TY - CONF A1 - Maiwald, Michael A1 - Gräßer, Patrick A1 - Wander, Lukas A1 - Zientek, Nicolai A1 - Guhl, Svetlana A1 - Meyer, Klas A1 - Kern, Simon T1 - Strangers in the Night—Smart Process Sensors in Our Current Automation Landscape N2 - The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Smart sensors enable concepts like self-diagnostics, self-calibration, and self-configuration/parameterization whenever our current automation landscape allows it. Here we summarize the currently discussed general requirements for process sensors 4.0 and introduce a smart online NMR sensor module as example, which was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). T2 - Eurosensors 2017 Conference CY - Paris, France DA - 03.09.2017 KW - Process Monitoring KW - Smart Sensors KW - CONSENS KW - Online NMR Spectroscopy KW - Mini-plant PY - 2017 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-415772 UR - http://www.mdpi.com/2504-3900/1/4/628 DO - https://doi.org/10.3390/proceedings1040628 VL - 1 SP - 628 EP - 631 PB - MDPI CY - Basel AN - OPUS4-41577 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wander, Lukas T1 - Assessment and validation of various flow cell designs for quantitative online NMR spectroscopy N2 - Compact nuclear magnetic resonance (NMR) instruments make NMR spectroscopy and relaxometry accessible in industrial and harsh environments for reaction and process control. Robust field integration of NMR systems have to face explosion protection or integration into process control systems with short set-up times. This paves the way for industrial automation in real process environments. The design of failsafe, temperature and pressure resistant flow through cells along with their NMR-specific requirements is an essential cornerstone to enter industrial production plants and fulfill explosion safety requirements. NMR-specific requirements aim at full quantitative pre-magnetization and acquisition with maximum sensitivity while reducing sample transfer times and dwell-times. All parameters are individually dependent on the applied NMR instrument. Luckily, an increasing number of applications are reported together with an increasing variety of commercial equipment. However, these contributions have to be reviewed thoroughly. The performance of sample flow cells commonly used in online analytics and especially for low-field NMR spectroscopy was experimentally and theoretically investigated by 1H-NMR experiments and numerical simulations. Here, we demonstrate and discuss an automated test method to determine the critical parameters of flow through cells for quantitative online NMR spectroscopy. The setup is based on randomized setpoints of flow rates in order to reduce temperature related effects. Five flow cells and tubings were assessed and compared for high-field as well as low-field NMR spectrometers. T2 - Small Molecule NMR Conference (SMASH) CY - Baveno, Italy DA - 17.09.2017 KW - Online NMR spectroscopy KW - Reaction monitoring KW - Flow cell KW - Process control KW - SMASH PY - 2017 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-419485 UR - http://www.smashnmr.org/conference/program AN - OPUS4-41948 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wander, Lukas T1 - NMR-Spektroskopie als Online-Referenzmethode in der Prozessindustrie N2 - In der Prozessindustrie findet die optische Spektroskopie (z. B. NIR- und Raman-Spektroskopie) als Online-Analytik zunehmend Anwendung zur Überwachung che-mischer Qualitäts¬attribute in der Produktion. Ihr ganzes Potential entfalten die Methoden aber meist nur in Kombination mit einer aufwendigen, multivariaten Kalibrierung. Diese muss alle relevanten Zustände des Systems abdecken und bedarf einer geeigneten Referenz¬analytik. Moderne instrumentelle Analysengeräte weisen eine hohe Empfind¬lich¬keit und Robustheit auf, sind aber dennoch stark von Fehler und Variabilität der Probennahme beeinflusst, was sich auf die Richtigkeit und Qualität des multivariaten Modells auswirkt. Diese Probleme lassen sich verringern, indem die Referenzanalytik ebenfalls online erfolgt. Eine mögliche Lösung stellt die hochauflösende NMR-Spektroskopie als quanti¬tative Online-Referenzanalytik dar. Insbesondere kom¬pakte NMR-Spektrometer auf Basis von Permanent¬magneten sind für diesen Zweck geeignet. Ausschlusskriterien