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

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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 … continued below

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Jarrett, Andrew Caleb August 2022.

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  • Jarrett, Andrew Caleb

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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.

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  • August 2022

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  • Sept. 3, 2022, 10:44 a.m.

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Jarrett, Andrew Caleb. Artificial Neural Network Based Thermal Conductivity Prediction of Propylene Glycol Solutions with Real Time Heat Propagation Approach, thesis, August 2022; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc1986109/: accessed May 28, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .

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