2020 : Desain Paduan AB2 untuk Penyimpanan Hidrogen Menggunakan Pendekatan Machine Learning
Hydrogen has high energy density and is a promising energy carrier for zero-emission vehicles and stationary energy storage. However, since hydrogen has a low boiling point of only -254Â°C, there are considerable challenges to pack hydrogen. Hydrogen can be stored using liquefaction, high-pressure tank, or in a metal hydride. The metal hydride has an advantage since it can store hydrogen at low pressure with high hydrogen density both in weight and volumetric density.
To date, many alloys have been studied, and some of them have been successfully commercialized. One example a good hydrogen storage material is the AB2 type alloys, which showed excellent hydrogen storage capacities, close to 2 wt. % H and 120 kg/m3, as well as the fast kinetics of hydrogen absorption, allowing the synthesis of saturated hydrides in less than 60 seconds.
This present project focuses on the study using a data science approach, i.e., using machine learning to predict hydrogen storage properties of AB2 alloy. The objective of this present work is finding an alloy composition that can apply at room temperature and absorb and desorb hydrogen with reasonable capacity.
The project would be integrated with a running European Project (http://hydride4mobility.fesb.unist.hr), where the department of the Mechanical Engineering Department of ITS Surabaya (Indonesia) is partner and Department of Energy System, IFE (Norway) is the coordinator. This collaboration allows an exchange of knowledge and resources so that the work will be conducted more effectively. The results from the present project will be published in a reputable impact factor journals such as Journal of alloys and compounds and the International journal of hydrogen energy.