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Energy-Efficient Manufacturing System Management

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: closed (4 September 2024) | Viewed by 6204

Special Issue Editors


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Pro-Deo Consultant, 52525 Heinsberg, North-Rhine Westphalia, Germany
Interests: analytical chemistry; artificial neural networks; computational science; computational materials science; molecular simulation; process control; chemicals; thermodynamics
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Guest Editor
Nimbus Research Centre, Munster Technological University, T12 P928 Cork, Ireland
Interests: the convergence of digital technologies such as IoT; blockchain and machine learning to support digital transformation

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Guest Editor
Automation Technology and Mechanical Engineering, Tampere University, Tampere, Finland
Interests: control engineering; process automation; system identification
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Special Issue Information

Dear Colleagues,

It is essential to reduce energy consumption and emissions and achieve a more efficient use of raw materials. To date, industry is one of the largest users of primarily fossil-fuel-based energy.

This Special Issue of Energies will focus on “Energy-Efficient Manufacturing System Management”, as developed within the framework of the factories of the future. The European Commission is currently funding a large number of research programs, including the Factory of the Future (FOF) cluster FOF-09-2020 on energy-efficient manufacturing system management. This cluster can be described as an innovation action, which is focused on developing digital solutions that support the energy-efficient management of manufacturing systems and the results are demonstrated in real-world contexts of industrial environments. This project cluster comprises the following four individual projects around this central theme:

  • DENiM: Digital intelligence for collaborative energy management in manufacturing;
  • ECOFACT: Eco-innovative energy factory management system based on enhanced LCA and LCCA towards resource-efficient manufacturing;
  • E2COMATION: Life-cycle optimization of industrial energy efficiency through a distributed control and decision-making automation platform;
  • ENERMAN: Energy-efficient manufacturing system management.

Collectively, these projects will develop energy-efficient practices to overcome the barriers that limit their application in the manufacturing sectors. The result of these activities is the definition of a pathway towards energy efficiency that allows industry to understand the current situation and to stimulate the definition of a strategic roadmap to incorporate energy efficiency as a key criterion in operational and organizational decision-making.

Manufacturing systems are complex because many parameters, related to the environment, components, usage of materials, machines, cells, lines and supply chains, collectively influence the energy performance of production processes. We are open to contributions (original research articles and high-quality reviews) that aim to combine innovative methodologies, tools, techniques and technologies in a holistic, intelligent and interoperable manner and that have the potential to provide significant energy savings in the manufacturing domain.

Prof. Dr. Robert J. Meier
Dr. Alan McGibney
Prof. Dr. Matti Vilkko
Guest Editors

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Published Papers (3 papers)

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Research

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16 pages, 1722 KiB  
Article
Developing Expert Systems for Improving Energy Efficiency in Manufacturing: A Case Study on Parts Cleaning
by Borys Ioshchikhes, Michael Frank, Ghada Elserafi, Jonathan Magin and Matthias Weigold
Energies 2024, 17(14), 3417; https://doi.org/10.3390/en17143417 - 11 Jul 2024
Cited by 1 | Viewed by 975
Abstract
Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy [...] Read more.
Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy savings as well as the high workloads of employees. This article describes the systematic development of an expert system, which offers a chance to overcome these obstacles and contribute significantly to increasing the energy efficiency of production machines. The system employs data-driven regression models to identify inefficient parameter settings, calculate achievable energy savings, and prioritize actions based on a fuzzy rule base. Proposed measures are first applied to an analytical real-time simulation model of a production machine to verify that the constraints required for the specified product quality are met. This provides the machine operator with the expert means to apply proposed energy efficiency measures to the physical entity. We demonstrate the development and application of the system for a throughput parts-cleaning machine in the metalworking industry. Full article
(This article belongs to the Special Issue Energy-Efficient Manufacturing System Management)
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28 pages, 5173 KiB  
Article
Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications
by Akshay Ranade, Javier Gómez, Andrew de Juan, William D. Chicaiza, Michael Ahern, Juan M. Escaño, Andriy Hryshchenko, Olan Casey, Aidan Cloonan, Dominic O’Sullivan, Ken Bruton and Alan McGibney
Energies 2024, 17(8), 1818; https://doi.org/10.3390/en17081818 - 10 Apr 2024
Cited by 1 | Viewed by 3343
Abstract
The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope [...] Read more.
The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency. Full article
(This article belongs to the Special Issue Energy-Efficient Manufacturing System Management)
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Review

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22 pages, 6622 KiB  
Review
A Systematic Review of Expert Systems for Improving Energy Efficiency in the Manufacturing Industry
by Borys Ioshchikhes, Michael Frank and Matthias Weigold
Energies 2024, 17(19), 4780; https://doi.org/10.3390/en17194780 - 24 Sep 2024
Viewed by 1095
Abstract
Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously [...] Read more.
Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research. Full article
(This article belongs to the Special Issue Energy-Efficient Manufacturing System Management)
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