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Mathematical and statistical tools for online NMR spectroscopy in chemical processes

  • Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environments for advanced process Monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS. We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for newMonitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environments for advanced process Monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS. We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Simon KernORCiD, Svetlana Guhl, Klas MeyerORCiD, Lukas WanderORCiD, Andrea PaulORCiD, Michael MaiwaldORCiD
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Deutsch):Tagungsband 13. Dresdner Sensor-Symposium
Jahr der Erstveröffentlichung:2017
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik
Herausgeber (Institution):AMA Verband für Sensorik und Messtechnik e.V.
Verlag:AMA Service GmbH
Verlagsort:Berlin
Erste Seite:P2, 209
Letzte Seite:212
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:First principles; Indirect hard modeling; Online NMR spectroscopy; Partial least squares regression; Process control; Quantum mechanics
Themenfelder/Aktivitätsfelder der BAM:Analytical Sciences
Analytical Sciences / Sensorik
Veranstaltung:13. Dresdner Sensor Symposium
Veranstaltungsort:Dresden, Germany
Beginndatum der Veranstaltung:04.12.2017
Enddatum der Veranstaltung:06.12.2017
DOI:https://doi.org/10.5162/13dss2017/P2.07
URL:https://www.ama-science.org/proceedings/details/2748
ISBN:978-3-9816876-5-1
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:04.12.2017
Referierte Publikation:Nein