TY - GEN A1 - Maiwald, Michael T1 - Already Producing or Still Calibrating? – Advances of Model-Based Data Evaluation Concepts for Quantitative Online NMR Spectroscopy N2 - The departure 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 analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). Based on concentration measurements of reagents and products by the NMR analyzer 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 - Practical Applications of NMR in Industry Conference (PANIC) 2018 CY - La Jolla, California, USA DA - 04.03.2018 KW - Process Monitoring KW - Process Control KW - Process analytical technology KW - Spectral Modeling KW - Smart Sensors KW - CONSENS KW - Industrie 4.0 PY - 2018 UR - https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/44435 AN - OPUS4-44435 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-444357 AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany