Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning

Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning

The accurate prediction of biodiesel fuel properties and determination of its optimal fatty acid (FA) profiles is a non-trivial process. To this aim, machine learning (ML) based predictive models were developed for cetane number (CN) and cold filter plugging point (CFPP), where the extreme gradient...

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Bibliographic Details
Main Authors: Manu Suvarna, Mohammad Islam Jahirul, Yeap, Aaron Wai Hung, Cheryl Valencia Augustine, Anushri Umesh, Mohammad Golam Rasul, Mehmet Erdem Günay, Ramazan Yildirim, Jidon Janaun
Format: Article
Language:English
English
Published: Elsevier 2022
Subjects:
TA1-2040 Engineering (General). Civil engineering (General)
TP315-360 Fuel
Online Access:https://eprints.ums.edu.my/id/eprint/33803/1/Predicting%20biodiesel%20properties%20and%20its%20optimal%20fatty%20acid%20profile%20via%20explainable%20machine%20learning.pdf
https://eprints.ums.edu.my/id/eprint/33803/2/Predicting%20biodiesel%20properties%20and%20its%20optimal%20fatty%20acid%20profile%20via%20explainable%20machine%20learning1.pdf

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https://eprints.ums.edu.my/id/eprint/33803/1/Predicting%20biodiesel%20properties%20and%20its%20optimal%20fatty%20acid%20profile%20via%20explainable%20machine%20learning.pdf
https://eprints.ums.edu.my/id/eprint/33803/2/Predicting%20biodiesel%20properties%20and%20its%20optimal%20fatty%20acid%20profile%20via%20explainable%20machine%20learning1.pdf

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