Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2967
Publisher DOI: 10.1007/s00216-020-02687-5
Title: Artificial neural networks for quantitative online NMR spectroscopy
Language: English
Authors: Kern, Simon 
Liehr, Sascha 
Wander, Lukas 
Bornemann-Pfeiffer, Martin 
Müller, Simon  
Maiwald, Michael 
Kowarik, Stefan 
Keywords: Artificial neural networks;Automation;Online NMR spectroscopy;Process industry;Real-time process monitoring
Issue Date: 9-May-2020
Publisher: Springer
Source: Analytical and Bioanalytical Chemistry 18 (412): 4447-4459 (2020-07-01)
Journal or Series Name: Analytical and bioanalytical chemistry 
Abstract (english): 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 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. [Figure not available: see fulltext.].
URI: http://hdl.handle.net/11420/7487
DOI: 10.15480/882.2967
ISSN: 1618-2650
Institute: Thermische Verfahrenstechnik V-8 
Type: (wissenschaftlicher) Artikel
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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