Application of machine learning and statistical approaches for optimization of heavy metals (Cd2+, Pb2+, Cu2+, and Zn2+) adsorption onto carbonized char prepared from PET plastic bottle waste

Abstract

This study focuses on the probable use of carbonized char prepared from PET plastic bottles for heavy metals (HMs) adsorption (Cd2+, Pb2+, Cu2+, and Zn2+). The prepared adsorbent is characterized by field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared spectroscopy (FTIR). Batch adsorption experiments were conducted with the influencing of different operational conditions: contact time (1–180 min), adsorbate concentration (25–300 mg/L), adsorbent dose (0.5–6 g/L), pH (3–7), and temperature (25–60 ºC). High coefficient value [Cd2+ (R2 = 0.99), Pb2+ (R2 = 0.97), Cu2+ (R2 = 0.94), and Zn2+ (R2 = 0.98)] of process optimization model suggest that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 86.68, 73.66, 67.10, and 57.04% for Cd2+, Pb2+, Cu2+, and Zn2+ at pH (7), respectively. Furthermore, ANN and BB-RSM revealed a good association between the tested and projected values. The maximum monolayer adsorption capacity of Cd2+, Pb2+, Cu2+, and Zn2+ was 263.157, 78.740, 196.078, and 84.745 mg/g, respectively. Pseudo-second-order was the well-suited kinetics, where Langmuir and Freundlich isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows HMs adsorption is favorable, exothermic, and spontaneous. HIGHLIGHTS Carbonized adsorbent was prepared from PET bottle waste by a simple thermal dissociation method.; Response surface methodology and artificial neural network express a strong correlation between the experimental and projected heavy metal removal efficiency.; Carbonized adsorbents reveal satisfactory results for experimental (Cd = 86.68%, Pb = 73.66%, Cu = 67.10%, and Zn = 57.04%) and real wastewater applications (Cd = 99.2%, Pb = 62.20%, Cu = 97.63%, and Zn = 64.47%).;

Keywords

• adsorption
• heavy metals
• machine learning methods
• pet plastic waste bottle