Optimization Study of Hydrogen Gas Adsorption on Zig-zag Single-walled Carbon Nanotubes: The Artificial Neural Network Analysis

Nasruddin, M. Lestari, Supriyadi, Sholahudin

Research output: Contribution to journalConference article

Abstract

The use of hydrogen gas in fuel cell technology has a huge opportunity to be applied in upcoming vehicle technology. One of the most important problems in fuel cell technology is the hydrogen storage. The adsorption of hydrogen in carbon-based materials attracts a lot of attention because of its reliability. This study investigated the adsorption of hydrogen gas in Single-walled Carbon Nano Tubes (SWCNT) with chilarity of (0, 12), (0, 15), and (0, 18) to find the optimum chilarity. Artificial Neural Networks (ANN) can be used to predict the hydrogen storage capacity at different pressure and temperature conditions appropriately, using simulated series of data. The Artificial Neural Network is modeled as a predictor of the hydrogen adsorption capacity which provides solutions to some deficiencies in molecular dynamics (MD) simulations. In a previous study, ANN configurations have been developed for 77k, 233k, and 298k temperatures in hydrogen gas storage. To prepare this prediction, ANN is modeled to find out the configurations that exist in the set of training and validation of specified data selection, the distance between data, and the number of neurons that produce the smallest error. This configuration is needed to make an accurate artificial neural network. The configuration of neural network was then applied to this research. The neural network analysis results show that the best configuration of artificial neural network in hydrogen storage is at 233K temperature i.e. on SWCNT with chilarity of (0.12).

Original languageEnglish
Article number012031
JournalIOP Conference Series: Materials Science and Engineering
Volume333
Issue number1
DOIs
Publication statusPublished - 2018 Apr 6
Externally publishedYes
Event2nd International Conference on Advanced Material for Better Future 2017, ICAMBF 2017 - Surakarta, Indonesia
Duration: 2017 Sep 42017 Sep 5

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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