Treatment of Licorice Wastewater via Coagulation–Flocculation and Ozonation Combination: ANN-GA Modeling and Optimization

This study aimed to remove licorice (Glycyrrhiza glabra) extract from pharmaceutical wastewater using a combination of treatment processes. An Artificial Neural Network (ANN) was coupled with a Genetic Algorithm (GA) to optimize the treatment parameters. Alum, polyaluminum chloride (PAC), and ferric chloride (FC), were first screened for chemical oxygen demand (COD) and color removal, after which alum was selected as the most effective coagulant for further experiments. The optimal ANN structure was identified based on three criteria: mean absolute percentage error, normalized root mean squared error, and correlation coefficient (R). The variables analyzed included pH (7–12), coagulant dose (200–1000 mg L−1), and oxidation time (6–60 min). ANOVA results revealed that coagulant type was the most statistically significant factor influencing both COD and color removal (p < 0.01), while pH and dose had relatively lower effects unless paired optimally. Results demonstrated a removal efficiency of approximately 98% under optimal conditions (pH = 10, coagulant dose = 610 mg L−1, and ozonation time = 53 min), with a remaining COD of 190 mg L−1. Overall, the combined coagulation–flocculation and ozonation process demonstrated high effectiveness for the treatment of licorice-based pharmaceutical wastewater.