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Eingeladener Vortrag
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Die BAM ist nahezu über die gesamte Wertschöpfungskette hinweg wissenschaftlich tätig. Von der sicheren und effizienten Wasserstofferzeugung (POWER-to-GAS), über die (Zwischen-)Speicherung von Wasserstoff in Druckgasspeichern bis hin zum Transport bspw. mittels Trailerfahrzeug zum Endverbraucher.
Komplettiert werden die Aktivitäten der BAM durch die sicherheitstechnische Beurteilung von wasserstoffhaltigen Gasgemischen, die Verträglichkeitsbewertung von Werkstoffen bis hin zur Detektion von Wasserstoffkonzentrationen über geeignete Sensorik, auch mittels ferngesteuerter Messdrohnen (sog. UAV-Drohnen).
Zudem untersucht die BAM proaktiv Schadensrisiken und Unfallszenarien für die Sicherheitsbetrachtung, um mögliche Schwachstellen aufzeigen und potenzielle Gefährdungen erkennen zu können.
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.
Die chemische Industrie steht derzeit, wie viele andere Industriebereiche, vor den Herausforderungen einer Digitalisierung der Produktion. Sie ist der Schlüssel für die Flexibilisierung von Prozessen und Anlagen, für die Verkürzung von Produkteinführungszeiten sowie für den Zuschnitt der Produktion auf wechselnde Nachfrage und kürzere Produktlebenszyklen. Die Messtechnik und Sensorik spielt neben der intelligenten Datenverarbeitung eine Schlüsselrolle für die Digitalisierung. Flexiblere Anlagen benötigen Sensorik zur Überwachung des Anlagenzustandes, zur Früherkennung nicht bestimmungsgemäßer Betriebszustände sowie für eine bedarfsgerechte Wartung. Da die Entwicklung neuer und verbesserter Messtechnik und Sensorik grundlegend aus verschiedenen Richtungen gedacht werden muss, haben sich Akteure aus verschiedenen Branchen zusammengetan und dieses Positionspapier erstellt. Es basiert auf einer grundlegenden Analyse des Ist-Stands sowie des Bedarfs der Industrie, die unter anderem auf einem eigens dafür durchgeführten Workshop mit Sensorentwicklern, Anlagenherstellern sowie Anlagenbetreibern am 18. Juni 2019 bei der DECHEMA in Frankfurt a. M. diskutiert wurden. Diese Aktivitäten wurden maßgeblich von der Initiative Wanted Technologies der ProcessNet sowie dem AMA Verband für Sensorik und Messtechnik e.V. initiiert.
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.
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.
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).
Optimizing the Green Synthesis of ZIF-8 by Reactive Extrusion Using In Situ Raman Spectroscopy
(2023)
We report the scale-up of a batch solid synthesis of zeolitic imidazolate framework-8 (ZIF-8) for reactive extrusion. The crystalline product forms in the extruder directly under the mixture of solid 2-methylimidazole and basic zinc carbonate in the presence of a catalytic amount of liquid. The process parameters such as temperature, liquid type, feeding rate, and linker excess were optimized using the setup specifically designed for in situ Raman spectroscopy. Highly crystalline ZIF-8 with a Brunauer–Emmett–Teller (BET) surface area of 1816 m2 g–1 was quantitatively prepared at mild temperature using a catalytic amount of ethanol and a small excess of the linker. Finally, we developed a simple and comprehensive approach to evaluating the environmental friendliness and scalability of metal–organic framework (MOF) syntheses in view of their large-scale production.
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.
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.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
For absorption processes with fluctuating feed gas compositions it is vital to continuously adjust
the operation point to achieve energy efficiency. In this contribution a Raman-based advanced
process control (APC) is introduced for the absorption of carbon dioxide (CO2) using an aqueous
solution of monoethanolamine (MEA). The APC is based on a Raman spectroscopic analysis of
the composition and CO2 load of the scrubbing liquid and a non-linear model predictive control
(NMPC) to adjust the scrubbing liquid cycle. In addition, an outer real-time optimization loop is
set in place to update the set points for the absorption process depending on the current feed gas composition minimizing the energy consumption of the process. Implementation and testing of the APC have been carried out in a mini-plant at TU Berlin. During a plant operation of more than 160 hours robustness and stability of the APC were shown.
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.
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).
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.