<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>34687</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>185</pageFirst>
    <pageLast/>
    <pageNumber>179</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>13</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Rapid characterisation of mixtures of hydrogen and natural gas by means of ultrasonic time-delay estimation</title>
    <abstract language="eng">The implementation of the “power-to-gas” concept, where hydrogen and natural gas are blended and transported in the existing network, requires a quick, on-site method to monitor the content of hydrogen in the mixture. We evaluate a rapid characterisation of this mixture based on the measurement of the speed of sound, using micromachined ultrasonic transducers (MUTs). Two MUT-based prototypes were implemented to analyse a mixture of natural gas and hydrogen under controlled conditions. Changes in the hydrogen content below 2 mol % (in a mixture that was adjusted between 6 mol % and 16 mol %) were discriminated by both devices, including the uncertainty due to the temperature compensation and the time-delay estimation. The obtained values of the speed of sound were consistent with those calculated from independent, non-acoustic measurements performed with a gas chromatograph and a density sensor. An MUT-based flow meter is thus capable of reporting both gas intake and the molar fraction of hydrogen, provided that the source of natural gas is kept constant.</abstract>
    <parentTitle language="eng">Journal of Sensors and Sensor Systems</parentTitle>
    <identifier type="doi">10.5194/jsss-13-179-2024</identifier>
    <identifier type="issn">2194-878X</identifier>
    <enrichment key="BTU">nicht an der BTU erstellt / not created at BTU</enrichment>
    <enrichment key="Referiert">Beitrag ist referiert / Article peer-reviewed</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Christine</firstName>
      <lastName>Ruffert</lastName>
    </author>
    <submitter>
      <firstName>Maria</firstName>
      <lastName>Nowotnick</lastName>
    </submitter>
    <author>
      <firstName>Jorge Mario</firstName>
      <lastName>Monsalve Guaracao</lastName>
    </author>
    <author>
      <firstName>Uwe</firstName>
      <lastName>Völz</lastName>
    </author>
    <author>
      <firstName>Marcel</firstName>
      <lastName>Jongmanns</lastName>
    </author>
    <author>
      <firstName>Björn</firstName>
      <lastName>Betz</lastName>
    </author>
    <author>
      <firstName>Sergiu</firstName>
      <lastName>Langa</lastName>
    </author>
    <author>
      <firstName>Jörg</firstName>
      <lastName>Amelung</lastName>
    </author>
    <author>
      <firstName>Marcus</firstName>
      <lastName>Wiersig</lastName>
    </author>
    <collection role="institutes" number="1522">FG Mikro- und Nanosysteme</collection>
  </doc>
</export-example>
