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Artificial neural networks for quantitative online NMR spectroscopy

  • Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset forIndustry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Simon KernORCiD, Sascha LiehrORCiD, Lukas WanderORCiD, Martin Bornemann-PfeifferORCiD, S. Müller, Michael MaiwaldORCiD, Stefan KowarikORCiD
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Analytical and Bioanalytical Chemistry
Jahr der Erstveröffentlichung:2020
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik
8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.6 Faseroptische Sensorik
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Springer
Verlagsort:Berlin, Heidelberg
Jahrgang/Band:412
Erste Seite:4447
Letzte Seite:4459
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Artificial Neural Networks; Automation; Online NMR Spectroscopy; Process Industry; Real-time Process Monitoring
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Materialien und Stoffe
Analytical Sciences
Analytical Sciences / Spurenanalytik und chemische Zusammensetzung
Analytical Sciences / Sensorik
DOI:https://doi.org/10.1007/s00216-020-02687-5
URN:urn:nbn:de:kobv:b43-507508
URL:https://link.springer.com/article/10.1007/s00216-020-02687-5#citeas
ISSN:1618-2642
Zugehöriger Identifikator:http://nbn-resolving.org/urn:nbn:de:kobv:b43-504569
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:11.05.2020
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:18.05.2020
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM