2025
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Trimmel, Simone; Spörl, Philipp; Haluza, Daniela; Meisel, Thomas C; Pitha, Ulrike; Prohaska, Thomas; Puschenreiter, Markus; Rueckert, Elmar; Spangl, Bernhard; Wiedenhofer, Dominik; Irrgeher, Johanna Determination of Technology-Critical Elements in Urban Plants and Water using Inductively Coupled Plasma Tandem Mass Spectrometry Conference SETAC Europe 35th Annual Meeting, 2025, (Extended Abstract). @conference{Trimmel2025,
title = {Determination of Technology-Critical Elements in Urban Plants and Water using Inductively Coupled Plasma Tandem Mass Spectrometry},
author = {Simone Trimmel and Philipp Spörl and Daniela Haluza and Thomas C Meisel and Ulrike Pitha and Thomas Prohaska and Markus Puschenreiter and Elmar Rueckert and Bernhard Spangl and Dominik Wiedenhofer and Johanna Irrgeher},
year = {2025},
date = {2025-05-13},
urldate = {2025-05-13},
booktitle = {SETAC Europe 35th Annual Meeting},
note = {Extended Abstract},
keywords = {Applied Deep Learning},
pubstate = {published},
tppubtype = {conference}
}
| |
Koinig, Gerald; Neubauer, Melanie; Martinelli, Walter; Radmann, Yves; Kuhn, Nikolai; Fink, Thomas; Rueckert, Elmar; Tischberger-Aldrian, Alexia CNN-based copper reduction in shredded scrap for enhanced electric arc furnace steelmaking Proceedings Article In: International Conference on Optical Characterization of Materials (OCM 2025), pp. 319-328, 2025, ISBN: 9783731514084. @inproceedings{nokey,
title = {CNN-based copper reduction in shredded scrap for enhanced electric arc furnace steelmaking},
author = {Gerald Koinig and Melanie Neubauer and Walter Martinelli and Yves Radmann and Nikolai Kuhn and Thomas Fink and Elmar Rueckert and Alexia Tischberger-Aldrian},
url = {http://www.scopus.com/inward/record.url?scp=105005090678&partnerID=8YFLogxK
https://books.google.de/books?hl=de&lr=&id=cQtZEQAAQBAJ&oi=fnd&pg=PA329&dq=CNN-based+copper+reduction+in+shredded+scrap+for+enhanced+electric+arc+furnace+steelmaking&ots=UK8_ZX8DWo&sig=9itL3MMW7ZDb1HK5rucwcYwVzG0
},
isbn = {9783731514084},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {International Conference on Optical Characterization of Materials (OCM 2025)},
pages = {319-328},
keywords = {Applied Deep Learning, neural network, Recycling},
pubstate = {published},
tppubtype = {inproceedings}
}
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2024
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Krukenfellner, Philip; Rueckert, Elmar; Flachberger, Helmut Predicting condition states, based on displacement data, generated by acceleration sensors on industrial linear vibrating screens through neural networks Journal Article In: IEEE Sensors Journal, pp. 1–13, 2024, ISBN: 1558-1748. @article{Krukenfellner2024,
title = {Predicting condition states, based on displacement data, generated by acceleration sensors on industrial linear vibrating screens through neural networks},
author = {Philip Krukenfellner and Elmar Rueckert and Helmut Flachberger},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/GJN9XCBzW5TqPys},
isbn = {1558-1748},
year = {2024},
date = {2024-10-04},
urldate = {2024-10-04},
journal = {IEEE Sensors Journal},
pages = {1--13},
keywords = {Applied Deep Learning, Industrial Applications, Vibrating Screens},
pubstate = {published},
tppubtype = {article}
}
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Trimmel, Simone; Spörl, Philipp; Haluza, Daniela; Lashin, Nagi; Meisel, Thomas C.; Pitha, Ulrike; Prohaska, Thomas; Puschenreiter, Markus; Rückert, Elmar; Spangl, Bernhard; Wiedenhofer, Dominik; Irrgeher, Johanna Green and blue infrastructure as model system for emissions of technology-critical elements Journal Article In: Science of The Total Environment, vol. 934, 2024, ISBN: 0048-9697, (https://doi.org/10.1016/j.scitotenv.2024.173364). @article{Trimmel2024,
title = {Green and blue infrastructure as model system for emissions of technology-critical elements},
author = {Simone Trimmel and Philipp Spörl and Daniela Haluza and Nagi Lashin and Thomas C. Meisel and Ulrike Pitha and Thomas Prohaska and Markus Puschenreiter and Elmar Rückert and Bernhard Spangl and Dominik Wiedenhofer and Johanna Irrgeher},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/WJwtk2JC4XnyzL6},
isbn = {0048-9697},
year = {2024},
date = {2024-05-21},
journal = {Science of The Total Environment},
volume = {934},
note = {https://doi.org/10.1016/j.scitotenv.2024.173364},
keywords = {Applied Deep Learning, environmental health risks, pollution},
pubstate = {published},
tppubtype = {article}
}
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