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Predictive model and near infrared spectroscopy in predicting the diesel fuel properties

Gamal Al-Kaf, Hasan ALi (2018) Predictive model and near infrared spectroscopy in predicting the diesel fuel properties. Masters thesis, Universiti Tun Hussein Onn Malaysia.


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Monitoring the diesel fuel properties play an important role in the performance of vehicle engines. Near-infrared (NIR) technology has been investigated as an alternative to monitor the diesel fuel properties. NIR spectroscopy shows an enormous potential for quantitative analysis of complex samples by coupling with artificial neural networks (ANNs). Although a single layer ANN shows promising in the establishing better relationship between a component of interest and NIR spectrum, a different algorithm for updating weight that has been proved to improve the performance of the multilayer could further reveal the potential of single linear ANN in NIR spectroscopic analysis. Therefore, this study investigates the performance of a single layer ANN that trained with Levenberg-Marquardt (SLM) and that trained with Scaled Conjugate Gradient (SSCG) and compares the proposed methods with multilayer ANN that trained with same learning algorithms. Results were evaluated and discussed with previous studies that used the same data sets to establish the relationship between the NIR spectral data and diesel fuel properties. Finding depicts that the proposed SLM and SSCG were capable of predicting the diesel fuel properties using NIR spectrum without data reduction, and achieving better accuracy in predicting the diesel fuel properties compared with other recent methods. In addition, using a proposed genetic algorithm for data reduction to improve the predictive model of the proposed method.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:28
Last Modified: 13 Aug 2018 03:28
URI: http://eprints.uthm.edu.my/id/eprint/10236
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