The reality of climate change is currently increasing greenhouse gas emissions and global warming, so the trend has now reached a high level. Considering that green hydrogen technology has become a widely recognized zero-emission energy source for commercial and industrial applications, this technology can be a solution for the problem of global warming.
The desire to reduce carbon emissions and conserve natural resources such as fossil fuels drives the demand for clean energy solutions. Green hydrogen technology can help us achieve this goal since it is a clean, renewable energy source. Artificial Intelligence (AI) techniques outperform conventional modeling and optimization approaches in providing more accurate models and better solutions. However, the application of AI techniques in hydrogen technologies is still in its infancy, which requires further efforts to improve their capability to provide more robust models and optimal solutions for green hydrogen production systems. Hydrogen can be produced from water using renewable resources such as solar and wind power. The main challenge is to make hydrogen production cost-effective with a reduced carbon footprint. Several AI techniques are emerging to help design green hydrogen technologies. For instance, a machine-learning algorithm could predict hydrogen production's optimal temperature and pressure conditions. Green hydrogen can be stored and used as an energy source at any time and place without emitting any GHG emissions. Green hydrogen technologies are also used to power zero-emission vehicles and to generate electricity. Availability of feedstock and the high cost of R and D activities restrain the green hydrogen market.
Due to the rising demand for clean energy sources and increased government activities to build a sustainable environment, the green hydrogen market is likely to rise significantly over the forecast period. In the future, hydrogen could play an essential role in a sustainable energy system because it can help to decarbonize the transport sector. New developments in artificial intelligence (AI) lead to a host of science and technology that includes green hydrogen technologies. These technologies aim to meet the challenge of reducing carbon dioxide emissions to address climate change and the energy crisis. It is therefore clear that AI presents one solution for a sustainable environment. This topical collection invites researchers to fulfil the challenges involved in implementing green hydrogen technologies with the help of AI.
Potential topics include, but are not limited to:
- Green hydrogen production using advanced electrolysis technology
- Green hydrogen storage and safety concerns
- Green hydrogen production and storage using smart integration of renewable energy systems
- Advantages/disadvantages of green hydrogen technology
- Green hydrogen production using wind and solar energy
- Financial challenges in implementing green hydrogen technology
- Application of AI in climatic mitigation
- AI-assisted expert systems for climate change risk prediction and evaluation
- Research and innovation in creative green industries
- A move towards carbon sequestration, biofuels, and replacements
- Policies enacted by the government to safeguard energy solutions
- Using renewable energy to reduce pollution in transportation
The deadline for submissions is October 1st 2022.
Submission Instructions
Before submitting your manuscript, please ensure you have carefully read the submission guidelines. The complete manuscript should be submitted through the Sustainable Environment Research submission system. To ensure that you submit to the topical collection please select the appropriate topical collection in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the topical collection on ‘Emerging Green Hydrogen Technologies for Sustainable Environment: AI-Based Approach'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
Lead Guest Editor
Dr. Iskander Tlili, National Engineering School of Monastir, Tunisia
Guest Editors
Dr. Ahmed Awan, Ajman University, United Arab Emirates
Dr. Sa'ed Awni Musmar, The University of Jordan, Jordan