This work examines the use of neural networks in modelling the adsorption process of gas mixtures (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>) on different activated carbons. Seven feed-forward neural network models, characterized by different structures, were constructed with the aim of predicting the adsorption of gas mixtures. A set of 417, 625, 143, 87, 64, 64, and 40 data points for NN1 to NN7, respectively, were used to test the neural networks. Of the total data, 60 %, 20 %, and 20 % were used, respectively, for training, validation, and testing of the seven models. Results show a good fit between the predicted and experimental values for each model; good correlations were found (<i>R</i> = 0.99656 for NN1, <i>R</i> = 0.99284 for NN2, <i>R</i> = 0.99388 for NN3, <i>R</i> = 0.99639 for <i>Q</i><sub>1</sub> for NN4, <i>R</i> = 0.99472 for <i>Q</i><sub>2</sub> for NN4, R = 0.99716 for <i>Q</i><sub>1</sub> for NN5, <i>R</i> = 0.99752 for <i>Q</i><sub>3</sub> for NN5, <i>R</i> = 0.99746 for <i>Q</i><sub>2</sub> for NN6, <i>R</i> = 0.99783 for <i>Q</i><sub>3</sub> for NN6, <i>R</i> = 0.9946 for <i>Q</i><sub>1</sub> for NN7, <i>R</i> = 0.99089 for <i>Q</i><sub>2</sub> for NN7, and <i>R</i> = 0.9947 for <i>Q</i><sub>3</sub> for NN7). Moreover, the comparison between the predicted results and the classical models (Gibbs model, Generalized dual-site Langmuir model, and Ideal Adsorption Solution Theory) shows that the neural network models gave far better results.
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