Advances in Detection, Control and Optimization of Low-Carbon Energy Systems

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 101

Special Issue Editors


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Guest Editor
School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: energy engineering and power technology; advanced power cycle; system integration and optimization

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Guest Editor
Ocean Institute, Northwestern Polytechnical University, Taicang 215400, China
Interests: clean combustion; thermal design

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Guest Editor
College of Computing and Data Science, Nanyang Technological University, Singapore 639798, Singapore
Interests: machine learning; signal processing; machinery state detection; big data analysis

Special Issue Information

Dear Colleagues,

In the pursuit of sustainable energy solutions, the importance of developing low-carbon energy systems has risen. With the looming threat of climate change and the urgent need to mitigate its effects, the scientific community has united in advancing innovative approaches to detect, control, and optimize low-carbon energy systems. This interdisciplinary field represents a fusion of engineering, environmental science, and technology, propelled by a collective dedication to fostering a greener, more sustainable future.

This Special Issue titled "Advances in Detection, Control and Optimization of Low-Carbon Energy Systems" seeks to explore recent breakthroughs in detecting, controlling, and optimizing low-carbon energy systems, discussing novel technologies, methodologies, and best practices. Topics encompass, but are not confined to, the following:

  • Integration of renewable energy, energy storage, carbon capture and storage, etc., in industrial processes;
  • Multi-criteria analysis of energy system, including thermodynamic, economic, thermoeconomic, and environmental assessments;
  • Signal processing and big data analysis in a low-carbon energy system;
  • Low-carbon combustion technology (e-fuels such as ammonia and hydrogen, the Allam cycle, etc.).

We sincerely hope that you will consider contributing to this Special Issue. Your participation would greatly enrich the general discourse and contribute to the advancement of this vital research field.

Sincerely,

Dr. Jing Zhou
Dr. Xiaofeng Wu
Dr. Xin Li
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.

Keywords

  • modeling and system optimization
  • low-carbon energy system
  • clean combustion technology
  • thermal analyses of energy systems
  • thermal management
  • signal processing
  • big data analysis

Published Papers

This special issue is now open for submission.
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