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Eingeladener Vortrag
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Modular plants using intensified continuous processes represent an appealing concept to produce pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes, and it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and includes a compact Nuclear Magnetic Resonance (NMR) spectrometer for online quality monitoring as well as a new model-based control approach. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including ro-bust evaluation of sensor data.
Here, we present alternatives for the quantitative determination of the analytes using modular, physically motivated models. These models can be adapted to new substances solely by the use of their corresponding pure component spectra, which can either be derived from experimental spectra as well as from quantum mechanical models or NMR predictors. Modular means that spec-tral models can simply be exchanged together with alternate reagents and products. Beyond that, we comprehensively calibrated an NIR spectrometer based on online NMR process data for the first time within an industrial plant. The integrated solution was developed for a metal organic reac-tion running on a commercial-scale modular pilot plant and it was tested under industrial conditions.
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.
Der Wandel von der aktuellen Automation zum smarten Sensor ist im vollen Gange. Automatisierungstechnik, sowie die Informations- und Kommunikationstechnik (IKT) verschmelzen zunehmend. Eine Topologie für smarte Sensoren, die das Zusammenwirken mit daten- und modellbasierten Steuerungen bis hin zur Softsensorik beschreibt gibt es bis heute jedoch noch nicht. Um zu einer störungsfreien Kommunikation aller Komponenten auf Basis eines einheitlichen Protokolls zu kommen sollte die Prozessindustrie die Weichen für eine smarte und sichere Kommunikationsarchitektur stellen. Sie verwehrt stattdessen die Entwicklungen ihrer Zulieferer und wartet lieber ab. Der Beitrag greift die Anforderungen der Technologie-Roadmap „Prozess-Sensoren 4.0“ auf und zeigt Möglichkeiten zu ihrer Realisierung am Beispiel eines Online-NMR-Analysators, der im Rahmen eines EU-Projekts entwickelt wurde.
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.
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.
Reaction monitoring in disperse systems, such as emulsions, is of significant technical importance in various disciplines like biotechnological engineering, chemical industry, food science, and a growing number other technical fields. These systems pose several challenges when it comes to process analytics, such as heterogeneity of mixtures, changes in optical behavior, and low optical activity. Concerning this, online nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for process monitoring in complex reaction mixtures due to its unique direct comparison abilities, while at the same time being non-invasive and independent of optical properties of the sample. In this study the applicability of online-spectroscopic methods on the homogeneously catalyzed hydroformylation system of 1-dodecene to tridecanal is investigated, which is operated in a mini-plant scale at Technische Universität Berlin. The design of a laboratory setup for process-like calibration experiments is presented, including a 500 MHz online NMR spectrometer, a benchtop NMR device with 43 MHz proton frequency as well as two Raman probes and a flow cell assembly for an ultraviolet and visible light (UV/VIS) spectrometer. Results of high-resolution online NMR spectroscopy are shown and technical as well as process-specific problems observed during the measurements are discussed.
A major industrial reaction based on homogeneous catalysis is hydroformylation for the production of aldehydes from alkenes and syngas. Hydroformylation in microemulsions, which is currently under investigation at Technische Universität Berlin on a mini-plant scale, was identified as a cost efficient approach which also enhances product selectivity. Herein, we present the application of online Raman spectroscopy on the reaction of 1-dodecene to 1-tridecanal within a microemulsion. To achieve a good representation of the operation range in the mini-plant with regard to concentrations of the reactants a design of experiments was used. Based on initial Raman spectra partial least squares regression (PLSR) models were calibrated for the prediction of 1-dodecene and 1-tridecanal. Limits of predictions arise from nonlinear correlations between Raman intensity and mass fractions of compounds in the microemulsion system. Furthermore, the prediction power of PLSR models becomes limited due to unexpected by-product formation. Application of the lab-scale derived calibration spectra and PLSR models on online spectra from a mini-plant operation yielded promising estimations of 1-tridecanal and acceptable predictions of 1-dodecene mass fractions suggesting Raman spectroscopy as a suitable technique for process analytics in microemulsions.
The Collaborative Research Center InPROMPT aims to establish a novel process concept for the hydroformylation of long-chained olefins, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro-emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the temperature and concentration sensitive multi-phase system demands a continuous observation of the reaction to achieve an operational and economically feasible plant operation. For that purpose, we tested the potential of both NMR and Raman spectroscopy for process control assistance. The lab-scale experiments were supported by sampling for off-line GC-analysis as reference analytics. The results of the NMR experiments will be part of another contribution.
