Machine learning-based prediction of speed of sound in fatty acid ethyl esters.

This research explores the application of fatty acid ethyl esters (FAEEs) in the pharmaceutical industry due to their biodegradable, renewable nature and versatility as excipients or drug delivery agents. The research seeks to create predictive models utilizing various methods in machine learning to calculate the speed of sound in FAEEs under different temperature, pressure, molar mass, and elemental composition conditions. Laboratory data figures from earlier research were used to train the models. Among the models developed, CNN was recognized as the most accurate model for predicting the speed of sound. This conclusion was drawn from extensive statistical evaluations and visualization techniques. CNN achieved an R² value of 0.9996, with low average absolute relative error and mean squared error, outperforming other tested algorithms. The dataset, consisting of 371 experimental data points, was validated using the Leverage algorithm to ensure reliability. Further analysis showed that pressure is the most influential factor, followed by temperature, as confirmed by sensitivity and SHAP analyses. The proposed framework provides a reliable, cost-effective alternative to experimental methods for estimating sound speed in FAEEs under various physical conditions.