Chemische Charakterisierung und Spurenanalytik
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qNMR - The handbook
(2023)
Quantitative NMR (qNMR) has been around for a long time, but also has great potential to solve future problems in any quantitative analysis. As a primary method, it differs fundamentally from chromatographic methods: it is better described as a quantum mechanical balance. Succesful implementation of qNMR requires certain attention to detail. "qNMR - the handbook" is intended to be a guide for analysts to help understand the fundamental principles of NMR and the significant points relating to its implementation for quantitation. Regulatory considerations of qNMR adoption are explained. NMR fundamentals are explained to provide understanding. Together with many useful examples, the book is a compelling addition to the laboratory's reference library, providing all the tools that any practitioner should know to successfully implement qNMR.
The authors are qNMR pioneers and come from a variety of backgrounds including business, government and academia.
Establishing ZIF‐8 as a reference material for hydrogen cryoadsorption: An interlaboratory study
(2024)
AbstractHydrogen storage by cryoadsorption on porous materials has the advantages of low material cost, safety, fast kinetics, and high cyclic stability. The further development of this technology requires reliable data on the H2 uptake of the adsorbents, however, even for activated carbons the values between different laboratories show sometimes large discrepancies. So far no reference material for hydrogen cryoadsorption is available. The metal‐organic framework ZIF‐8 is an ideal material possessing high thermal, chemical, and mechanical stability that reduces degradation during handling and activation. Here, we distributed ZIF‐8 pellets synthesized by extrusion to 9 laboratories equipped with 15 different experimental setups including gravimetric and volumetric analyzers. The gravimetric H2 uptake of the pellets was measured at 77 K and up to 100 bar showing a high reproducibility between the different laboratories, with a small relative standard deviation of 3–4 % between pressures of 10–100 bar. The effect of operating variables like the amount of sample or analysis temperature was evaluated, remarking the calibration of devices and other correction procedures as the most significant deviation sources. Overall, the reproducible hydrogen cryoadsorption measurements indicate the robustness of the ZIF‐8 pellets, which we want to propose as a reference material.
Modular chemical production is a tangible implementation of the digital transformation of the specialty chemicals process industry. In particular, it enables acceleration of process development and thus faster time to market by flexibly interconnecting and orchestrating standardized physical modules and bringing them to life. For this purpose, specific (chemical) sensors of process analytics are needed, preferably without lengthy calibration or spectroscopic model development.
An excellent example of a "direct" analytical method is online nuclear magnetic resonance (NMR) spectroscopy. NMR spectroscopy meets the requirements of a direct analytical method because of the direct correlation between the signal area in the spectrum ("counting" the nuclear spins) and the analyte amount of substance concentrations. It is also extremely linear over the concentration range.
With the availability of compact benchtop NMR instruments, it is now possible to bring NMR spectroscopy directly into the field, in close proximity to specialized laboratory facilities, pilot plants, and even industrial-scale production facilities. The first systems are in TRL 8 (Qualified System with Proof of Functionality in the Field).
The presentation will discuss the many building blocks of online nuclear magnetic resonance spectroscopy, from flow cells to automated data analysis.
Anhand von Beispielen wird in diesem Vortrag ein möglicher ganzheitlicherer Ansatz für die Digitalisierung und den Einsatz maschinengestützter Prozesse bei der Herstellung von Spezialchemikalien und Arzneimitteln durch die Einführung integrierter und vernetzter Systeme und Prozesse skizziert. Ein aktueller Ansatz und ein Beispiel in diesem Vortrag sind flexible und modulare chemische Produktionseinheiten, die Mehrzweckanlagen nutzen, um verschiedene hochwertige Produkte mit kurzen Stillstandszeiten zwischen den Kampagnen herzustellen und die Markteinführungszeit für neue Produkte zu verkürzen.
Als zweites Beispiel wird kurz die Testplattform Wasserstofftankstelle vorgestellt, die als moderne Anlage der Prozessindustrie betrachtet werden kann, vergleichbar mit einer Anlage aus der chemischen oder pharmazeutischen Industrie. An ihr lassen sich alle derzeit entlang der Digitalen Transformation diskutierten Konzepte implementieren, um sie zu standardisieren und zu validieren. Dazu gehören Konzepte zur Konnektivität von Sensoren und Aktoren aus der Feldebene in höhere Ebenen der Automatisierung oder zur Einbringung zusätzlicher Sensoren oder Sensornetzwerke, die zunehmend flexibler gestaltet werden soll, die sichere und nachvollziehbare Parametrierung von Automatisierungskomponenten – vielleicht aus einem digitalen Abbild (Verwaltungsschale bzw. „Digitaler Zwilling“) heraus, Konzepte zur vorausschauenden Wartung („Predictive Maintenance“), Konzepte zu digitalen Entscheidungsprozessen, Zertifikaten und Signaturen oder der zunehmende Einsatz von komplexen Auswertungsalgorithmen und Applikationen in der Feldebene („Embedded Computing“) oder der „Kante“ zu Cloudbasierten Systemen der Informationstechnik („Edge-Computing“).
Die beiden Beispiele sollen aktuelle Entwicklungsachsen des industriellen Messwesens im Rahmen der industriellen Automation aufzeigen, in denen Messwerte, deren Messunsicherheiten und Kontextinformationen eine wichtige Rolle einnehmen.
Introduction
NMR spectroscopy is one of the most important analytical methods in organic chemistry. While most analyses are carried out qualitatively with the aim of substance identification and structure elucidation, quantitative NMR spectroscopy (qNMR) is increasingly gaining importance in research and industry. qNMR provides the most universally applicable form of direct purity determination without need for reference materials of impurities or the calculation of response factors but only exhibiting suitable NMR properties.
Methods
One of the most attractive features of quantitative NMR spectroscopy is that the NMR peak areas can be used directly for concentration quantification without further calibration. Another advantage of NMR spectroscopy is that the method has a high linearity between absolute signal area and sample concentration, which makes it an absolute analytical comparison method that is independent of the matrix. This enables automated robust data evaluation strategies that can be used for online applications of qNMR spectroscopy.
Jancke et al. proposed NMR spectroscopy as a relative primary analytical method because it can be fully described by mathematical equations from which a complete uncertainty budget can be derived, allowing it to be used at the highest metrological level. Weber et al. discussed in detail important aspects of the procedure that enable the realisation of low measurement uncertainties in qNMR measurements. Since certification of CRM requires expanded mea¬sure¬ment uncertainties of less than 0.5 % (relative), the work of Weber et al. demonstrated for the first time that qNMR can fulfil this criterion.
Results
To date, further comparative studies have been carried out in metrology and industry, demonstrating the performance of quantitative NMR spectroscopy and further reducing measurement uncertainties. The development of validation concepts and the commercial availability of suitable certified reference materials facilitate the application, especially in the usually highly regulated industrial environment. Users can thus accelerate the development of analytical methods. The talk will cover a wide range of topics from current metrological activities to new challenges for qNMR spectroscopy and also deals with aspects such as validation and accreditation.
Innovative aspects
• qNMR provides the most universally applicable form of direct purity determination
• Expanded measurement uncertainties lower than 0.15 % (relative) possible
• Benchtop NMR instruments increasingly used for qNMR spectroscopy
The European project MefHySto addresses the need of large-scale energy storage, which is required for a shift to renewable energy supply. The project is funded by the European Metrology Programme on Innovation and Research (EMPIR) and consists of 14 consortium partners from all over Europe (www.mefhysto.eu). It is demonstrated, how MefHySto is contributing to the EMN for Energy Gases aiming at prioritisation of the measurement gaps and challenges interacting with the EMN stakeholders.
Cryoadsorption on the inner surface of porous materials is a promising solution for safe, fast, and reversible hydrogen storage. Within the class of highly porous metal−organic frameworks, zeolitic imidazolate frameworks (ZIFs) show high thermal, chemical, and mechanical stability. In this study, we selected ZIF-8 synthesized mechanochemically by twin-screw extrusion as powder and pellets. The hydrogen storage capacity at 77 K and up to 100 bar has been analyzed in two laboratories applying three different measurement setups showing a high reproducibility. Pelletizing ZIF-8 increases the packing density close to the corresponding value for a single crystal without loss of porosity, resulting in an improved volumetric hydrogen storage capacity close to the upper limit for a single crystal. The high volumetric uptake combined with a low and constant heat of adsorption provides ca. 31 g of usable hydrogen per liter of pellet assuming a temperature−pressure swing adsorption process between 77 K − 100 bar and 117 K − 5 bar. Cycling experiments do not indicate any degradation in storage capacity. The excellent stability during preparation, handling, and operation of ZIF-8 pellets demonstrates its potential as a robust adsorbent material for technical application in pilot- and full-scale adsorption vessel prototypes.
Unternehmen der Prozessindustrie müssen neue Wege finden, 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 – insbesondere hochwertige, hochwertige Produkte wie Feinchemikalien oder Arzneimittel. Dies wird zukünftig durch Veränderungen in den Wertschöpfungsketten entlang einer potenziellen Kreislaufwirtschaft erschwert.
Anhand von Beispielen wird in diesem Vortrag ein möglicher ganzheitlicher Ansatz zur Digitalisierung und zum Einsatz maschineller Verfahren in der Produktion von Spezialchemikalien durch die Einführung integrierter und vernetzter Systeme und Prozesse skizziert.
Es wird auch auf die aktuelle Technologie-Roadmap „Prozess-Sensoren 2027+“ eingegangen, die Ende 2021 erschienen ist. Im Zentrum dieser Roadmaps stehen Sensoren zur Erfassung von physikalischen und chemischen Messgrößen mittels spezifischer und unspezifischer Messverfahren, die zur Steuerung und dem besseren Verständnis von Prozessen dienen. Die Roadmap fasst die gemeinsame Technologie- und Marktsicht von Anwendern, Herstellern und Forschungs¬einrichtungen im Bereich Prozess-Sensorik in der verfahrenstechnischen Industrie zusammen. Digitalisierung und Nachhaltigkeit sind übergreifende Kernthemen der künftigen Entwicklung.
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.
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products.
NMR spectroscopy appeared as excellent online analytical tool and allowed a modular data analysis approach, which even served as reliable reference method for further Process Analytical Technology (PAT) applications. Using the available datasets, 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. In future, such fully integrated and interconnecting “smart” systems and processes can increase the efficiency of the production of specialty chemicals and pharmaceuticals.
At the end of the article, ideas for solutions are discussed in order to speed up the implementation of new special products from the point of view of process analytics and to network the existing process chains more closely.
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products.
NMR spectroscopy appeared as excellent online analytical tool and allowed a modular data analysis approach, which even served as reliable reference method for further Process Analytical Technology (PAT) applications. Using the available datasets, 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. In future, such fully integrated and interconnecting “smart” systems and processes can increase the efficiency of the production of specialty chemicals and pharmaceuticals.
