TY - JOUR A1 - Bornemann-Pfeiffer, Martin A1 - Kern, Simon A1 - Maiwald, Michael A1 - Meyer, Klas T1 - Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production JF - Chemie Ingenieur Technik N2 - 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. KW - Modular Production KW - Chemical Process Control KW - Process Analytical Technology KW - Digital Transformation KW - Industry 4.0 PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-517264 DO - https://doi.org/10.1002/cite.202000150 SN - 1522-2640 VL - 93 IS - 1-2 SP - 62 EP - 70 PB - Wiley-VCH CY - Weinheim AN - OPUS4-51726 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meyer, Klas A1 - Bornemann-Pfeifer, Martin A1 - Thiele, I. A1 - Gutschmann, B. A1 - Riedel, S. L. A1 - Falkenstein, S. A1 - Döring, T. A1 - Abele, M. A1 - Neubauer, M. A1 - Maiwald, Michael T1 - Benchtop-NMR-Spektroskopie – Praktische Probleme in Labor- und Prozessanwendungen N2 - In den letzten Jahren erfreut sich die Benchtop-NMR-Spektroskopie steigender Beliebtheit. Zusätzlich zu Anwendungen im Labor werden die kompakten Geräte auch zunehmend in industriellen Bereichen eingesetzt. Neben geringeren Investitions- und Betriebskosten sind insbesondere die Robustheit und einfache Bedienbarkeit dieser Systeme entscheidende Faktoren. Ein prozessnaher Einsatz kann sowohl nach Probenahme im Rahmen der Betriebsanalytik erfolgen als auch als Online-Analytik mit direkter Ankopplung an Prozesse. Insbesondere bei der direkten Kopplung mittels Durchflusszellen spielt der Probentransfer eine entscheidende Rolle. Dies zeigt sich auch am Beispiel eines Extraktionsprozesses im Labormaßstab. Das in einem Bioprozess aus tierischen Altfetten hergestellte Copolymer PHB(Co-HHX) wurde in einem Batch-Prozess mit organischen Lösungsmitteln aus gefrier-getrockneten Zellen extrahiert. Unter Variation der Parameter wurde der Prozess direkt in der mehrphasigen Suspension mittels Benchtop-NMR-Spektroskopie verfolgt. Eine große Limitierung für den Einsatz als Online-Methode ist die Temperaturempfindlichkeit der Magnetsysteme. Während die Umgebungstemperatur gut kontrollierbar ist, stellt sich die Thematik der Probentemperatur komplexer dar. Bei größeren Abweichungen von der Magnettemperatur kommt es bereits nach kurzer Zeit zu Drift-Effekten, die die Lock-Systeme nur begrenzt kompensieren können. Ein Ansatz ist die Nutzung temperierter Luftströme, ähnlich bekannter VT-Einheiten ergänzt um eine aktive Isolierung zum Magneten. An einem Modellaufbau wurden dazu verschiedene Konstruktionen zunächst mittels Thermographie charakterisiert und anschließend die Anwendbarkeit in einem Prototyp-Gerät überprüft. In der rauen industriellen Umgebung von Produktionsanlagen sind weitere Maßnahmen zum Betrieb eines Laborgerätes nötig (z.B. Temperierung, Witterungsschutz). Zusätzlich bestehen häufig strikte Anforderungen an den Explosionsschutz. Zu diesem Zweck wurde gemeinsam mit der Evonik Rheinfelden eine Einhausung entwickelt, die es ermöglicht neben der NMR-Spektroskopie auch eine weitere analytische Methode möglichst flexibel direkt an großtechnische Anlagen anzubinden. T2 - 44. Tagung Praktische Probleme der Kernresonanzspektroskopie 2023 CY - Berlin, Germany DA - 21.03.2023 KW - Benchtop-NMR KW - Prozessanalytik KW - Extraktion PY - 2023 AN - OPUS4-57202 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meyer, Klas A1 - Abele, M. A1 - Bornemann-Pfeiffer, Martin A1 - Döring, T. A1 - Falkenstein, S. A1 - Kern, S. A1 - Maiwald, Michael T1 - Benchtop NMR spectroscopy in chemical manufacturing: From hardware field integration to data modeling N2 - The use of benchtop-NMR instruments is constantly increasing during the recent years. Advantages of being affordable, portable and easy-to-operate without the need for trained staff make them especially interesting for industrial applications in quality control. However, applications of NMR spectroscopy as an online PAT tool are still very rare but offer a huge potential for process optimization and control. A key task to exploit this potential is hardware field integration of the lab-instruments in a rough environment of a chemical plant. Additionally, developments in automation and data evaluation are mandatory to ensure a robust unattended operation with low maintenance requirements. Here, we show an approach of a fully automated analyzer enclosure considering explosion safety, field communication, as well as environmental conditions in the field. Temperature sensitivity is still a limitation of benchtop-NMR instruments in flow applications. Recent developments of manufacturers allow for limited operation at static temperature levels, however, a dynamic system for continuous operation is still not available. Using a prototype system offering a larger bore, active temperature shielding studies with thermostated air were performed evaluating the performance. Automated data evaluation of NMR spectra using a modular indirect hard modeling (IHM) approach showed good results and flexibility. 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, offering new ways to simultaneously evaluating large numbers of different models. T2 - Quantitative NMR Methods for Reaction and Process Monitoring (NMRPM) CY - Kaiserslautern, Germany DA - 24.05.2023 KW - Field integration KW - Benchtop-NMR KW - Process Analytical Technology PY - 2023 AN - OPUS4-57560 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meyer, Klas T1 - BAM quality toolbox framework for qNMR N2 - Accreditation of analytical methods, either according to GxP or ISO regulations, requires a comprehensive quality management system. General quality documents are often already in place, which need to be extended by method-specific documentation. In this presentation we like to show an idea of a modular set of standard operating procedures (SOP) specifically developed meeting the requirements for quantitative NMR. The future goal is to collaborate with different accreditated NMR laboratories to compile a universal set of SOP and other quality documents that can be modified and used as a starting point for developing your own quality system for applications of qNMR in a regulated environment. T2 - ValidNMR Workshop at Practical Applications of NMR in Industry Conference (PANIC) CY - Online meeting DA - 22.10.2020 KW - ValidNMR KW - Validation KW - NMR spectroscopy KW - ISO 17025 KW - GxP PY - 2020 AN - OPUS4-51466 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Meyer, Klas T1 - Atomkerne zählen - Quantitative NMR-Spektroskopie als vielseitige Methode in Labor und Prozess JF - GIT-Labor-Fachzeitschrift N2 - Meist präsentieren sich Laboratorien für NMR-Spektroskopie auf den ersten Blick von außen nur wenig einladend. Es lohnt sich aber einen Blick hinein zu riskieren, denn die Methode ist heute unverzichtbar für die analytische Chemie. Die quantitativen Möglichkeiten werden häufig unterschätzt, erfahren allerdings durch den aufstrebenden Markt kompakter Benchtop-NMR-Spektrometer verstärktes Interesse für Anwendungen von der Laborbank bis hin zum Industrieprozess. KW - NMR-Spektroskopie KW - Benchtop-NMR KW - qNMR PY - 2020 UR - https://analyticalscience.wiley.com/do/10.1002/was.000600052 SN - 0016-3538 VL - 64 IS - 10 SP - 28 EP - 30 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-51464 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fricke, F. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Hierzegger, R. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method JF - Transactions on Emerging Topics in Computing N2 - 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. KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy KW - Modular Production PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 DO - https://doi.org/10.1109/TETC.2021.3131371 SN - 2168-6750 VL - 10 IS - 1 SP - 87 EP - 98 PB - IEEE AN - OPUS4-53941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, S. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy JF - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) N2 - 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. T2 - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) CY - Grenoble, France DA - 01.02.2021 KW - Industry 4.0, KW - Cyber-physical systems KW - Artificial neural networks KW - Mass spectrometry KW - Nuclear magnetic resonance spectroscopy PY - 2021 DO - https://doi.org/10.23919/DATE51398.2021.9473958 SP - 615 EP - 620 PB - IEEE AN - OPUS4-55360 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gottu Mukkula, A. R. A1 - Kern, Simon A1 - Salge, M. A1 - Holtkamp, M. A1 - Guhl, Svetlana A1 - Fleischer, C. A1 - Meyer, Klas A1 - Remelhe, M. A1 - Maiwald, Michael A1 - Engell, S. T1 - An Application of Modifier Adaptation with Quadratic Approximation on a Pilot Scale Plant in Industrial Environment JF - IFAC-PapersOnLine N2 - 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. KW - Process Analytical Technology KW - Online NMR Spectroscopy KW - Process Industry KW - Iterative real-time optimization KW - Modifier adaptation KW - Plant-model mismatch KW - Reactor control KW - CONSENS PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524531 DO - https://doi.org/10.1016/j.ifacol.2020.12.685 SN - 1522-2640 VL - 53 IS - 2 SP - 11773 EP - 11779 PB - Elsevier CY - Amsterdam AN - OPUS4-52453 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meyer, Klas A1 - Kern, Simon A1 - Guhl, Svetlana A1 - Wander, Lukas A1 - Paul, Andrea A1 - Maiwald, Michael T1 - Already Producing or Still Calibrating? – Advances of Model-Based Automation for Online NMR Spectroscopy N2 - The transition 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 comparison method”, independent of the matrix, it runs with very short set-up times in combination with “modular” spectral models. These are based on pure component NMR spectra without the need for tedious calibrations runs. We present approaches from statistical, (i.e., Partial Least Squares Regression) to physically motivated models (i.e., Indirect Hard Modelling). Based on concentration measurements of reagents and products by the NMR analyser 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). T2 - 2nd Reaction Monitoring Symposium CY - Bath, UK DA - 28.01.2019 KW - Process Analytical Technology KW - NMR Spectroscopy KW - Modular Production PY - 2019 AN - OPUS4-47309 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -