Dokument-ID Dokumenttyp Autoren/innen Persönliche Herausgeber/innen Haupttitel Abstract Auflage Verlagsort Verlag Herausgeber (Institution) Erscheinungsjahr Titel des übergeordneten Werkes Jahrgang/Band ISBN Veranstaltung Veranstaltungsort Beginndatum der Veranstaltung Enddatum der Veranstaltung Ausgabe/Heft Erste Seite Letzte Seite URN DOI Lizenz Datum der Freischaltung OPUS4-50750 Zeitschriftenartikel Kern, Simon; Liehr, Sascha; Wander, Lukas; Bornemann-Pfeiffer, Martin; Müller, S.; Maiwald, Michael; Kowarik, Stefan 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 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. Berlin Springer 2020 Analytical and bioanalytical chemistry 412 18 4447 4459 urn:nbn:de:kobv:b43-507508 10.1007/s00216-020-02687-5 https://creativecommons.org/licenses/by/4.0/deed.de 2020-05-11 OPUS4-44847 Zeitschriftenartikel Kern, Simon; Meyer, Klas; Guhl, Svetlana; Gräßer, Patrick; Paul, Andrea; King, R.; Maiwald, Michael 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 Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy. Berlin, Heidelberg Springer 2018 Analytical and Bioanalytical Chemistry 410 14 3349 3360 10.1007/s00216-018-1020-z 2018-05-07 OPUS4-37356 Zeitschriftenartikel Meyer, Klas; Kern, Simon; Zientek, Nicolai; Guthausen, G.; Maiwald, Michael Process control with compact NMR Compact nuclear magnetic resonance (NMR) instruments make NMR spectroscopy and relaxometry accessible in industrial and harsh environments for reaction and process control. An increasing number of applications are reported. To build an interdisciplinary bridge between "process control" and "compact NMR",we give a short overviewon current developments in the field of process Engineering such as modern process design, integrated processes, intensified processes along with requirements to process control, model based control, or soft sensing. Finally, robust field integration of NMR systems into processes environments, facing explosion protection or Integration into process control systems, are briefly discussed. Elsevier 2016 Trends in Analytical Chemistry 83 Part A / SI 39 52 urn:nbn:de:kobv:b43-373562 10.1016/j.trac.2016.03.016 https://creativecommons.org/licenses/by-nc-nd/4.0/deed.de 2016-09-15 OPUS4-36333 Zeitschriftenartikel Zientek, Nicolai; Meyer, Klas; Kern, Simon; Maiwald, Michael Quantitative online NMR spectroscopy in a nutshell Online NMR spectroscopy is an excellent tool to study complex reacting multicomponent mixtures and gain process insight and understanding. For online studies under process conditions, flow NMR probes can be used in a wide range of temperature and pressure. This paper compiles the most important aspects towards quantitative process NMR spectroscopy in complex multicomponent mixtures and provides examples. After NMR spectroscopy is introduced as an online method and for technical samples without sample preparation in deuterated solvents, influences of the residence time distribution, pre-magnetization, and cell design are discussed. NMR acquisition and processing parameters as well as data preparation methods are presented and the most practical data analysis strategies are introduced. Weinheim, Germany Wiley-VCH Verlag GmbH & Co. KGaA 2016 Chemie Ingenieur Technik 88 6 698 709 10.1002/cite.201500120 2016-06-02