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Neural Network Prediction of Flash Point of Diesel Fuel From Its Chemical Composıtıon and Physical Propertıes

BROWSE_DETAIL_CREATION_DATE: 08-02-2018

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: Chemistry, Chemical engineering,

BROWSE_DETAIL_CREATORS: Al-Ani, Younis Muhsin Younis (Author),

BROWSE_DETAIL_CONTRIBUTERS: KAYI, Hakan (Advisor),

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Artificial neural networks, Flash point prediction, Diesel fuel, Chemicalcomposition, Physical properties


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The flash point is important in engineering calculations, so this study has two majorpurposes. The first purpose of the study is to predict the flash point from its chemicalcomposition and physical properties by using artificial neural network to decrease timeand cost spent on experimental analysis of flash point, and the second purpose is to findthe simplest formula to predict the flash point.Artificial Neural Networks is applied as a black-box type modeling for flash point predictionof diesel fuel. The experimental data used in this study is obtained from Erbil power station.Every truck holding diesel fuel needs to be monitored, especially for the flash point test.In this study, the Levenberg-Marquardt training algorithm is utilized to train the neuralnetwork and to predict the flash point.The network performance is evaluated through network test results, mean squared erroranalysis, regression corrections and error histograms. The findings obtained in this studyindicated that the designed neural network performs quite well in the prediction of flash pointof diesel fuel from its chemical composition and physical properties.


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