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Industry 4.0: Smart Green Applications
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Industry 4.0: Smart Green Applications

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 11471

Special Issue Editors

Dr. Silviu Rǎileanu

E-Mail Website
Guest Editor
Department of Automation and Applied Informatics, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: decentralized manufacturing optimizations; multi-agent systems; robotics; Industry 4.0; digital twins
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Theodor Borangiu

E-Mail Website
Guest Editor
Department of Automation and Applied Informatics, University Politehnica of Bucharest, Bucharest, Romania
Interests: distributed automation; digital manufacturing; cloud manufacturing; robot-vision; service-oriented; holonic and multi-agent manufacturing control; cyber-physical production systems
Prof. Dr. Damien Trentesaux

E-Mail Website
Guest Editor
LAMIH-UMR CNRS, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France
Interests: Industry 4.0; sustainable/energy aware scheduling; intelligent/active product; physical Internet; manufacturing systems; transportation systems; logistics; supply-chains
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The total amount of carbon dioxide and other greenhouse gases (GHGs) that is produced and remains in the atmosphere drives climate change. A third of the primary energy consumption and of the GHG emissions connected to energy can be attributed to the manufacturing sector. By using efficient and clean manufacturing technologies it is possible to reduce production-related energy consumption and emissions over the complete life cycle of a product.

Sustainable manufacturing is the production of manufactured goods using economically sensible procedures that reduce the harmful effects on the environment while optimizing energy consumption and preserving resources. The amount of materials and energy needed to make a product is decreased by incorporating sustainable material management and additive manufacturing principles into product design and development.

The objective of this Special Issue is to promote the usage of IT in the physical flow of materials in manufacturing value chains in order to have a clear understanding of how energy and resources are consumed at each stage, ranging from the production of raw materials and manufacturing to logistics and reverse logistics, and how to minimize the product’s carbon footprint. The usage of IT in industry to optimize manufacturing processes from a cost point of view while ensuring product and resource visibility is the objective of Industry 4.0. This Special Issue aims to shift the traditional objective of Industry 4.0 from a cost-related problem to an environmental problem.

Industry 4.0 technologies can significantly enhance sustainability and environmental protection by optimizing resource use, reducing waste, and promoting circular economy principles, as evidenced by the existing literature that includes theoretical frameworks, case studies, and empirical analyses.

Authors are invited to submit original contributions on methods, models, and control architectures dealing with the intelligent control of future supply chains, including, but not limited to, the following topics:

  • Reducing energy consumption through data collection and analysis.
  • Usage of green energy in industrial processes.
  • Reducing used materials through production process design and resource optimization.
  • Circular economy: design, implementation, and current issues of reverse supply chains.
  • Development of sustainable products.
  • Improvements in product design to reduce returns and failures.
  • Usage of agent-based modelling and simulation (ABMS) technologies to test and visualize the impacts of green technologies applied in supply chains.
  • Industrial system resiliency for sustainability.

Case studies, theoretical models, design methodologies, and literature reviews are especially welcome.

Dr. Silviu Rǎileanu
Prof. Dr. Theodor Borangiu
Prof. Dr. Damien Trentesaux
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. Sustainability 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 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

  • Industry 4.0
  • sustainability
  • Industrial Internet of Things
  • intelligent manufacturing system
  • energy awareness
  • optimization

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Published Papers (5 papers)

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Research

30 pages, 7379 KiB  
Article
Autonomous Agent-Based Adaptation of Energy-Optimized Production Schedules Using Extensive-Form Games
by William Motsch, Achim Wagner and Martin Ruskowski
Sustainability 2024, 16(9), 3612; https://doi.org/10.3390/su16093612 - 25 Apr 2024
Cited by 1 | Viewed by 1519
Abstract
Modular cyber-physical production systems are an important paradigm of Industry 4.0 to react flexibly to changes. The flexibility of those systems is further increased with skill-based engineering and can be used to adapt to customer requirements or to adapt manufacturing to disturbances in [...] Read more.
Modular cyber-physical production systems are an important paradigm of Industry 4.0 to react flexibly to changes. The flexibility of those systems is further increased with skill-based engineering and can be used to adapt to customer requirements or to adapt manufacturing to disturbances in supply chains. Further potential for application of these systems can be found in the topic of electrical energy supply, which is also characterized by fluctuations. The relevance of energy-optimized production schedules for manufacturing systems in general becomes more important with the increased use of renewable energies. Nevertheless, it is often difficult to adapt when short-term energy price updates or unforeseen events occur. To address these challenges with an autonomous approach, this contribution focuses on extensive-form games to adapt energy-optimized production schedules in an agent-based manner. The paper presents agent-based modeling to transform and monitor energy-optimized production schedules into game trees to respond to changing energy prices and disturbances in production. The game is setup with a scheduler agent and energy agents who are considered players. The implementation of the mechanism is presented in two use cases, realizing decision making for an energy price update in a simulation example and for unforeseen events in a real-world demonstrator. Full article
(This article belongs to the Special Issue Industry 4.0: Smart Green Applications)
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20 pages, 3376 KiB  
Article
The Role of Low-Cost Digital Solutions in Supporting Industrial Sustainability
by Tasnim A. Abdel-Aty, Duncan McFarlane, Sam Brooks, Liz Salter, Awwal Sanusi Abubakar, Steve Evans, Greg Hawkridge, Kate Price Thomas, Elisa Negri, Anandarup Mukherjee, Gokcen Yilmaz and Marco Macchi
Sustainability 2024, 16(3), 1301; https://doi.org/10.3390/su16031301 - 3 Feb 2024
Cited by 4 | Viewed by 1975
Abstract
Small and medium enterprise (SME) manufacturers are impeded from participating in sustainability initiatives using new technologies due to the high cost and the lack of clarity on where to start. The integration of low-cost digital solutions has enabled SME manufacturers to adopt Industry [...] Read more.
Small and medium enterprise (SME) manufacturers are impeded from participating in sustainability initiatives using new technologies due to the high cost and the lack of clarity on where to start. The integration of low-cost digital solutions has enabled SME manufacturers to adopt Industry 4.0 technologies to support operations. However, using low-cost technologies to address sustainability challenges is underexplored. This article addresses three key research questions: What digital solutions do SMEs need to address industrial sustainability challenges? To what extent can existing low-cost digital solutions be used to address industrial sustainability challenges? How should new digital solutions for developing greater sustainability be prioritised? Three main tasks were conducted. Initially, a new sustainability-focused sub-catalogue was created using an existing catalogue of low-cost solution areas for manufacturing. Secondly, a workshop with 17 participants was used to identify the top ten priority solution areas, with process monitoring, energy monitoring, and quality inspection at the top. Lastly, existing low-cost digital solutions within the top ten priority areas were evaluated to identify how they could contribute to lean manufacturing. Predominantly existing solutions could contribute to waste or use reduction in lean manufacturing. This study provides a foundation for the future development of low-cost solutions for sustainability by indicating manufacturers’ key priority areas and outlining how existing solutions could be adapted to support waste reduction. Full article
(This article belongs to the Special Issue Industry 4.0: Smart Green Applications)
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20 pages, 6739 KiB  
Article
Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication
by Clément Rup and Eddy Bajic
Sustainability 2024, 16(3), 1160; https://doi.org/10.3390/su16031160 - 30 Jan 2024
Cited by 4 | Viewed by 2522
Abstract
The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime [...] Read more.
The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems. Full article
(This article belongs to the Special Issue Industry 4.0: Smart Green Applications)
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20 pages, 5002 KiB  
Article
Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design
by Silviu Răileanu, Theodor Borangiu, Ionuț Lențoiu and Mihnea Constantinescu
Sustainability 2024, 16(3), 1053; https://doi.org/10.3390/su16031053 - 25 Jan 2024
Cited by 4 | Viewed by 2661
Abstract
The paper describes the development of an optimization model for the layout of an industrial robot relative to known locations of served machines and operations to be performed. Robotized material handling applications, defined by trajectories (paths, speed profiles) and final points, are considered [...] Read more.
The paper describes the development of an optimization model for the layout of an industrial robot relative to known locations of served machines and operations to be performed. Robotized material handling applications, defined by trajectories (paths, speed profiles) and final points, are considered in this research. An energy-monitoring framework set up by joint velocities provides input data that are fed to the optimization model. The physical placement of the robot base stands for the decisional variables, while the objective function is represented by the total distance covered by individual joints along established task routes transposed into energy consumption. The values of the decisional variables are restricted by trajectory constraints (waypoints on paths), joint operating values and link dimensions. Modelling technique and practical results using the Microsoft Solver optimization tool from Excel for Microsoft 365, Version 2312 are reported for SCARA-type robots. The performance of the optimization model is compared with actual measurements of consumed energy on an Adept Cobra S600 SCARA robot. Full article
(This article belongs to the Special Issue Industry 4.0: Smart Green Applications)
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22 pages, 5528 KiB  
Article
Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience
by Toyosi Ademujimi and Vittaldas Prabhu
Sustainability 2024, 16(2), 513; https://doi.org/10.3390/su16020513 - 7 Jan 2024
Cited by 4 | Viewed by 1943
Abstract
We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures sustainability by capturing product quality (e.g., [...] Read more.
We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures sustainability by capturing product quality (e.g., less scrap) and measures resilience by capturing availability. A major advantage of the proposed approach is that the engineering models are likely to be available long before the corresponding digitalized smart factory becomes fully operational. Specifically, for BN structure learning, we propose to use analytical queueing theory models of the factory to elicit the structure, and to carry out intervention we propose to use designed experiments based on discrete-event simulation models of the factory. For parameter learning, we apply a qualitative maximum a posteriori (QMAP) method and propose additional expert constraints based on the law of propagation of uncertainty from queueing theory. Furthermore, the proposed approach overcomes the challenge of obtaining balanced-class data in BN learning for fault diagnostics. We apply the proposed BN learning approach to (i) a 4-robot cell in our laboratory and (ii) a robotic machining cell in a commercial vehicle factory. In both cases, the proposed method is found to be efficacious in accurately learning the BN structure and parameter, as measured using structural-hamming distance and Kullback–Leibler divergence score, respectively. The proposed approach can pave the way for a new class of resilient and sustainable smart manufacturing systems. Full article
(This article belongs to the Special Issue Industry 4.0: Smart Green Applications)
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