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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.
Der Wandel von der aktuellen Automation zum smarten Sensor ist im vollen Gange. Automatisierungstechnik, sowie die Informations- und Kommunikationstechnik (IKT) verschmelzen zunehmend. Eine Topologie für smarte Sensoren, die das Zusammenwirken mit daten- und modellbasierten Steuerungen bis hin zur Softsensorik beschreibt gibt es bis heute jedoch noch nicht. Um zu einer störungsfreien Kommunikation aller Komponenten auf Basis eines einheitlichen Protokolls zu kommen sollte die Prozessindustrie die Weichen für eine smarte und sichere Kommunikationsarchitektur stellen. Sie verwehrt stattdessen die Entwicklungen ihrer Zulieferer und wartet lieber ab. Der Beitrag greift die Anforderungen der Technologie-Roadmap „Prozess-Sensoren 4.0“ auf und zeigt Möglichkeiten zu ihrer Realisierung am Beispiel eines Online-NMR-Analysators, der im Rahmen eines EU-Projekts entwickelt wurde.
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.
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).
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.
Annually vast amounts of plastics are produced world-wide. However, recycling and waste management is still insufficient resulting in large quantities of plastics being released into the environment. Degradation by sunlight, mechanical and biological factors lead to the breakdown of this waste into little fragments. By convention particles smaller than 5 mm are referred to as microplastics (MP). The occurrence of MP has been reported by researchers virtually all around the globe. Gaining knowledge on MP is currently a time-consuming process because analysis mainly relies on micro-infrared and micro-Raman methods. Prior to that the particles need to undergo purification and enrichment. Thus, only small numbers and volumes of samples can be investigated. Here we tested NIR spectroscopy combined with a multivariate data analysis as a means of speeding up the process of MP analysis.
Experiments were performed using the most abundant polymers polyethylene, polypropylene, polyethylene terephthalate and polystyrene. MP samples were obtained by adding the cryomilled and sieved (<125 µm) particles to approximately 1 g of standard soil at 0,5–10 mass%. Spectra were recorded with a fiber optic reflection probe connected to a FT-NIR spectrometer. 5–10 spectra recorded of each sample were used for the calibration of chemometric models (partial least squares regression, PLSR). “Unknown” test samples were then used to test the model’s capability to predict the type and amount of polymer.
In samples containing 1–5 % of the polymers the prediction yielded the highest degree of agreement with the gravimetric reference values. At low polymer loads some false positive results in the identification were observed. Large amounts of polymers limited the prediction capability by a nonlinear behaviour of the absorption. Further testing was done with real world samples such as compost and washing machine filters. Even though the calibration did not account for these highly complex sample compositions, satisfactory results could be achieved.
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.
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.
Process analytical techniques are extremely useful tools for chemical production and manufacture and are of particular interest to the pharmaceutical, food and (petro-) chemical industries.
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.
A major advantage of NMR spectroscopy is that the method features a high linearity between absolute signal area and sample concentration, which makes it an absolute analytical comparison method which is independent of the matrix. This is an important prerequisite for robust data evaluation strategies within a control concept and reduces the need for extensive maintenance of the evaluation model over the time of operation. Additionally, NMR spectroscopy provides orthogonal, but complimentary physical information to conventional, e.g., optical spectroscopy. It increases the accessible information for technical processes, where aromatic-toaliphatic conversions or isomerizations occur and conventional methods fail due to only minor changes in functional groups.
As a technically relevant example, the catalytic hydrogenation of 2-butyne-1,4-diol and further pharmaceutical reactions were studied using an online NMR sensor based on a commercially available low-field NMR spectrometer within the framework of the EU project CONSENS (Integrated Control and Sensing).