Simulation Of Carbon Nanotubes For Hydrogen Storage Using Neural Network: A Preliminary Study

Abstract

The discovery of carbon nanotubes (CNT) by 5umio Iijima in 1991 has attracted many researchers worldwide to study and explore the newly found materials. The unique characteristics that CNT possess include excellent properties for energy production and hydrogen storage. Currently, there are 4 technologies available for hydrogen storage: compressed gas, liquefaction, metal hydrides and physisorption. It has been claimed that physisorption is the most promising hydrogen storage method for meeting the goals of the US Department of Energy (DOE) Hydrogen Plan for fuel cell powered vehicles. CNT are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. A number of theoretical and experimental investigations have been made in this area mainly to study whether CNT can reach the benchmark of gravimetric density of 6.5 wt% and volumetric density of 62 kg H2/m3 set by the DOE Hydrogen Plan. Based on previous researches, a numerical simulation of CNT for hydrogen storage using Artificial Neural Network (ANN) will be developed.