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Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis

  • Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies. We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least SquaresMonitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies. We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Simon KernORCiD, Klas MeyerORCiD, Svetlana Guhl, Patrick Gräßer, Andrea PaulORCiD, R. King, Michael MaiwaldORCiD
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Analytical and Bioanalytical Chemistry
Jahr der Erstveröffentlichung:2018
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik
Verlag:Springer
Verlagsort:Berlin, Heidelberg
Jahrgang/Band:410
Ausgabe/Heft:14
Erste Seite:3349
Letzte Seite:3360
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Benchtop NMR Spectroscopy; CONSENS; Indirect Hard Modeling; Online NMR spectroscopy; Partial Least Squares Regression; Process analytical technology; Smart Sensors
Themenfelder/Aktivitätsfelder der BAM:Analytical Sciences
Analytical Sciences / Spurenanalytik und chemische Zusammensetzung
Analytical Sciences / Sensorik
DOI:https://doi.org/10.1007/s00216-018-1020-z
URL:https://link.springer.com/article/10.1007/s00216-018-1020-z
URL:http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=MEDLINE&DestLinkType=FullRecord;UT=MEDLINE:29616294
ISSN:1618-2642
ISSN:1618-2650
Verfügbarkeit des Volltexts:Volltext-PDF im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:07.05.2018
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:07.05.2018