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    <id>52453</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>11773</pageFirst>
    <pageLast>11779</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>53</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An Application of Modifier Adaptation with Quadratic Approximation on a Pilot Scale Plant in Industrial Environment</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">IFAC-PapersOnLine</parentTitle>
    <identifier type="issn">1522-2640</identifier>
    <identifier type="doi">10.1016/j.ifacol.2020.12.685</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-524531</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">24.06.2021</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>A. R. Gottu Mukkula</author>
    <author>Simon Kern</author>
    <author>M. Salge</author>
    <author>M. Holtkamp</author>
    <author>Svetlana Guhl</author>
    <author>C. Fleischer</author>
    <author>Klas Meyer</author>
    <author>M. Remelhe</author>
    <author>Michael Maiwald</author>
    <author>S. Engell</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process Analytical Technology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR Spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process Industry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Iterative real-time optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Modifier adaptation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plant-model mismatch</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reactor control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CONSENS</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
    <collection role="institutes" number="">1.4 Non-Target-Analytik</collection>
    <collection role="themenfelder" number="">Umwelt</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="themenfelder" number="">Sensorik</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/52453/IFAC_53_2020_11773-11779_Gottu-Mukula_Modifier_Adaptation_CONSENS.pdf</file>
  </doc>
  <doc>
    <id>47715</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Flexible Automation with compact NMR instruments</title>
    <abstract language="eng">Modular plants using intensified continuous processes represent an appealing concept to produce pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes, and it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and includes a compact Nuclear Magnetic Resonance (NMR) spectrometer for online quality monitoring as well as a new model-based control approach. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including ro-bust evaluation of sensor data. &#13;
Here, we present alternatives for the quantitative determination of the analytes using modular, physically motivated models. These models can be adapted to new substances solely by the use of their corresponding pure component spectra, which can either be derived from experimental spectra as well as from quantum mechanical models or NMR predictors. Modular means that spec-tral models can simply be exchanged together with alternate reagents and products. Beyond that, we comprehensively calibrated an NIR spectrometer based on online NMR process data for the first time within an industrial plant. The integrated solution was developed for a metal organic reac-tion running on a commercial-scale modular pilot plant and it was tested under industrial conditions.</abstract>
    <enrichment key="eventName">7th Annual PANIC Conference</enrichment>
    <enrichment key="eventPlace">Hilton Head Island, South Carolina, USA</enrichment>
    <enrichment key="eventStart">03.03.2019</enrichment>
    <enrichment key="eventEnd">07.03.2019</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <author>Simon Kern</author>
    <author>Lukas Wander</author>
    <author>Klas Meyer</author>
    <author>Svetlana Guhl</author>
    <author>A. R. Gottu Mukkula</author>
    <author>M. Holtkamp</author>
    <author>M. Salge</author>
    <author>C. Fleischer</author>
    <author>N. Weber</author>
    <author>R. King</author>
    <author>S. Engell</author>
    <author>Andrea Paul</author>
    <author>M. Pereira Remelhe</author>
    <author>Michael Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR Spectroscopy</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="">Präsentation</collection>
  </doc>
  <doc>
    <id>48062</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3037</pageFirst>
    <pageLast>3046</pageLast>
    <pageNumber/>
    <edition/>
    <issue>14</issue>
    <volume>411</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals</title>
    <abstract language="eng">Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach&#13;
was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance.</abstract>
    <parentTitle language="eng">Analytical and Bioanalytical Chemistry</parentTitle>
    <identifier type="doi">10.1007/s00216-019-01752-y</identifier>
    <identifier type="issn">1618-2642</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-480623</identifier>
    <identifier type="issn">1618-2650</identifier>
    <enrichment key="date_peer_review">27.05.2019</enrichment>
    <enrichment key="RelatedIdentifier">https://nbn-resolving.org/urn:nbn:de:kobv:b43-480630</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Kern</author>
    <author>Lukas Wander</author>
    <author>Klas Meyer</author>
    <author>Svetlana Guhl</author>
    <author>A. R. Gottu Mukkula</author>
    <author>M. Holtkamp</author>
    <author>M. Salge</author>
    <author>C. Fleischer</author>
    <author>N. Weber</author>
    <author>S. Engell</author>
    <author>Andrea Paul</author>
    <author>M. Pereira Remelhe</author>
    <author>Michael Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NMR Spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NIR 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>Real-time quality control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Continuous processes</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CONSENS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data Fusion</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</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>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/48062/ABC_411_2019_3037-3046_Kern.pdf</file>
  </doc>
  <doc>
    <id>48063</id>
    <completedYear/>
    <publishedYear>2018</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">Raw data of pilot plant runs for CONSENS project (Case study 1)</title>
    <abstract language="eng">In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum. &#13;
&#13;
The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.&#13;
&#13;
In the following the contents of the files are explained.</abstract>
    <identifier type="doi">10.5281/zenodo.1438233</identifier>
