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 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 - 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 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 - 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 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 - JOUR A1 - Bruno, Francesco A1 - Fiorucci, Letizia A1 - Vignoli, Alessia A1 - Meyer, Klas A1 - Maiwald, Michael A1 - Ravera, Enrico T1 - pyIHM: Indirect Hard Modeling, in Python N2 - NMR is a powerful analytical technique that combines an exquisite qualitative power, related to the unicity of the spectra of each molecule in a mixture, with an intrinsic quantitativeness, related to the fact that the integral of each peak only depends on the number of nuclei (i.e., the amount of substance times the number of equivalent nuclei in the signal), regardless of the molecule. Signal integration is the most common approach in quantitative NMR but has several drawbacks (vide infra). An alternative is to use hard modeling of the peaks. In this paper, we present pyIHM, a Python package for the quantification of the components of NMR spectra through indirect hard modeling, and we discuss some numerical details of the implementation that make this approach robust and reliable. KW - Algorithms KW - Chemical Structure KW - Deconvolution KW - Mixtures KW - NMR spectroscopy PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-626793 DO - https://doi.org/10.1021/acs.analchem.4c06484 SN - 1520-6882 VL - 97 IS - 8 SP - 4598 EP - 4605 PB - ACS Publications CY - Washington D.C. AN - OPUS4-62679 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael T1 - Messverfahren für Wasserstoff: Qualität durch Zertifizierung – Standortvorteile in Deutschland N2 - Die Veranstaltungsreihe "Neue Märkte erschließen – mit Normen und Standards hoch hinaus", die vom DIN gemeinsam mit der NOW GmbH organisiert wird, richtets sich vorwiegend an KMUs. Fokus der Veranstaltung sind Messverfahren für Wasserstoff. Der Beitrag der BAM führt sehr kurz die Rolle nationaler und internationaler Normung sowie weltweiter Metrologie im Rahmen der Meterkonvention bezüglich analytischer Qualitätssicherung und Zertifizierung von Gasen und Referenzmaterialien ein. Es werden aktuelle Beispiele für die Qualitätssicherung von Wasserstoff, mögliche dazu notwendige Ausrüstung und weiterführende Literaturquellen vorgestellt. T2 - Neue Märkte erschließen – mit Normen und Standards hoch hinaus CY - Online meeting DA - 18.03.2021 KW - Wasserstoff KW - Normung KW - Standortvorteil KW - Analytik KW - Wasserstoffanalytik KW - Wasserstoff Referenzmaterial KW - Gasanalytik PY - 2021 AN - OPUS4-52296 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eifert, T. A1 - Eisen, K. A1 - Maiwald, Michael A1 - Herwig, C. T1 - Current and future requirements to industrial analytical infrastructure—part 2: smart sensors N2 - 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. KW - Smart sensors KW - Industry 4.0 KW - Digital twins KW - Process intelligence KW - Process analytical technology KW - Physical twin KW - Cyber-physical system PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-503980 DO - https://doi.org/10.1007/s00216-020-02421-1 SN - 1618-2642 VL - 412 IS - 9 SP - 2037 EP - 2045 PB - Springer CY - Berlin Heidelberg AN - OPUS4-50398 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eisen, K A1 - Eifert, T A1 - Herwig, C A1 - Maiwald, Michael T1 - Current and future requirements to industrial analytical infrastructure—part 1: process analytical laboratories N2 - 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). KW - Smart test laboratories KW - Laboratory 4.0 KW - Sustainable Production KW - Industry 4.0 PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-504020 DO - https://doi.org/10.1007/s00216-020-02420-2 SN - 1618-2642 VL - 412 IS - 9 SP - 2027 EP - 2035 PB - Springer CY - Berlin Heidelberg AN - OPUS4-50402 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Maiwald, Michael T1 - Integrated and Networked Systems and Processes—A Perspective for Digital Transformation in Thermal Process Engineering N2 - Separation technology as a sub-discipline of thermal process engineering is one of the most critical steps in the production of chemicals, essential for the quality of intermediate and end products. The discipline comprises the construction of facilities that convert raw materials into value-added products along the value chain. Conversions typically take place in repeated reaction and separation steps—either in batch or continuous processes. The end products are the result of several production and separation steps that are not only sequentially linked, but also include the treatment of unused raw materials, by-products and wastes. Production processes in the process