Integrated optimization of electroplating wastewater treatment: A comparative assessment of physicochemical treatment...

This study assesses physicochemical treatment methods for industrial electroplating wastewater (EPW). Monitoring at a full-scale Tunisian facility revealed considerable instability in conventional systems. Residual nickel concentrations ranged from 0.02 ± 0.01 to 60 ± 0.4 mg/L. Chloride levels consistently exceeded regulatory thresholds, ranging from 442 ± 9-1418 ± 10 mg/L. Over 24 weeks, three treatment configurations were evaluated: ferric chloride-based coagulation (Configuration 1), polyaluminum chloride coagulation (Configuration 2), and lime slurry-based chemical attack (Configuration 3). Configuration 3 showed the most consistent performance. It achieved 99% chromium removal, 97% nickel removal, and 50% chloride reduction, while reducing chemical operating costs by 69% (0. 134 €· m ³ compared to 0. 432 €·m-3). A novel optimization strategy is presented that explicitly incorporates variability in influent nickel concentration ([Ni]ᵢ, 33-92 mg/L) as an independent factor. This is applied within a Response Surface Methodology-Central Composite Design, alongside pH (6.3-9.7) and flocculant dosage (2.2-9.4 mg/L). Fluctuations in wastewater composition are treated as a controlled design variable rather than experimental noise. This approach produces a robust predictive model ([Ni]f = f([Ni]ᵢ, pH, [A-PAM]) with high accuracy (R² = 0.9917). The resulting equation enables real- time adaptive chemical dosing. Operators can calculate optimal pH and flocculant requirements based on measured influent concentrations. Statistical analysis identified significant pH-metal loading interactions (F = 5.70, p = 0.044). This confirms that optimal parameters systematically shift with influent composition. Under model-predicted conditions, residual nickel consistently remained below 2 mg/L, despite substantial influent variability. This ensures regulatory compliance. The integrated approach shows that variability- responsive optimization enhances treatment efficacy, operational stability, and economic efficiency.