March 1, 2009 Thesis Open Access

Mesfin Agide

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  <identifier identifierType="DOI">10.20372/nadre:4930</identifier>
  <creators>
    <creator>
      <creatorName>Mesfin Agide</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Neural Network predictive process modeling: Application to food processing</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2009</publicationYear>
  <dates>
    <date dateType="Issued">2009-03-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Thesis</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/4930</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.20372/nadre:4929</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/aau</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Currently, food processing industry is driven by several requirements. This&lt;br&gt;
requirement includes ensuring safety, meeting quality standard and customer&lt;br&gt;
expectation and reducing production cost to be competent in market. To achieve&lt;br&gt;
this requirement they have to operate at optimum process conditions all the time.&lt;br&gt;
In food processing, due to the nature of the process, it is difficult to find and&lt;br&gt;
operate at the best conditions solely by experience.&lt;br&gt;
The Ethiopia food industry is no coping up with such requirement due cost&lt;br&gt;
of optimization and low level of education of works operating in the production&lt;br&gt;
system. Thus, it is necessary modeling of the process or part of the process to&lt;br&gt;
capture the relation of between important process parameters and use the model to&lt;br&gt;
control and improve the process better. In addition, it is found necessary to make&lt;br&gt;
the model accessible for the operators working in Ethiopian industry. Using&lt;br&gt;
artificial neural network method is found to be very good modeling to tool to solve&lt;br&gt;
food engineering problems&lt;/p&gt;</description>
  </descriptions>
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