AI-Driven Optimization Of Lead Removal From Aqueous Solution Using Calcium Peroxide: A Response Surface Methodology Approach

Abstract

Lead contamination of water poses serious environmental and health risks, necessitating efficient removal methods. This study explores the use of calcium peroxide (CaO₂) nanoparticles as a low-cost adsorbent for lead (Pb(II)) removal from aqueous solutions, optimized via Response Surface Methodology (RSM). A Central Composite Design (CCD) was employed to evaluate the combined effects of CaO₂ dosage (0.05–1.0 g/100 mL), initial Pb(II) concentration (10–100 mg/L), and contact time (3–7 min) on lead removal efficiency. Batch experiments were conducted according to the design matrix, and Pb(II) removal was measured. A quadratic regression model was developed and validated by analysis of variance (ANOVA). The model was highly significant (model F-value = 35.19, p < 0.001) with R² > 0.96 and a non-significant lack-of-fit, indicating excellent goodness of fit. Response surface plots were generated to visualize the interactions between variables. Results showed that all three factors significantly influence Pb(II) removal. Lead removal efficiency increased with higher CaO₂ dose and moderate initial Pb concentration, and reached a maximum at short contact time before plateauing. The optimized conditions predicted by the model were around contact time 3.4 min, CaO₂ dose 0.40 g/100 mL, and initial Pb(II) concentration 49.2 mg/L, yielding 89.5% Pb(II) removal. A confirmation experiment at these conditions achieved 89.6% removal, validating the model. This work demonstrates that RSM is an effective approach for optimizing heavy metal removal processes. The findings highlight CaO₂’s potential as an economical adsorbent for lead, achieving high removal efficiencies under optimized conditions, and underscore the value of statistical optimization in water treatment process design.