making it useful for the simulation of industrial reactors. Oxygenates were found to be the main responsible for catalyst deactivation through coke formation by parallel decay reactions according to first-order kinetics. The detrimental effect of water in suppressing MTP reactions is overshadowed by its benefit in surviving the catalyst activity. Reducing the feed WHSV and increasing the reaction temperature and water content enhance feed conversion and propylene selectivity. A good agreement between the calculated results and experimental data was observed with average errors of less than 10 % and 3 % for kinetic models of reaction and catalyst deactivation, respectively. This confirms the accuracy of these kinetic models, making them reliable for designing and optimizing industrial reactors.