5 Ocak 2023 Dergi makalesi Açık Erişim

Cheng, Yi; Ekici, Ecrin; Yildiz, Güray; Yang, Yang; Coward, Brad; Wang, Jiawei

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/252347</identifier>
  <creators>
    <creator>
      <creatorName>Cheng, Yi</creatorName>
      <givenName>Yi</givenName>
      <familyName>Cheng</familyName>
      <affiliation>Aston University</affiliation>
    </creator>
    <creator>
      <creatorName>Ekici, Ecrin</creatorName>
      <givenName>Ecrin</givenName>
      <familyName>Ekici</familyName>
      <affiliation>İzmir Yüksek Teknoloji Enstitüsü</affiliation>
    </creator>
    <creator>
      <creatorName>Yildiz, Güray</creatorName>
      <givenName>Güray</givenName>
      <familyName>Yildiz</familyName>
      <affiliation>İzmir Yüksek Teknoloji Enstitüsü</affiliation>
    </creator>
    <creator>
      <creatorName>Yang, Yang</creatorName>
      <givenName>Yang</givenName>
      <familyName>Yang</familyName>
      <affiliation>Aston University</affiliation>
    </creator>
    <creator>
      <creatorName>Coward, Brad</creatorName>
      <givenName>Brad</givenName>
      <familyName>Coward</familyName>
      <affiliation>Aston University</affiliation>
    </creator>
    <creator>
      <creatorName>Wang, Jiawei</creatorName>
      <givenName>Jiawei</givenName>
      <familyName>Wang</familyName>
      <affiliation>Aston University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Applied Machine Learning For Prediction Of Waste Plastic Pyrolysis Towards Valuable Fuel And Chemicals Production</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>Waste plastics, Pyrolysis, Machine learning, Decision tree</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-01-05</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/252347</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.jaap.2023.105857</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;a href="https://www.sciencedirect.com/topics/chemistry/thermolysis"&gt;Pyrolysis&lt;/a&gt;&amp;nbsp;is a suitable conversion technology to address the severe ecological and environmental hurdles caused by waste plastics&amp;#39; ineffective pre- and/or post-user management and massive landfilling. By using machine learning (ML) algorithms, the present study developed models for predicting the products of continuous and non-catalytically processes for the pyrolysis of waste plastics. Along with different input datasets, four algorithms, including decision tree (DT), artificial neuron network (ANN), support vector machine (SVM), and Gaussian process (GP), were compared to select input variables for the most accurate models. Among these algorithms, the DT model exhibited generalisable and satisfactory accuracy (R&lt;sup&gt;2&lt;/sup&gt;&amp;nbsp;&amp;gt;&amp;nbsp;0.99) with training data. The dataset with the elemental&amp;nbsp;&lt;a href="https://www.sciencedirect.com/topics/chemistry/phase-composition"&gt;composition&lt;/a&gt;&amp;nbsp;of waste plastics achieved better accuracy than that with the plastic-type for predicting liquid yields. These observations allow the predictions by the data from ultimate analysis when inaccessible to the plastic-type data in unknown plastic wastes. Besides, the combination of ultimate analysis input and the DT model also achieved excellent accuracy in liquid and gas composition predictions.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Türkiye Bilimsel ve Teknolojik Araştirma Kurumu</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">https://doi.org/10.13039/501100004410</funderIdentifier>
      <awardNumber>119N302</awardNumber>
    </fundingReference>
  </fundingReferences>
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