Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique

Abstract

Methane dry reforming is a thermo-catalytical process that utilizes two principal components of greenhouse gases for the production of hydrogen-rich syngas . One major shortcoming of the methane dry reforming as a potential route for renewable hydrogen-rich syngas production is catalyst deactivation through carbon deposition . In this study, an artificial neural network approach was employed for predictive modeling of the deactivation of alumina supported cobalt catalyst used to catalyze methane dry reforming reaction. The effect of methane/carbon dioxide (CH 4 /CO 2 ) ratio, reaction temperature and nitrogen (N 2 ) flowrate on the carbon deposition were investigated using full factorial experimental design . Two artificial neural network modeling techniques namely multilayer perceptron neural network (MLPNN) and radial basis function (RBFNN) were employed for the prediction of carbon deposition per gram catalyst using data obtained from 170 experimental runs. The hidden neurons were optimized to obtain 16 and 20 units respectively for the MLPNN and the RBFNN resulting in the network architecture of 3, 16, 1 and 3, 20, 1, respectively. The statistical analysis of the network performance resulted in mean standard error (MSE) values of 0.048 and 0.00285 for training the MLPNN algorithm above and below stoichiometric conditions with corresponding R 2 values of 0.945 and 0.965. While MSE values of 0.0073 and 0.00015 were obtained for the training of the RBFNN algorithm above and below stoichiometric conditions with R 2 of 0.987 for both cases. Base on the statistical analysis the RBFNN model was adjudicated as a better predictor of the carbon deposition during the hydrogen-rich syngas production than the MLPNN model. The three input parameters were found to have varying levels of importance in the prediction of the carbon deposition. The reaction temperature was observed to be the most important parameters that influence the prediction of carbon deposition above stochiometric while CH 4 /CO 2 was the most important parameters that influence the prediction of carbon deposition below stoichiometric conditions. • MLPNN and RBFNN models were developed and employed to predict carbon deposition on Co/αAl 2 O 3 . • Optimized hidden neurons with 16 and 20 units respectively were obtained for MLPNN and RBFNN. • Levenberg-Marquardt algorithm was employed to train the MLPNN and RBFNN. • Both MLPNN and RBFNN accurately predict carbon deposition with minimal residuals. • RBFNN with MSE of 0.00015 and R 2 of 0.985 predicts better than MLPNN with MSE of 0.00285 and R 2 of 0.965