Chemie und Prozesstechnik
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Wir laden zum Trainingsworkshop Datenanalyse ein. Angetrieben durch die Digitalisierung und die sogenannte Industrie 4.0 steigt die Erwartungshaltung gegenüber der Nutzung von Daten. Die Unsicherheit, Widersprüchlichkeit und Redundanz separat verarbeiteter einzelner Quellen soll durch die synergetische Zusammenführung heterogener Datensätze überwunden werden. Dabei steht der zunehmenden Aufgabenkomplexität eine ebenso zunehmende Verfügbarkeit an Daten und Analyseverfahren gegenüber.
Der Workshop vermittelt ein konzeptionelles Verständnis für moderne Datenanalyseverfahren (Machine Learning (ML), Multivariate Statistik) und soll durch ein anschließendes Hands-On Training mit Python (https://www.python.org) einen einfachen Einstieg in die Thematik ermöglichen. Der Kurs richtet sich an den wissenschaftlichen Nachwuchs.
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.
Analysis of dynamic systems
(2019)
Monitoring specific information (i.e., physico-chemical properties, chemical reactions, etc.) is the key to chemical process control when looking at dynamic systems, and quantitative online NMR spectroscopy is the method of choice for the investigation and understanding of dynamic multi-component systems. NMR provides rapid and non-invasive information, and due to the inherent linearity between sample concentration and signal intensity, peak areas can be directly used for quantification of multiple components in a mixture (without the need for any further calibration). This is one of the most attractive features of quantitative NMR spectroscopy. With the launch of devices covering magnetic field strengths from 40 to 90 MHz, so called compact or benchtop NMR systems, this analytical method is now reaching a sufficient degree of compactness and operability for an application outside of very specialized laboratories.
Whilst there are also many other tools available to examine various analytical parameters from dynamic processes, such as mass spectrometry, (near) infrared or Raman spectroscopy, each of these tools can only really be used independently. How can we examine and compare all data describing a particular chemical reaction? How can we visualize information rich, specific, or direct methods together with less specific but established analytical methods? And how can we transfer calibration information to the most appropriate process analytical method or method combination? Quantitative NMR spectroscopy (qNMR) has the potential to substitute offline laboratory analysis for calibration purposes by delivering quantitative reference data as an online method.
The workshop briefly presents the current state of the art of the analysis of dynamic systems by online NMR spectroscopy and analytical data fusion, with the remaining time being used for questions and open discussion with the attendees.
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.
This work wants to show the benefit of NMR spectroscopy as online analytical technique in industrial applications for improving process understanding and efficiency. Especially development and set‐up times based on “modular” data analysis models will enable new production concepts, which are currently discussed with respect to digitization of process industry.
PANIC is the ideal forum for such discussions in the application of NMR spectroscopy and its data analysis to the everyday problems in process industry.
Chemical companies must find new paths to successfully survive in a changing environment. The potential of digital technologies belongs to these. 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.
In future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.