TY - JOUR A1 - Zientek, Nicolai A1 - Meyer, Klas A1 - Kern, Simon A1 - Maiwald, Michael T1 - Quantitative online NMR spectroscopy in a nutshell N2 - 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. KW - Prozessanalytik KW - Process analytical technology KW - Online NMR spectroscopy KW - Online monitoring PY - 2016 U6 - https://doi.org/10.1002/cite.201500120 SN - 0009-286X VL - 88 IS - 6 SP - 698 EP - 709 PB - Wiley-VCH Verlag GmbH & Co. KGaA CY - Weinheim, Germany AN - OPUS4-36333 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Meyer, Klas A1 - Kern, Simon A1 - Zientek, Nicolai A1 - Guthausen, G. A1 - Maiwald, Michael T1 - Process control with compact NMR N2 - 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. KW - Prozessanalytik KW - Quantitative NMR-Spektroskopie KW - Industrie 4.0 KW - Reaction monitoring KW - Process control KW - Online NMR spectroscopy KW - Compact NMR PY - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-373562 UR - http://www.sciencedirect.com/science/article/pii/S0165993616300073 SN - 0165-9936 VL - 83 IS - Part A / SI SP - 39 EP - 52 PB - Elsevier AN - OPUS4-37356 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kern, Simon A1 - Meyer, Klas A1 - Guhl, Svetlana A1 - Gräßer, Patrick A1 - Paul, Andrea A1 - King, R. A1 - Maiwald, Michael T1 - Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis N2 - 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. KW - Online NMR spectroscopy KW - Process analytical technology KW - Partial Least Squares Regression KW - Indirect Hard Modeling KW - Benchtop NMR Spectroscopy KW - Smart Sensors KW - CONSENS PY - 2018 UR - https://link.springer.com/article/10.1007/s00216-018-1020-z U6 - https://doi.org/10.1007/s00216-018-1020-z SN - 1618-2642 SN - 1618-2650 VL - 410 IS - 14 SP - 3349 EP - 3360 PB - Springer CY - Berlin, Heidelberg AN - OPUS4-44847 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Artificial neural networks for quantitative online NMR spectroscopy N2 - 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. KW - Online NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Automation KW - Process industry PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-507508 SN - 1618-2642 VL - 412 IS - 18 SP - 4447 EP - 4459 PB - Springer CY - Berlin AN - OPUS4-50750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -