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

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 (R-2>0.90) is significantly improved compared to the existing CFPP prediction model (R-2 = 0.87). It provides support for the next step in a wider range of verification and promotion. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study evaluates different machine learning methods to establish a prediction model between biodiesel composition and CFPP. DT-based methods perform well, while the stacking method shows the best prediction results and stability. It improves significantly compared to existing CFPP prediction models and provides support for wider verification and promotion.