Kheira Benazouz , Nasma Bouchelkia , Hamza Moussa , Razika Boutheldja , Meriem Zamouche , Abdeltif Amrane , Chelliah Parvathiraja , Hamad A. Al-Lohedan , Jean-Claude Bollinger and Lotfi Mouni2026-07-272026-07-272025-03-16EISSN 2073-4441https://dspace.crtaa.dz/handle/123456789/133Chitosan was hydro-thermally extracted from grey shrimp carapaces and charac- terized using various techniques (degree of deacetylation (DD), viscosity, thermogravimet- ric analysis (TGA), scanning electron microscopy (SEM), and surface area analysis (BET)). It was then used for Cu(II) removal in a batch system, achieving a maximum capacity of 89 mg/g under standard conditions. Both pseudo-first-order and pseudo-second-order nonlinear kinetic models described the adsorption of Cu(II) ions on chitosan well, with a better fit of the pseudo-first-order model at low concentrations, while the equilibrium data suggested that the Langmuir model was suitable for describing the adsorption system, with a maximum adsorption capacity of 123 mg/g. A response surface methodology and central composite design were used to optimise and evaluate the effects of six independent parameters: initial Cu(II) concentration, pH, chitosan concentration (S/L), temperature (T), contact time (t), and NaCl concentration on the adsorption efficiency of Cu(II) by the synthesised chitosan. The proposed model was confirmed to accurately describe the phenomenon within the experimental range, achieving an R2 value of 1. ANOVA indicated that the initial concentrations of Cu(II) and chitosan concentration (S/L) were the most significant factors, while the other variables had no significant effect on the process. The adsorption capacity of Cu(II) onto the prepared chitosan was also optimised and modelled using artificial neural networks (ANNs). The maximum amount, qmax = 468 mg·g−1 , shows that chitosan is a highly effective adsorbent, chelating and complexing for copper ions.enchitosancopper ions Cu(II)wastewater treatmentoptimizationresponse surface methodology (RSM)artificial neural networks (ANNs)Article