Investigation of Adsorption of Methane, Carbon Dioxide and N2 on Zeolite 13X Using Artificial Neural Network

One of the most important processes for avoiding global warming and increasing the heating value of natural gas is adsorption and separation of carbon dioxide and nitrogen using zeolite. In this study, experimental results of adsorption of methane, carbon dioxide and nitrogen by zeolite 13X was assessed using artificial neural network. The temperature and pressure was considered as inputs, and adsorption capacity was considered as the output of system. In all models, Levenberg-Marquardt back-propogation was used for training of the network. To find the optimum transfer function in hidden and output layers and optimum number of neurons, coefficient of determination, sum of squared errors, mean square error were calculated. Optimized number of neurons for methane, carbon dioxide and nitrogen was obtained 10, 10, and 15 respectively. Moreover, the best transfer functions were Logsig and Tansig for methane, Logsing and Pureline for carbon dioxide and nitrogen for hidden and output layers. In the end, average deviation percentage for predicted results with neural network was compared with the results obtained by Langmuir and dependent on temperature sip models. It indicates that neural network has high accuracy in comparison with other two models.