|About this Abstract
||MS&T22: Materials Science & Technology
||Nanotechnology for Energy, Environment, Electronics, Healthcare and Industry
||Removal of Copper (II) and Lead (II) from Hydrometallurgical Effluent onto Cellulose Nanocomposites: Mechanistic and Artificial Neural Network Modeling
||Musamba Banza, Hilary Rutto, Tumisang Seodigeng
|On-Site Speaker (Planned)
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 nanocrystals (CNCS)and chitosan were used to develop green and biodegradable nanocomposites for wastewater treatment. The nanocomposite was characterized using FTIR, TGA, and SEM. The Batch experiments were performed to study the removal of copper and lead from aqueous solutions. The effects of the sorbent dosage, contact time, pH, and initial on the removing efficiency of the metal cations were examined. The mechanism of absorption was described via four mechanistic models Film diffusion, Weber and Morris, Dummwald-Wagner, and Bangham models. The Artificial neural network model predicted the adsorption of heavy metals ions with incredible accuracy with adsorption capacity of 250mg/g for Copper and 270 mg/g for lead. Film diffusion was identified as the rate-limiting process via mechanistic modelling.
||Planned: At-meeting proceedings