Abstract:
The aim of this thesis is to extract a general knowledge about the steam reforming of methane and to create models representing the data accumulated in the literature. The experimental data were collected from published articles in the literature. The conversion of methane was modeled as a function of various catalyst preparation and operational variables using decision tree classification and artificial neural networks, which were created by writing computer codes in MATLAB environment. Decision tree analyses for methane conversion were performed for the entire data, for Ni, Ru and Rh based catalysts, incipient to wetness impregnation method data and packed bed reactor data, separately. Analysis of total data resulted in 20.83% training error and 22.91% testing error. 21.41% training error and 24.52% testing error were obtained for Ni metal based data. 6.68% and 8.93% errors were found for training and testing of Rh metal based data. 8.03% training error and 14.77% testing error were calculated for Ru metal based data. Training error and testing error of incipient to wetness impregnation method data were 11.47% and 14.50%. For packed bed reactor data, training error and testing error were 20.01% and 21.78. The neural network analysis was also performed and the optimal neural network topology was found as 59-16-16-1 (59 input neurons, 16 neurons each in the first and second hidden layers and 1 output; with the activation function of hyperbolic tangent sigmoid function for all the layers), where “trainlm“ and “trainbr” functions were used for training and testing respectively. R2 and RMSE values of training were found to be 0.97 and 6.03, whereas they were 0.93 and 8.78 for testing. Then, an input significance analysis was performed and it was found that base metal type within the input variables had the most significant effect on the methane conversion while catalyst preparation method was the least important parameter. Finally, the optimal neural network was forced to predict the results of experiments by using the data of the other experiments in the database. RMSE and R2 were 69.6% and 9.03% for experiments and 44.07% and 18.64% for articles, respectively.
