Improving the CFPP property of biodiesel via composition design: An intelligent raw material selection strategy based on different machine learning algorithms

Ziheng Cui, Shuai Huang, Meng Wang, Kaili Nie, Yunming Fang and Tianwei Tan

Renewable Energy, 2021, vol. 170, issue C, 354-363

Abstract: Low temperature performance determines that the application area of biodiesel is one of the most important parameters for biodiesel. Therefore, establishing a model between biodiesel composition and low temperature performance can help manufacturers easily select or deploy raw materials. In this study, different ML methods were evaluated for the first time to establish a prediction model between biodiesel composition and CFPP. The DT-based methods has good performance in predicting CFPP of biodiesel. The stacking method was shown to have the best prediction results and stability via verification of ET, stacking and MLP methods on the test set. Importance analysis shows that palmitic acid has the greatest influence on DT-based methods. This work determind that the coefficient of determination of stacking method (R2>0.90) is significantly improved compared to the existing CFPP prediction model (R2=0.87). It provides support for the next step in a wider range of verification and promotion.

Keywords: Biodiesel; CFPP Prediction; Machine learning; Random forest; Extra trees; Multilayer perceptron (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:170:y:2021:i:c:p:354-363

DOI: 10.1016/j.renene.2021.02.008

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