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Azo dyes are widely used in the textile industry due to their vibrant colors and chemical stability; however, wastewater containing these dyes poses significant environmental and health risks due to their toxic, persistent, and potentially carcinogenic properties. In this study, the treatment of wastewater containing trypan blue dye was investigated using the electrooxidation process with boron-doped diamond electrodes, and the efficiency of the process was modeled through the Extreme Gradient Boosting (XGBoost) algorithm. In the experimental phase, the effects of key operational parameters, including current density, pH, electrolysis time, and supporting electrolyte concentration, on TB dye removal efficiency were systematically evaluated. Based on the experimental data obtained, a machine learning-based XGBoost prediction model was developed, and hyperparameter optimization was performed to enhance its predictive performance. The model achieved high accuracy (R2 = 0.996 for training and 0.954 for testing) and yielded low error metrics (RMSE and MAE), confirming its reliability in predicting removal efficiency. This study presents an integrated and data-driven approach for improving the efficiency and sustainability of electrooxidation processes and offers an environmentally friendly and effective method for the treatment of azo dye-contaminated wastewater.
Sevtap Tırınk (Wed,) studied this question.