Project LoTuS - Energetic Optimization of Parts Drying

Projekt LoTuS: Ansätze zur energetischen Optimierung von Reinigungsanlagen mit integrierter Trocknung

Ghada Elserafi, Adrian von Hayn, Matthias Weigold
JournalIndustrie 4.0 Management
Issue Volume 37, 2021, Edition 4, Pages 8-11
Open Accesshttps://doi.org/10.30844/I40M_21-4_S8-11
Bibliography Share Cite Download

Abstract

Due to rising quality requirements in the metalworking industry, parts drying has been gaining significance, leading to the increasing importance of reducing the energy consumption of drying processes. Therefore, the LoTuS project investigates different approaches to increase drying efficiency. Along with alternative drying technologies, process digitization is employed to provide sufficient transparency for part-specific drying. Using sensor data, artificial intelligence is utilized for process monitoring. Peak demand is further reduced by implementing load management techniques.

Keywords

, , , , ,

Bibliography

[1] Bilz, M.: Markt- und Trendanalyse in der Industriellen Teilereinigung 2012. Fraunhofer-Allianz Reinigungstechnik. Berlin 2013.
[2] Potenzial für kosten- und qualitätsoptimierte Prozesse. In: JOT Journal für Oberflächentechnik 50 (2010) 5, S. 50-53.
[3] Bürkle, G.: Reinigung und Reiniger in der Metallindustrie. In: Technische Akademie Esslingen (TAE). URL: www.tae.de/uploads/media/Reinigung_und_Reiniger_in_der_Metallindustrie_-_TAE.pdf, Abrufdatum 22.02.2021.
[4] Seefeldt, F.; Wünsch, M.; Matthes, U.; Baumgartner, W.: Potenziale für Energieeinsparung und Energieeffizienz im Lichte aktueller Preisentwicklungen. Endbericht 18/06. Basel Berlin 2007.
[5] Nast, M.; Frisch, S.; Pehnt, M.; Otter, P.: Prozesswärme im Marktanreizprogramm: Zwischenbericht zu Perspektivische Weiterentwicklung des Marktanreizprogramms (2010). URL: https://elib.dlr.de/82173/1/Prozessw%C3%A4rme_im_MAP.pdf, Abrufdatum 22.02.2021.
[6] Daimler AG; Henkel AG; PTW TU Darmstadt; TTD TU Darmstadt; MAFAC GmbH: Workshop Energieeffiziente Bauteilreinigung. Sitzungsprotokoll. Darmstadt 2017.
[7] Durkee, J. B.: Management of industrial cleaning technology and processes. 1st Edition. Amsterdam 2006.
[8] Blesl, M.; Kessler, A.: Energieeffizienz in der Industrie. 2. Auflage. Berlin 2013.
[9] Paul, S.; Paul, R.: Grundlagen der Elektrotechnik und Elektronik 2: Elektromagnetische Felder und ihre Anwendungen. 2. Auflage. Bremen Hamburg 2019.
[10] Mujumdar, A. S. (Hrsg): Handbook of industrial drying. 4th Edition. Boca Raton, USA 2015.
[11] Schraml, P.: Methode zur Reduktion maximaler elektrischer Lasten spanender Werkzeugmaschinen. Technische Universität Darmstadt. Dissertation. Aachen 2018.
[12] Abele, E.: Gemeinsamer Schlussbericht zum Projekt ETA-Fabrik: Energieeffiziente Fabrik für interdisziplinäre Technologie- und Anwendungsforschung. Darmstadt 2019.
[13] Denkena, B. (Hrsg); Garber, T. (Hrsg): NCplus+ Prozess- und wertschöpfungsorientiert gesteuerte Werkzeugmaschine: Abschlussbericht zum BMBF-Verbundprojekt (Berichte aus dem IFW). Garbsen 2013.
[14] Weigold, M.; Hanson, J.; Rinderknecht, S.; Panten, N.; Walther, J.; Grosch, B.: Gemeinsamer Schlussbericht zum Projekt PHI-Factory: Flexible elektrische Fabriknetzführung – zur systemübergreifenden Steigerung der Energieeffizienz unter den Anforderungen zukünftiger Verteilnetze mit regenerativer Energieerzeugung: Förderbereich: Energieeffizienz in Industrie und Gewerbe, Handel und Dienstleistungen (GHD): Laufzeit: 01.12.2016-31.03.2020. Darmstadt 2020.

