Optimization of poultry slaughterhouse wastewater treatment via advanced electrocoagulation using response surface methodology and a machine learning–based random forest–genetic algorithm hybrid model

Chemical oxygen demand (COD), a major indicator of organic pollution in poultry slaughterhouse wastewater, must be effectively reduced prior to discharge. In this study, electrocoagulation was applied for COD removal, and the operating conditions were systematically optimized. The effects of current density, influent pH, flow rate, supporting electrolyte concentration, and hydrogen peroxide addition were investigated using response surface methodology (RSM) and a machine learning–based random forest–genetic algorithm (RF–GA) hybrid approach. RSM was employed to develop a statistically significant regression model (R 2 = 0.995, p <0.0001) describing the relationship between operating variables and COD concentration, although its predictive capability was constrained by assumptions of the polynomial model. In contrast, the RF–GA model captured nonlinear interactions more effectively and identified operating conditions yielding COD concentrations of approximately 460 mg/L (R 2 = 0.983), corresponding to a COD removal efficiency of 94.77%. Model interpretability was enhanced using shapley additive explanations (SHAP), which confirmed that influent pH and hydrogen peroxide concentration were the dominant factors governing COD reduction. In summary, the consistency between RSM, RF–GA, and SHAP analyses demonstrates the robustness and physical plausibility of the proposed optimization framework.