Modeling the effects of biodiesel chemical composition on iodine value using novel machine learning algorithm

Abstract

Biodiesel is a promising renewable energy the use of which facilitates the goals of carbon neutrality and timely carbon peak. The iodine value (IV), one of the major properties of biodiesel, denotes the degree of unsaturation and correlates with the chemical composition of biodiesel. In this work, a novel approach based on random forest (RF) and least squares support vector machine (LSSVM) algorithms optimized by whale optimization algorithm (WOA) is proposed for estimating the IV of biodiesel as a function of fatty acid methyl ester profiles. In doing so, the compositions and IVs of 52 biodiesel/binary biodiesel blends were determined by gas chromatography mass spectrometry and EN14111 standard. Then, LSSVM algorithm was assessed as the most appropriate predictive method compared to the adaptive neuro fuzzy inference system, multi-layer perceptron neural network, and decision tree algorithms. Continuously, four feature selection approaches (Pearson’s correlation coefficient, principal component analysis, ReliefF algorithm, and RF) were utilized to choose the most influencing fatty acid methyl ester combination as input. The RF-LSSVM model was found to be superior to other Ⅰ hybrid models. Then, WOA was incorporated into the modeling process and the RF-WOA-LSSVM model achieved a high performance with a root mean square error and correlation coefficient of 1.1893 and 0.9977, respectively. The accuracy and error of the RF-WOA-LSSVM model have been validated. Comparison with previous biodiesel IV machine learning models and application of other new experimental datasets from the literature prove the feasibility of the proposed hybrid RF-WOA-LSSVM model for estimating the IV of different biodiesels types.