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Intelligent Technologies and Processes for Advanced Nuclear Power and Energy...
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Intelligent Technologies and Processes for Advanced Nuclear Power and Energy Engineering (Volume II)

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2856

Special Issue Editors

Dr. Amir M. Fathollahi-Fard

E-Mail Website
Guest Editor
Peter B. Gustavson School of Business, University of Victoria, P.O. Box 1700, Victoria, BC V8P5C2, Canada
Interests: supply chain management; healthcare systems; sustainable logistics and production management; optimization algorithms; heuristics; metaheuristics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vigen H. Arakelian

E-Mail Website
Guest Editor
Head of the research team «Mecaproce», Institut National des Sciences Appliquées (INSA) 20 av. des Buttes de Coesmes, CS 70839, F-35708 Rennes, France
Interests: design, kinematics and dynamics of mechanical systems; robot hands and the mechanics of manipulation; industrial robotic innovation; (4) mechatronic approaches to the design of robot manipulators; dynamic balancing and synthesis of high-speed machines; rehabilitation engineering, prosthetics and orthotics; numerical simulation and optimization of mechanisms using ADAMS software
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiwu Li

E-Mail Website
Guest Editor
Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao
Interests: petri net theory and application; supervisory control of discrete event systems; workflow analysis; system reconfiguration; game theory; production scheduling and planning; data and process mining
Special Issues, Collections and Topics in MDPI journals
Dr. Zixian Zhang

E-Mail Website
Guest Editor
Department of Mechanical Science, Tokyo Institute of Technology, Tokyo 101-0021, Japan
Interests: reconstruction of CAD models from triangular surface mesh; product optimal design; advanced manufacturing technology;networked manufacturing.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangdong Tian

E-Mail Website
Guest Editor
School of Mechanical-Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: remanufacturing and green manufacturing; green logistics and transportation; intelligent inspection and automotive repair
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nuclear power equipment is a national name card which plays an important role in the nuclear power supply of the new generation of onshore nuclear power plants, submarine aircraft carriers, marine development, deep space exploration and other major national projects. From the designation to decommissioning of nuclear power equipment, lifecycle digital and intelligent technologies implement a visual and flexible analysis mode and integrate information on the whole process to supply systematic engineering service in design, manufacturing, operation and management. Because of the numerous subsystems, complex working conditions and lengthy service period of advanced nuclear power equipment, how to realize its lifecycle digital and intelligent technologies is a daunting task. This has recently motivated researchers to explore new lifecycle digital and intelligent technologies of advanced nuclear power equipment.

With the success of the previous Special Issue, “Intelligent Technologies and Processes for Advanced Nuclear Power and Energy Engineering (Volume I)”, Volume II of this Special Issue will continue to present the latest advances and developments dedicated to the lifecycle digital and intelligent technologies for advanced nuclear power equipment, such as digital collaborative design, multidisciplinary design optimization and autonomous decision making, intelligent and sustainable nuclear manufacturing, predictive maintenance and autonomous diagnostics.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Digital collaborative design and manufacturing in the nuclear power industry;
  • Multidisciplinary design optimization and autonomous decision making for nuclear power equipment;
  • Modular technologies for nuclear engineering;
  • The whole lifecycle model system of nuclear power equipment;
  • Intelligent nuclear manufacturing with digital twin;
  • Four-dimensional digital construction schedule management in the nuclear power industry;
  • Visual simulation technologies in start-up commissioning of nuclear systems;
  • Sustainable nuclear manufacturing using data-driven approaches;
  • Predictive maintenance for long-term operation of nuclear power equipment;
  • Autonomous diagnostics and prognostics for nuclear power equipment;
  • Information integration and cognitive system in nuclear power scenarios;
  • Quality control and traceability of state co-evolution for nuclear power equipment;
  • Domain knowledge coupling association and deep mining based on data space in the nuclear power industry.

