TY - JOUR A1 - Bornemann-Pfeiffer, Martin A1 - Kern, Simon A1 - Jurtz, N. A1 - Thiede, Tobias A1 - Kraume, M. A1 - Maiwald, Michael T1 - Design and validation of an additively manufactured flowCell–static mixer combination for inline NMR spectroscopy N2 - There have been an increasing number of publications on flow chemistry applications of compact NMR. Despite this, there is so far no comprehensive workflow for the technical design of flow cells. Here, we present an approach that is suitable for the design of an NMR flow cell with an integrated static mixing unit. This design moves the mixing of reactants to the active NMR detection region within the NMR instrument, presenting a feature that analyses chemical reactions faster (5–120 s region) than other common setups. During the design phase, the targeted mixing homogeneity of the components was evaluated for different types of mixing units based on CFD simulation. Subsequently, the flow cell was additively manufactured from ceramic material and metal tubing. Within the targeted working mass flow range, excellent mixing properties as well as narrow line widths were confirmed in validation experiments, comparable to common glass tubes. KW - Inline NMR Spectroscopy KW - Integrated Processes KW - Reaction Monitoring KW - Process Analytical Technology KW - Flow Chemistry KW - Static Mixing KW - Modular Production PY - 2019 UR - https://pubs.acs.org/doi/abs/10.1021/acs.iecr.9b03746 U6 - https://doi.org/10.1021/acs.iecr.9b03746 SN - 0888-5885 SN - 1520-5045 N1 - Geburtsname von Bornemann-Pfeiffer, Martin: Bornemann, M. - Birth name of Bornemann-Pfeiffer, Martin: Bornemann, M. VL - 58 IS - 42 SP - 19562 EP - 19570 PB - American Chemical Society CY - Washington AN - OPUS4-49041 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bornemann-Pfeiffer, Martin A1 - Kern, Simon A1 - Maiwald, Michael A1 - Meyer, Klas T1 - Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production N2 - Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and thus a quicker time to market by flexibly connecting and orchestrating standardised physical modules and bringing them to life (i.e., parameterising them) with digitally accumulated process knowledge. We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes. KW - Modular Production KW - Chemical Process Control KW - Process Analytical Technology KW - Digital Transformation KW - Industry 4.0 PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-517264 SN - 1522-2640 VL - 93 IS - 1-2 SP - 62 EP - 70 PB - Wiley-VCH CY - Weinheim AN - OPUS4-51726 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Guhl, Svetlana A1 - Kern, Simon A1 - Meyer, Klas A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Maiwald, Michael T1 - Produzieren Sie schon oder kalibrieren Sie noch? – Online-NMR-Spektrometer als Smarte Feldgeräte N2 - Der Vortrag zeigt allgemeine Anforderungen an "smarte Feldgeräte" und deren Entwicklung in den vergangenen Jahren. Am Beispiel eines smarten Online-NMR-Sensors, der in einem EU-Projekt von der BAM entwickelt wurde, wird die Umsetzung der Anforderung aufgezeigt. Schließlich werden weitere Technologieanforderungen und Lösungsansätze vorgestellt. T2 - ProcessNet-Jahrestagung und 33. DECHEMA-Jahrestagung der Biotechnologen CY - Aachen, Germany DA - 10.09.2018 KW - Prozessanalytik KW - Prozessindustrie KW - Online-NMR-Spektroskopie KW - Datenkonzepte KW - Datenanalyse KW - CONSENS PY - 2018 UR - https://onlinelibrary.wiley.com/doi/abs/10.1002/cite.201855229 U6 - https://doi.org/10.1002/cite.201855229 SN - 0009-286X N1 - Geburtsname von Bornemann-Pfeiffer, Martin: Bornemann, M. - Birth name of Bornemann-Pfeiffer, Martin: Bornemann, M. VL - 90 IS - 9 SP - 1236 EP - 1236 PB - Wiley-VCH Verlag GmbH & Co. KGaA CY - Weinheim AN - OPUS4-45901 LA - deu 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 - TY - GEN A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Training data of quantitative online NMR spectroscopy for artificial neural networks N2 - Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans. Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model. Synthetic low-field NMR spectra First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum. Xi (“pure component spectra dataset”) Xii (“spectral model dataset”) Experimental low-field NMR spectra from MNDPA-Synthesis This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included. KW - NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Online NMR spectroscopy KW - Automation KW - Process industry PY - 2020 U6 - https://doi.org/10.5281/zenodo.3677139 PB - Zenodo CY - Geneva AN - OPUS4-50456 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -