PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 21, 2024Energies2 citationsOpen Access

Systematic Literature Review on Pipeline Transport Losses of Hydrogen, Methane, and Their Mixture, Hythane

View Full Paper
CHCristina HoraFDFlorin Ciprian DanDSDinu-Calin Secui

Key Points

Key points are not available for this paper at this time.

Abstract

The transition to cleaner energy sources necessitates an in-depth understanding of the transport characteristics, losses, and opportunities associated with various gaseous fuels, including hydrogen, methane, and their mixtures, such as hythane. Hydrogen (H2), the most abundant element in the universe, is increasingly recognized as a viable alternative to fossil fuels, primarily due to its potential to reduce carbon footprints as a cleaner energy source. Gradually gaining prominence in the energy market, it is displacing other fuels such as methane. In some transport systems, hydrogen is mixed with methane (CH4) in order to reduce the carbon footprint while using the same existing production equipment. As more and more large methane consumers are implementing this mixture, we would like to see how the research has followed the market trend. An up-to-date research, development, and implementation status review is critical. This study aims to identify the main indicators of H2 and CH4 transport losses in pipes, providing a review of the state of the art in the specific literature. To deliver this, a systematic literature review (SLR) was carried out using preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, pinpointing the research trends and results in peer review-published articles over a period of twelve years (2012–2024). Findings: this review identifies and points out, in numbers, the boundaries of the 2012–2024 timeline research.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Hora et al. (2024) studied this question.

synapsesocial.com/papers/68e57c15b6db64358751afe7https://doi.org/10.3390/en17184709
Ask AI
Helpful
Bookmark
Share
View Full Paper