Dr. Amir M. Fathollahi-Fard
Prof. Dr. Vigen H. Arakelian
Dr. Zhiwu Li
Dr. Zixian Zhang
Dr. Guangdong Tian
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

  • lifecycle management
  • sustainability
  • quality control
  • intellgent systems
  • nuclear manufacturing

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

Published Papers (2 papers)

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27 pages, 4126 KiB  
Article
A Fault Warning Approach Using an Enhanced Sand Cat Swarm Optimization Algorithm and a Generalized Neural Network
by Youchun Pi, Yun Tan, Amir-Mohammad Golmohammadi, Yujing Guo, Yanfeng Xiao and Yan Chen
Processes 2023, 11(9), 2543; https://doi.org/10.3390/pr11092543 - 25 Aug 2023
Cited by 3 | Viewed by 1269
Abstract
With the continuous development and complexity of industrial systems, various types of industrial equipment and systems face increasing risks of failure during operation. Important to these systems is fault warning technology, which can timely detect anomalies before failures and take corresponding preventive measures, [...] Read more.
With the continuous development and complexity of industrial systems, various types of industrial equipment and systems face increasing risks of failure during operation. Important to these systems is fault warning technology, which can timely detect anomalies before failures and take corresponding preventive measures, thereby reducing production interruptions and maintenance costs, improving production efficiency, and enhancing equipment reliability. Machine learning techniques have proven highly effective for fault detection in modern production processes. Among numerous machine learning algorithms, the generalized neural network stands out due to its simplicity, effectiveness, and applicability to various fault warning scenarios. However, the increasing complexity of systems and equipment presents significant challenges to the generalized neural network. In real-world scenarios, it suffers from drawbacks such as difficulties in determining parameters and getting trapped in local optima, which affect its ability to meet the requirements of high efficiency and accuracy. To overcome these issues, this paper proposes a fault warning method based on an enhanced sand cat swarm optimization algorithm combined with a generalized neural network. First, we develop an enhanced sand cat swarm optimization algorithm that incorporates an improved chaotic mapping initialization strategy, as well as Cauchy mutation and reverse elite strategies based on adaptive selection. Subsequently, we utilize this algorithm to optimize the generalized neural network and determine its optimal parameters, effectively improving the accuracy and reliability of system fault warnings. The proposed method is validated using actual industrial system data, specifically for generator fault warning, and is demonstrated to outperform other advanced fault warning techniques. This research provides valuable insights and promising directions for enhancing industrial fault warning capabilities. Full article
(This article belongs to the Special Issue Intelligent Technologies and Processes for Advanced Nuclear Power and Energy Engineering (Volume II))
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21 pages, 2632 KiB  
Article
Developing a Hybrid Algorithm Based on an Equilibrium Optimizer and an Improved Backpropagation Neural Network for Fault Warning
by Jiang Liu, Changshu Zhan, Haiyang Wang, Xingqin Zhang, Xichao Liang, Shuangqing Zheng, Zhou Meng and Guishan Zhou
Processes 2023, 11(6), 1813; https://doi.org/10.3390/pr11061813 - 14 Jun 2023
Cited by 7 | Viewed by 1091
Abstract
In today’s rapidly evolving manufacturing landscape with the advent of intelligent technologies, ensuring smooth equipment operation and fostering stable business growth rely heavily on accurate early fault detection and timely maintenance. Machine learning techniques have proven to be effective in detecting faults in [...] Read more.
In today’s rapidly evolving manufacturing landscape with the advent of intelligent technologies, ensuring smooth equipment operation and fostering stable business growth rely heavily on accurate early fault detection and timely maintenance. Machine learning techniques have proven to be effective in detecting faults in modern production processes. Among various machine learning algorithms, the Backpropagation (BP) neural network is a commonly used model for fault detection. However, due to the intricacies of the BP neural network training process and the challenges posed by local minima, it has certain limitations in practical applications, which hinder its ability to meet efficiency and accuracy requirements in real-world scenarios. This paper aims to optimize BP networks and develop more effective fault warning methods. The primary contribution of this research is the proposal of a novel hybrid algorithm that combines a random wandering strategy within the main loop of an equilibrium optimizer (EO), a local search operator inspired by simulated annealing, and an adaptive learning strategy within the BP neural network. Through analysis and comparison of multiple sets of experimental data, the algorithm demonstrates exceptional accuracy and stability in fault warning tasks, effectively predicting the future operation of equipment and systems. This innovative approach not only overcomes the limitations of traditional BP neural networks, but also provides an efficient and reliable solution for fault detection and early warning in practical applications. Full article
(This article belongs to the Special Issue Intelligent Technologies and Processes for Advanced Nuclear Power and Energy Engineering (Volume II))
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