A feed-forward artificial neural network (ANN) with one hidden layer was constructed and tested to model the equilibrium data of hydrogen onto Ni-decorated CNTs. CNT properties like surface area, pore volume and experimental conditions are used as inputs to predict the corresponding hydrogen uptake at equilibrium conditions. The constructed ANN was found to be precise in modeling the hydrogen adsorption isotherms for all inputs during the training process. The trained network successfully simulates the hydrogen adsorption isotherm for new inputs, which are kept unaware of the neural network during the training process, thus showing its applicability to determine adsorption isotherms for any operating conditions under the studied constraints. The percentage of absolute deviation between experimental and predicted data during the training and testing stages was less than 5% for most input conditions.

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