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 - JOUR A1 - Hoffmann, H A1 - Paulisch, M C A1 - Gebhard, M A1 - Osiewacz, J A1 - Kutter, M A1 - Hilger, A A1 - Arlt, T A1 - Kardjilov, N A1 - Ellendorff, B A1 - Beckmann, F A1 - Markötter, Henning A1 - Luik, M A1 - Turek, T A1 - Manke, I A1 - Roth, C T1 - Development of a Modular Operando Cell for X-ray Imaging of Strongly Absorbing Silver-Based Gas Diffusion Electrodes N2 - Metal-based gas diffusion electrodes are utilized in chlor-alkali electrolysis or electrochemical reduction of carbon dioxide, allowing the reaction to proceed at high current densities. In contrast to planar electrodes and predominantly 2D designs, the industrially required high current densities can be achieved by intense contact between the gas and liquid phase with the catalytically active surfaces. An essential asset for the knowledge-based design of tailored electrodes is therefore in-depth information on electrolyte distribution and intrusion into the electrode’s porous structure. Lab-based and synchrotron radiography allow for monitoring this process operando. Herein, we describe the development of a cell design that can be modularly adapted and successfully used to monitor both the oxygen reduction reaction and the electrochemical reduction of CO2 as exemplary and currently very relevant examples of gas-liquid reactions by only minor modifications to the cell set-up. With the reported cell design, we were able to observe the electrolyte distribution within the gas diffusion electrode during cell operation in realistic conditions. KW - X-Ray imaging KW - Gas diffusion electrodes KW - Operando cell PY - 2022 DO - https://doi.org/10.1149/1945-7111/ac6220 VL - 169 IS - 4 SP - 044508 PB - IOP science AN - OPUS4-55027 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -