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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

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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 (2 papers)

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Research

17 pages, 41503 KiB

Open AccessArticle

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

Viewed by 492

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. 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)

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17 pages, 5842 KiB

Open AccessArticle

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 - 08 Dec 2023

Viewed by 547

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 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)

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