TY - JOUR A1 - Alfeld, M. A1 - Eckhardt, H.-S. A1 - Kraft, J. A1 - Maiwald, Michael A1 - Meermann, Björn A1 - Merz, K. A1 - Prikler, S. A1 - Richert, J. A1 - Steiner, G. A1 - von Tümpling, W. T1 - Trendbericht Analytische Chemie N2 - Zusätzlich zu Methodenentwicklung, Miniaturisierung und Kopplungsverfahren zeigen sich die Hyperspektroskopie zusammen mit Imaging‐Verfahren, der Einzelmolekülnachweis und der Einsatz von 3‐D‐Druckern als neue Schwerpunkte. Hinzu kommen künstliche Intelligenz bei Sensoren, Bildgebungsverfahren und Prozesssteuerung sowie die Vernetzung von Analyse‐ und Laborgeräten. Trends und Forschungsthemen aus der analytischen Chemie, zusammengestellt von elf Autoren, koordiniert von Günter Gauglitz. KW - Multielementanalytik KW - Multimodale Analytik KW - Kristallolgraphie KW - Prozessanalytik KW - Prozessindustrie KW - Industrielle Analytik KW - Chemometrik KW - Chemometrie PY - 2020 DO - https://doi.org/10.1002/nadc.20204095786 SN - 1868-0054 VL - 68 IS - 4 SP - 52 EP - 60 PB - Wiley CY - Weinheim AN - OPUS4-50609 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Maiwald, Michael T1 - The internet of things in the lab and in process - The digital transformation challenges for the laboratory 4.0 T1 - Das Internet of Things in Labor und Prozess - Herausforderungen des digitalen Wandels für das Labor 4.0 N2 - It is a fact that much of the time spent on analytical laboratory instrumentation these days goes into system maintenance. Digital transformation could give us more time again for creativity and our actual laboratory work – if we shape it the right way. N2 - Fakt ist: Einen Großteil der Zeit, der an analytischen Laborgeräten verbracht wird, nimmt heute die Systempflege in Anspruch. Der digitale Wandel kann uns endlich wieder mehr Zeit für Kreativität und die eigentliche Laborarbeit geben – wenn wir ihn richtig gestalten. KW - Lab of the Future KW - Digitalisation KW - Automation KW - Data Analysis KW - Instrument Communication KW - Labor der Zukunft KW - Digitale Transformation KW - Automatisierung KW - Gerätekommunikation PY - 2020 IS - 4 SP - 1 EP - 3 PB - Lumitos AG CY - Darmstadt AN - OPUS4-50618 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Maiwald, Michael T1 - On the way to the cyber-physical lab T1 - Auf dem Weg zum cyber-physical Lab N2 - Laboratories tend to be central hubs for chemical, biotechnological, pharmaceutical or foodstuff production. They play a key role in research and development, chemical analysis, quality assurance, maintenance and process control. For process development and optimization, process analytical technology (PAT) has proven to be a powerful tool to improve our understanding of processes, increase productivity, reduce waste and costs and shorten processing times. N2 - Im Allgemeinen sind Laboratorien zentrale Drehscheiben für die chemische, biotechnologische, pharmazeutische oder Lebensmittelproduktion. Sie spielen eine Schlüsselrolle in Forschung und Entwicklung, chemischer Analytik, Qualitätssicherung, Instandhaltung und Prozesskontrolle. Für die Prozessentwicklung und -Optimierung hat sich die Prozessanalysentechnik (PAT) als leistungsfähiges Werkzeug erwiesen, um das Prozessverständnis zu verbessern, die Produktivität zu steigern, Abfall und Kosten zu reduzieren und die Prozesszeit zu verkürzen. KW - Lab of the Future KW - Digitalisation KW - Cyber-physical Lab KW - Smart Laboratoy PY - 2020 SP - 1 EP - 3 PB - Lumitos AG CY - Darmstadt AN - OPUS4-51036 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Maiwald, Michael A1 - Soruco Aloisio, Ricardo A1 - Meyer, Klas A1 - Klaus, Christian T1 - Auf dem Weg zu einer digitalen Qualitätsinfrastruktur - Eine Labor-Testplattform für die Integration von Sensoren und Messgeräten N2 - Um die internationale Spitzenposition deutscher Unternehmen der Prozessindustrie auch in Zukunft sicher zu stellen, müssen die Unternehmen ihre Prozesse und Geschäftsabläufe digitalisieren und gemeinsam mit der Forschung innovative neue Methoden, Apparate, Anlagen, Sensoren und Automatisierungstechnik sowie Datenkonzepte entwickeln. All dies erfordert den Aufbau neuer Fähigkeiten, Investition in Talente, interdisziplinäre Kommunikation zwischen verschiedenen Personen und Abteilungen und eine Bereitschaft zur Veränderung. KW - QI-Digital KW - Wasserstofftankstelle KW - IT-OT-Testplattform KW - Prozessindustrie PY - 2023 VL - 67 IS - 9 SP - 20 EP - 23 PB - Wiley-VCH CY - Weinheim AN - OPUS4-58461 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 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 - 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 -