Machine Learning-Optimized Methyl Oleate Production Using Synthesized [Lys][CF₃SO₃]₂ Ionic Liquid as catalyst

An amino acid ionic liquid, with lysine as the cation and CF₃SO₃⁻ as the anion, was synthesized by ion‐exchange between lysine and trifluoromethanesulfonic acid. This ionic liquid was then applied as a catalyst for the esterification of oleic acid with methanol to produce methyl oleate, commonly known as biodiesel. Using this catalytic ionic liquid helps to mitigate common issues in conventional biodiesel synthesis—such as difficult product separation, catalyst recovery, and waste liquid disposal. This study examines the effect of such a catalyst on the conversion rate of oleic acid, and determines the optimal experimental conditions for the temperature, molar ratio, catalyst loading and reaction time. The process was first optimized through traditional single‐factor experiments on the main operating variables, then, more data-driven methods of Response Surface Methodology (RSM) from a Box-Behnken designed experiment alongside a Random Forest Regression modeling were used to determine optimal conditions for maximum methyl oleate yield. The computational approach served to significantly reduce the required number of experimental trials, and allows to systematically identify optimal reaction conditions. The verified computational results had only 0.17% deviation from the predicted values.