Removal of Copper (II) and Lead (II) from hydrometallurgical effluent onto cellulose nanocomposites: mechanistic and Levenberg-Marquardt in Artificial Neural Network modelling

A well-designed adsorption system should meet the requirements for high efficiency while remaining cost and time effective. nanocellulose materials have a proven track record as viable adsorbent alternatives. Cellulose is a renewable raw material that can be used to develop an adsorbent for heavy metal ions removal. In this study, CNCs were modified with EDTA and used as adsorbents to remove Pb(II) and Cu (II) ions from a mixture of metal ions synthesized solution. The modified CNCs were characterized using Fourier transform infrared (FTIR), X-ray diffraction (XRD), Scanning electron microscopy (SEM) and thermogravimetric analysis (TGA) surface area. SEM results showed that CNCs are porous, have narrow particles size, and FTIR results revealed that the functional group responsible for the lead ions removal was mainly carboxylates (-COO2-). The XRD diffraction pattern showed that the CNCs possessed the cellulose crystalline configuration. The effects of the sorbent dosage, contact time, pH, and initial on the removal efficiency of the metal cations were examined. The absorption mechanism was described via four mechanistic models: Film diffusion, Weber and Morris, Dummwald-Wagner, and Bangham. The Artificial Neural Network (ANN) model predicted the adsorption of heavy metal ions with incredible accuracy, with an adsorption capacity of 250 mg/g for Copper and 270 mg/g for lead. Film diffusion was identified as the rate-limiting process via mechanistic modelling.