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Artificial Neural Network Based Thermal Conductivity Prediction of Propylene Glycol Solutions with Real Time Heat Propagation Approach

Machine learning is fast growing field as it can be applied to solve a large amount of problems. One large subsection of machine learning are artificial neural networks (ANN), these work on pattern recognition and can be trained with data sets of known solutions. The objective of this thesis is to discuss the creation of an ANN capable of classifying differences in propylene glycol concentrations, up to 10%. Utilizing a micro pipette thermal sensor (MTS) it is possible to measure the heat propagation of a liquid from a laser pulse. The ANN can then be trained beforehand with simulated data and be tested in real time with temperature data from the MTS. This method could be applied to find the thermal conductivity of unknown fluids and biological samples, such as cells and tissues.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc1986109
Date08 1900
CreatorsJarrett, Andrew Caleb
ContributorsChoi, Tae-Youl, Bostanci, Huseyin, Sadat, Hamid
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
FormatText
RightsPublic, Jarrett, Andrew Caleb, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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