Datei im Netzwerk der BAM verfügbar ("Closed Access")
Filtern
Dokumenttyp
- Beitrag zu einem Tagungsband (11)
- Zeitschriftenartikel (5)
- Vortrag (5)
- Posterpräsentation (5)
Schlagworte
- Online NMR Spectroscopy (8)
- Online NMR spectroscopy (7)
- CONSENS (6)
- Prozessanalytik (6)
- Industrie 4.0 (5)
- Online-NMR-Spektroskopie (4)
- Process Analytical Technology (4)
- Process control (4)
- Reaction Monitoring (4)
- Indirect Hard Modeling (3)
- Online monitoring (3)
- Process analytical technology (3)
- EuroPACT (2)
- Indirect hard modeling (2)
- Online NMR (2)
- Partial Least Squares Regression (2)
- Process Control (2)
- Reaction monitoring (2)
- Smart Sensors (2)
- “Click” chemistry (2)
- Artificial Neural Networks (1)
- Artificial neural networks (1)
- Benchtop NMR Spectroscopy (1)
- Betriebspunktoptimierung (1)
- Click Chemistry (1)
- Click chemistry (1)
- Cyber-Physical Systems (1)
- Cyber-physical systems (1)
- Data analysis (1)
- Data processig (1)
- Datenanalyse (1)
- Datenkonzepte (1)
- Echtzeitoptimierungsverfahren (1)
- First Principles (1)
- First principles (1)
- Flow Chemistry (1)
- Hydration (1)
- Indirect Hard Modelling (1)
- Industry 4.0 (1)
- Industry 4.0, (1)
- Inline NMR Spectroscopy (1)
- Integrated Processes (1)
- Mass Spectrometry (1)
- Mass spectrometry (1)
- Modifier-Adaptation (1)
- Modular Production (1)
- Modulare Produktion (1)
- Nuclear Magnetic Resonance Spectroscopy (1)
- Nuclear magnetic resonance spectroscopy (1)
- Online NMR Spectrsocopy (1)
- Partial least squares regression (1)
- Process Monitoring (1)
- Process monitoring (1)
- Prozess-Sensoren (1)
- Prozess-Steuerung (1)
- Prozessindustrie (1)
- Qualitätskontrolle (1)
- Quantum Mechanics (1)
- Quantum mechanics (1)
- Silane (1)
- Smart sensors (1)
- Smarte Feldgeräte (1)
- Spectral Modeling (1)
- Static Mixing (1)
- Thiol-ene coupling (1)
- Trialkoxysilane (1)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (5)
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.
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.
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.
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.
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 environ¬ments 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.
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.
Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies.
We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.
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).
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).