INTEGRATION OF MACHINE LEARNING IN THE DESIGN AND OPTIMIZATION OF ELECTROCHEMICAL OXIDATION FOR CAFFEINE REMOVAL - Rafayola, Shafira
Electrochemical oxidation (EO) is an advanced oxidation process-based technology proven effective in removing persistent organic compounds such as caffeine. However, the limitations of conventional experimental methods, which require significant time and energy, make the optimization of the EO process challenging. By utilizing machine learning (ML), this study aims to model the optimal EO conditions for caffeine removal. One of the most commonly used EO methods is electro-Fenton, which utilizes Fe²? for the formation of •OH radicals that can oxidize organic compounds. Electro-Fenton experiments with variations in pH and Fe²? concentration were conducted to obtain optimal EO process condition data, which were later used for ML modeling. The experimental results showed that under optimal conditions of pH 3 and an Fe²? concentration of 1 mM, the maximum caffeine removal efficiency of 85% was achieved in 50 minutes. The selected ML model based on the best performance was random forest (RF) with an RMSE value of 42.45, R² of 0.61, and MAE of 24.30. From the developed RF model, it was found that the three variables that most influence the EO process time to achieve >80% efficiency are the type of anode, current density, and anode surface area. With the optimal conditions predicted by the RF model, the time required to reach >80% efficiency is 3.44 minutes.