Chemical and pharmaceutical companies need to find new ways to survive successfully in a changing environment, while finding more flexible ways of product and process development to bring their products to market faster - especially high-value, high-end products such as fine chemicals or pharmaceuticals. This is complicated by changes in value chains along a potential circular economy.
One current approach is flexible and modular chemical production units that use multi-purpose equipment to produce various high-value products with short downtimes between campaigns and can shorten time-to-market for new products. Online NMR spectroscopy will play an important role for plant automation and quality control, as the method brings very high linearity, matrix independence and thus works almost calibration-free. Moreover, these properties ideally enable automated and machine-aided data analysis for the above-mentioned applications.
Using examples, this presentation will outline a possible more holistic approach to digitalization and the use of machine-based processes in the production of specialty chemicals and pharmaceuticals through the introduction of integrated and networked systems and processes.
Chemical and pharmaceutical companies need to find new ways to survive successfully in a changing environment, while finding more flexible ways of product and process development to bring their products to market faster - especially high-value, high-end products such as fine chemicals or pharmaceuticals. This is complicated by changes in value chains along a potential circular economy.
One current approach is flexible and modular chemical production units that use multi-purpose equipment to produce various high-value products with short downtimes between campaigns and can shorten time-to-market for new products. Online NMR spectroscopy will play an important role for plant automation and quality control, as the method brings very high linearity, matrix independence and thus works almost calibration-free. Moreover, these properties ideally enable automated and machine-aided data analysis for the above-mentioned applications.
Using examples, this presentation will outline a possible more holistic approach to digitalization and the use of machine-based processes in the production of specialty chemicals and pharmaceuticals through the introduction of integrated and networked systems and processes.
Das europäische Projekt MefHySto befasst sich mit dem Bedarf an großmaßstäblichen Energiespeichern, die für eine Umstellung der Energieversorgung auf erneuerbare Energien erforderlich sind. Eine solche Speicherung ist entscheidend, um Energie zu Spitzenzeiten zu liefern, wenn die erneuerbaren Energiequellen schwanken. Eine mögliche Lösung für die Energiespeicherung ist der großtechnische Einsatz von Wasserstoff. Die messtechnische Rückführbarkeit in der Energieinfrastruktur für die Wasserstoffspeicherung ist dann von entscheidender Bedeutung und eine bessere Kenntnis der chemischen und physikalischen Eigenschaften von Wasserstoff sowie rückführbare Messungen und validierte Techniken unverzichtbar.
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.
The prerequisites for a successful energy transition and the economic use of hydrogen as a clean green energy carrier and for H2 readiness are a rapid market ramp-up and the establishment of the required value chains. Reliable quality and safety standards for innovative technologies are the prerequisite for ensuring supply security, for environmental compatibility and sustainable climate protection, for building trust in these technologies and thus enable product and process innovations.
With the Competence Centre "H2Safety@BAM", BAM is creating the safety-related prere-quisites for the successful implementation of hydrogen technologies at national as well as European level. BAM uses decades of experience in dealing with hydrogen technologies to develop the necessary quality and safety standards.
The presentation will draw a bow from the typical basic tasks of BAM in the field of competence "Sensors, analytics and certified reference materials", such as maintenance and dissemination of the national gas composition standards for calorific value determination as Designated Institute for Metrology in Chemistry within the framework of the Metre Convention, to the further development of measurement and sensor technology for these tasks. For the certification of reference materials, a mostly slow and time-consuming but solid reference analysis is common. With hydrogen and its special properties, completely new requirements are added. In addition, fast and simple online analysis is required for process control, for example to register quality changes, e.g., during load changes or refuelling processes.
Intelligent sensor systems, certified reference materials and instrumental analytical-chemical methods contribute to safety and functionality in hydrogen technologies.
This article gives a brief overview of SensRef activities in the Competence Centre H2Safety@BAM on the issues: Analytical methods for the determination of hydrogen purity, certified reference materials as measurement standards with regard to gas quality (primary calibrators) of BAM, test methods for gas sensor systems to detect hydrogen in air as well as the application of fibre-optic sensor systems to monitor the expansion and ageing behaviour of composite containers in hydrogen technologies.
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.
ISO/IEC 17025 is the worldwide quality standard for testing and calibration laboratories. It is the basis for accreditation by an accreditation body. The current version was published in 2018.
Implementing ISO/IEC 17025 as part of laboratory quality initiatives offers both laboratory and business benefits, such as expanding the potential customer base for testing and/or calibration, increasing the reputation and image of the laboratory at national and international level, continuous improvement of the data quality and the effectiveness of the laboratory or creation of a good basis for most other quality systems in the laboratory sector, such as GxP. The main difference between a proper approach to analysis and a formal accreditation is shown in a targeted documentation, especially on the qualification of the personnel, the test equipment and the validation of the analytical methods.
Using quantitative NMR spectroscopy as an example, it is shown how accreditation can be carried out and what documentation is required. In our case, we have described the procedure in an SOP ("Determination of the quantitative composition of simple mixtures of structurally known compounds with 1H-NMR spectroscopy") and supported it with a modular system of organizational and equipment SOPs. The special feature is that the accredited method is independent for the choice of the analyte and the matrix and therefore it is possible to operate with a single validated method. In our case, we have proposed three quality levels ("leagues") with different levels of analytical effort, which differ in their measurement uncertainty, in order to simplify the workflow and analysis design.
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. The potential of digital technologies belongs to these.
A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products. At the same time, we need to move towards knowledge-based production that takes into account all essential equipment, process and control data from plants and laboratories and makes valuable expertise available and transferable. The potential of data from production together with its contextual information is often not yet consistently used today for a comprehensive understanding of production.
