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  <doc>
    <id>43254</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>P2, 209</pageFirst>
    <pageLast>212</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>AMA Service GmbH</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation>AMA Verband für Sensorik und Messtechnik e.V.</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mathematical and statistical tools for online NMR spectroscopy in chemical processes</title>
    <abstract language="eng">Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environments for advanced process Monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.&#13;
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.</abstract>
    <parentTitle language="deu">Tagungsband 13. Dresdner Sensor-Symposium</parentTitle>
    <identifier type="doi">10.5162/13dss2017/P2.07</identifier>
    <identifier type="url">https://www.ama-science.org/proceedings/details/2748</identifier>
    <identifier type="isbn">978-3-9816876-5-1</identifier>
    <enrichment key="eventName">13. Dresdner Sensor Symposium</enrichment>
    <enrichment key="eventPlace">Dresden, Germany</enrichment>
    <enrichment key="eventStart">04.12.2017</enrichment>
    <enrichment key="eventEnd">06.12.2017</enrichment>
    <author>Simon Kern</author>
    <author>Svetlana Guhl</author>
    <author>Klas Meyer</author>
    <author>Lukas Wander</author>
    <author>Andrea Paul</author>
    <author>Michael Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Partial least squares regression</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Indirect hard modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantum mechanics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>First principles</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>50750</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4447</pageFirst>
    <pageLast>4459</pageLast>
    <pageNumber/>
    <edition/>
    <issue>18</issue>
    <volume>412</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial neural networks for quantitative online NMR spectroscopy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Analytical and bioanalytical chemistry</parentTitle>
    <identifier type="doi">10.1007/s00216-020-02687-5</identifier>
    <identifier type="issn">1618-2642</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-507508</identifier>
    <enrichment key="date_peer_review">18.05.2020</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/50456</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/54481</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Kern</author>
    <author>Sascha Liehr</author>
    <author>Lukas Wander</author>
    <author>Martin Bornemann-Pfeiffer</author>
    <author>S. Müller</author>
    <author>Michael Maiwald</author>
    <author>Stefan Kowarik</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Real-time process monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process industry</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
    <collection role="institutes" number="">1.4 Non-Target-Analytik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.6 Faseroptische Sensorik</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="themenfelder" number="">Chemische Charakterisierung und Spurenanalytik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">8.0 Abteilungsleitung und andere</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/50750/AnalBioanalChem_412_2020.pdf</file>
    <file>https://opus4.kobv.de/opus4-bam/files/50750/216_2020_2687_MOESM1_ESM.pdf</file>
  </doc>
  <doc>
    <id>50456</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>researchdata</type>
    <publisherName>Zenodo</publisherName>
    <publisherPlace>Geneva</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Training data of quantitative online NMR spectroscopy for artificial neural networks</title>
    <abstract language="eng">Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.&#13;
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.&#13;
&#13;
Synthetic low-field NMR spectra&#13;
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.&#13;
Xi (“pure component spectra dataset”)&#13;
Xii (“spectral model dataset”)&#13;
&#13;
Experimental low-field NMR spectra from MNDPA-Synthesis&#13;
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.</abstract>
    <identifier type="doi">10.5281/zenodo.3677139</identifier>
    <enrichment key="ScientificResourceTypeGeneral">Datensatz</enrichment>
    <enrichment key="RelatedIdentifier">http://nbn-resolving.org/urn:nbn:de:kobv:b43-507508</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/54481</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Kern</author>
    <author>Sascha Liehr</author>
    <author>Lukas Wander</author>
    <author>Martin Bornemann-Pfeiffer</author>
    <author>S. Müller</author>
    <author>Michael Maiwald</author>
    <author>Stefan Kowarik</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NMR spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Real-time process monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process industry</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
    <collection role="institutes" number="">1.4 Non-Target-Analytik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.6 Faseroptische Sensorik</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="themenfelder" number="">Chemische Charakterisierung und Spurenanalytik</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="unnumberedseries" number="">Forschungsdatensätze der BAM</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
  </doc>
  <doc>
    <id>41948</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assessment and validation of various flow cell designs for quantitative online NMR spectroscopy</title>
    <abstract language="eng">Compact nuclear magnetic resonance (NMR) instruments make NMR spectroscopy and relaxometry accessible in industrial and harsh environments for reaction and process control. Robust field integration of NMR systems have to face explosion protection or integration into process control systems with short set-up times. This paves the way for industrial automation in real process environments.&#13;
The design of failsafe, temperature and pressure resistant flow through cells along with their NMR-specific requirements is an essential cornerstone to enter industrial production plants and fulfill explosion safety requirements. NMR-specific requirements aim at full quantitative pre-magnetization and acquisition with maximum sensitivity while reducing sample transfer times and dwell-times. All parameters are individually dependent on the applied NMR instrument. Luckily, an increasing number of applications are reported together with an increasing variety of commercial equipment. However, these contributions have to be reviewed thoroughly.&#13;
The performance of sample flow cells commonly used in online analytics and especially for low-field NMR spectroscopy was experimentally and theoretically investigated by 1H-NMR experiments and numerical simulations. Here, we demonstrate and discuss an automated test method to determine the critical parameters of flow through cells for quantitative online NMR spectroscopy. The setup is based on randomized setpoints of flow rates in order to reduce temperature related effects. Five flow cells and tubings were assessed and compared for high-field as well as low-field NMR spectrometers.</abstract>
    <identifier type="url">http://www.smashnmr.org/conference/program</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-419485</identifier>
    <enrichment key="eventName">Small Molecule NMR Conference (SMASH)</enrichment>
    <enrichment key="eventPlace">Baveno, Italy</enrichment>
    <enrichment key="eventStart">17.09.2017</enrichment>
    <enrichment key="eventEnd">20.09.2017</enrichment>
    <licence>Creative Commons - Namensnennung - Nicht kommerziell - Keine Bearbeitung 3.0</licence>
    <author>Lukas Wander</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reaction monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Flow cell</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SMASH</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="unnumberedseries" number="">BAM Präsentationen</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/41948/Poster_SMASH_flowcell_2017.pdf</file>
  </doc>
</export-example>
