energies-logo

Journal Browser

Journal Browser

Applications of Electromagnetism in Energy Efficiency

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2566

Special Issue Editors


E-Mail Website
Guest Editor
Institute of Computer Science and Innovative Technologies, WSEI University, Lublin, Poland
Interests: electromagnetism; energy efficiency; tomography; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to present solutions based on the applications of electromagnetism in energy efficiency, computational methods and techniques, smart buildings, digital twins, sensor systems, electrotechnical and electronic solutions using machine learning, deep learning and optimization solutions, and tomography from an energy point of view.

This Special Issue will be devoted to the use of new solutions and computational methods in the following areas:

- Applications of electromagnetism in energy efficiency;
- Energy optimization in smart buildings;
- Applications of electromagnetism in medicine;
- Computational electromagnetism;
- Applications of electromagnetism in computer science;
- Electromagnetic materials.

Prof. Dr. Tomasz Rymarczyk
Prof. Dr. Ewa Korzeniewska
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

  • energetic efficiency
  • electromagnetism
  • bioelectromagnetism
  • intelligent building
  • sensors
  • machine learning
  • deep learning

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 5440 KiB  
Article
Detection and Determination of User Position Using Radio Tomography with Optimal Energy Consumption of Measuring Devices in Smart Buildings
by Michał Styła, Edward Kozłowski, Paweł Tchórzewski, Dominik Gnaś, Przemysław Adamkiewicz, Jan Laskowski, Sylwia Skrzypek-Ahmed, Arkadiusz Małek and Dariusz Kasperek
Energies 2024, 17(11), 2757; https://doi.org/10.3390/en17112757 - 5 Jun 2024
Viewed by 250
Abstract
The main objective of the research presented in the following work was the adaptation of reflection-radar technology in a detection and navigation system using radio-tomographic imaging techniques. As key aspects of this work, the energy optimization of high-frequency transmitters can be considered for [...] Read more.
The main objective of the research presented in the following work was the adaptation of reflection-radar technology in a detection and navigation system using radio-tomographic imaging techniques. As key aspects of this work, the energy optimization of high-frequency transmitters can be considered for use inside buildings while maintaining user safety. The resulting building monitoring and control system using a network of intelligent sensors supported by artificial intelligence algorithms, such as logistic regression or neural networks, should be considered an outcome. This paper discusses the methodology for extracting information from signal echoes and how they were transported and aggregated. The data extracted in this way were used to support user navigation through a building, optimize energy based on presence information, and increase the facility’s overall security level. A band from 5 GHz to 6 GHz was chosen as the carrier frequency of the signals, representing a compromise between energy expenditure, range, and the properties of wave behavior in contact with different types of matter. The system includes proprietary hardware solutions that allow parameters to be adjusted over the entire range and guarantee adaptation for RTI (radio tomography imaging) technology. Full article
(This article belongs to the Special Issue Applications of Electromagnetism in Energy Efficiency)
Show Figures

Figure 1

17 pages, 11293 KiB  
Article
Optimal Rotor Design and Analysis of Energy-Efficient Brushless DC Motor-Driven Centrifugal Monoset Pump for Agriculture Applications
by Richard Pravin Antony, Pongiannan Rakkiya Goundar Komarasamy, Narayanamoorthi Rajamanickam, Roobaea Alroobaea and Yasser Aboelmagd
Energies 2024, 17(10), 2280; https://doi.org/10.3390/en17102280 - 9 May 2024
Viewed by 615
Abstract
The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design [...] Read more.
The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design and analysis of an energy-efficient BLDC motor with specifications of 1 hp, 3000 rpm, and 48 V, specifically tailored for a centrifugal monoset pump for irrigation. The focus lies in achieving optimal energy efficiency through grey wolf optimization (GWO) algorithm in the rotor design to determine optimal dimensions of the Neodymium Iron Boron (NdFeB) magnet as well as its grade. The finite element method analysis software, MagNet, is used to model and analyze the BLDC motor. The motor parameters, such as speed, torque, flux functions, temperature, and efficiency, are analyzed. For performance comparison, the same model with different magnet models is also analyzed. Validation via 3D finite element analysis highlights improvements in magnet flux linkage, stator tooth flux density, and rotor inertia with increased magnet thickness. Simulation results affirm the consistent performance of the designed BLDC motor, preferably when efficiency is increased. This efficiency and the constant speed lead to an improvement in the overall conversion efficiency of 7% within its operating range, affirming that the motor pump system is energy-efficient. Full article
(This article belongs to the Special Issue Applications of Electromagnetism in Energy Efficiency)
Show Figures

Figure 1

17 pages, 41503 KiB  
Article
Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors
by Monika Kulisz, Grzegorz Kłosowski, Tomasz Rymarczyk, Jolanta Słoniec, Konrad Gauda and Wiktor Cwynar
Energies 2024, 17(3), 681; https://doi.org/10.3390/en17030681 - 31 Jan 2024
Cited by 1 | Viewed by 602
Abstract
This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model’s loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. [...] Read more.
This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model’s loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency. Full article
(This article belongs to the Special Issue Applications of Electromagnetism in Energy Efficiency)
Show Figures

Figure 1

17 pages, 5842 KiB  
Article
Algorithms for Optimizing Energy Consumption for Fermentation Processes in Biogas Production
by Grzegorz Rybak, Edward Kozłowski, Krzysztof Król, Tomasz Rymarczyk, Agnieszka Sulimierska, Artur Dmowski and Piotr Bednarczuk
Energies 2023, 16(24), 7972; https://doi.org/10.3390/en16247972 - 8 Dec 2023
Viewed by 631
Abstract
Problems related to reducing energy consumption constitute an important basis for scientific research worldwide. A proposal to use various renewable energy sources, including creating a biogas plant, is emphasized in the introduction of this article. However, the indicated solutions require continuous monitoring and [...] Read more.
Problems related to reducing energy consumption constitute an important basis for scientific research worldwide. A proposal to use various renewable energy sources, including creating a biogas plant, is emphasized in the introduction of this article. However, the indicated solutions require continuous monitoring and control to maximise the installations’ effectiveness. The authors took up the challenge of developing a computer solution to reduce the costs of maintaining technological process monitoring systems. Concept diagrams of a metrological system using multi-sensor techniques containing humidity, temperature and pressure sensors coupled with Electrical Impedance Tomography (EIT) sensors were presented. This approach allows for effective monitoring of the anaerobic fermentation process. The possibility of reducing the energy consumed during installation operation was proposed, which resulted in the development of algorithms for determining alarm states, which are the basis for controlling the frequency of technological process measurements. Implementing the idea required the preparation of measurement infrastructure and an analytical engine based on AI techniques, including an expert system and developed algorithms. Numerous time-consuming studies and experiments have confirmed reduced energy consumption, which can be successfully used in biogas production. Full article
(This article belongs to the Special Issue Applications of Electromagnetism in Energy Efficiency)
Show Figures

Figure 1

Back to TopTop