Thesis Archive
A Binary Hyperbox Classifier Model for Hydrogen Storage in Magnesium (Mg) and Complex Hydrides
John Andrei S. Acantilado
K Anthea C. Rana
Jared Ethan M. Santos
Abstract:
Hydrogen cannot be easily stored for energy applications. One potential solution is the storage of hydrogen within metal hydrides. The main method for determining the viability of a metal hydride for hydrogen storage is through costly and time-consuming experimentation. Machine learning provides an economical solution as it determines the association between the hydrogen storage capacity and the other properties of the material. In this thesis, a binary classifier model was developed for predicting a metal hydride’s viability for storage applications. The classifier was trained on a subset of the US Department o Energy metal hydride database using the enhanced binary hyperbox approach. This work focuses specifically on complex and Mg hydrides. The algorithm was able to generate a classifier model consisting of mechanistically plausible if/then rules that predict hydrogen storage capacity from heat of formation, operating temperature, and pressure as inputs. The model had a false positive rate of 22.0% and false negative rate of 21.1%.
Adviser:
Aviso, Kathleen B.; Tan, Raymond Girard R.