für herkömmlicher NMR-Systeme, wie der große Wartungs-aufwand (Kryotechnik) und der Platz¬bedarf, werden damit vermieden. Im Rahmen des EU-Projekts CONSENS wurde die Nutzung einer Online-Referenz¬analytik mit NMR-Spektroskopie am Beispiel einer industriellen Pilotanlage erfolgreich realisiert. Untersuchungsgegenstand war die kontinuierliche Synthese eines Aus¬gangsstoffs für die pharmazeutische Industrie. Die enthaltenen metallorganischen Verbindungen sind für die bisher genutzte HPLC Analytik unzu-gänglich und die Analyse ausgewählter Proben erfolgte nach dem Quenchen der Lösung oft mit einem großen zeitlichen Abstand zur Probennahme. Konzen-trationswerte auf Basis von Online-NMR-Spektren standen hingegen mit einer zeitlichen Auflösung von drei Spektren pro Minute über den gesamten Reaktionsverlauf hinweg zur Verfügung. Außerdem konnten durch die NMR-Spektroskopie intermediär auftretende Spezies erstmal quantitativ bestimmt und diese Daten für die Kalibrierung eines NIR-Spektrometers genutzt werden. T2 - ANAKON 2019 CY - Münster, Germany DA - 25.03.2019 KW - NMR-Spektroskopie KW - Prozessanalytik KW - Online-Referenzmethode PY - 2019 AN - OPUS4-47659 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wander, Lukas T1 - Prozess-Spektroskopie: Analytik für die kontinuierliche Produktion N2 - Der Vortrag zeigt aktuelle Entwicklungen und Betätigungsfelder des Fachbereichs Prozessanalytik zum Thema Automation in der Analytik. Dies Umfasst die Laborautomation am Beispiel der Probenpräparation für die Röntgenfluoreszenzanalyse und moderne Synthesekonzepte in der organischen Chemie. Außerdem wird die Rolle der Prozessanalytik in der kontinuierlichen Produktion thematisier. Als Anschauungsgegenstand dienen die Überwachung einer Hydroformylierung mittels Raman- und NMR-Spektroskopie und der Einsatz eines NMR-Sensors in einer modularen Produktionsanlage im Pilotmaßstab. T2 - 4. Analytiktag CY - Duisburg, Germany DA - 07.11.2019 KW - NMR-Spektroskopie KW - Raman-Spektroskopie PY - 2019 N1 - Geburtsname von Bornemann-Pfeiffer, Martin: Bornemann, M. - Birth name of Bornemann-Pfeiffer, Martin: Bornemann, M. AN - OPUS4-49579 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wander, Lukas T1 - Multivariate analysis of large µ-FTIR datasets in search of microplastics N2 - µ-FTIR spectroscopy is a widely used technique in microplastics research. It allows to simultaneously characterize the material of the small particles, fibers or fragments, and to specify their size distribution and shape. Modern detectors offer the possibility to perform two-dimensional imaging of the sample providing detailed information. However, datasets are often too large for manual evaluation calling for automated microplastic identification. Library search based on the comparison with known reference spectra has been proposed to solve this problem. To supplement this ‘targeted analysis’, an exploratory approach was tested. Principal component analysis (PCA) was used to drastically reduce the size of the data set while maintaining the significant information. Groups of similar spectra in the prepared data set were identified with cluster analysis. Members of different clusters could be assigned to different polymer types whereas the variation observed within a cluster gives a hint on the chemical variability of microplastics of the same type. Spectra labeled according to the respective cluster can be used for supervised learning. The obtained classification was tested on an independent data set and results were compared to the spectral library search approach. T2 - CEST 2019 CY - Rhodes, Greece DA - 04.09.2019 KW - FTIR KW - Microplastics KW - Multivariate data analysis PY - 2019 AN - OPUS4-48889 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wander, Lukas T1 - Analyzing large μ-FTIR data sets in search of microplastics N2 - Working towards a comprehensive understanding of introduction pathways, number, and fate of micro¬plastics in the environment, suitable analytical methods are a precondition. Micro-spectroscopic methods are probably the most widely used techniques. Besides their ability to measure single spectra of a particle or fiber, most modern FTIR- and Raman microscopes are also capable of two-dimensional imaging. This is very appealing to microplastics research