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Eingeladener Vortrag
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The transition 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 comparison method”, independent of the matrix, it runs with very short set-up times in combination with “modular” spectral models. These are based on pure component NMR spectra without the need for tedious calibrations runs. We present approaches from statistical, (i.e., Partial Least Squares Regression) to physically motivated models (i.e., Indirect Hard Modelling).
Based on concentration measurements of reagents and products by the NMR analyser 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 goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.
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.
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 valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally 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 realized 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-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a 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.
Atomkerne zählen - Quantitative NMR-Spektroskopie als vielseitige Methode in Labor und Prozess
(2020)
Meist präsentieren sich Laboratorien für NMR-Spektroskopie auf den ersten Blick von außen nur wenig einladend. Es lohnt sich aber einen Blick hinein zu riskieren, denn die Methode ist heute unverzichtbar für die analytische Chemie. Die quantitativen Möglichkeiten werden häufig unterschätzt, erfahren allerdings durch den aufstrebenden Markt kompakter Benchtop-NMR-Spektrometer verstärktes Interesse für Anwendungen von der Laborbank bis hin zum Industrieprozess.
Um die internationale Spitzenposition deutscher Unternehmen der Prozessindustrie auch in Zukunft sicher zu stellen, müssen die Unternehmen ihre Prozesse und Geschäftsabläufe digitalisieren und gemeinsam mit der Forschung innovative neue Methoden, Apparate, Anlagen, Sensoren und Automatisierungstechnik sowie Datenkonzepte entwickeln. All dies erfordert den Aufbau neuer Fähigkeiten, Investition in Talente, interdisziplinäre Kommunikation zwischen verschiedenen Personen und Abteilungen und eine Bereitschaft zur Veränderung.
Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
Accreditation of analytical methods, either according to GxP or ISO regulations, requires a comprehensive quality management system. General quality documents are often already in place, which need to be extended by method-specific documentation. In this presentation we like to show an idea of a modular set of standard operating procedures (SOP) specifically developed meeting the requirements for quantitative NMR. The future goal is to collaborate with different accreditated NMR laboratories to compile a universal set of SOP and other quality documents that can be modified and used as a starting point for developing your own quality system for applications of qNMR in a regulated environment.
The use of benchtop-NMR instruments is constantly increasing during the recent years. Advantages of being affordable, portable and easy-to-operate without the need for trained staff make them especially interesting for industrial applications in quality control. However, applications of NMR spectroscopy as an online PAT tool are still very rare but offer a huge potential for process optimization and control. A key task to exploit this potential is hardware field integration of the lab-instruments in a rough environment of a chemical plant. Additionally, developments in automation and data evaluation are mandatory to ensure a robust unattended operation with low maintenance requirements. Here, we show an approach of a fully automated analyzer enclosure considering explosion safety, field communication, as well as environmental conditions in the field.
Temperature sensitivity is still a limitation of benchtop-NMR instruments in flow applications. Recent developments of manufacturers allow for limited operation at static temperature levels, however, a dynamic system for continuous operation is still not available. Using a prototype system offering a larger bore, active temperature shielding studies with thermostated air were performed evaluating the performance.
Automated data evaluation of NMR spectra using a modular indirect hard modeling (IHM) approach showed good results and flexibility. A second data analysis approach based on artificial neural networks (ANN) was evaluated.Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously, offering new ways to simultaneously evaluating large numbers of different models.
In den letzten Jahren erfreut sich die Benchtop-NMR-Spektroskopie steigender Beliebtheit. Zusätzlich zu Anwendungen im Labor werden die kompakten Geräte auch zunehmend in industriellen Bereichen eingesetzt. Neben geringeren Investitions- und Betriebskosten sind insbesondere die Robustheit und einfache Bedienbarkeit dieser Systeme entscheidende Faktoren. Ein prozessnaher Einsatz kann sowohl nach Probenahme im Rahmen der Betriebsanalytik erfolgen als auch als Online-Analytik mit direkter Ankopplung an Prozesse. Insbesondere bei der direkten Kopplung mittels Durchflusszellen spielt der Probentransfer eine entscheidende Rolle. Dies zeigt sich auch am Beispiel eines Extraktionsprozesses im Labormaßstab. Das in einem Bioprozess aus tierischen Altfetten hergestellte Copolymer PHB(Co-HHX) wurde in einem Batch-Prozess mit organischen Lösungsmitteln aus gefrier-getrockneten Zellen extrahiert. Unter Variation der Parameter wurde der Prozess direkt in der mehrphasigen Suspension mittels Benchtop-NMR-Spektroskopie verfolgt.
Eine große Limitierung für den Einsatz als Online-Methode ist die Temperaturempfindlichkeit der Magnetsysteme. Während die Umgebungstemperatur gut kontrollierbar ist, stellt sich die Thematik der Probentemperatur komplexer dar. Bei größeren Abweichungen von der Magnettemperatur kommt es bereits nach kurzer Zeit zu Drift-Effekten, die die Lock-Systeme nur begrenzt kompensieren können. Ein Ansatz ist die Nutzung temperierter Luftströme, ähnlich bekannter VT-Einheiten ergänzt um eine aktive Isolierung zum Magneten. An einem Modellaufbau wurden dazu verschiedene Konstruktionen zunächst mittels Thermographie charakterisiert und anschließend die Anwendbarkeit in einem Prototyp-Gerät überprüft.