By giving examples this paper outlines a possible more holistic approach to digitalisation and the use of machine-based methods in the production of specialty chemicals and pharmaceuticals through the introduction of integrated and networked systems and processes.
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.
The competitiveness of the process industry is based on ensuring the required product quality while making optimum use of equipment, raw materials and energy. Chemical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. The potential of digital technologies belongs to these.
One way is knowledge-based production, taking into account all essential equipment, process and regulatory data of plants and laboratories. Today, the potential of this data is often not yet consistently used for a comprehensive understanding of production. Another approach uses flexible and modular chemical plants, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products. Digital transformation is enabling completely new production concepts that are being used increasingly. Intensified continuous production plants also allow for difficult to produce compounds.
This contribution aims to encourage a more holistic approach to the digitalization and use of machine-assisted methods in (bio) process engineering by introduction of integrated and networked systems and processes, which have the potential to speed up the high-quality production of specialty chemicals and pharmaceuticals.
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.
Due to its advantages of being a direct comparison method, quantitative NMR spectroscopy (qNMR) becomes more and more popular in industry. While conventional high-field NMR systems are often associated with high investment and operational costs, the upcoming market of permanent-magnet based benchtop NMR systems show a considerable option for a lot of applications. The mobility of these systems allows to bring them more closely to the real production environment, e.g. for at-line quality control.
In this work we present an interlaboratory comparison study investigating the qNMR performance of state-of-the-art benchtop NMR spectrometers. Therefore, BAM prepared two samples of a mixture of NMR reference standards tetramethylbenzene (TMB) and tetrachloronitrobenzene (TCNB) at concentration levels of 200 mM and 10 mM. These “ready-to-use” samples were sent to participant laboratories, which performed analysis on their benchtop NMR equipment of different vendors and fields from 43 to 80 MHz. Raw data was reported back and further investigated by using different data analysis methods at BAM.
After this very first qNMR comparison study of benchtop NMR spectrometers show promising results, following studies are planned to cover more parts of the qNMR process, e.g. sample preparation and weighing, but also data analysis, as commonly done in similar studies for high-field NMR spectroscopy in industry and metrology.
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.
Recently, AI procedures have also been successfully used for NMR data evaluation. In order to overcome the typical limitation of too small data sets from process developments, a new method was tested, which allows a physically motivated multiplication of the available reference data together with context information in order to obtain a sufficiently large data set for the training of machine learning algorithms.
In future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
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.
Laboratories tend to be central hubs for chemical, biotechnological, pharmaceutical or foodstuff production. They play a key role in research and development, chemical analysis, quality assurance, maintenance and process control. For process development and optimization, process analytical technology (PAT) has proven to be a powerful tool to improve our understanding of processes, increase productivity, reduce waste and costs and shorten processing times.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Zusätzlich zu Methodenentwicklung, Miniaturisierung und Kopplungsverfahren zeigen sich die Hyperspektroskopie zusammen mit Imaging‐Verfahren, der Einzelmolekülnachweis und der Einsatz von 3‐D‐Druckern als neue Schwerpunkte. Hinzu kommen künstliche Intelligenz bei Sensoren, Bildgebungsverfahren und Prozesssteuerung sowie die Vernetzung von Analyse‐ und Laborgeräten. Trends und Forschungsthemen aus der analytischen Chemie, zusammengestellt von elf Autoren, koordiniert von Günter Gauglitz.
Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.
Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model.
Synthetic low-field NMR spectra
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.
Xi (“pure component spectra dataset”)
Xii (“spectral model dataset”)
Experimental low-field NMR spectra from MNDPA-Synthesis
This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.
Current and future requirements to industrial analytical infrastructure—part 2: smart sensors
(2020)
Complex processes meet and need Industry 4.0 capabilities. Shorter product cycles, flexible production needs, and direct assessment of product quality attributes and raw material attributes call for an increased need of new process analytical technologies (PAT) concepts. While individual PAT tools may be available since decades, we need holistic concepts to fulfill above industrial needs. In this series of two contributions, we want to present a combined view on the future of PAT (process analytical technology), which is projected in smart labs (Part 1) and smart sensors (Part 2). Part 2 of this feature article series describes the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality. The smart sensor consists of (i) chemical and process information in the physical twin by smart field devices, by measuring multiple components, and is fully connected in the IIoT 4.0 environment. In addition, (ii) it includes process intelligence in the digital twin, as to being able to generate knowledge from multi-sensor and multi-dimensional data. The cyber-physical system (CPS) combines both elements mentioned above and allows the smart sensor to be self-calibrating and self-optimizing. It maintains its operation autonomously. Furthermore, it allows—as central PAT enabler—a flexible but also target-oriented predictive control strategy and efficient process development and can compensate variations of the process and raw material attributes. Future cyber-physical production systems—like smart sensors—consist of the fusion of two main pillars, the physical and the digital twins. We discuss the individual elements of both pillars, such as connectivity, and chemical analytics on the one hand as well as hybrid models and knowledge workflows on the other. Finally, we discuss its integration needs in a CPS in order to allow is versatile deployment in efficient process development and advanced optimum predictive process control.