    <enrichment key="ScientificResourceTypeGeneral">Datensatz</enrichment>
    <enrichment key="RelatedIdentifier">https://nbn-resolving.org/urn:nbn:de:kobv:b43-480623</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Kern</author>
    <author>Lukas Wander</author>
    <author>Klas Meyer</author>
    <author>Svetlana Guhl</author>
    <author>A. R. Gottu Mukkula</author>
    <author>M. Holtkamp</author>
    <author>M. Salge</author>
    <author>C. Fleischer</author>
    <author>N. Weber</author>
    <author>S. Engell</author>
    <author>Andrea Paul</author>
    <author>R. King</author>
    <author>Michael Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process Analytical Technology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multivariate Data Analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nuclear Magnetic Resonance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Near Infrared Spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Continuous Manufacturing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CONSENS</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="">Graue Literatur</collection>
    <collection role="unnumberedseries" number="">Forschungsdatensätze der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
  </doc>
  <doc>
    <id>53941</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>87</pageFirst>
    <pageLast>98</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>10</volume>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Transactions on Emerging Topics in Computing</parentTitle>
    <identifier type="issn">2168-6750</identifier>
    <identifier type="url">https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=9638378</identifier>
    <identifier type="doi">10.1109/TETC.2021.3131371</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-539412</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">28.03.2022</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>F. Fricke</author>
    <author>M. Brandalero</author>
    <author>Sascha Liehr</author>
    <author>Simon Kern</author>
    <author>Klas Meyer</author>
    <author>Stefan Kowarik</author>
    <author>R. Hierzegger</author>
    <author>S. Westerdick</author>
    <author>Michael Maiwald</author>
    <author>M. Hübner</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Industry 4.0</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cyber-Physical Systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Neural Networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass Spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nuclear Magnetic Resonance Spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Modular Production</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</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="">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/53941/IEEE_TETC_10_2022_87-98_Fricke_et_al_AI_for_MS_and_NMR.pdf</file>
  </doc>
  <doc>
    <id>55360</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>615</pageFirst>
    <pageLast>620</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2021 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)</parentTitle>
    <identifier type="doi">10.23919/DATE51398.2021.9473958</identifier>
    <enrichment key="eventName">2021 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)</enrichment>
    <enrichment key="eventPlace">Grenoble, France</enrichment>
    <enrichment key="eventStart">01.02.2021</enrichment>
    <enrichment key="eventEnd">05.02.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">25.07.2022</enrichment>
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    <author>F. Fricke</author>
    <author>S. Mahmood</author>
    <author>J. Hoffmann</author>
    <author>M. Brandalero</author>
    <author>Sascha Liehr</author>
    <author>Simon Kern</author>
    <author>Klas Meyer</author>
    <author>S. Kowarik</author>
    <author>S. Westerdick</author>
    <author>Michael Maiwald</author>
    <author>M. Hübner</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Industry 4.0,</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cyber-physical systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nuclear magnetic resonance spectroscopy</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
    <collection role="institutes" number="">1.4 Non-Target-Analytik</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 im Netzwerk der BAM verfügbar ("Closed Access")</collection>
  </doc>
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    <id>52180</id>
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    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>615</pageFirst>
    <pageLast>620</pageLast>
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    <edition/>
    <issue/>
    <volume/>
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    <publisherName>Research Publishing</publisherName>
    <publisherPlace/>
    <creatingCorporation>EDAA</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Proceedings of the 2021 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)</parentTitle>
    <identifier type="isbn">978-3-9819263-5-4</identifier>
    <identifier type="url">www.date-conference.com</identifier>
    <enrichment key="eventName">2021 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">01.02.2021</enrichment>
    <enrichment key="eventEnd">05.02.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>F. Fricke</author>
    <author>S. Mahmood</author>
    <author>J. Hoffmann</author>
    <author>M. Brandalero</author>
    <author>Sascha Liehr</author>
    <author>Simon Kern</author>
    <author>Klas Meyer</author>
    <author>Stefan Kowarik</author>
    <author>S. Westerdicky</author>
    <author>Michael Maiwald</author>
    <author>M. Hübner</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Industry 4.0</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cyber-Physical Systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Neural Networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass Spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nuclear Magnetic Resonance Spectroscopy</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</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="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>36135</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>513</pageFirst>
    <pageLast>520</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>54</volume>
    <type>article</type>
    <publisherName>John Wiley &amp; Sons, Ltd</publisherName>
    <publisherPlace>Hoboken, New Jersey, USA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated data evaluation and modelling of simultaneous F-19-H-1 medium-resolution NMR spectra for online reaction monitoring</title>
    <abstract language="eng">Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.</abstract>
