TY - JOUR A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Artificial neural networks for quantitative online NMR spectroscopy N2 - 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. KW - Online NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Automation KW - Process industry PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-507508 SN - 1618-2642 VL - 412 IS - 18 SP - 4447 EP - 4459 PB - Springer CY - Berlin AN - OPUS4-50750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael A1 - Meyer, Klas A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Kern, Simon A1 - Müller, S. A1 - Kowarik, Stefan T1 - Künstliche Neuronale Netze für quantitative Online-Spektroskopie in der Prozessindustrie – Lästige Modellbildung ade? N2 - Bei Industry 4.0 dreht sich alles um Interkonnektivität, sensorgestützte Prozesssteuerung und datengesteuerte Systeme. Prozessanalysentechnik (PAT) wie die Online-Kernresonanzspektroskopie (NMR) gewinnt zunehmend an Bedeutung, da sie zur Automatisierung und Digitalisierung in der Produktion beiträgt. Eine klassische Auswertung von Prozessdaten und deren Umsetzung in Wissen ist jedoch bisher in vielen Fällen aufgrund der unzureichend großen verfügbaren Datensätze nicht möglich oder nicht wirtschaftlich. Bei der Entwicklung eines automatisierten Verfahrens für die Prozesskontrolle stehen manchmal nur die Basisdaten einer begrenzten Anzahl von Batch-Versuchen aus typischen Produkt- und Prozessentwicklungskampagnen zur Verfügung. Diese Datensätze sind jedoch nicht groß genug, um maschinengestützte Verfahren zu trainieren. Um diese Einschränkung zu überwinden, wurde ein neues Verfahren entwickelt, das eine physikalisch motivierte Multiplikation der verfügbaren Referenzdaten erlaubt, um einen ausreichend großen Datensatz für das Training von maschinellen Lernalgorithmen zu erhalten. Das zugrundeliegende Beispiel einer chemischen Synthese wurde spektroskopisch verfolgt und mit der neuen Methode sowie mit einem physikalisch basierten Modell analysiert, wobei sowohl eine anwendungsrelevante Niederfeld-NMR als auch eine Hochfeld-NMR-Spektroskopie als Referenzmethode verwendet wurde. Künstliche neuronale Netze (ANNs) haben das Potenzial, bereits aus relativ begrenzten Eingabedaten wertvolle Prozessinformationen abzuleiten. Um jedoch die Konzentration unter komplexen Bedingungen (viele Edukte und weite Konzentrationsbereiche) vorherzusagen, sind größere ANNs und damit ein größerer Trainingsdatensatz erforderlich. Wir zeigen, dass ein mäßig komplexes Problem mit vier Edukten unter Verwendung von ANNs in Kombination mit der vorgestellten PAT-Methode (Niederfeld-NMR-Spektroskopie) und mit dem vorgeschlagenen Ansatz zur Erzeugung aussagekräftiger Trainingsdaten bewältigt werden kann. T2 - 16. Kolloquium Arbeitskreis Prozessanalytik CY - Online meeting DA - 23.11.2020 KW - Prozessindustrie KW - Online-NMR-Spektroskopie KW - Industrie 4.0 KW - Künstliche Neuronale Netze KW - Prozessanalytik KW - Digitale Transformation PY - 2020 AN - OPUS4-51647 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Training data of quantitative online NMR spectroscopy for artificial neural networks N2 - 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. KW - NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Online NMR spectroscopy KW - Automation KW - Process industry PY - 2020 U6 - https://doi.org/10.5281/zenodo.3677139 PB - Zenodo CY - Geneva AN - OPUS4-50456 LA - eng 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 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 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 - CONF A1 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Westerdicky, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy 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 - Online meeting 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 UR - www.date-conference.com SN - 978-3-9819263-5-4 SP - 615 EP - 620 PB - Research Publishing AN - OPUS4-52180 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael A1 - Meyer, Klas A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Kern, Simon A1 - Müller, S. A1 - Kowarik, Stefan T1 - Integrated and Networked Systems and Processes – A Perspective for Digital Transformation in (Bio) Process Engineering N2 - 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. T2 - 6th BioProScale Symposium - industrial scale bioprocess intensification from process development to large-scale understanding CY - Online meeting DA - 29.03.2021 KW - Industry 4.0 KW - Biotechnology KW - Bio engineering KW - Process Analytical Technology KW - BioProScale KW - Artificial Neural Networks PY - 2021 UR - https://biotechnologie.ifgb.de/node/648 AN - OPUS4-52371 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Tiebe, Carlo A1 - Maiwald, Michael A1 - Schukar, Marcus T1 - Beiträge der Prozessanalytik und Sensorik zur Sicherheit in Wasserstofftechnologien N2 - 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. T2 - Berlin Brandenburger Optik-Tag "Einsatz von Sensorik und Mikroelektronik in der Wasserstoffwirtschaft" CY - Online meeting DA - 29.11.2021 KW - H2Safety@BAM KW - SensRef KW - Gas analysis KW - Gas purity KW - Fibre-optic sensors KW - Sensor response KW - Certified reference materials PY - 2021 AN - OPUS4-54138 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael A1 - Meyer, Klas A1 - Kern, Simon A1 - Guhl, Svetlana A1 - Bornemann-Pfeiffer, Martin A1 - Wander, Lukas A1 - Kowarik, Stefan A1 - Liehr, Sascha T1 - Modular process control with compact NMR spectroscopy: From field integration to fully automated data analysis N2 - 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. T2 - Benchtop NMR: From Academia to Industry CY - Online meeting DA - 28.09.2022 KW - Digital Transformatioin KW - Process Industry KW - Benchtop NMR Spectroscopy KW - Procee Analytical Technology KW - Modular Production KW - Specialty Chemicals KW - Industry 4.0 PY - 2022 UR - https://eventos.fct.unl.pt/benchtop_nmr_workshop2022/pages/welcome AN - OPUS4-55850 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael A1 - Meyer, Klas A1 - Kern, Simon A1 - Guhl, Svetlana A1 - Bornemann-Pfeiffer, Martin A1 - Wander, Lukas A1 - Kowarik, Stefan A1 - Liehr, Sascha A1 - Abele, M. A1 - Falkenstein, S. T1 - Modular process control with compact NMR spectroscopy – From field integration to automated data analysis N2 - 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. T2 - GIDRM Day (Gruppo Italiano Discussione Risonanze Magnetiche) - Data analysis and NMR: from fundamental aspects to health and material applications CY - Online meeting DA - 14.10.2022 KW - Process Control KW - Online NMR Spectroscopy KW - Industry 4.0 KW - Process Analytical Technology KW - Data Analysis KW - Machine-Assisted Workflows PY - 2022 U6 - https://doi.org/http://www.gidrm.org/index.php/activities/workshops/2022-workshops/gidrm-day-data-analysis-and-nmr-from-fundamental-aspects-to-health-and-material-applications AN - OPUS4-56002 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Tiebe, Carlo A1 - Mieller, Björn A1 - Maiwald, Michael A1 - Kipphardt, Heinrich A1 - Tuma, Dirk A1 - Prager, Jens A1 - Schukar, Marcus A1 - Strohhäcker, J. T1 - Sensoren und Analytik für Sicherheit und Prozesskontrolle in Wasserstofftechnologien N2 - Die Nutzung von Sensortechnologien, insbesondere im Bereich der Gasdetektion mit einem Schwerpunkt auf Wasserstoff, spielt eine entscheidende Rolle in verschiedenen Anwendungsbereichen der Wasserstofftechnologie. Sicherheitsüberwachung, Leckdetektion und Prozesskontrolle gehören zu den prominenten Anwendungsgebieten dieser Sensortechnologien. Ein zentrales Ziel ist die Erkennung von freigesetztem Wasserstoff sowie die genaue Bestimmung des Wasserstoff-Luftverhältnisses mithilfe von Gassensoren. Dies ist von entscheidender Bedeutung, um potenzielle Gefahren frühzeitig zu erkennen und angemessene Maßnahmen zu ergreifen. Ein weiterer Schwerpunkt dieses Beitrags liegt auf der Analytik und der Verwendung zertifizierter Referenzmaterialien in Verbindung mit Metrologie für die Wasserstoffspeicherung. Dies gewährleistet eine präzise und zuverlässige Charakterisierung von Wasserstoff und unterstützt die Entwicklung sicherer Speichertechnologien. Im Rahmen des Euramet-Vorhabens Metrology for Advanced Hydrogen Storage Solutions (MefHySto) wird eine Kurzvorstellung präsentiert. Der Vortrag stellt zwei zerstörungsfreie Prüfverfahren zum strukturellen Zustandsüberwachung (Structural Health Monitoring, SHM) für Wasserstofftechnologien vor. Insbesondere die Fehlstellenerkennung mittels geführter Ultraschallwellen spielt eine bedeutende Rolle bei der Lebensdauerüberwachung von Wasserstoffspeichern. Ein weiterer Aspekt ist die Anwendung faseroptischer Sensorik zur Schadensfrüherkennung von Wasserstoffspeichern. Diese zerstörungsfreien Prüfverfahren ermöglichen eine präzise und frühzeitige Identifizierung von Schäden, was die Sicherheit und Effizienz von Wasserstoffspeichersystemen entscheidend verbessert. T2 - DVGW Kongress H2 Sicherheit CY - Online meeting DA - 15.11.2023 KW - H2Safety@BAM KW - Gassensorik KW - Metrologie KW - zertifizierte Referenzmaterialien KW - Zerstörungsfreie Prüfung KW - Ultraschall KW - Faseroptik PY - 2023 AN - OPUS4-59230 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -