Ioshchikhes, Borys ; Frank, Michael Georg ; Elserafi, Ghada ; Magin, Jonathan ; Weigold, Matthias (2024)
Developing Expert Systems for Improving Energy Efficiency in Manufacturing: A Case Study on Parts Cleaning.
In: Energies, 17 (14)
doi: 10.3390/en17143417
Artikel, Bibliographie
Kurzbeschreibung (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 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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Ioshchikhes, Borys ; Frank, Michael Georg ; Elserafi, Ghada ; Magin, Jonathan ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Developing Expert Systems for Improving Energy Efficiency in Manufacturing: A Case Study on Parts Cleaning |
| Sprache: | Englisch |
| Publikationsjahr: | 2024 |
| Ort: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Energies |
| Jahrgang/Volume einer Zeitschrift: | 17 |
| (Heft-)Nummer: | 14 |
| Kollation: | 16 Seiten |
| DOI: | 10.3390/en17143417 |
| Kurzbeschreibung (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 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. |
| Freie Schlagworte: | sustainability, climate neutrality, fuzzy reasoning, energy analysis, optimization |
| ID-Nummer: | Artikel-ID: 3417 |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) |
| Hinterlegungsdatum: | 17 Jul 2024 07:21 |
| Letzte Änderung: | 17 Jul 2024 07:21 |
| PPN: | 51995744X |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
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