The competitiveness of the chemical and pharmaceutical industry is based on ensuring the required product quality while making optimum use of plants, raw materials, and energy. In this context, effective process control using reliable chemical process analytics secures global competitiveness. The setup of those control strategies often originate in process development but need to be transferable along the whole product life cycle. In this series of two contributions, we want to present a combined view on the future of PAT (process analytical technology), which is projected in smart labs (part 1) and smart sensors (part 2). In laboratories and pilot plants, offline chemical analytical methods are frequently used, where inline methods are also used in production. Here, a transferability from process development to the process in operation would be desirable. This can be obtained by establishing PAT methods for production already during process development or scale-up. However, the current PAT (Bakeev 2005, Org Process Res 19:3–62; Simon et al. 2015, Org Process Res Dev 19:3–62) must become more flexible and smarter. This can be achieved by introducing digitalization-based knowledge management, so that knowledge from product development enables and accelerates the integration of PAT. Conversely, knowledge from the production process will also contribute to product and process development. This contribution describes the future role of the laboratory and develops requirements therefrom. In part 2, we examine the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality—also within process development or scale-up facilities (Eifert et al. 2020, Anal Bioanal Chem).
Um in einem veränderten Umfeld erfolgreich bestehen zu können, müssen Chemieunternehmen neue Pfade beschreiten. Dazu gehört insbesondere das Potential digitaler Technologien. Mit flexiblen, modularen chemischen Vielzweck-Produktionsanlagen lassen sich häufig wechselnde Produkte mit kürzeren Vorlauf- und Stillstandzeiten zwischen den Kampagnen und dennoch hoher Qualität realisieren. Intensivierte, kontinuierliche Produktionsanlagen erlauben auch den Umgang mit schwierig zu handhabenden Substanzen.
Grundvoraussetzung für solche Konzepte ist eine hochautomatisierte "chemische" Prozesskontrolle zusammen mit Echtzeit-Qualitätskotrolle, die "chemische" Informationen über den Prozess bereitstellt. In einem Anwendungsbeispiel wurde eine pharmazeutische Lithiierungsreaktion aus einer modularen Pilot-Anlage betrachtet und dabei die Vorzüge eines vollautomatischen NMR-Sensors untersucht. Dazu wurde ein kommerziell erhältliches Benchtop-NMR-Spektrometer mit Permanentmagnet auf die industriellen Anforderungen, wie Explosionsschutz, Feldkommunikation und vollautomatischer, robuster Datenauswertung angepasst. Der NMR-Sensor konnte schließlich erfolgreich im vollautomatischen Betrieb nach fortschrittlichen Regelkonzepten und für die Echtzeitoptimierung der Anlage getestet werden. Die NMR-Spektroskopie erwies sich als hervorragende Online-Methode und konnte zusammen mit einer modularen Datenauswertung sehr flexibel genutzt werden. Die Methode konnte überdies als zuverlässige Referenzmethode zur Kalibrierung konventioneller Online-Analytik eingesetzt werden.
Zukünftig werden voll integrierte und intelligent vernetzte "smarte" Sensoren und Prozesse eine kontinuierliche Produktion von Chemikalien und Pharmazeutika mit vertretbaren Qualitätskosten möglich machen.
Unternehmen der chemischen Industrie müssen neuen Pfade beschreiten, um in einem veränderten Umfeld erfolgreich bestehen zu können. Dazu gehört insbesondere, das Potenzial digitaler Technologien zu nutzen. Die volle Integration und intelligente Vernetzung von Systemen und Prozessen kommt allerdings nur zögerlich voran. Dieser Beitrag ist ein Loblied auf die Feldebene. Er möchte dazu ermutigen, die Digitalisierung der Prozessindustrie auf Basis smarter Sensorik, Aktorik und Kommunikation ganzheitlicher zu denken und informiert über aktuelle technische Perspektiven, wie das Ein-Netzwerk-Paradigma, Ad-hoc-Vernetzungen, Edge-Computing, FPGAs, virtuelle Maschinen oder Blockchain. Diese geben smarter Sensorik, Aktorik und Kommunikation eine völlig neue Perspektive.
Die Wettbewerbsfähigkeit der Prozessindustrie basiert auf der Sicherung der geforderten Produktqualität bei einer optimalen Nutzung von Anlagen, Rohstoffen und Energie. Ein Weg zur wissensbasierten Produktion führt über die Betrachtung der wesentlichen Apparate-, Prozess- und Freigabedaten aus Betrieben und Labors. Das Potenzial dieser Daten wird heute vielfach noch nicht konsequent für ein umfassendes Verständnis der Produktion genutzt.
Neben Fragen zur Datenerfassung, Datenkonnektivität und Datenintegrität müssen solche Daten für eine ganzheitliche Prozessanalyse zunächst mit Kontextinformationen zusammengebracht werden. Datenquellen enthalten Zeitwertpaare, aber auch diskrete Daten aus LIMS (Laboratory Information Management Systems) oder ELN (Electronic Laboratory Notebooks) und werden zunehmend durch 2D- und 3D-Daten aus der Chromatographie-Massenspektrometrie oder bildbasierter Analytik ergänzt.
Für die automatisierte Merkmalsextraktion, etwa zur Extraktion chemischer Informationen aus den oben genannten Datenquellen werden multivariate Werkzeuge und Algorithmen genutzt. Multivariate Statistiken wie PCA (Principle Component Analysis), PLS (Partial Least Squares) und LDA (Latent Discriminant Analysis) bilden die erste Grundlage für die Datenanalyse. Für diese Verfahren sind heute Datenvorbehandlungsschritte nötig. Die Modellbildung geschieht manuell und ist sehr aufwendig.
