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

Abstract

KEYWORDSAlumina-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.Catalyst Co-V/Al2O3 ANFIS model Nano catalyst Fischer-Tropsch Neuro-Fuzzy prediction

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

“…A i and B i represent the linguistic labels whose membership function parameters are premise parameters and are represented by fuzzy sets [27].…”

“…ANFIS consists of five layers, namely, a fuzzy layer, a product layer, a normalized layer, a defuzzy layer and a total output layer [27,37]. In the first and fourth layers, the nodes are adaptive and some used parameters in these nodes are determined through training phase, whereas the nodes in other layers are fixed.…”

“…Finally, the last layer calculates the total sum of the all input signals overall as final output [27]:…”

“…In this regard, these parameters were selected for the training and testing of the models, and the appropriate ranges for these factors were defined accordingly. The percentages of MO removal, R%, and MO uptake at t time (mg/g), q t , were chosen as outputs and calculated as follows: [27] where C o and C t (mg/L) are the MO concentrations at initial and t time, respectively; V (L) is the solution volume, and m (g) is the adsorbents dosage. As listed in Table 1, the experiments were randomized in two deigned blocks by experiment software (DesignExpert 8 trial version).…”

“…ANFIS is a powerful approach to modeling/mapping the input and output relationship in complex and nonlinear systems [26,27]. It converges much faster and has the most efficient learning algorithm, comparing with other models [28].…”