F. Fricke, S. Mahmood, J. Hoffmann, M. Brandalero, Sascha Liehr, Simon Kern, Klas Meyer, S. Kowarik, S. Westerdick, Michael Maiwald, M. Hübner
- Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation ofMass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.…
MetadatenAutor*innen: | F. Fricke, S. Mahmood, J. Hoffmann, M. Brandalero, Sascha Liehr, Simon KernORCiD, Klas MeyerORCiD, S. Kowarik, S. Westerdick, Michael MaiwaldORCiD, M. Hübner |
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Dokumenttyp: | Zeitschriftenartikel |
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Veröffentlichungsform: | Verlagsliteratur |
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Sprache: | Englisch |
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Titel des übergeordneten Werkes (Englisch): | 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) |
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Jahr der Erstveröffentlichung: | 2021 |
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Organisationseinheit der BAM: | 1 Analytische Chemie; Referenzmaterialien |
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| 1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik |
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Verlag: | IEEE |
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Erste Seite: | 615 |
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Letzte Seite: | 620 |
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DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
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Freie Schlagwörter: | Artificial neural networks; Cyber-physical systems; Industry 4.0,; Mass spectrometry; Nuclear magnetic resonance spectroscopy |
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Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
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| Chemie und Prozesstechnik / Chemische Charakterisierung und Spurenanalytik |
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Veranstaltung: | 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) |
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Veranstaltungsort: | Grenoble, France |
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Beginndatum der Veranstaltung: | 01.02.2021 |
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Enddatum der Veranstaltung: | 05.02.2021 |
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DOI: | 10.23919/DATE51398.2021.9473958 |
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Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
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Datum der Freischaltung: | 25.07.2022 |
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Referierte Publikation: | Ja |
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Datum der Eintragung als referierte Publikation: | 25.07.2022 |
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