Special Issue "Clean Energy – Today’ Processes and Systems for Tomorrow"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5549

Special Issue Editors

School of Engineering, College of Sciences and Engineering, University of Tasmania, Hobart TAS7001, Australia
Interests: renewable energy utilization; energy storage and conversion; cooling engineering; desalination; and remote renewable power systems
Special Issues, Collections and Topics in MDPI journals
1. Department of Applied Mechanics, University Dunarea de Jos of Galati, Strada Domnească 47, Galați, Romania
2. CENTEC - Centre for Marine Technology and Ocean Engineering, University of Lisbon, Lisbon, Portugal
Interests: marine renewable energy; offshore wind; waves; coastal processes; climate change; extreme events in marine environment; coastal hazards; wave and currents modeling; data assimilation
Special Issues, Collections and Topics in MDPI journals
Department of Management and Innovation Systems, University of Salerno, 84084 Salerno, Italy
Interests: smart grids; energy management; power systems; demand response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The World is suffering from huge population growth and fast development, that in turn heavily depend on energy availability.

Nowadays, the energy mix is still mostly supported on fossil fuels, not only because the technology is well established, but mainly because it is readily available and highly controllable. However, the uneven distribution of fossil energy reserves and the unavoidable related emissions have driven researchers and decision makers towards increasing use of energy produced from renewable sources. The non-controllability characteristics of renewable energy, such as solar and wind, together with the expected reduction in installed thermoelectric capacity, and the location of such generation systems far from the consumption centers may cause problems in managing the electrical systems with a possible increase in grid congestion. The thermoelectric capacity will most probably be compensated with the use of distributed generation, thus making the electrical systems insufficiently prepared and therefore requiring investments. It will be mandatory to implement digital technologies in smart grids. In this new model, the provision of ancillary services to the Transmission System Operator (TSO) or Distribution System Operator (DSO) should take into account the possible flexibility furnished by new distributed resources including demand response, dispersed and small generators, also based on Renewable Energy Sources (RESs) and frequently endowed with energy storage systems. Moreover, new frameworks should be designed to manage the interaction between aggregators and system operators, to make clean and sustainable energy constantly available to consumers.

This Special Issue welcomes original and innovative contributions addressing the challenges of clean energy systems, both from the point of view of production and distribution, how to guarantee its availability, affordability and its efficient use. Topics include but are not limited to blue hydrogen; business models for energy production, distribution, and supply; carbon and water footprint of energy systems; efficient energy usage; energy communitites; energy decarbonization; energy-efficient buildings; energy management; energy markets; energy policy; energy storage; green hydrogen; integration of electric vehicles; LCA of energy systems; renewable energy; smart energy systems.

Prof. Dr. Nídia Caetano
Prof. Dr. Xiaolin Wang
Prof. Dr. Eugen Rusu
Prof. Dr. Pierluigi Siano
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. Processes is an international peer-reviewed open access monthly 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 2400 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.


  • smart grid architectures
  • electric vehicles and vehicle-to-grid
  • renewable energy grid
  • energy-efficient technologies
  • new energy materials and devices
  • energy storage and distributed energy resources
  • power electronics

Published Papers (1 paper)

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Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review
Processes 2021, 9(6), 909; https://doi.org/10.3390/pr9060909 - 21 May 2021
Cited by 17 | Viewed by 4365
Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in [...] Read more.
Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed. Full article
(This article belongs to the Special Issue Clean Energy – Today’ Processes and Systems for Tomorrow)
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