industry are particularly susceptible to fluctuations in raw materials and other influences affecting product quality. This is a challenge, despite increasing fluctuations, to deliver targeted quality and simultaneously meet the increasing dynamics of the market, at least for high value fine chemicals. In order to survive successfully in a changed environment, chemical companies must tread new paths. This includes the potential of digital technologies. The full integration and intelligent networking of systems and processes is progressing hesitantly. This contribution aims to encourage a more holistic approach to the digitalization in thermal process engineering by introduction of integrated and networked systems and processes. KW - Smarter Sensor KW - Digitalisation KW - Digital transformation KW - Process Industry KW - Thermal Process Engineering KW - Digital Twins PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-504964 DO - https://doi.org/10.3390/chemengineering4010015 SN - 2305-7084 VL - 4 IS - 15 SP - 1 EP - 9 PB - MDPI CY - Basel AN - OPUS4-50496 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - van der Veen, A. M H A1 - Zalewska, E. T. A1 - Kipphardt, Heinrich A1 - Beelen, R. R. A1 - Tuma, Dirk A1 - Maiwald, Michael A1 - Fükő, J. A1 - Büki, T. A1 - Szilágyi, Z. N. A1 - Beránek, J. A1 - Cieciora, D. A1 - Ochman, G. A1 - e.t al., T1 - Metrologia International Bureau of Weights and Measures (BIPM), find out more - KEY COMPARISON International comparison CCQM-K118 natural gas N2 - CCQM-K118 was an international key comparison on natural gas composition with two types of gases, i.e., a low calorific hydrogen-enriched natural gas and a high calorific LNG type of gas. There were 14 participating laboratories. The traveling standards (i.e., 14 mixtures each) were obtained from an external source and checked for homogeneity and stability before and after the participants' measurements at the two coordinating laboratories. The data evaluation was performed using a consensus value and a laboratory effect model. The results of the participants were benchmarked against a key comparison reference value computed from the largest consistent subset (LCS) of the submitted results, adjusted for the differences between the travelling standards. For the first time in a key comparison in gas analysis, the model included a term to account for excess variability in the LCS. Most of the participants reported one or a few (slightly) discrepant results. Partly this is due to the heterogeneity and heteroscedasticity of the datasets. In all, the results in this key comparison demonstrate the good comparability of the national measurement standards for natural gas composition maintained by the participating NMIs. Main text. To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/. The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (MRA). To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/. The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA). KW - CCQM-K PY - 2022 DO - https://doi.org/10.1088/0026-1394/59/1A/08017 SN - 0026-1394 VL - 59 IS - 1A PB - IOP Publishing LTD AN - OPUS4-56308 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael 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 - CHAP A1 - Maiwald, Michael ED - Tauschnitz, T. T1 - Erweiterung des NOA-Konzepts - M+O-Sensoren, NE 183 N2 - Der Sensorik kommt bei der Digitalisierung der Prozessindustrie eine Schlüsselrolle zu. Entsprechend ist sie ein zentraler Baustein der NAMUR Open Architecture Konzepts (NOA). M+O-Sensoren (Monitoring + Optimization) - stellen eine neue Geräteklasse für die zusätzliche Überwachung und Optimierung von Anlagen der Prozessindustrie dar. Diese deckt klassische und alternative Messprinzipien bis hin zur Nachbildung der menschlichen Sinne ab. Hier werden die Anforderungen an M+O-Sensoren als Bestandteile der NOA beschrieben. KW - Prozessanalytik KW - Prozessindustrie KW - Sensoren KW - M+O-Sensoren KW - NAMUR Open Architecture KW - NOA PY - 2021 SN - 978-3-8356-7451-6 SP - 39 EP - 45 PB - Vulkan-Verlag GmbH CY - Essen ET - 1 AN - OPUS4-52912 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael T1 - Fully integrated and intelligently networked systems and processes - Perspective: Smart sensors, actuators and communication N2 - Companies in the chemical industry have to tread new paths in order to survive successfully in a changed environment. This includes, in particular, the potential of digital technologies. The full integration and intelligent networking of systems and processes is making slow progress. This talk is a tribute to the field level. It wants to encourage a more holistic approach to the digitalisation of the process industry based on smart sensors, actuators and communication and provides information on current technical