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Eingeladener Vortrag
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Assessment and validation of various flow cell designs for quantitative online NMR spectroscopy
(2017)
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.
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environments for advanced process Monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.
For reaction monitoring using NMR instruments, in particular, after acquisition of the FID the data needs to be corrected in real-time for common effects using automated methods. When it comes to NMR data evaluation under industrial process conditions, the shape of signals can change drastically due to nonlinear effects. However, the structural and quantitative information is still present but needs to be extracted by applying predictive models. Acquired raw spectra were processed with the following tools:
· Phase correction using the Entropy minimization method
· Baseline correction using a low-order Polynomial fit
· Alignment (icoshift) Pure component models based on Pseudo-Voigt functions can be derived via peak fitting of measured pure components or by the use of spin calculations.
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.
Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals
(2019)
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.
Monitoring chemical reactions is the key to chemical process control. Today, mainly
optical online methods are applied. NMR spectroscopy has a high potential for direct
loop process control. Compact NMR instruments based on permanent magnets
are robust and relatively inexpensive analysers, which feature advantages like low
cost, low maintenance, ease of use, and cryogen-free operation. Instruments for
online NMR measurements equipped with a flow-through cell, possessing a good
signal-to-noise-ratio, sufficient robustness, and meeting the requirements for
integration into industrial plants (i.e., explosion safety and fully automated data
analysis) are currently not available off the rack.
Intensified continuous processes are in focus of current research. Flexible (modular)
chemical plants can produce different products using the same equipment with short
down-times between campaigns and quick introduction of new products to the
market. In continuous flow processes online sensor data and tight closed-loop control
of the product quality are mandatory. If these are not available, there is a huge risk of
producing large amounts of out-of-spec (OOS) products. This is addressed in the
European Unionʼs Research Project CONSENS (Integrated Control and Sensing)
by development and integration of smart sensor modules for process monitoring and
control within such modular plant setups.
The presented NMR module is provided in an explosion proof housing of 57 x 57 x
85 cm module size and involves a compact 43.5 MHz NMR spectrometer together
with an acquisition unit and a programmable logic controller for automated data
preparation (phasing, baseline correction) and evaluation. Indirect Hard Modeling
(IHM) was selected for data analysis of the low-field NMR spectra. A set-up for
monitoring continuous reactions in a thermostated 1/8” tubular reactor using
automated syringe pumps was used to validate the IHM models by using high-field
NMR spectroscopy as analytical reference method.
Keramikdurchflusszellen für das industrielle Reaktionsmonitoring mit Niederfeld-NMR-Spektroskopie
(2017)
Derzeit verfügbare Niederfeld-NMR-Spektrometer sind oft für Laborapplikationen konzipiert. Für den Einsatz im industriellen Prozessmonitoring müssen deshalb Anpassungen vorgenommen werden. Ein wichtiger Aspekt ist die Gestaltung der Messzelle. Sie muss über eine hohe thermische-, chemische und vor allem mechanische Beständigkeit verfügen. Hinzu kommt die Besonderheit des NMR-Experiments, das auf die Durchlässigkeit von Radiofrequenzen angewiesen ist. Keramik ist ein in der Hochfeld-NMR-Spektroskopie bewährtes Material das diese Eigenschaften vereint. Um im Prozessmonitoring die Interessen kleiner Bypass-Volumina, großer Durchflussgeschwindigkeit und großes Signal-zu-Rausch-Verhältnis mit der nötigen Vormagnetisierungszeit in Einklang zu bringen, ist eine im Messbereich aufgeweitete Zellgeometrie vorteilhaft. Die Fertigung einer Keramikmesszelle für die Niederfeld-NMR-Spektroskopie für Drücke bis 7 MPa mit dieser besonderen Geometrie wurde mit Hilfe eines modernen additiven Fertigungsverfahrens realisiert.
Quantitative NMR-Messungen an konstant durch das Spektrometer strömenden Flüssigkeiten werden durch eine maximale Durchflussgeschwindigkeit limitiert. Diese wird von vielen Einflussgrößen bestimmt und sollte vor jeder quantitativen Messreihe experimentell für das individuelle System ermittelt werden. Zu diesem Zweck wurde eine automatisierte Laboranordnung konzipiert. Der Vergleich der neuen NMR-Keramikzelle mit bestehenden Messzellen u. a. anhand dieses Parameters untermauert ihre Eignung für das industrielle Prozessmonitoring.
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.
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.
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.