Efficient Removal of Cu(II) from Wastewater Using Chitosan Derived from Shrimp Shells: A Kinetic, Thermodynamic, Optimization, and Modelling Study
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Date
2025-03-16
Journal Title
Journal ISSN
Volume Title
Publisher
water
Abstract
Chitosan 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.
Description
Keywords
chitosan, copper ions Cu(II), wastewater treatment, optimization, response surface methodology (RSM), artificial neural networks (ANNs)