Können diese Daten im Zeitalter von ML (Machine Learning) und KI (Artificial Intelligence) anderweitig sinnvoll genutzt werden und ohne klassische Modellbildung? Die Bezeichnung „Big Data“ als Voraussetzung für datengetriebene Auswerteverfahren ist für die Prozessindustrie allerdings unpassend, denn auch bei mengenmäßig großen Datensätzen liegen für Kampagnen typischerweise nur Informationen über einige Batches mit einer Serie von Messdaten vor, die genügend Varianz für eine datengetriebene Auswertung aufweisen – nicht vergleichbar mit den Datenmengen im WWW oder von großen Internet-Konzernen.
Ein Weg zur wissensbasierten Produktion führt über die Betrachtung der wesentlichen Apparate-, Prozess- und Freigabedaten aus Betrieben und Labors. Das Potenzial dieser Daten wird heute vielfach noch nicht konsequent für ein umfassendes Verständnis der Produktion genutzt.
Neben Fragen zur Datenerfassung, Datenkonnektivität und Datenintegrität müssen solche Daten für eine ganzheitliche Prozessanalyse zunächst mit Kontextinformationen zusammengebracht werden. Datenquellen enthalten vor allem Zeitwertpaare, die numerisch vorbehandelt und möglichst vollautomatisch ausgewertet werden müssen. Am Beispiel NMR-spektroskopischer Daten wird der Stand der Auswertung mit physikalisch motivierten Modellen, wie z. B. dem IHM erläutert.
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.
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.
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.
Im Vortrag werden Anforderungen und Lösungsvorschläge für das Labor der Zukunft diskutiert. Industrie 4.0 bzw. das Labor 4.0 hilft uns, komplexere Prozesse schneller umzusetzen. Entwicklung von Anlagen und Prozessen beginnt im Labor 4.0. Dazu werden offene, nicht proprietäre Schnittstellen und Standards bei Laborgeräten und Feldgeräten dringend benötigt.
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.
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.
Um in einem veränderten Umfeld erfolgreich bestehen zu können, müssen Chemieunternehmen neue Pfade beschreiten. Dazu gehört insbesondere das Potential digitaler Technologien. Mit flexiblen, modularen chemischen Vielzweck-Produktionsanlagen lassen sich häufig wechselnde Produkte mit kürzeren Vorlauf- und Stillstandzeiten zwischen den Kampagnen und dennoch hoher Qualität realisieren. Intensivierte, kontinuierliche Produktionsanlagen erlauben auch den Umgang mit schwierig zu handhabenden Substanzen.
Grundvoraussetzung für solche Konzepte ist eine hochautomatisierte "chemische" Prozesskontrolle zusammen mit Echtzeit-Qualitätskotrolle, die "chemische" Informationen über den Prozess bereitstellt. In einem Anwendungsbeispiel wurde eine pharmazeutische Lithiierungsreaktion aus einer modularen Pilot-Anlage betrachtet und dabei die Vorzüge eines vollautomatischen NMR-Sensors untersucht. Dazu wurde ein kommerziell erhältliches Benchtop-NMR-Spektrometer mit Permanentmagnet auf die industriellen Anforderungen, wie Explosionsschutz, Feldkommunikation und vollautomatischer, robuster Datenauswertung angepasst. Der NMR-Sensor konnte schließlich erfolgreich im vollautomatischen Betrieb nach fortschrittlichen Regelkonzepten und für die Echtzeitoptimierung der Anlage getestet werden. Die NMR-Spektroskopie erwies sich als hervorragende Online-Methode und konnte zusammen mit einer modularen Datenauswertung sehr flexibel genutzt werden. Die Methode konnte überdies als zuverlässige Referenzmethode zur Kalibrierung konventioneller Online-Analytik eingesetzt werden.
Zukünftig werden voll integrierte und intelligent vernetzte "smarte" Sensoren und Prozesse eine kontinuierliche Produktion von Chemikalien und Pharmazeutika mit vertretbaren Qualitätskosten möglich machen.
Um in einem veränderten Umfeld erfolgreich bestehen zu können, müssen Chemieunternehmen neue Pfade beschreiten. Dazu gehört insbesondere das Potential digitaler Technologien. Mit flexiblen, modularen chemischen Vielzweck-Produktionsanlagen lassen sich häufig wechselnde Produkte mit kürzeren Vorlauf- und Stillstandzeiten zwischen den Kampagnen und dennoch hoher Qualität realisieren. Intensivierte, kontinuierliche Produktionsanlagen erlauben auch den Umgang mit schwierig zu handhabenden Substanzen.
Grundvoraussetzung für solche Konzepte ist eine hochautomatisierte "chemische" Prozesskontrolle zusammen mit Echtzeit-Qualitätskotrolle, die "chemische" Informationen über den Prozess bereitstellt. In einem Anwendungsbeispiel wurde eine pharmazeutische Lithiierungsreaktion aus einer modularen Pilot-Anlage betrachtet und dabei die Vorzüge eines vollautomatischen NMR-Sensors untersucht. Dazu wurde ein kommerziell erhältliches Benchtop-NMR-Spektrometer mit Permanentmagnet auf die industriellen Anforderungen, wie Explosionsschutz, Feldkommunikation und vollautomatischer, robuster Datenauswertung angepasst. Der NMR-Sensor konnte schließlich erfolgreich im vollautomatischen Betrieb nach fortschrittlichen Regelkonzepten und für die Echtzeitoptimierung der Anlage getestet werden. Die NMR-Spektroskopie erwies sich als hervorragende Online-Methode und konnte zusammen mit einer modularen Datenauswertung sehr flexibel genutzt werden. Die Methode konnte überdies als zuverlässige Referenzmethode zur Kalibrierung konventioneller Online-Analytik eingesetzt werden.
