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Clean and Low Carbon Energy, 3rd Edition
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Topic Editors

Dr. Ruitong Yang
School of Architecture and Civil Engineering, Northeast Petroleum University, Daqing 163318, China
School of Civil Engineering and Architecture, Northeast Petroleum University, Daqing 163318, China
School of New Energy, Harbin Institute of Technology, Weihai 264209, China
School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China
Department of Building Thermal Energy Engineering, Harbin Institute of Technology, Harbin 150006, China
Prof. Dr. Changyu Liu
School of Civil Engineering and Architecture, Northeast Petroleum University, Daqing 163318, China
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Clean and Low Carbon Energy, 3rd Edition

Abstract submission deadline
31 March 2027
Manuscript submission deadline
30 June 2027
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802

Topic Information

Dear Colleagues,

This is the third edition of the previous successful topic “Clean and Low Carbon Energy”.

The rapid growth of the national economy has relied heavily on traditional fossil fuels like coal, oil, and natural gas. However, these energy sources cause environmental pollution, global warming, and economic security risks. To address these challenges, it is essential to break through key bottlenecks related to pollutants, carbon emissions, and energy-grade loss, and to develop a clean, low carbon energy system based on renewables such as solar, wind, and geothermal energy. This transition is vital for environmental protection and energy innovation.

Research in clean and low carbon energy has advanced significantly in the past decade and continues to drive the development of other renewable energy sources. However, there are still challenges in applying new theories, methods, and meeting emerging needs. This Topic aims to enhance scientific and multidisciplinary work, particularly in improving clean energy systems. We also encourage contributions integrating Big Data and Cognitive Computing, which are transforming energy management through optimization, predictive analytics, and real-time decision-making, crucial for boosting energy efficiency in buildings, industries, and the environment. We invite submissions on innovative developments, reviews, case studies, and analyses in the following areas:

  • Renewable energy resources and technologies
  • Renewable energy harvesting and conversion
  • Energy systems and efficiency improvement
  • Advanced energy technologies
  • Energy storage and applications
  • Energy and buildings
  • Energy use in industry
  • Energy and the environment
  • Energy and nanotechnology
  • Energy management, policy, and economics
  • Big data in energy analytics
  • Cognitive computing for energy control
  • Data-driven renewable energy integration
  • AI and cognitive computing for energy efficiency.

Dr. Ruitong Yang
Prof. Dr. Dong Li
Prof. Dr. Fuqiang Wang
Prof. Dr. Zhonghao Rao
Prof. Dr. Chao Shen
Prof. Dr. Changyu Liu
Topic Editors

Keywords

  • clean energy
  • low carbon energy
  • renewable energy technologies
  • energy efficiency
  • big data in energy
  • cognitive computing in energy

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Big Data and Cognitive Computing
BDCC 		5.3 11.4 2017 23.3 Days CHF 1800 Submit
Buildings
buildings 		3.4 5.6 2011 14.7 Days CHF 2600 Submit
Clean Technologies
cleantechnol 		5.9 9.4 2019 20.9 Days CHF 1800 Submit
Energies
energies 		3.9 8.3 2008 16.7 Days CHF 2600 Submit
Processes
processes 		3.4 5.7 2013 14.7 Days CHF 2400 Submit
Sustainability
sustainability 		4.1 8.9 2009 16.9 Days CHF 2400 Submit

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Published Papers (1 paper)

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Article
Process Monitoring of Internal Wall Loss in Hot-Fluid Pipelines Using External Fiber Bragg Grating Thermometry and Residual-Peak Morphology
by Lijie Zhu, Jiangang Sun, Dong Li, Ruitong Yang and Zhiguo Wang
Processes 2026, 14(17), 2718; https://doi.org/10.3390/pr14172718 - 25 Aug 2026
Viewed by 318
Abstract
Internal wall loss in hot-fluid pipelines is difficult to monitor during operation because weak thermal perturbations are masked by global heating, axial cooling, and external heat dissipation. This study develops an external thermometry framework combining a flexible-base fiber Bragg grating (FBG) array with [...] Read more.
Internal wall loss in hot-fluid pipelines is difficult to monitor during operation because weak thermal perturbations are masked by global heating, axial cooling, and external heat dissipation. This study develops an external thermometry framework combining a flexible-base fiber Bragg grating (FBG) array with residual-peak morphology analysis. A closed-loop hot-water rig with five artificial wall-loss regions was tested under exposed-air and buried-soil boundaries, and a validated conjugate heat-transfer model generated 269 controlled scenarios. Experiments showed residual anomalies above measurement uncertainty, with a maximum repeatability standard deviation of approximately 0.12 °C and the clearest signals at 90–120 s after hot-water injection. Boundary conditions strongly affected observability at 115 mm and 70 °C, and the residual peak increased from about 0–1 °C in exposed air to 8–9 °C under the buried boundary. Simulations showed that defect width expanded the disturbed region from approximately 100 to 210 mm, while peak amplitude remained coupled to width and depth. Six morphology descriptors jointly estimated position, width, and depth, with mean absolute errors (MAEs) of 0.547, 1.647, and 0.245 mm, respectively. The method provides a recalibratable early-screening route for locating suspicious wall-loss regions before confirmatory inspection. Full article
(This article belongs to the Topic Clean and Low Carbon Energy, 3rd Edition)
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