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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. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control. Here we introduce our smart online NMR sensor module used for process characterisation and optimisation.
Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations).
Based on concentration measurements of reagents and products by online NMR spectroscopy a continuous production and direct loop process control were successfully realized for several validation runs in a modular industrial pilot plant and compared to conventional analytical methods (HPLC, near infrared spectroscopy). The NMR module was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (“Integrated CONtrol and SENsing”, www.consens-spire.eu).
Already Analyzing or Still Calibrating? - Demonstration of an online NMR analyzer in pilot scale
(2018)
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. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.
Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations).
Based on concentration measurements of reagents and products by the NMR analyzer a continuous production and direct loop process control were successfully realized for several validation runs in a modular industrial pilot plant and compared to conventional analytical methods (HPLC, near infrared spectroscopy). The NMR analyser was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (“Integrated CONtrol and SENsing”, www.consens-spire.eu).
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
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.
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.
Currently, research in chemical manufacturing moves towards flexible plug-and-play approaches focusing on modular plants, capable of producing small scales on-demand with short down-times between individual campaigns. This approach allows for efficient use of hardware, a faster optimization of the process conditions, and thus, an accelerated introduction of new products to the market. Driven mostly by the search for chemical syntheses under biocompatible conditions, so-called “click” chemistry rapidly became a growing field of research. The resulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via comparably straightforward and robust analysis techniques. Here we report on a fast and reliable calibration-free online high field NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement with a time interval of 20 s per spectrum, and a robust, automated algorithm to interpret the obtained data. All spectra were acquired using a 500 MHz NMR spectrometer (Varian) with a dual band flow probe having a 1/16-inch polymer tubing working as a flow cell. Single scan 1H NMR spectra were recorded with an acquisition time of 5 s, relaxation delay of 15 s. As a proof-of-concept, the thiol-ene coupling between N-boc cysteine methyl ester and allyl alcohol was conducted in non-deuterated solvents while its time-resolved behaviour was characterised with step tracer experiments. Through the application of spectral modeling the signal area for each reactant can be deconvoluted in the online spectra and thus converted to the respective concentrations or molar ratios. The signals which were suitable for direct integration were used herein for comparison purposes of both methods.
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.
Trialkoxysilane haben sich in den vergangenen Jahren als vielseitig einsetzbare Organosilane erwiesen. Die Einsatzgebiete erstrecken sich vom Witterungsschutz von Bauwerken über haftvermittelnde Eigenschaften in der Glasfaserindustrie, bei Dicht- und Klebstoffen, in Farben und Lacken bis hin zur Modifizierung von polymeren Werkstoffen.
Kommerzielle Benchtop-NMR-Spektrometer haben das Potential auch im Bereich der Silanchemie als Online-Methode zur Reaktionsüberwachung und für die Qualitätskontrolle eingesetzt zu werden. Interessante NMR-Kerne für die oben genanntem Produkte sind 1H und 29Si. In einer gemeinsamen Forschungskooperation zwischen EVONIK und BAM wurde anhand verschiedener Fallstudien die Anwendbarkeit der Niederfeld-NMR-Spektroskopie zur chemischen Analyse von Silanen evaluiert. Im Zuge der Fallstudien wurde gezeigt, wie Niederfeld-NMR-Spektroskopie die Möglichkeiten der Konzentrationsmessung auf neue Anwendungsgebiete erweitert, in denen bestehende Technologien wie z. B. NIR, Raman, UV/VIS, etc. mangels Referenzdaten nicht quantitativ eingesetzt werden können.
Fallstudie 1: Oligomerisierung
Eine Fallstudie setzte dazu an, den Hydrolyse- und Kondensationsverlauf mit einer geeigneten Online-NMR-Analytik zu beobachten, den Reaktionsfortschritt der Hydrolyse und Kondensation auf dieser Basis besser zu verstehen und zu optimieren. Zu diesem Zweck werden durch Zugabe von Wasser zunächst die Alkoxysubstituenten eines Trialkoxysilans hydrolysiert und entsprechende Silanole gebildet. Diese können dann über eine SiOH-Funktion an den zu modifizierenden Werkstoff anbinden und über weitere Silanolgruppen unter Ausbildung von Siloxaneinheiten vernetzen.
Fallstudie 2: Spaltung von cyclischen Silanverbindungen
In einer weiteren Fallstudie wurde die Kinetik der Aufspaltungen einer cyclischen Silanverbindung untersucht. Die Online-NMR-Analytik kam hierbei sowohl im Labor als auch in der industriellen Produktionsanlage zum Einsatz. Hierfür wurde eine vollständig automatisierte Einhausung verwendet, welche den Einsatz eines kommerziellen NMR-Spektrometers in explosionsgeschützten Bereichen ermöglicht.
Fallstudie 3: Qualitätskontrolle für Produktmischungen von Trialkoxysilanen
Für Produktmischungen eines Trialkoxysilans und weiteren Bestandteilen wie u. a. org. Stabilisatoren, Organozinnverbindungen, eines aromatischen Amins und org. Peroxide wurden quantitative 1H-Spektren akquiriert und eine automatische Auswertungsmethode basierend auf Indirect Hard Modeling (IHM) entwickelt. Für die Nebenkomponenten, deren Stoffmengenanteile bis zu 3 Mol-% betragen, wurden durch die zugrunde gelegte Methode typischerweise korrekte experimentelle Stoffmengenanteile gefunden, die weniger als 0,2 Mol-% vom Referenzwert abweichen.
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.