Your downloads

You might also be interested in

Digital Competence Lab (DCL) for Speech Therapy

Digital Competence Lab (DCL) for Speech Therapy

Designing a learning platform to advance digital skills
Anika Thurmann ORCID Icon, Antonia Weirich ORCID Icon, Kerstin Bilda, Fiona Dörr ORCID Icon, Lars Tönges ORCID Icon
The digital transformation of healthcare results in lasting changes in speech therapy. Smart technologies and artificial intelligence (AI) are creating new opportunities to ensure therapy quality, address care bottlenecks, and actively involve patients in exercise processes. At the same time, these developments are expanding the role of speech therapists, who increasingly use digital systems as supportive tools in addition to their core therapeutic tasks. Based on a feasibility study of the AI-supported application ISi-Speech-Sprechen in a real-world setting of complex Parkinson's therapy (PKT), this article outlines the key challenges associated with implementing smart technologies.
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 110-118 | DOI 10.30844/I4SE.26.1.102
AI Implementation in Industrial Quality Control

AI Implementation in Industrial Quality Control

A design science approach bridging technical and human factors
Erdi Ünal ORCID Icon, Kathrin Nauth ORCID Icon, Pavlos Rath-Manakidis, Jens Pöppelbuß ORCID Icon, Felix Hoenig, Christian Meske ORCID Icon
Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844/I4SE.26.1.112
XAI for Predicting and Nudging Worker Decision-Making

XAI for Predicting and Nudging Worker Decision-Making

Feasibility and perceived ethical issues
Jan-Phillip Herrmann ORCID Icon, Catharina Baier, Sven Tackenberg ORCID Icon, Verena Nitsch ORCID Icon
Explainable artificial intelligence (XAI)-based nudging, while ethically complex, may offer a favorable alternative to rigid, algorithmically generated schedules that simultaneously respects worker autonomy and improves overall scheduling performance on the shop floor. This paper presents a controlled laboratory study demonstrating the successful nudging of 28 industrial engineering students in a job shop simulation. The study shows that the observed concordance between students’ sequencing decisions and a predefined target sequence increases by 9% through nudging. This is done by using XAI to analyze students’ preferences and adjusting task deadlines and priorities in the simulation. The paper discusses the ethical issues of nudging, including potential manipulation, illusory autonomy, and reducing people to numbers. To mitigate these issues, it offers recommendations for implementing the XAI-based nudging approach in practice and highlights its strengths relative to rigid, ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 70-78
Improving Documentation Quality and Creating Time for Core Activities

Improving Documentation Quality and Creating Time for Core Activities

Success factors for implementing AI-based documentation systems in nursing care
Sophie Berretta ORCID Icon, Elisabeth Liedmann ORCID Icon, Paul-Fiete Kramer ORCID Icon, Anja Gerlmaier, Christopher Schmidt
Demographic change is accompanied by both a growing demand for care and a shortage of qualified nursing staff. Consequently, AI-based technologies are increasingly becoming a focus of care-related innovations. Their aim is to reduce workload pressure, save time, and enhance the attractiveness of the nursing profession. Using the example of AI-supported documentation systems for admission interviews, this article examines to what extent such systems can contribute to improvements in work processes and care quality, focusing on the perspectives of nursing professionals and nursing experts. The results indicate potential for workload relief, enhanced documentation quality, and the reallocation of time resources toward direct patient care. However, realizing these potentials requires a human-centered and context-sensitive implementation approach.
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 154-160 | DOI 10.30844/I4SE.26.1.146
Applied AI for Human-Centric Assembly Workplace Design

Applied AI for Human-Centric Assembly Workplace Design

An ethics-informed approach
Tadele Belay Tuli ORCID Icon, Michael Jonek ORCID Icon, Sascha Niethammer, Henning Vogler, Martin Manns ORCID Icon
Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection®. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly.
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 60-68 | DOI 10.30844/I4SE.26.1.58
Co-Determination Dialogues

Co-Determination Dialogues

A tool for human-centered AI implementation
Manfred Wannöffel ORCID Icon, Fabian Hoose ORCID Icon, Alexander Ranft, Claudia Niewerth ORCID Icon, Dirk Stüter
As part of the regional competence center humAIne, funded by the Federal Ministry of Research, Technology, and Space (BMFTR), a process was developed using co-determination dialogues to establish a common understanding of the challenges involved in the introduction of artificial intelligence (AI) between management, employees, and interest groups. Experiences from project partner companies such as Doncasters Precision Castings in Bochum GmbH (DPC) exemplify how co-determination dialogues not only help to develop legally binding regulations for manageable, operationally anchored, sustainable AI use but also initiate continuous qualification processes for all stakeholder groups in accordance with Articles 4 and 5 of the EU AI Act.
Industry 4.0 Science | Volume 42 | Edition 1 | Pages 92-98 | DOI 10.30844/I4SE.26.1.84
Christoph Brocks