Artificial Intelligence Techniques and Response Surface Methodology for the Optimization of Methyl Ester Sulfonate Synthesis from Used Cooking Oil by Sulfonation

Herein, the impacts of sulfonation temperature (100-120 °C), sulfonation time (3-5 h), and NaHSO₃/methyl ester (ME) molar ratio (1:1-1.5:1 mol/mol) on methyl ester sulfonate (MES) yield were studied. For the first time, MES synthesis via the sulfonation process was modeled using the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM). Moreover, particle swarm optimization (PSO) and RSM methods were used to improve the independent process variables that affect the sulfonation process. The RSM model (coefficient of determination (R²) = 0.9695, mean square error (MSE) = 2.7094, and average absolute deviation (AAD) = 2.9508%) was the least efficient in accurately predicting MES yield, whereas the ANFIS model (R² = 0.9886, MSE = 1.0138, and AAD = 0.9058%) was superior to the ANN model (R² = 0.9750, MSE = 2.6282, and AAD = 1.7184%). The results of process optimization using the developed models revealed that PSO outperformed RSM. The ANFIS model coupled with PSO (ANFIS-PSO) achieved the best combination of sulfonation process factors (96.84 °C temperature, 2.68 h time, and 0.92:1 mol/mol NaHSO₃/ME molar ratio) that resulted in the maximum MES yield of 74.82%. Analysis of MES synthesized under optimum conditions using FTIR, ¹H NMR, and surface tension determination showed that MES could be prepared from used cooking oil.