Die Abtrennung von CO2 aus industriellen Gasströmen ist eine großtechnisch wichtige Trennaufgabe. Neben der Aufarbeitung von Erdgas werden diese Verfahren zur Verminderung der CO2-Emission durch industrielle Rauchgase, insbesondere für alternativlose Prozesse wie die Stahl- und Zementproduktion diskutiert.
Hydroformylation represents an important homogeneous catalyzed process, which is widely used within chemical industry. Usually applied with simple alkenes like Propene and Butene aldehydes obtained from alkenes >C6 are relevant intermediates in production of plasticizers, surfactants and polymers. Today the active catalyst species is often based on valuable Rhodium complexes in aqueous solution. This implies the problem of limited water solubility of the reactands, which is acceptable for short chain lengths, but states a problem in case of higher alkenes. Along with that efficient separation and recycling of the catalyst becomes more complicated. There are different approaches tackling this problem, e.g., by using of salt formation in the BASF process or downstream distillation within the Shell process
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.
Compact nuclear magnetic resonance (NMR) instruments make NMR spectroscopy and relaxometry accessible in industrial and harsh environments for reaction and process control. An increasing number of applications are reported. To build an interdisciplinary bridge between “process control” and “compact NMR”,we give a short overviewon current developments in the field of process Engineering such as modern process design, integrated processes, intensified processes along with requirements to process control, model based control, or soft sensing. Finally, robust field integration of NMR systems into processes environments, facing explosion protection or Integration into process control systems, are briefly discussed.
Process monitoring of an intensified continuous production unit with compact NMR spectroscopy
(2016)
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied. NMR spectroscopy has a high potential for direct loop process control. Compact NMR instruments based on permanent magnets are robust and relatively inexpensive analyzers, which feature advantages like low cost, low maintenance, ease of use, and cryogen-free operation. Instruments for online NMR measurements equipped with a flow-through cell, possessing a good signal-to-noise-ratio, sufficient robustness, and meeting the requirements for integration into industrial plants (i.e., explosion safety and fully automated data analysis) are cur-rently not available off the rack. Recently, promising benchtop NMR instruments with acceptable performance came to market and process integrated sensors developed on basis of such laboratory instruments are on their way.
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. Smart sensors enable concepts like self-diagnostics, self-calibration, and self-configuration/parameterization whenever our current automation landscape allows it. Here we summarize the currently discussed general requirements for process sensors 4.0 and introduce a smart online NMR sensor module as example, which was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu).
Bei der Entwicklung pharmazeutischer Wirkstoffe werfen Substanzen mit einer geringen Löslichkeit, Auflösungsrate und damit einhergehend schlechten Bioverfügbarkeit immer größere Probleme auf. Diese Eigenschaften und nicht etwa toxikologische Eigenschaften oder schlechte Wirksamkeit sorgen dafür, dass es weniger als 1 % aller Wirkstoffe bis in die Markteinführung schaffen. Eine Reihe von Ansätzen zur Verbesserung der schlechten Wasserlöslichkeit konnten bereits etabliert werden. Hierunter zählen die gezielte Salzbildung bzw. Salzscreening, Solubilisierung mit Cosolventien oder die Nutzung von Polymeren als mögliche Transportwege. Eine weitere Möglichkeit bietet die Bildung verschiedener polymorpher Formen, sowohl der Einzelkomponente als auch von den verschiedenen Mehrkomponentenverbindungen.
Medium resolution nuclear magnetic resonance (MR-NMR) spectroscopy is currently a fast developing field, which has an enormous potential to become an important analytical tool for reaction monitoring, in hyphenated techniques, and for systematic investigations of complex mixtures. The recent developments of innovative MR-NMR spectrometers are therefore remarkable due to their possible applications in quality control, education, and process monitoring. MR-NMR spectroscopy can beneficially be applied for fast, non-invasive, and volume integrating analyses under rough environmental conditions.
Within this study, a simple 1/16'' fluorinated ethylene propylene (FEP) tube with an ID of 0.04'' (1.02 mm) was used as a flow cell in combination with a 5 mm glass Dewar tube inserted into a benchtop MR-NMR spectrometer with a 1H Larmor frequency of 43.32 MHz and 40.68 MHz for 19F. For the first time, quasi-simultaneous proton and fluorine NMR spectra were recorded with a series of alternating 19F and 1H single scan spectra along the reaction time coordinate of a homogeneously catalysed esterification model reaction containing fluorinated compounds. The results were compared to quantitative NMR spectra from a hyphenated 500 MHz online NMR instrument for validation. Automation of handling, pre-processing, and analysis of NMR data becomes increasingly important for process monitoring applications of online NMR spectroscopy and for its technical and practical acceptance. Thus, NMR spectra were automatically baseline corrected and phased using the minimum entropy method. Data analysis schemes were designed such that they are based on simple direct integration or first principle line fitting, with the aim that the analysis directly revealed molar concentrations from the spectra.
