energies-logo

Journal Browser

Journal Browser

Transforming Energy Security: Hydrogen Innovations and AI Strategies for a Resilient Energy Infrastructure

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A5: Hydrogen Energy".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1420

Special Issue Editor


E-Mail Website
Guest Editor
Oak Ridge National Laboratory, 1 Bethel Valley Rd., Oak Ridge, TN 37830, USA
Interests: miro-CHP; building energy; carbon intensity; fuel cells; low carbon fuels; renewable energy; hybrid power systems; grid resiliency; decarbonization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydrogen is becoming an increasingly critical element in achieving net-zero carbon emissions by 2050. The primary decarbonization pillars are energy efficiency, electrification with renewables, carbon capture and storage and green hydrogen. The deep penetration of intermittent renewables such as wind and solar energy requires high-energy-density chemical storage technologies such as hydrogen. World governments are realizing the significance of hydrogen for accomplishing net-zero emissions. Hydrogen as a primary energy source is in high demand, and its use is estimated to avoid the release of up to 60 Gt CO2 emissions by 2050. The hydrogen-enabled decarbonization of all three major economic sectors, viz., industry, buildings and transportation, is the focus of this Special Issue.

In this context, this Special Issue aims to focus on recent technology advancements in the key areas of production, storage, distribution and utilization. Economically producing, safely distributing and efficiently utilizing hydrogen are critical to realizing its potential in achieving global carbon reduction targets.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Electrolyzer technologies such as PEM, alkaline and solid oxide, including the reversible SOCs;
  • Sorption-based hydrogen storage materials; traditional high pressure and cryogenic storage solutions; hydrogen embrittlement;
  • Hydrogen-enabling harsh environment materials;
  • Hydrogen leakage detection and suppression;
  • Hydrogen combustion engines and fuel cells for passenger, long-haul and heavy-duty transportation; hydrogen-based heating and cooking equipment in the building industry;
  • Hydrogen-fueled cogeneration and trigeneration technologies;
  • Integrated microgrid technologies involving hydrogen;
  • Techno economics of hydrogen technologies.

I look forward to receiving your contributions. 

Dr. Praveen Cheekatamarla
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydrogen economy
  • low-carbon fuel
  • zero-carbon fuel
  • hydrogen production
  • hydrogen storage
  • hydrogen utilization
  • fuel cells
  • combustion
  • heating
  • electrolysis
  • solid oxide cells
  • PEM

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

42 pages, 8010 KB  
Article
Predicting Methane Dry Reforming Performance via Multi-Output Machine Learning: A Comparative Study of Regression Models
by Sheila Devasahayam, John Samuel Thella and Manoj K. Mohanty
Energies 2025, 18(18), 4807; https://doi.org/10.3390/en18184807 - 9 Sep 2025
Cited by 1 | Viewed by 798
Abstract
Dry reforming of methane (DRM) offers a sustainable route to convert two major greenhouse gases—CH4 and CO2—into synthesis gas (syngas), enabling low-carbon hydrogen production and carbon utilization. This study applies fifteen machine learning (ML) regression models to simultaneously predict CH [...] Read more.
Dry reforming of methane (DRM) offers a sustainable route to convert two major greenhouse gases—CH4 and CO2—into synthesis gas (syngas), enabling low-carbon hydrogen production and carbon utilization. This study applies fifteen machine learning (ML) regression models to simultaneously predict CH4 conversion, CO2 conversion, H2 yield, and CO yield using a published dataset of 27 experiments with Ni/CaFe2O4-catalyzed DRM. The comparative evaluation covers linear, tree-based, ensemble, and kernel-based algorithms under a unified multi-output learning framework. Feature importance analysis highlights reaction temperature, CH4/CO2 feed ratio, and Ni metal loading as the most influential variables. Predictions from the top-performing models (CatBoost and Random Forest) identify optimal performance windows—feed ratio near 1.0 and temperature between 780–820 °C—consistent with thermodynamic and kinetic expectations. Although no new catalysts are introduced, the study demonstrates how ML can extract actionable parametric insights from small experimental datasets, guiding future DRM experimentation and process optimization for hydrogen-rich syngas production. Full article
Show Figures

Figure 1

Back to TopTop