Zukünftig werden voll integrierte und intelligent vernetzte "smarte" Sensoren und Prozesse eine kontinuierliche Produktion von Chemikalien und Pharmazeutika mit vertretbaren Qualitätskosten möglich machen.
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.
Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals
(2019)
Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, 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 is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach
was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance.
Um in einem veränderten Umfeld erfolgreich bestehen zu können, müssen Chemie-unternehmen neue Pfade beschreiten. Dazu gehört insbesondere das Potential digi-taler Technologien. Mit flexiblen, modularen chemischen Vielzweck-Produktionsanlagen lassen sich häufig wechselnde Produkte mit kürzeren Vorlauf- und Stillstandzeiten zwischen den Kampagnen und dennoch hoher Qualität realisie-ren. Intensivierte, kontinuierliche Produktionsanlagen erlauben auch den Umgang mit schwierig zu handhabenden Substanzen.
Grundvoraussetzung für solche Konzepte ist eine hochautomatisierte "chemische" Prozesskontrolle zusammen mit Echtzeit-Qualitätskotrolle, die "chemische" Informati-onen über den Prozess bereitstellt. In einem Anwendungsbeispiel wurde eine phar-mazeutische Lithiierungsreaktion aus einer modularen Pilot-Anlage betrachtet und dabei die Vorzüge eines vollautomatischen NMR-Sensors untersucht. Dazu wurde ein kommerziell erhältliches Benchtop-NMR-Spektrometer mit Permanentmagnet auf die industriellen Anforderungen, wie Explosionsschutz, Feldkommunikation und voll-automatischer, robuster Datenauswertung angepasst. Der NMR-Sensor konnte schließlich erfolgreich im vollautomatischen Betrieb nach fortschrittlichen Regelkon-zepten und für die Echtzeitoptimierung der Anlage getestet werden. Die NMR-Spektroskopie erwies sich als hervorragende Online-Methode und konnte zusammen mit einer modularen Datenauswertung sehr flexibel genutzt werden. Die Methode konnte überdies als zuverlässige Referenzmethode zur Kalibrierung konventioneller Online-Analytik eingesetzt werden.
Zukünftig werden voll integrierte und intelligent vernetzte "smarte" Sensoren und Pro-zesse eine kontinuierliche Produktion von Chemikalien und Pharmazeutika mit ver-tretbaren Qualitätskosten möglich machen.
There is a need within the NMR community to progress forward in exploring new facets in which we can use analytical techniques to advance our understanding of various systems. One aspect the NMR community hasn’t fully encompassed is the validation process, which also involves setting reference standards, establishing a common language that directly relates to NMR, communication relating to validation, and much more.
This workshopcontribution starts with an overview on international metrology for qNMR spectroscopy. Since NMR is completely described by mathematical equations, the measurement unceartainty can directly be dreived from formula. Examples are presented. These are differentiated between type A and B evaluations. Finally the Expanded Unceartainty is defined. Since the user needs a risk-based unceartainty assessment, different "leagues" for routine, advanced, and high level needs are proposed to make clear, that no all sources of uncertainty have to be taken in considerention at practical levels.
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.
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.
The talk introduces a smart online NMR sensor module provided in an explosion proof housing as example. This sensor was developed for an intensified industrial process (pharmaceutical lithiation reaction step) funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). 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.
The talk also generally covers current aspects of high-field and low-field online NMR spectroscopy for reaction monitoring and process control.
Prozessindustrie Gemeinsam Digital – Forschungsbedarf für die Digitalisierung der Prozessindustrie
(2019)
Die Digitale Transformation durchdringt weite Teile der Industrie mit einer zunehmenden Vernetzung und Digitalisierung entlang der Wertschöpfungskette.
Basierend auf dem Whitepaper der DECHEMA „Digitalisierung in der Chemieindustrie“ entsteht die Vision eines gläsernen Apparates mit einem zeitlich, räumlich und methodisch dichtem Monitoring der Betriebszustände, das eine daten- und modellbasierte Optimierung transienter oder instationärer Prozesse (das heißt An-/Abfahren, Laständerungen, etc.) erlaubt. Auch der nicht bestimmungsmäßige Betrieb von Apparaten oder Modulen wird zuverlässig erkannt. Basis ist hier die Implementierung von innovativer Messtechnik und neuartigen Sensoren in Kombination mit der Anwendung datengetriebener oder rigoroser Modelle zur Prozesssimulation und -kontrolle.
Anlagen, die auf diesem Konzept basieren, lassen sich sicher an Stabilitäts- bzw. Kapazitätsgrenzen und mit höherem Durchsatz betreiben. Trotz schwankender Rohstoffqualitäten kann eine gleichbleibende Zielqualität der Produkte mit rentablen Qualitätssicherungskosten garantiert werden. Dazu ist eine Vernetzung über weitere Branchen (Softwareingenieure, MSR-Technik, Betreiber, Hersteller) notwendig. Sie ergänzt komplementär die ENPRO-Initiative mit Themen der modularen Anlagenplanung und -konstruktion und modular verteilter Intelligenz. Ein Digitalisierungsbedarf besteht auch für bereits vorhandenen Anlagenbestand bzw. zukünftig nicht modular gestalteter Produktionsapparate der chemischen Industrie, um auch dort einen wesentlichen Beitrag zur Ressourcen- und Energieeffizienz zu leisten.
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.
