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AI and Digital Twins Technologies for Hydrogen (H2) Production Power Plants

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

Deadline for manuscript submissions: closed (25 December 2023) | Viewed by 1138

Special Issue Editors

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Guest Editor
Sensor Systems Unit, Digital Systems Department Smart Hardware, RISE Research Institutes of Sweden, Gothenburg, Sweden
Interests: artificial intelligence; machine learning; digital twins; predictive analytics/maintenance; diagnostics; prognostics; condition monitoring; RAMS; development of model-based and data-driven approaches; digitization; Bayesian statistical machine learning techniques for forecasting

Special Issue Information

Dear Colleagues,

Hydrogen is emerging as a sustainable carbon-free energy source and, as such, its use is attracting growing interest as a key enabler for the achievement of the objective of a reduction in CO2 emissions. On the other hand, digital-twin technology is offering a concrete opportunity to build a virtual model of the complete dynamics of the assets of a production facility, including its process equipment. Digital Twin uses mechanistic models combined with sensor data to create a digital model of the industrial assets and its production line. Furthermore, in recent years, Applied Artificial Intelligence has been widely adopted in production lines for monitoring and predictive control. Companies from various sectors worldwide are already adopting smart and intelligent technologies that allow them to automate, and improve processes efficiency, also from the point of view of energy consumption. The developments in Artificial Intelligence for Digital Twin are quite promising, as they can be effectively utilized to integrate Internet of Things (IoT), automation, and environmental and safety mitigation measures. Various Artificial Intelligence/Machine Learning models and algorithms can improve the automation, control, reliability and safety.

In this view, the Special Issue invites papers concerning a broad range of methods and applications of Artificial Intelligence, Machine Learning, Digital Twins for hydrogen energy production plants and other topics related to the industrial use of hydrogen.

Dr. Madhav Mishra
Prof. Dr. Enrico Zio
Guest Editors

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.


  • Artificial Intelligence
  • Machine Learning
  • Digital Twins
  • Hydrogen (H2) Energy
  • Dynamics Models
  • Data-Driven Models
  • Digital Models
  • Predictive Maintenance
  • Green Energy

Published Papers

There is no accepted submissions to this special issue at this moment.
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