Neural Network Modeling of Adsorption of Binary Vapour Mixtures

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Abstract

Three neural network models were used for prediction of adsorption equilibria of binary vapour mixtures on an activated carbon. The predictions were compared both with published experimental data and calculated values from the Ideal Adsorption Solution (IAS) model. The neural network was trained using both binary and single component experimental adsorption data. Even for a limited number of data points (about 60) the network models were capable of approximating experimental data very precisely.

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