Transesterification of Castor Oil into Biodiesel: Predictive Modeling with Machine Learning and Genetic Algorithm
Year: 2025
Type: article
Abstract: The growing demand for energy and the environmental impacts of fossil fuels have driven the search for sustainable alternatives such as biodiesel. Castor oil stands out as a promising non-edible feedstock but requires optimization strategies to overcome challenges in its conversion to biodiesel. This study developed a predictive model to determine the optimal parameters for homogeneous alkaline or acid transesterification of castor oil, aiming to maximize fatty acid methyl ester (FAME) yield. A dataset of 406 operating conditions from the literature was used to train and evaluate six models: Multilayer Perceptron with logistic sigmoid activation (MLP-logsig), hyperbolic tangent activation (MLP-tansig), Radial Basis Function network (RBF), hybrid RBF + MLP, Random Forest (RF), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLP-tansig achieved the best performance in training, validation, and testing (R > 0.98). However, when combined with a Genetic Algorithm (GA), it generated infeasible parameters. Conversely, the RBF + GA combination yielded results consistent with the literature: molar ratio 19.35:1, alkaline catalyst 1.13% w/w, temperature 50 °C, reaction time 70 min, and stirring speed 548.32 rpm, achieving 100% FAME yield. This approach reduces the need for extensive experimental testing, offering a cost- and time-efficient solution for optimizing biodiesel production.
Source: Biomass
Institution: Universidade Federal da Bahia
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W4415876148