Finally, the performance of 1/16'' FEP tube set-up with an ID of 1.02 mm was characterised regarding the limit of detection (LOQ (1H) = 0.335 mol L-1 and LOQ (19F) = 0.130 mol L-1 for trifluoroethanol in D2O (single scan)) and maximum quantitative flow rates up to 0.3 mL min-1. Thus, a series of single scan 19F and 1H NMR spectra acquired with this simple set-up already presents a valuable basis for quantitative reaction monitoring.
Die quantitative NMR-Spektroskopie (qNMR) gewinnt in den letzten Jahren immer mehr an Bedeutung, speziell hinsichtlich der Anwendung auf komplexe Fragestellungen der analytischen Chemie. Ein großer Vorteil dieser Methode ist die Möglichkeit der Relativquantifizierung durch das „Zählen von Kernspins" in der Probe. Unter der Voraussetzung eines korrekt ausgeführten NMR-Experiments ist so der direkte Vergleich von Signalflächen im Spektrum möglich, ohne dass zuvor zwingend eine Kalibrierung notwendig ist.
Die Entwicklung innovativer Medium-Resolution-NMR-Spektrometer eröffnet eine Reihe bemerkenswerter Applikationen für die Qualitätskontrolle, die akademische Ausbildung und die Prozessanalytik. Die Verwendung von Permanentmagneten erlaubt den Einsatz der NMR-Spektroskopie in industriellen Prozessumgebungen.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
Online NMR spectroscopy is an excellent tool to study complex reacting multicomponent mixtures and gain process insight and understanding. For online studies under process conditions, flow NMR probes can be used in a wide range of temperature and pressure. This paper compiles the most important aspects towards quantitative process NMR spectroscopy in complex multicomponent mixtures and provides examples. After NMR spectroscopy is introduced as an online method and for technical samples without sample preparation in deuterated solvents, influences of the residence time distribution, pre-magnetization, and cell design are discussed. NMR acquisition and processing parameters as well as data preparation methods are presented and the most practical data analysis strategies are introduced.
Ugly Spectra and Lousy interfaces – Challenges for Compact NMR Spectroscopy in Process Control
(2016)
With the introduction of advanced process analytical technology, the closeness of key process variables to their limits can be directly controlled and the product can be classified or even released in real time.
Compact NMR instruments can make NMR spectroscopy accessible in industrial and harsh environments for process control.
Homogen katalysierte Reaktionen sind wichtige Werkzeuge der chemischen Industrie. Milde Reaktionsbedingungen und hohe Selektivitäten führen zu einer energie- und ressourcenschonenden Produktion. Ein bedeutender Prozessschritt ist die Hydroformylierung. Hier kommen Kobalt- und Rhodiumkomplexe mit mehrzähnigen Liganden zum Einsatz, die zumeist in wässriger Lösung vorliegen. Die Anwendbarkeit beschränkt sich demnach auf kurzkettige Edukte mit hinreichender Wasserlöslichkeit.
In this contribution a Raman spectrometer based control structure for the heating of a desorption column is proposed. For this purpose calibration experiments for the absorption of carbon dioxide using monoethanolamine solutions are carried out and calibration models are developed to measure both carbon dioxide liquid loads and monoethanolamine mass fractions. The calibration experiments are supported by online NMR spectroscopy to accurately measure the appearance of all species in the electrolyte system. Both models are tested during the plant operation of a mini-plant for the oxidative coupling of methane and the proof of concept for the control structure is given. The Raman spectroscopy implemented in the ATEX conform mini-plant shows a reliable and robust performance being even indifferent to impurities hindering the GC analysis.
Quantitative NMR spectroscopy for gas analysis for production of primary reference gas mixtures
(2017)
Due to its direct correlation to the number of spins within a sample quantitative NMR spectroscopy (qNMR) is a promising method with absolute comparison abilities in complex systems in technical, as well as metrological applications. Most of the samples studied with qNMR are in liquid state in diluted solutions, while gas-phase applications represent a rarely applied case. Commercially available NMR equipment was used for purity assessment of liquid and liquefied hydrocarbons serving as raw materials for production of primary reference gas standards. Additionally, gas-phase studies were performed within an online NMR flow probe, as well as in a high-pressure NMR setup to check feasibility as verification method for the composition of gas mixtures.
