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Accelerating chemical process development and manufacturing along with quick adaption to changing customer needs means consequent transformation of former batch to continuous (modular) manufacturing processes. These are justified by an improved process control through smaller volumes, better heat transfer, and faster dynamics of the examined reaction systems.
As an example, for such modular process units we present the design and validation of an integrated nuclear magnetic resonance (NMR) micro mixer tailor‐made for a desired chemical reaction based on computational modelling. The micro mixer represents an integrated modular production unit as an example for the most important class of continuous reactors. The quantitative online NMR sensor represents a smart process analytical field device providing rapid and non‐invasive chemical composition information without need for calibration. We describe the custom design through computational fluid dynamics (CFD) for the demands of the NMR sensor as well as for the given reaction conditions. The system was validated with an esterification reaction as an example for a chemical reaction process.
Systems utilizing such an online NMR analyser benefits through short development and set‐up times based on “modular” spectral models. Such models can simply be built upon pure component NMR spectra within minutes to a few hours (i.e., assignment of the NMR signals to the components) instead of tedious DoE 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). The approach was validated for typical industrial reactions, such as hydrogenations or lithiations.
Die Nutzung von Sensortechnologien, insbesondere im Bereich der Gasdetektion mit einem Schwerpunkt auf Wasserstoff, spielt eine entscheidende Rolle in verschiedenen Anwendungsbereichen der Wasserstofftechnologie. Sicherheitsüberwachung, Leckdetektion und Prozesskontrolle gehören zu den prominenten Anwendungsgebieten dieser Sensortechnologien. Ein zentrales Ziel ist die Erkennung von freigesetztem Wasserstoff sowie die genaue Bestimmung des Wasserstoff-Luftverhältnisses mithilfe von Gassensoren. Dies ist von entscheidender Bedeutung, um potenzielle Gefahren frühzeitig zu erkennen und angemessene Maßnahmen zu ergreifen.
Ein weiterer Schwerpunkt dieses Beitrags liegt auf der Analytik und der Verwendung zertifizierter Referenzmaterialien in Verbindung mit Metrologie für die Wasserstoffspeicherung. Dies gewährleistet eine präzise und zuverlässige Charakterisierung von Wasserstoff und unterstützt die Entwicklung sicherer Speichertechnologien. Im Rahmen des Euramet-Vorhabens Metrology for Advanced Hydrogen Storage Solutions (MefHySto) wird eine Kurzvorstellung präsentiert.
Der Vortrag stellt zwei zerstörungsfreie Prüfverfahren zum strukturellen Zustandsüberwachung (Structural Health Monitoring, SHM) für Wasserstofftechnologien vor. Insbesondere die Fehlstellenerkennung mittels geführter Ultraschallwellen spielt eine bedeutende Rolle bei der Lebensdauerüberwachung von Wasserstoffspeichern. Ein weiterer Aspekt ist die Anwendung faseroptischer Sensorik zur Schadensfrüherkennung von Wasserstoffspeichern. Diese zerstörungsfreien Prüfverfahren ermöglichen eine präzise und frühzeitige Identifizierung von Schäden, was die Sicherheit und Effizienz von Wasserstoffspeichersystemen entscheidend verbessert.
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.
Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short down-times between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds.
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. The advances of a fully automated NMR sensor were exploited, using a given pharmaceutical lithiation reaction as an example process within a modular pilot plant. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as , e.g., explosion safety, field communication, and robust evaluation of sensor data. It was thereof used for direct loop advanced process control and real-time optimization of the process. NMR appeared as preeminent online analytical tool and allowed using a modular data analysis tool, which even served as reliable reference method for further PAT applications.
Integrierte und vernetzte Systeme und Prozesse – Eine Perspektive für die smarte Laborinfrastruktur
(2022)
Chemie- und Pharmaunternehmen müssen neue Wege gehen, um in einem sich wandelnden Umfeld erfolgreich zu überleben, und gleichzeitig flexiblere Wege der Produkt- und Prozessentwicklung finden, um ihre Produkte schneller auf den Markt zu bringen - vor allem hochwertige High-End-Produkte wie Feinchemikalien oder Arzneimittel. Dazu gehört auch das Potenzial der digitalen Technologien, um ein umfassenderes Wissensmanagement zu ermöglichen.
Eine wichtige Wissensquelle sind analytische Labors, die Unternehmensweit aktiv sind – von F&E bis zur Produktion. Die ganzheitliche Einbindung von analytischen Labors und ein unternehmensweites Daten- und Wissensmanagement sind wichtige Bausteine zur Integration und Vernetzung aller Systeme und Prozesse. Das Potenzial von Daten aus der Produktion mit ihren Kontextinformationen wird heute oft noch nicht konsequent für ein umfassendes Verständnis der Produktion genutzt.
Dieser Beitrag skizziert anhand von Beispielen einen möglichen ganzheitlicheren Ansatz zur Digitalisierung und zum Einsatz maschineller Verfahren in der Produktion von Spezialchemikalien und Pharmazeutika durch die Einführung integrierter und vernetzter Systeme und Prozesse.
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.
The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIRprocess Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis.
Digitalisierung und Industrie 4.0 verändern komplette Geschäftsmodelle, heben neue Effizienzpotenziale und stärken die Wettbewerbsfähigkeit. Auf dem 57. Tutzing-Symposion vom 15.–18.04.2018 wurde mit Vorträgen und Kreativworkshops erkundet, welche speziellen Anforderungen die Prozessindustrie hat, welche digitalen Innovationen bereits umgesetzt wurden und wo noch Handlungsbedarf besteht. Ein Workshop befasste sich mit den Themenfeldern Datenkonzepte, Datenanalyse, Big Data und künstliche Intelligenz. Es geht nicht um die Digitalisierung von heute. Im Angesicht der wachsenden Digitalisierung unserer Prozesse stellt sich die Frage, ob wir den Prozess wirklich gut kennen. Ob alle Verfahrensschritte detailliert hinterlegt wurden. Nur mit einem heuristischen Ansatz kann das vorhandene Wissen nicht digitalisiert werden.
Sehr schnell werden die Mechanismen eines Massenmarktes mit denen einer Nische verwechselt. Nicht jeder Mechanismus, den wir von großen Suchmaschinen oder Einkaufsportalen kennen, gibt uns einen Hinweis auf Nutzen und Verfügbarkeit für die Prozess- oder pharmazeutische Industrie. Eine gute Analyse der Anforderungen in der Zukunft mit einem Abgleich der derzeitigen technischen Möglichkeiten ist Voraussetzung für eine Verbesserung der derzeitigen digitalen Umsetzung. Dabei ist es sinnvoll unkonventionelle Methoden einzusetzen.
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). 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.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.
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).