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Low-Energy Technologies in Heavy Industries

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 1162

Special Issue Editor


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Guest Editor
Department of Industrial Informatics, Silesian University of Technology, 40-019 Katowice, Poland
Interests: management; digitalisation; Industry 4.0; industrial transformation; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

Low-Energy Technologies are fundamentally transforming the industries heavily reliant on energy for their manufacturing processes. Advanced technologies are increasingly penetrating the renewable energy sector and energy-intensive industries like cement, steel, and chemicals. Low-energy technologies enhance energy efficiency and reduce manufacturing costs in these sectors.

The implementation of  low-energy technologies in energy-intensive industries aligns with the targets of global climate policy. Energy-intensive industries emphasize sustainable manufacturing through innovative technologies to increase energy efficiency and reduce electricity consumption, including the development of information systems for supervising energy consumption, production, and storage with management optimization functions, including technologies for process heat recovery and its subsequent use. The import solutions are technologies based on renewable energy sources (RES).

The focus of this special issue is energy efficiency in heavy industries. Authors can present various low-energy technologies, including energy management systems, operational optimization, modeling of energy intensity and prediction, renewable energy solutions, energy storage, autonomous computer energy systems, and other energy projects.

This special issue aims to provide a valuable solutions for understanding the energy saving in the heavy industries, including:

  • low-energy technologies and computer applications for energy management in heavy industries,
  • challenges and barriers for energy-intensive industries in green transformation,
  • innovations in industrial process technologies,
  • projects of energy efficiency in industrial process technologies,
  • benefits of green transformation in heavy industries,
  • future technologies for energy efficient manufacturing.

Prof. Dr. Bożena Gajdzik
Guest Editor

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

  • heavy industries
  • industrial process technologies
  • energy-intensive manufacturing
  • low-energy technologies
  • energy intensity
  • energy consumption
  • renewable energy
  • sustainable energy
  • energy efficiency
  • energy saving
  • energy management
  • operational optimization
  • computer energy systems
  • green transformation

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Published Papers (1 paper)

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Research

28 pages, 1683 KiB  
Article
Energy-Saving Geospatial Data Storage—LiDAR Point Cloud Compression
by Artur Warchoł, Karolina Pęzioł and Marek Baścik
Energies 2024, 17(24), 6413; https://doi.org/10.3390/en17246413 - 20 Dec 2024
Viewed by 891
Abstract
In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds. Their large volumes on disk, both at the acquisition and processing stages, and in the final [...] Read more.
In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds. Their large volumes on disk, both at the acquisition and processing stages, and in the final versions translate into a high demand for disk space and therefore electricity. It is therefore obvious that in order to reduce energy consumption, lower the carbon footprint of the activity and sensitize sustainability in the digitization of the industry, lossless compression of the aforementioned datasets is a good solution. In this article, a new format for point clouds—3DL—is presented, the effectiveness of which is compared with 21 available formats that can contain LiDAR data. A total of 404 processes were carried out to validate the 3DL file format. The validation was based on four LiDAR point clouds stored in LAS files: two files derived from ALS (airborne laser scanning), one in the local coordinate system and the other in PL-2000; and two obtained by TLS (terrestrial laser scanning), also with the same georeferencing (local and national PL-2000). During research, each LAS file was saved 101 different ways in 22 different formats, and the results were then compared in several ways (according to the coordinate system, ALS and TLS data, both types of data within a single coordinate system and the time of processing). The validated solution (3DL) achieved CR (compression rate) results of around 32% for ALS data and around 42% for TLS data, while the best solutions reached 15% for ALS and 34% for TLS. On the other hand, the worst method compressed the file up to 424.92% (ALS_PL2000). This significant reduction in file size contributes to a significant reduction in energy consumption during the storage of LiDAR point clouds, their transmission over the internet and/or during copy/transfer. For all solutions, rankings were developed according to CR and CT (compression time) parameters. Full article
(This article belongs to the Special Issue Low-Energy Technologies in Heavy Industries)
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