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

• 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; https://eprints.ums.edu.my/id/eprint/33803/; https://www.sciencedirect.com/science/article/pii/S0960148122002737?casa_token=jZDbLUKi4vAAAAAA:TMsOj_09hXypyKOx2c-DEzQUJK7b9HGwssbT15h3i2nVWNzbnNiiqK9ybWYMn_L6pvYxXOK3W6M; https://doi.org/10.1016/j.renene.2022.02.124

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...