because it allows to simultaneously characterize the analytes chemically as well as their size (distribution) and shape. Two-dimensional imaging on extensive sample areas with FTIR-micros¬copes is facilitated by focal plane array (FPA) detectors resulting in large data sets comprised of up to several million spectra. With numbers too large for manual inspection of each individual spectrum, automated data evaluation is inevitable. Identifying different polymers based on the comparison with known reference spectra (library search) has proven to be a suitable approach. For that purpose, FTIR-spectra of common plastics can be collected to create an individual reference library. To Supplement this ‘targeted analysis’, looking for known substances via library search, an exploratory approach was tested. Principal component analysis (PCA) proved to be a helpful tool to drastically reduce the size of the data set while maintaining the significant information. Subsequently, cluster analysis was used to find groups of similar spectra. Spectra found in different clusters could be assigned to different polymer types. The variation observed within clusters gives a hint on chemical variability of microplastics of the same polymer found in the sample. Spectra labeled according to the respective cluster/polymer type were used to build a classification model which allowed to quickly predict the polymer type based on the FTIR spectrum. Classification was tested on a second, independent data set and results were compared to the spectral library search procedure. T2 - ANAKON 2019 CY - Münster, Germany DA - 25.03.2019 KW - Microplastics KW - FTIR KW - Principal component analysis (PCA) PY - 2019 AN - OPUS4-47658 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kern, Simon A1 - Wander, Lukas A1 - Meyer, Klas A1 - Guhl, Svetlana A1 - Gottu Mukkula, A. R. A1 - Holtkamp, M. A1 - Salge, M. A1 - Fleischer, C. A1 - Weber, N. A1 - King, R. A1 - Engell, S. A1 - Paul, Andrea A1 - Pereira Remelhe, M. A1 - Maiwald, Michael T1 - Flexible Automation with compact NMR instruments N2 - Modular plants using intensified continuous processes represent an appealing concept to produce pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes, and it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and includes a compact Nuclear Magnetic Resonance (NMR) spectrometer for online quality monitoring as well as a new model-based control approach. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including ro-bust evaluation of sensor data. Here, we present alternatives for the quantitative determination of the analytes using modular, physically motivated models. These models can be adapted to new substances solely by the use of their corresponding pure component spectra, which can either be derived from experimental spectra as well as from quantum mechanical models or NMR predictors. Modular means that spec-tral models can simply be exchanged together with alternate reagents and products. Beyond that, we comprehensively calibrated an NIR spectrometer based on online NMR process data for the first time within an industrial plant. The integrated solution was developed for a metal organic reac-tion running on a commercial-scale modular pilot plant and it was tested under industrial conditions. T2 - 7th Annual PANIC Conference CY - Hilton Head Island, South Carolina, USA DA - 03.03.2019 KW - Online NMR Spectroscopy PY - 2019 AN - OPUS4-47715 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kern, Simon A1 - Wander, Lukas A1 - Meyer, Klas A1 - Guhl, Svetlana A1 - Gottu Mukkula, A. R. A1 - Holtkamp, M. A1 - Salge, M. A1 - Fleischer, C. A1 - Weber, N. A1 - Engell, S. A1 - Paul, Andrea A1 - Pereira Remelhe, M. A1 - Maiwald, Michael T1 - Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals N2 - Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance. KW - NMR Spectroscopy KW - NIR Spectroscopy KW - Real-time process monitoring KW - Real-time quality control KW - Continuous processes KW - CONSENS KW - Data Fusion PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-480623 DO - https://doi.org/10.1007/s00216-019-01752-y SN - 1618-2642 SN - 1618-2650 VL - 411 IS - 14 SP - 3037 EP - 3046 PB - Springer Nature CY - Heidelberg AN - OPUS4-48062 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -