Unlocking the potential of transesterification catalysts for biodiesel production through machine learning approach. (June 2023)

• Record Type: Journal Article
• Authors: Sukpancharoen, Somboon; Katongtung, Tossapon; Rattanachoung, Nopporn; Tippayawong, Nakorn
• Is Part Of: Bioresource technology. Volume 378(2023)
• Publication Date: 2023-06
• Subjects: Artificial intelligence -- Extreme gradient boosting -- Renewable energy -- Biofuel -- Transesterification catalysts; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2023.128961
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: ML predicts %BDY in transesterification with catalysts. 19–24 features for accurate ML models identified. XGB algorithm yields 0.98 R 2 accuracy in predictions. Study reveals key factors affecting transesterification catalysts. Abstract: The growing demand for fossil fuels has motivated the search for a renewable energy source, and biodiesel has emerged as a promising and environmentally friendly alternative. In this study, machine learning techniques were employed to predict the biodiesel yield from transesterification processes using three different catalysts: homogeneous, heterogeneous, and enzyme. Extreme gradient boosting algorithms showed the highest accuracy in predictions, with a coefficient of determination accuracy of nearly 0.98, as determined through a 10-fold cross-validation of the input data. The results indicated that linoleic acid, behenic acid, and reaction time were the most crucial factors affecting biodiesel yield predictions for homogeneous, heterogeneous, and enzyme catalysts, respectively. This research provides insights into the individual and combined effects of key factors on transesterification catalysts, contributing to a deeper understanding of the system.