Artificial neural network modelling of hydrogen storage properties of Mg-based alloys

An artificial neural network model has been created for prediction of the hydrogen storage capacity and the temperature and pressure of dehydrogenation of Mg-based alloys as a function of alloy composition. The effects of 24 chemical elements are considered in the model, which is based on a two-layer feedforward hierarchical architecture. The neural network was trained using the Levenberg–Marquardt training algorithm in combination with Bayesian regularization. The model was used to study the influence of the alloying elements on the hydrogen storage properties of MgH2. For almost all of the investigated alloying elements, increasing their content results in a decrease of the hydrogen storage capacity, but several elements lead to a reduction of the temperature for hydrogen desorption. A graphical user interface (GUI) has been established for the prediction of the hydrogen storage capacity, temperature and pressure of dehydrogenation for magnesium alloys as function of their chemical composition, as well as for investigation the influence of the different alloying elements on the hydrogen storage properties in magnesium alloys.