Prediction of gas-phase adsorption isotherms using neural nets

This study investigated 6, number. of models. (the modified Sips', Dubinin-Astakhov's, VSM theory, the generalized Khan et al.'s model and a simple artificial neural network (ANN)) to predict the effect of temperature on equilibrium adsorption of hydrocarbon gases and vapors on activated carbon. Published. data on the adsorption of methane, ethane and propane on activated carbon at 311 K to 505 K were used to estimate the parameters of the conventional models and train the network. Then, the, conventional models and the ANN were used to predict the isotherm at a single temperature for each adsorbate, and these results were compared with experimental data. It was found that the ANN model had a lower mean relative. error than the conventional models.