Neuro-Fuzzy prediction of alumina-supported cobalt vanadate catalyst behavior in the Fischer-Tropsch process

Abstract

Alumina-supported cobalt vanadate multitransition-metal catalyst was prepared by impregnation method. The catalyst was characterized using X-ray diffraction, Fourier transform infrared spectroscopy, Brunauer-Emmett-Teller, X-ray fluorescence and Transmission electron microscopy. The cobalt/vanadium catalyst was employed for Fischer-Tropsch process in an autoclave reactor. The evaluation of this catalyst occurred at different temperature (423-623 K), over a pressure range of 10-50 bars with the Syngas H₂/CO ratio varying from 2 to 6. The catalyst gave a high and selective conversion of syngas into methane. The degree of syngas conversion increased with increasing temperature and pressure. The adaptive Neuro-Fuzzy inference system (ANFIS) model has been applied for the training of the fuzzy system and the test set was applied to evaluate the performance of the system including moving average error (MAE), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results exposed that the predicted values from the model were in good agreement with the experimental data.