What is the future of Analytical Sciences? The talk starts with a definition, comparing the current view with that from 1968. How do wie set trends? How do we get Analytics inside? Some examples of "Big Science" are given and discussed in relation to a definition of AS. How does AS face the current Grand Challenges?
As exaples for something significant, several exaples are presented, such as Climate Change of Hydrogen Storage. Another important trand are eScience and automation concepts for AS, which are highlighted.
But (Analytical) Science has to be politcal in our times to face Fake News and to breake barriers!
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.
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.
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.
The talk also generally covers current aspects of high-field and low-field online NMR spectroscopy for reaction monitoring and process control and gives also an overview on direct dissolution studies of API cocrystals.
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.
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.
qNMR provides the most universally applicable form of direct concentration or purity determination without need for reference materials of impurities or the calculation of response factors but only exhibiting suitable NMR properties. The workshop presents basic terms of statistics and uncertainty analysis, which are the basis for qNMR spectroscopy and data analysis such as, e.g., standard deviations, linear regression, significance tests, etc. and gives typical examples of applications in qNMR spectroscopy.
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.
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.
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.
Die ehemals wegen komplexer Technik und vergleichsweise hohen Wartungskosten beim Anwender ungeliebte Prozessanalysentechnik (PAT) erfährt sich mittlerweile immer mehr als etablierender Bereich mit einem großen Zuwachs und Dynamik. Die Prozesskontrolle und -steuerung über physikalische Kenngrößen wie Druck und Temperatur lässt eine weitere Optimierung der Anlagen kaum mehr zu. Nur mittels stoffspezifischer Analysen lassen sich Rohstoffschwankungen, Ausbeuten und Energieeinsatz konsequent optimieren.
Der systematische Einsatz der Prozessanalysentechnik verändert Prozesse und Produktionsumgebungen und hat damit die Chance, Kernstück dezentral automatisierter Produktionseinheiten zu werden. Ein neuer Arbeitskreis der NAMUR AK 3.7 „Smarte Sensorik, Aktorik und Kommunikation“ wird diesem verstärkt Rechnung tragen. Es werden reale Anwendungsbeispiele aufgezeigt, die eine schnelle Amortisation von PAT im Prozess untermauern.
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).
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).
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.
The talk introduces a smart online NMR sensor module provided in an explosion proof housing as example. This sensor was developed for an intensified industrial process (pharmaceutical lithiation reaction step) funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). 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.
Industry 4.0, IIoT, or Lab 4.0 will enable us to handle more complex processes in shorter time. Intensified production concepts require for adaptive analytical instruments and control technology to realize short set-up times, modular control strategies. They are based on a digitized Laboratory 4.0.
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.
Der Vortrag stellt einige aktuelle Herausforderungen für die Prozessanalytik und mögliche Antworten vor.
Gepulste Raman-Spektrometer akkumulieren das Raman-Signal mit Hilfe schneller optischer Schalter im Picosekunden-Bereich, bevor langlebigere Fluoreszenzanregung entsteht. Damit lassen sich stark fluoreszierende Materialien untersuchen, die bislang nicht zugänglich sind. Eine weitere interessante Entwicklung ist etwa die Shifted excitation Raman difference spectroscopy (SERDS) die besonders für biologische Anwendungen interessant ist.
Flexible, modulare Produktionsanlagen stellen einen vielversprechenden Ansatz für die kontinuierliche Produktion von Fein- und Spezialchemikalien dar. In einem EU-Projekt wurde die Feldintegration eines Online-NMR-Sensormoduls als smartes Modul für die Prozesskontrolle vorangebracht. Dieses Modul basiert auf einem kommerziell erhältlichen Niederfeld-NMR-Spektrometer, welche zurzeit für die Anwendung im Laborbereich erhältlich ist. Für die Feldintegration wurde ein ATEX-zertifiziertes, explosionsgeschütztes Gehäuse entwickelt sowie Automationsschemen für den unbeaufsichtigten Betrieb und für die kalibrierfreie spektrale Datenauswertung erstellt.
Eine sehr gut anwendbare analytische Messtechnik zur Kontrolle der elementaren Zusammensetzung von verschiedensten Materialien ist die laserinduzierte Plasmaspektroskopie (LIPS, engl. LIBS - Laser-induced Breakdown Spectroscopy). Bei der LIBS wird ein kurz gepulster Laser auf eine Probe fokussiert, um ein Leuchtplasma zu erzeugen. Das dabei erzeugte Atomemissionsspektrum ermöglicht eine qualitative und quantitative Analyse der Zusammensetzung der Probe bezüglich praktisch aller Elemente des Periodensystems. In einem aktuellen Projekt wird diese Methode neben anderen zur Online-Analyse von Ackerböden für die ortsspezifischer Bewirtschaftung (Precision Agriculture) weiterentwickelt und bewertet.
qNMR provides the most universally applicable form of direct concentration or purity determination without need for reference materials of impurities or the calculation of response factors but only exhibiting suitable NMR properties.
The workshop presents basic terms of statistics and uncertainty analysis, which are the basis for qNMR spectroscopy and data analysis such as, e.g., standard deviations, linear regression, significance tests, etc. and gives typical examples of applications in qNMR spectroscopy.
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.
The talk introduces a smart online NMR sensor module provided in an explosion proof housing as example. This sensor was developed for an intensified industrial process (pharmaceutical lithiation reaction step) funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). 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.
The talk also generally covers current aspects of high-field and low-field online NMR spectroscopy for reaction monitoring and process control and gives also an overview on direct dissolution studies of API cocrystals.