Modeling of Catalytic Performance of SAPO-۳۴ Nanocatalysts Synthesized Sonochemically Using MVR & ANFIS models

Nowadays, the catalytic conversion of methanol to lower olefins is a process of great interest. Among catalysts, SAPO-۳۴ has received significant attention in the methanol-to-olefins (MTO) process. Although a number of papers have been published on the synthesis and modification of the catalytic performance of the SAPO-۳۴ nanocatalyst in the MTO process, very few have attempted to simulate catalyst performance, such as methanol conversion, light olefin content, and lifetime, to maximize benefits. In this study, the catalytic performance of SAPO-۳۴ nanocatalysts synthesized sonochemically in the MTO process is modeled by adjusting ultrasound power intensity, ultrasonic irradiation time, sonotrode size, and sonication temperature using two different models: Multiple Variables Regression (MVR) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results confirm that ANFIS models have a better predictive power than MVR.

کلیدواژه ها:

SAPO-۳۴ nanocatalysts ، ultrasound-related variables ، Adaptive Neuro-Fuzzy Inference System ، Multiple Variables Regression