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Processes
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Advances in Detection, Control and Optimization of Low-Carbon Energy Systems
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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 2865

Special Issue Editors

Dr. Jing Zhou

E-Mail Website
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
Dr. Xiaofeng Wu

E-Mail Website
Guest Editor
Ocean Institute, Northwestern Polytechnical University, Taicang 215400, China
Interests: clean combustion; thermal design
Dr. Xin Li

E-Mail Website
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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

18 pages, 2659 KiB  
Article
Small-Sample Short-Term Photovoltaic Output Prediction Model Based on GRA-SSA-GNNM Method
by Qi Wang, Meiheriayi Mutailipu, Qiang Xiong, Xuehui Jing and Yande Yang
Processes 2024, 12(11), 2485; https://doi.org/10.3390/pr12112485 (registering DOI) - 8 Nov 2024
Viewed by 289
Abstract
The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the [...] Read more.
The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the low prediction accuracy in the previous research. A hybrid prediction model based on grey relation analysis (GRA) combined with the sparrow search algorithm (SSA) and the grey neural network model (GNNM) is proposed. In this paper, GRA is utilized to reduce the dimension of meteorological features of the samples. Then, the GNNM is used to perform regression analysis on the input features after reducing the dimension of meteorological features of the samples, and the parameters of the GNNM are optimized via SSA. A limited dataset was used to compare several models in different seasons and weather conditions. The prediction results agree well with the data from the PV power plant in Xinjiang, indicating that the GRA-SSA-GNNM model developed in this work effectively achieves a high precision estimation in short-term PV power generation output prediction and has a promising application in this field. Full article
(This article belongs to the Special Issue Advances in Detection, Control and Optimization of Low-Carbon Energy Systems)
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22 pages, 4048 KiB  
Article
Measuring Domain Shift in Vibration Signals to Improve Cross-Domain Diagnosis of Piston Aero Engine Faults
by Pengfei Shen, Fengrong Bi, Xiaoyang Bi and Yunyi Lu
Processes 2024, 12(9), 1902; https://doi.org/10.3390/pr12091902 - 5 Sep 2024
Viewed by 560
Abstract
Transfer learning is an effective approach to address the decline in generalizability of intelligent fault diagnosis methods. However, there has been a persistent lack of comprehensive and effective metrics for assessing the transferability of cross-domain data, making it challenging to answer the fundamental [...] Read more.
Transfer learning is an effective approach to address the decline in generalizability of intelligent fault diagnosis methods. However, there has been a persistent lack of comprehensive and effective metrics for assessing the transferability of cross-domain data, making it challenging to answer the fundamental question in transfer learning: “When to transfer”. This study proposes a novel hybrid transferability metric (HTM) based on weighted correlation-diversity shift. The metric introduces a correlation shift measurement based on sparse principal component analysis, effectively quantifying distribution differences in domain-invariant features based on the sparse representation theory. It also designs a diversity shift measurement based on label space differences, addressing the previously overlooked impact of label variation on transferability. The proposed transferability metric is validated on four types of cross-domain diagnosis tasks involving piston aero engines. The results show that in diagnostic scenarios involving both supervised transfer learning and extreme class imbalance problems, HTM accurately predicted the transferability of the target tasks, which aligned with the actual diagnostic accuracy trends. It provides a feasible method for predicting and evaluating the applicability of transfer learning methods in real-world scenarios. Full article
(This article belongs to the Special Issue Advances in Detection, Control and Optimization of Low-Carbon Energy Systems)
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18 pages, 7890 KiB  
Article
Research on Intelligent Scheduling Strategy for Electric Heavy Trucks Considering Photovoltaic Outputs
by Jiaheng Jing, Meiheriayi Mutailipu, Qi Wang, Qiang Xiong, Mingyao Huang and Jiuming Zhang
Processes 2024, 12(7), 1493; https://doi.org/10.3390/pr12071493 - 17 Jul 2024
Viewed by 637
Abstract
Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not [...] Read more.
Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station in an industrial zone in Xinjiang. While considering the electricity procurement cost of the charging station, the aim is to minimize fluctuations in the electricity procurement load. A simulation analysis was conducted using MATLAB 2021a software, and the results indicated that, compared to an uncoordinated charging strategy for electric heavy trucks, the proposed strategy reduced electricity procurement costs by CNY 1348.25, decreased load fluctuations by 169.45, and improved the utilization efficiency of photovoltaic energy by 30%. A statistical analysis was also used to support the reduction in electricity procurement costs and load variations. Finally, a sensitivity analysis of the weight factors in the objective function was performed, proving that the proposed strategy effectively reduces electricity procurement costs and improves the utilization efficiency of photovoltaic energy. Full article
(This article belongs to the Special Issue Advances in Detection, Control and Optimization of Low-Carbon Energy Systems)
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20 pages, 13405 KiB  
Article
Simulation Research on Cylinder Liner Shape and Position Tolerance under Thermo-Mechanical Load
by Feng Han, Hui Wang, Jian Wang, Jingchao Wang, Jiewei Lin, Huwei Dai and Junhong Zhang
Processes 2024, 12(7), 1290; https://doi.org/10.3390/pr12071290 - 21 Jun 2024
Viewed by 741
Abstract
The cylinder liner bears alternating thermal load and mechanical load, and evaluating the cylinder liner deformation is a key issue in the design stage of an engine. In this work, the shape and position tolerance of the cylinder liner to various loads were [...] Read more.
The cylinder liner bears alternating thermal load and mechanical load, and evaluating the cylinder liner deformation is a key issue in the design stage of an engine. In this work, the shape and position tolerance of the cylinder liner to various loads were studied based on the finite element method, the simplex algorithm and the least square method. Firstly, the heat transfer boundary conditions of the cylinder liner were obtained through combustion simulation. Combined with the mechanical load, the transient deformation of the cylinder liner under the thermo-mechanical load was obtained. Subsequently, the out-of-roundness and coaxiality were selected to evaluate the shape and position changes in the cylinder liner. Finally, the transient tolerance analysis of the cylinder liner under alternating thermo-mechanical load was carried out. The results show that the maximum out-of-roundness of the cylinder liner under thermal load, mechanical load and thermos-mechanical load was 15.12, 43.40 and 51.76 μm, respectively. The maximum coaxiality under thermal load, mechanical load and thermos-mechanical load were 6.17, 80.49 and 80.22 μm. The side thrust was more likely to cause uneven deformation of the cylinder liner section, the liner coaxiality was mainly affected by the cylinder burst pressure, and the liner shape tolerance was much more sensitive to the mechanical load than the mechanical load. Full article
(This article belongs to the Special Issue Advances in Detection, Control and Optimization of Low-Carbon Energy Systems)
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