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Keywords:

  • iron;
  • catalysis;
  • computational chemistry;
  • neural network;
  • vanadium oxide

Abstract

The application of an Fe-V2O5-promoted γ-alumina catalyst was studied for the reverse water–gas–shift reaction. The reaction was performed under a wide range of synthesis conditions, temperatures, and CO2/H2 ratios in a batch reactor. The experimental results were compared with neuro-fuzzy simulation results. The Mamdani algorithm (a gradient descent algorithm) was applied to train the fuzzy system, and a test set was used to evaluate the performance of the system by applying the efficiency coefficient (E), root-mean-square error (RMSE) and mean absolute error (MAE). The predicted values from the model are in good agreement with the experimental data. The outcome of this study demonstrates how the neuro-fuzzy method, as a promising prediction technique, can be effectively applied to the reverse water–gas–shift reaction. This study applies neuro-fuzzy modelling to predict the product composition of CO, CO2 and CH4 in the reverse water–gas–shift reaction, for which the input vector was three-dimensional (including the variables of operating temperature, time, and CO2/H2 ratio) for 34 different experiments, and the output vectors consisted of CO, CO2 and CH4 conversions.