A framework for modeling of catalyst deactivation and coking free of main-reaction kinetics in methanol to gasoline process

Optimizing the methanol-to-gasoline technology necessitates a profound comprehension of catalyst deactivation and coke formation kinetics. However, conventional mathematical models cannot capture catalyst deactivation without relying on complex main-reaction kinetic models, and they treat the prediction of coking as a separate problem. Addressing these challenges, this research introduces a computational framework to identify the optimal model for the kinetics of deactivation and coking. The key novelty is leveraging the concept of active site loss to eliminate the requirement for main-reaction kinetic models while facilitating the prediction of coking according to the site loss model. Experimental data of an HZSM-5 catalytic bed under diverse operating conditions (WHSV: 10-100 h-1, temperature: 325-375 degrees C) were employed to validate method efficiency for predicting reactor outputs, including oxygenates, light olefins, and gasoline. The proposed approach demonstrates a 0.52 % Root-Mean-Square-Error in simulating product weights and a 66 % reduction in deactivation kinetic constants. This study reveals assuming a critical volume for each grown coke precursor justifies experimental data by R2 of 99.71 %. This volume was computed around 100 angstrom 3 and exhibits a linear Arrhenius temperature dependency. These results highlight the high accuracy and extreme simplicity this framework offers. Moreover, the method transforms deactivated catalyst data at varying WHSVs into a larger unified dataset for the fresh catalyst at each isotherm. Hence, it also positively impacts the kinetic study of main reactions. Consequently, the developed framework is a promising tool for direct assessment of catalyst deactivation and coking in similar processes toward advancing catalyst design strategies and fuel production efficiency.

Automated summary

This research introduces a computational framework to identify the optimal model for catalyst deactivation and coke formation kinetics. The framework leverages the concept of active site loss and demonstrates high accuracy and simplicity. It also transforms deactivated catalyst data into a unified dataset, positively impacting the kinetic study of main reactions.