Prediction of the Performance of Nanocatalysts in Fischer-Tropsch Synthesis using AI Model Capable of Simultaneously Handling Quantitative and Qualitative Variables

The primary objective of this study is to predict the conversion of carbon monoxide (xCO) of cobalt supported SiO۲ and Al۲O۳ nanocatalysts in the Fischer-Tropsch synthesis (FTs) using a type-۲ fuzzy model capable of simultaneously handling both qualitative and quantitative variables. The predicted results were compared with experimental data. The findings indicate a promising accuracy of ۸۸% for the model, despite being developed with minimal complexity and relying solely on user intuition and expertise.

کلیدواژه ها:

Nanocatalyst ، Artificial Intelligence ، Mamdani fuzzy inference ، Fischer Tropsch synthesis