    <parentTitle language="eng">Magnetic Resonance in Chemistry</parentTitle>
    <identifier type="doi">doi:10.1002/mrc.4216</identifier>
    <identifier type="url">http://onlinelibrary.wiley.com/doi/10.1002/mrc.4216/abstract</identifier>
    <enrichment key="date_peer_review">15.08.2016</enrichment>
    <author>Nicolai Zientek</author>
    <author>C. Laurain</author>
    <author>Klas Meyer</author>
    <author>Andrea Paul</author>
    <author>D. Engel</author>
    <author>G. Guthausen</author>
    <author>M. Kraume</author>
    <author>Michael Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>1H-NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>19F-NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medium-resolution NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantitative NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reaction monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process analytical technology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IHM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Indirect hard modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Chemometrics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PLS-R</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Partial least squares regression</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
  </doc>
  <doc>
    <id>38360</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>P04, 24</pageFirst>
    <pageLast>25</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>BAM Bundesanstalt für Materialforschung und -prüfung</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation>Gesellschaft Deutscher Chemiker (GDCh), Arbeitskreis Prozessanalytik</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated data evaluation and modeling of simultaneous 19F-1H medium resolution NMR spectra for online reaction monitoring</title>
    <abstract language="eng">Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.</abstract>
    <parentTitle language="eng">Tagungsband - 10. Kolloquium Arbeitskreis Prozessanalytik</parentTitle>
    <identifier type="url">http://arbeitskreis-prozessanalytik.de/images/stories/Veranstaltungen/Kolloquien/10_kolloquium_2014/tagungsband_10_kolloquium_ak_prozessanalytik_2014_hq-druck_f.pdf</identifier>
    <enrichment key="eventName">10. Kolloquium Arbeitskreis Prozessanalytik</enrichment>
    <enrichment key="eventPlace">Gerlingen, Germany</enrichment>
    <enrichment key="eventStart">25.11.2014</enrichment>
    <enrichment key="eventEnd">26.11.2014</enrichment>
    <author>Nicolai Zientek</author>
    <author>Clement Laurain</author>
    <author>Klas Meyer</author>
    <author>Andrea Paul</author>
    <author>D. Engel</author>
    <author>G. Guthausen</author>
    <author>M. Kraume</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>Data evaluation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reaction monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Indirect hard modeling</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="literaturgattung" number="">Eigenverlag BAM</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
  </doc>
  <doc>
    <id>32008</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>53</pageFirst>
    <pageLast>62</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>249</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>San Diego, Calif.</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Simultaneous 19F-1H medium resolution NMR spectroscopy for online reaction monitoring</title>
    <abstract language="eng">Medium resolution nuclear magnetic resonance (MR-NMR) spectroscopy is currently a fast developing field, which has an enormous potential to become an important analytical tool for reaction monitoring, in hyphenated techniques, and for systematic investigations of complex mixtures. The recent developments of innovative MR-NMR spectrometers are therefore remarkable due to their possible applications in quality control, education, and process monitoring. MR-NMR spectroscopy can beneficially be applied for fast, non-invasive, and volume integrating analyses under rough environmental conditions.&#13;
Within this study, a simple 1/16'' fluorinated ethylene propylene (FEP) tube with an ID of 0.04'' (1.02 mm) was used as a flow cell in combination with a 5 mm glass Dewar tube inserted into a benchtop MR-NMR spectrometer with a 1H Larmor frequency of 43.32 MHz and 40.68 MHz for 19F. For the first time, quasi-simultaneous proton and fluorine NMR spectra were recorded with a series of alternating 19F and 1H single scan spectra along the reaction time coordinate of a homogeneously catalysed esterification model reaction containing fluorinated compounds. The results were compared to quantitative NMR spectra from a hyphenated 500 MHz online NMR instrument for validation. Automation of handling, pre-processing, and analysis of NMR data becomes increasingly important for process monitoring applications of online NMR spectroscopy and for its technical and practical acceptance. Thus, NMR spectra were automatically baseline corrected and phased using the minimum entropy method. Data analysis schemes were designed such that they are based on simple direct integration or first principle line fitting, with the aim that the analysis directly revealed molar concentrations from the spectra.&#13;
Finally, the performance of 1/16'' FEP tube set-up with an ID of 1.02 mm was characterised regarding the limit of detection (LOQ (1H) = 0.335 mol L-1 and LOQ (19F) = 0.130 mol L-1 for trifluoroethanol in D2O (single scan)) and maximum quantitative flow rates up to 0.3 mL min-1. Thus, a series of single scan 19F and 1H NMR spectra acquired with this simple set-up already presents a valuable basis for quantitative reaction monitoring.</abstract>
    <parentTitle language="eng">Journal of magnetic resonance</parentTitle>
    <identifier type="old">35067</identifier>
    <identifier type="doi">10.1016/j.jmr.2014.10.007</identifier>
    <identifier type="issn">1090-7807</identifier>
    <identifier type="issn">0022-2364</identifier>
    <enrichment key="date_peer_review">20.11.2014</enrichment>
    <author>Nicolai Zientek</author>
    <author>Clement Laurain</author>
    <author>Klas Meyer</author>
    <author>M. Kraume</author>
    <author>G. Guthausen</author>
    <author>Michael Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>1H</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>19F</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medium-resolution NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantitative NMR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reaction monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process analytical technology</value>
    </subject>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
  </doc>
</export-example>