Flexible, modulare Produktionsanlagen auf der Basis von verfahrenstechnischen Teilmodulen stellen einen vielversprechenden Ansatz für die kontinuierliche Produktion von Fein- und Spezialchemikalien dar. Der Schwerpunkt des Horizont-2020-Projekts der Europäischen Kommission CONSENS (Integrated Control and Sensing) liegt in der Implementierung von innovativen Sensorkonzepten zur Prozessüberwachung und -regelung innerhalb von containerbasierten modularen Produktionsan lagen. In diesem Artikel wird die Feldintegration eines Online-NMR-Sensors als smartes Modul für die Prozesskontrolle beschrieben. Dieses Modul basiert auf einem kommerziell erhältlichen Niederfeld-NMRSpektrometer, das 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 spektrale Datenauswertung erstellt. Nachdem die Machbarkeit und Leistungsfähigkeit des Sensorkonzeptes in Laborexperimente an einer aromatischen Substitutionsreaktion bereits erfolgreich demonstriert wurde, ist die Inbetriebnahme des NMR-Sensormoduls in
industrieller Umgebung für 2017 geplant.
Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.
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.
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.
Process analytical techniques are extremely useful tools for chemical production and manufacture and are of particular interest to the pharmaceutical, food and (petro-) chemical industries.
Today, mainly optical online methods are applied. NMR spectroscopy has a high potential for direct loop process control. Compact NMR instruments based on permanent magnets are robust and relatively inexpensive analysers, which feature advantages like low cost, low maintenance, ease of use, and cryogen-free operation. Instruments for online NMR measurements equipped with a flow-through cell, possessing a good signal-to-noise-ratio, sufficient robustness, and meeting the requirements for integration into industrial plants (i.e., explosion safety and fully automated data analysis) are currently not available off the rack.
A major advantage of NMR spectroscopy is that the method features a high linearity between absolute signal area and sample concentration, which makes it an absolute analytical comparison method which is independent of the matrix. This is an important prerequisite for robust data evaluation strategies within a control concept and reduces the need for extensive maintenance of the evaluation model over the time of operation. Additionally, NMR spectroscopy provides orthogonal, but complimentary physical information to conventional, e.g., optical spectroscopy. It increases the accessible information for technical processes, where aromatic-toaliphatic conversions or isomerizations occur and conventional methods fail due to only minor changes in functional groups.
As a technically relevant example, the catalytic hydrogenation of 2-butyne-1,4-diol and further pharmaceutical reactions were studied using an online NMR sensor based on a commercially available low-field NMR spectrometer within the framework of the EU project CONSENS (Integrated Control and Sensing).
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.
Hydroformylation of short-chained olefins has been established as a standard industrial process for the production of C2 to C6 aldehydes. Using aqueous solutions of transition metal complexes these processes are carried out homogeneously catalyzed. A biphasic approach allows for highly efficient catalyst recovery. Regarding renewable feedstocks, the hydroformylation of long-chained alkenes (> C10) in a biphasic system, using highly selective rhodium catalysts has yet not been shown. Therefore, the Collaborative Research Center SFB/TR 63 InPROMPT develops new process concepts, involving innovative tuneable solvent systems to enable rather difficult or so far nonviable synthesis paths. One possible concept is the hydroformylation of long-chained alkenes in microemulsions. For this, a modular mixer-settler concept was proposed, combining high reaction rates and efficient catalyst recycling via the application of technical grade surfactants. The feasibility of such a concept is evaluated in a fully automated, modular mini-plant system within which the characteristics of such a multiphase system pose several obstacles for the operation. Maintaining a stable phase separation for efficient product separation and catalyst recycling is complicated by small and highly dynamic operation windows as well as poor measurability of component concentrations in the liquid phases. In this contribution, a model-based strategy is presented to enable concentration tracking and phase state control within dynamic mini-plant experiments. Raman spectroscopy is used as an advanced process analytical tool, which allows for online in-situ tracking of concentrations. Combined with optical and conductivity analysis optimal plant trajectories can be calculated via the solution of dynamic optimization problem under uncertainty. Applying these, a stable reaction yield of 40 % was achieved, combined with an oil phase purity of 99,8 % (total amount of oily components in the oil phase) and catalyst leaching below 0.1 ppm.
Um die internationale Spitzenposition deutscher Unternehmen der Prozessindustrie auch in Zukunft sicher zu stellen, müssen die Unternehmen ihre Prozesse und Geschäftsabläufe digitalisieren und gemeinsam mit der Forschung innovative neue Methoden, Apparate, Anlagen, Sensoren und Automatisierungstechnik sowie Datenkonzepte entwickeln. All dies erfordert den Aufbau neuer Fähigkeiten, Investition in Talente, interdisziplinäre Kommunikation zwischen verschiedenen Personen und Abteilungen und eine Bereitschaft zur Veränderung.