In der rauen industriellen Umgebung von Produktionsanlagen sind weitere Maßnahmen zum Betrieb eines Laborgerätes nötig (z.B. Temperierung, Witterungsschutz). Zusätzlich bestehen häufig strikte Anforderungen an den Explosionsschutz. Zu diesem Zweck wurde gemeinsam mit der Evonik Rheinfelden eine Einhausung entwickelt, die es ermöglicht neben der NMR-Spektroskopie auch eine weitere analytische Methode möglichst flexibel direkt an großtechnische Anlagen anzubinden.
Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production
(2021)
Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and thus a quicker time to market by flexibly connecting and orchestrating standardised physical modules and bringing them to life (i.e., parameterising them) with digitally accumulated process knowledge.
We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes.
Chemische Prozesskontrolle für modulare Produktionskonzepte mit direkten analytischen Methoden
(2020)
Flexible Produktionskonzepte werden oft mit modularen, schnell rekonfigurierbaren Einheiten verbunden. Um die Vorteile dieser flexiblen Produktion tatsächlich nutzen zu können ist neben einer geeigneten Infrastruktur auch eine ebenso anpassungsfähige Qualitätskontrolle unverzichtbar. Analysengeräte und Sensoren werden heutzutage immer kompakter und kostengünstiger in ihrer Anschaffung. Dadurch wird es möglich eine große Anzahl von Messstellen innerhalb einer Anlage zu realisieren. Die erfassten Datenmengen erfordern zuverlässige und robuste Auswerteverfahren, die sich in das Gesamtkonzept der Automatisierung einer derart flexiblen Produktion nahtlos integrieren. Referenzdaten aus direkten Methoden wie der NMR-Spektroskopie können hier einen wertvollen Beitrag zur Generierung von Prozesswissen für die Entwicklung neuer Modelle für kostengünstigere Analysenverfahren leisten.
Ergot alkaloids are mycotoxins formed by fungi of the Claviceps genus, which are some of the most common contaminants of food and feed worldwide. These toxins are a structurally heterogeneous group of compounds, sharing an ergoline backbone. Six structures and their corresponding stereoisomers are typically quantified by either HPLC-FLD or HPLC-MS/MS and the values subsequently summed up to determine the total ergot alkaloid content. For the development of a screening method targeting all ergot alkaloids simultaneously, the alkaloids need to be transferred to one homogeneous structure: a lysergic acid derivative. In this study, two promising cleaving methods—acidic esterification and hydrazinolysis—are compared, using dihydroergocristine as a model compound. While the acidic esterification proved to be unsuitable, due to long reaction times and oxidation sensitivity, hydrazinolysis reached a quantitative yield in 40-60 min. Parallel workup of several samples is possible. An increasing effect on the reaction rate by the addition of ammonium iodide was demonstrated. Application of hydrazinolysis to a major ergot alkaloid mix solution showed that all ergopeptines were cleaved, but ergometrine/-inine was barely affected. Still, hydrazinolysis is a suitable tool for the development of a sum parameter screening method for ergot alkaloids in food and feed.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2022)
Companies of chemical industry find themselves more often in a rapidly changing environment, e.g., due to variability of raw material quality or energy costs and efficiency. Process optimization and new process concepts become more and more important. For example, flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds like exothermic reactions with high heat dissipation.
Therefore, fully automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. Field studies in modular and conventional production plant setups show promising results gaining process knowledge for further optimization. NMR appeared as preeminent online analytical method and allow using a modular data analysis approach, which can even serve as reliable reference method for further calibration-dependent PAT applications (e.g., NIR spectroscopy). Based on experiences from these field studies an improved analyzer enclosure setup was developed and built, including the option of a secondary method besides NMR spectroscopy. Integrated control systems allow for a flexible implementation based on the available automation infrastructure at the chemical plant or pilot plant setup. In the future, fully integrated and intelligently interconnecting “smart” PAT systems and processes have the potential speed up the setup of production equipment for chemicals and pharmaceuticals and therefore help to reduce the time-to-market.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2022)
Chemical companies must find new paths to successfully survive in a changing environment, especially by utilizing the potential of digital technologies. Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market for new products.
Therefore, fully automated process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. It was used for direct loop advanced process control and real-time optimization of the process. Field studies in modular and conventional production plants show promising results gaining process knowledge. NMR appeared as preeminent online analytical method and allow using a modular data analysis tool, which even served as reliable reference method for further PAT applications (e.g. NIR spectroscopy). In the future, fully integrated and interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2022)
Chemical companies must find new paths to successfully survive in a changing environment, especially by utilizing the potential of digital technologies. Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market for new products.
Therefore, fully automated process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. It was used for direct loop advanced process control and real-time optimization of the process. Field studies in modular and conventional production plants show promising results gaining process knowledge. NMR appeared as preeminent online analytical method and allow using a modular data analysis tool, which even served as reliable reference method for further PAT applications (e.g. NIR spectroscopy). In the future, fully integrated and interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.