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Produzieren Sie schon oder kalibrieren Sie noch? – Online-NMR-Spektrometer als Smarte Feldgeräte
(2018)
Der Übergang von der aktuellen Automatisierungslandschaft zur nächsten Generation von Automatisierungskonzepten für die Prozessindustrie hat bereits begonnen. Intelligente Funktionen der Sensoren vereinfachen ihre Anwendung und ermöglichen eine Plug-and-Play-Integration, auch wenn sie auf den ersten Blick komplexer erscheinen mögen. Dies ist die Basis für die Digitalisierung der Prozessindustrie und hilft uns, komplexere Prozesse schneller umzusetzen.
Der Vortrag fasst die derzeit diskutierten allgemeinen Anforderungen an „Smarte Feldgeräte“ zusammen und diskutiert dieses am Beispiel eines smarten Online-NMR-Sensors. NMR-Spektroskopie bietet sich durch den Vorteil der direkten Vergleichsmethode (ohne Kalibrierung) für die Prozess-Steuerung an und verringert somit die Rüstzeiten. Zudem basiert der Sensor auf physikalisch motivierten Modellen (Indirect Hard Modeling, IHM), die sich modular kombinieren lassen. Die Methoden wurden anhand eines vorgegebenen pharmazeutischen Reaktionsschrittes im Rahmen des „Horizon 2020“-Projekts CONSENS der Europäischen Union demonstriert und validiert.
Zuletzt werden Anforderungen an die Weiterentwicklung der Datenauswertemethoden diskutiert, um letztlich die semantische Information aus den Messdaten herauszulesen oder das in der Industrie 4.0 geforderte „durchgehende Engineering“ für die Automatisierungskomponenten zu ermöglichen.
Produzieren Sie schon oder kalibrieren Sie noch? – Online-NMR-Spektrometer als Smarte Feldgeräte
(2018)
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.
Improvement in deep process understanding is a mandatory prerequisite for the application of modern concepts like Industrial Internet of Things (IIoT) or “Industrie 4.0”. This is particularly relevant in new process concepts such as intensified production in modularized plants. The direct hyphenation with online methods of process analytical technology (PAT) allows profound insights into the actual reactions within chemical and pharmaceutical production steps and provides necessary information for associated advanced control strategies.
While the industrial application of online Raman spectroscopy has already been successfully demonstrated, low-field NMR spectroscopy is not yet adequately developed as an online method for use in process industry. The high Information content combined with the low calibration effort makes NMR spectroscopy a highly promising method for modern process automation with a high flexibility due to short set-up times and novel calibration concepts. This is a Major advantage especially within multi-purpose production plants, as well as for processes suffering from fluctuating quality of raw materials.
The concept was evaluated on several example processes of pharmaceutical and chemical industry. The one presented here represents a stage of the synthesis of the industrially important solvent tetrahydrofuran consisting of the catalytic hydrogenation of 2-butine-1,4-diol was monitored. This reaction is proceeding via an intermediate product and suffers from competitive reaction paths. In this application, different spectroscopic methods were combined with the data obtained from classical process sensors, e.g., pressure, temperature, and flow transducers for the development of innovative control concepts.
Such an analyzer for direct implementation in an industrial process environment based on a commercially available laboratory benchtop NMR instrument was recently developed within the EU project CONSENS (www.consens-spire.eu), challenging hyphenation to the production plant as well as compliance to all requirements of chemical industry such as explosion safety regulations (ATEX), robust automation, and modern, as well as classical communication interfaces such as OPC-UA or 4–20 mA communication.
Improvement in deep process understanding is a mandatory prerequisite for the application of modern concepts like Industrial Internet of Things (IIoT) or “Industrie 4.0”. This is particularly relevant in new process concepts such as intensified production in modularized plants. The direct hyphenation with online methods of process analytical technology (PAT) allows profound insights into the actual reactions within chemical and pharmaceutical production steps and provides necessary information for associated advanced control strategies.
While the industrial application of online Raman spectroscopy has already been successfully demonstrated, low-field NMR spectroscopy is not yet adequately developed as an online method for use in process industry. The high information content combined with the low calibration effort makes NMR spectroscopy a highly promising method for modern process automation with a high flexibility due to short set-up times and novel calibration concepts. This is a major advantage especially within multi-purpose production plants, as well as for processes suffering from fluctuating quality of raw materials.
The concept was evaluated on several example processes of pharmaceutical and chemical industry. The one presented here represents a stage of the synthesis of the industrially important solvent tetrahydrofuran consisting of the catalytic hydrogenation of 2-butine-1,4-diol was monitored. This reaction is proceeding via an intermediate product and suffers from competitive reaction paths. In this application, different spectroscopic methods were combined with the data obtained from classical process sensors, e.g., pressure, temperature, and flow transducers for the development of innovative control concepts.
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.
Im Zuge der Digitalisierung der Prozessindustrie werden zunehmend modellbasiere Echtzeitoptimierungsverfahren eingesetzt, sog. „Advanced Process Control“. Mithilfe der sogenannten Modifier-Adaptation ist eine iterative Betriebspunktoptimierung auch mit ungenauen Modellen möglich, sofern zuverlässige Prozessdaten zur Verfügung stehen. Am Beispiel eines smarten Online-NMR-Sensors, der in einem EU-Projekt von der BAM entwickelt wurde, konnte das Konzept in einer modularen Produktionsanlage zur Herstellung eines pharmazeutischen Wirkstoffs erfolgreich getestet werden.
In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum.
The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.
In the following the contents of the files are explained.