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 environments 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.
Validation report on NMR
(2017)
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. Smart sensors enable concepts like self-diagnostics, self-calibration, and self-configuration/ parameterization whenever our current automation landscape allows it.
Here we summarize the currently discussed general requirements for process sensors 4.0 and introduce a smart online NMR sensor module as example, which was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (www.consensspire.eu).
The preparation of new active pharmaceutical ingredient (API) multicomponent Crystal forms, especially co-crystals and salts, is being considered as a reliable strategy to improve API solubility and bioavailability. In this study, three novel imidazole-based salts of the poorly water-soluble salicylic acid (SA) are reported exhibiting a remarkable improvement in solubility and dissolution rate properties. All structures were solved by powder X-ray diffraction. Multiple complementary techniques were used to solve co-crystal/salt ambiguities: density functional Theory calculations, Raman and 1H/13C solid-state NMR spectroscopies. In all molecular salts, the Crystal packing interactions are based on a common charged assisted +N-H SA)...O-(co-former) hydrogen bond interaction. The presence of an extra methyl group in different positions of the co-former, induced different supramolecular arrangements, yielding salts with different physicochemical properties.
All salts present much higher solubility and dissolution rate than pure SA. The most promising results were obtained for the salts with imidazole and 1-methylimidazole co-formers.
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.
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.
The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole. Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature. This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.
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.
Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylation of long-chained olefins is studied in a mini-plant, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro¬emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the high sensitivity of this multi-phase system with regard to changes in temperature and composition demands a continuous observation of the reaction to achieve a reliable and economic plant operation. For that purpose, we tested the potential of both online NMR and Raman spectroscopy for process control. The lab-scale experiments were supported by off-line GC-analysis as a reference method.
A fiber optic coupled probe of a process Raman spectrometer was directly integrated into the reactor. 25 mixtures with varying concentrations of olefin (1-dodecene), product (n-tridecanal), water, n-dodecane, and technical surfactant (Marlipal 24/70) were prepared according to a D-optimal design. Online NMR spectroscopy was implemented by using a flow probe equipped with 1/16” PFA tubing serving as a flow cell. This was hyphenated to the reactor within a thermostated bypass to maintain process conditions in the transfer lines.
Partial least squares regression (PLSR) models were established based on the initial spectra after activation of the reaction with syngas for the prediction of unknown concentrations of 1-dodecene and n-tridecanal over the course of the reaction in the lab-scale system. The obtained Raman spectra do not only contain information on the chemical composition but are further affected by the emulsion properties of the mixtures, which depend on the phase state and the type of micelles. Based on the spectral signature of both Raman and NMR spectra, it could be deduced that especially in reaction mixtures with high 1-dodecene content the formation of isomers as a competitive reaction was dominating. Similar trends were also observed during some of the process runs in the mini-plant. The multivariate calibration allowed for the estimation of reactants and products of the hydroformylation reaction in both laboratory setup and mini-plant.
Monitoring chemical reactions is the key to chemical process control. Today, mainly
optical online methods are applied. NMR spectroscopy has a high potential for direct
loop process control. Compact NMR instruments based on permanent magnets
are robust and relatively inexpensive analysers, which feature advantages like low
cost, low maintenance, ease of use, and cryogen-free operation. Instruments for
online NMR measurements equipped with a flow-through cell, possessing a good
signal-to-noise-ratio, sufficient robustness, and meeting the requirements for
integration into industrial plants (i.e., explosion safety and fully automated data
analysis) are currently not available off the rack.
Intensified continuous processes are in focus of current research. Flexible (modular)
chemical plants can produce different products using the same equipment with short
down-times between campaigns and quick introduction of new products to the
market. In continuous flow processes online sensor data and tight closed-loop control
of the product quality are mandatory. If these are not available, there is a huge risk of
producing large amounts of out-of-spec (OOS) products. This is addressed in the
European Unionʼs Research Project CONSENS (Integrated Control and Sensing)
by development and integration of smart sensor modules for process monitoring and
control within such modular plant setups.
The presented NMR module is provided in an explosion proof housing of 57 x 57 x
85 cm module size and involves a compact 43.5 MHz NMR spectrometer together
with an acquisition unit and a programmable logic controller for automated data
preparation (phasing, baseline correction) and evaluation. Indirect Hard Modeling
(IHM) was selected for data analysis of the low-field NMR spectra. A set-up for
monitoring continuous reactions in a thermostated 1/8” tubular reactor using
automated syringe pumps was used to validate the IHM models by using high-field
NMR spectroscopy as analytical reference method.
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.
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.
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.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.