perspectives, such as the "one-network paradigm", ad-hoc networking, edge computing, FPGAs, virtual machines or blockchain. These give smart sensors, actuators and communication a completely new perspective. T2 - 5th European Conference on Process Analytics and Control Technology - EuroPACT 2021 CY - Online meeting DA - 15.11.2021 KW - Process Analytical Technology KW - Smart Sensors KW - Digital transformation KW - Industry 4.0 KW - EuroPact KW - Distributed Networks PY - 2021 UR - https://dechema.de/europact2021.html AN - OPUS4-53754 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Kessler, R. W. A1 - Kessler, W. A1 - Maiwald, Michael ED - Meyers, R. A. T1 - Inline and Online Process Analytical Technology with a Focus on Optical Spectroscopy N2 - Process analytical technology (PAT) is a cross-sectional technology and thus essential for future smart production. While in the past decades, the focus of process optimization strategies was on increasing efficiency, in the future, the focus will be on the sustainability of a production and its products. In addition, products will be increasingly personalized in order to match the property profile exactly to the intended use. PAT is able to provide context-sensitive information at the molecular level for process control. Spectroscopic sensors can determine inline and simultaneously both the chemical composition and its sub-microscopic morphology. The article will focus on the optical spectroscopy and therefore starts with a brief introduction on the basic concepts of molecular spectroscopy. In addition, the particularities of measuring liquids, surfaces, or particulate systems in PAT applications are described. This should enable the reader to select the appropriate method for the specific problem. Many examples from everyday industrial practice illustrate the applications. The areas covered are the manufacturing industry, process and pharmaceutical industry, food industry, as well as biotechnology and medical technology. Future will show that PAT is especially important for applications in the field of medicine (point of care) circular economy (recycling, water–wastewater, etc.). It is important to emphasize that sustainability in industrial production can only be successful with an inter- and transdisciplinary close exchange between the different disciplines. KW - Process Analytical Technology KW - Optical Spectroscopy KW - Encyclopedia of Analytical Chemistry KW - Sensors KW - Process Industry PY - 2022 SN - 978-0-47002-731-8 DO - https://doi.org/10.1002/9780470027318.a9791 SP - 1 EP - 31 PB - JohnWiley & Sons, Ltd. AN - OPUS4-56688 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 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 - CONF A1 - Maiwald, Michael A1 - Lubenau, U. T1 - Large-scale hydrogen storage in geological reservoirs: Current perspectives and requirements for metrology N2 - The course of the changeover of UGS from natural gas to hydrogen varies depending on the type of the underground gas storage (UGS). In caverns a changeover to high H2-contents can be achieved quickly, while pore storage tanks must be converted over long periods of time. The analytical requirements are correspondingly different. This information has been compiled through expert statement by underground storage operators. A significant number of new UGS is currently not expected. Public funds (project funding) are currently being raised for the conversion of caverns to hydrogen. In addition, investigations and evaluations of the material are currently being carried out at various storage facilities in order to determine the possibilities and costs of a conversion. H2 admixtures to natural gas, but also pure H2 caverns are considered. The bottleneck seems to be the availability of large volumes of hydrogen. The analytical requirements along with the different hydrogen qualities, which are currently discussed were compiled through expert discussions with underground storage operators and are at hand as early impact results. T2 - EEMUA Energy Transition Seminar CY - Rotterdam, Netherland DA - 23.06.2022 KW - Hydrogen Storage KW - Underground gas storage (UGS) KW - MefHySto KW - EMPIR PY - 2022 AN - OPUS4-55109 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael T1 - Integrierte und vernetzte Systeme und Prozesse – Eine Perspektive für das Messen in der Prozessindustrie N2 - Anhand von Beispielen wird in diesem Vortrag ein möglicher ganzheitlicherer Ansatz für die Digitalisierung und den Einsatz maschinengestützter Prozesse bei der Herstellung von Spezialchemikalien und Arzneimitteln durch die Einführung integrierter und vernetzter Systeme und Prozesse skizziert. Ein aktueller Ansatz und ein Beispiel in diesem Vortrag sind flexible und modulare chemische Produktionseinheiten, die Mehrzweckanlagen nutzen, um verschiedene hochwertige Produkte mit kurzen Stillstandszeiten zwischen den Kampagnen herzustellen und die Markteinführungszeit für neue Produkte zu verkürzen. Als zweites Beispiel wird kurz die Testplattform Wasserstofftankstelle vorgestellt, die als moderne Anlage der Prozessindustrie betrachtet werden kann, vergleichbar mit einer Anlage aus der chemischen oder pharmazeutischen Industrie. An ihr lassen sich alle derzeit entlang der Digitalen Transformation diskutierten Konzepte implementieren, um sie zu standardisieren und zu validieren. Dazu gehören Konzepte zur Konnektivität von Sensoren und Aktoren aus der Feldebene in höhere Ebenen der Automatisierung oder zur Einbringung zusätzlicher Sensoren oder Sensornetzwerke, die zunehmend flexibler gestaltet werden soll, die sichere und nachvollziehbare Parametrierung von Automatisierungskomponenten – vielleicht aus einem digitalen Abbild (Verwaltungsschale bzw. „Digitaler Zwilling“) heraus, Konzepte zur vorausschauenden Wartung („Predictive Maintenance“), Konzepte zu digitalen Entscheidungsprozessen, Zertifikaten und Signaturen oder der zunehmende Einsatz von komplexen Auswertungsalgorithmen und Applikationen in der Feldebene („Embedded Computing“) oder der „Kante“ zu Cloudbasierten Systemen der Informationstechnik („Edge-Computing“). Die beiden Beispiele sollen aktuelle Entwicklungsachsen des industriellen Messwesens im Rahmen der industriellen Automation aufzeigen, in denen Messwerte, deren Messunsicherheiten und Kontextinformationen eine wichtige Rolle einnehmen. T2 - 324. PTB-Seminar: Berechnung der Messunsicherheit – Empfehlungen für die Praxis CY - Berlin, Germany DA - 22.05.2023 KW - Messunsicherheit KW - Prozessindustrie KW - Datenerfassung KW - QI-Digital KW - Online-NMR-Spektroskopie PY - 2023 UR - https://www.ptb.de/cms/de/ptb/fachabteilungen/abt8/fb-84/ag-842/seminare/324-ptb-seminar-berechnung-der-messunsicherheit-empfehlungen-fuer-die-praxis.html AN - OPUS4-57533 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maiwald, Michael 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 DO - 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 - Maiwald, Michael 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 T1 - Sicherheit und Prozesskontrolle in der Wasserstoff-Wirtschaft: Anforderungen an Sensoren und Prozessanalytik N2 - Der Vortrag ist ein Impulsvortrag, der kurz in die Prozessketten der potentiellen Wasserstoffwirtschaft einführt. An vielen Stellen werden spezifische Sensoren benötigt, die die Prozess-Sicherheit und die Zuverlässigkeit von Qualitätsparametern gewährleisten. Es wird auch kurz auf die Forschungsföerderungslandschaft zu diesem Thema eingegangen. T2 - Sensorik für die Digitalisierung Chemische Produktionsanlagen – Wir bringen neue Projektideen auf dem Weg! CY - Frankfurt am Main, Germany DA - 13.06.2022 KW - Sensoren KW - Wasserstoff-Wirtschaft KW - Forschungslandschaft Wasserstoff KW - ProcessNet KW - Digitale Transformation PY - 2022 AN - OPUS4-55110 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zhou, Z. A1 - Han, Q. A1 - Wang, D. A1 - Macé, T. A1 - Kipphardt, Heinrich A1 - Maiwald, Michael A1 - Tuma, Dirk A1 - Uehara, S. A1 - Akima, D. A1 - Shimosaka, T. A1 - Jung, J. A1 - Oh, S.-H. A1 - van der Veen, A. A1 - van Wijk, J.I.T. A1 - Ziel, P. R. A1 - Konopelko, L. A1 - Valkova, M. A1 - Mogale, D.M. A1 - Botha, A. A1 - Brewer, P. A1 - Murugan, A. A1 - Minnaro, M.D. A1 - Miller, M. A1 - Guenther, F. A1 - Kelly, M.E. T1 - CCQM K101 Final report international comparison CCQM-K101:Oxygen in nitrogen-a track B comparison and that the matrix contains argon N2 - This key comparison aims to assess the capabilities of the participants to determine the amount-of-substance fraction oxygen in nitrogen. The GAWG has classified this as a track B comparison, due to the unexpected 50 μmol/mol argon mole fraction content of the transfer standards, which effects the achievable performance of some measurement techniques such a GC-TCD. The separation of oxygen and argon is challenging, and not all systems in use are equally well designed for it. As this analytical challenge due to a substantial fraction of argon in the transfer standards became a reality, the Gas Analysis Working Group (GAWG) decided to qualify this key comparison as a regular key comparison and not as a core comparison, which may be used to support calibration and measurement capabilities (CMCs) for oxygen in nitrogen, or for oxygen in nitrogen mixtures containing argon only (see also the section on support to CMCs). KW - CCQM-K101 KW - Gas analysis PY - 2016 UR - http://iopscience.iop.org/article/10.1088/0026-1394/53/1A/08013 SN - 1681-7575 SN - 0026-1394 VL - 53 IS - Techn Suppl SP - 08013, 1 EP - 71 PB - IOP publishing AN - OPUS4-40013 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -