Excessive discharge of phosphates in municipal and industrial effluents into water bodies continues to amplify the rate and extent of eutrophication that is impairing aquatic ecosystems throughout the world. Consequently, research into technologies to combat the problem of eutrophication continues unabated. This study aimed to develop a protocol to encapsulate dewatered lime-iron sludge in calcium alginate beads and assess and optimize its phosphate adsorption performance. Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the encapsulation process through parameter variation. RSM was superior in capturing the nonlinear behavior of the process. Numerical optimization in RSM revealed that maximum adsorption could be obtained from beads prepared using 0.25 g sodium alginate and 0.5 g lime-iron sludge in 25 mL of distilled water to produce a homogeneous mixture and added dropwise into a solution of 0.31 g CaCl 2 in 25 mL of distilled water. The accuracy of the RSM prediction was subsequently validated by laboratory experiments that revealed a residual error of 2.9% and thus highlights the applicability of the model. Batch experiments were conducted and modeled to expound the mechanisms of adsorption. Kinetic data were best simulated using the pseudo-second order model while equilibrium data followed the Langmuir isotherm at room temperature and the Sips isotherm at higher temperatures. Physisorption, hydrogen bonding, dipole interaction, and ligand exchange were the dominant attachment mechanisms while film and intraparticle diffusion were the pertinent transport mechanisms. The beads exhibited a maximum monolayer adsorption capacity of 8.3 mg=g that compared well to other phosphate-targeting adsorbents reported in the literature.
In this study, the biosorption performance of banana floret was assessed as a new biosorbent for the removal of Cu(II) ions (a model heavy metal) from aqueous solutions. Batch experiments were conducted to assess the effects of agitation, particle size, pH, temperature and initial concentration. Kinetic and equilibrium data were modeled, and mass transfer studies were conducted to elucidate the mechanisms of biosorption. Kinetic data were best simulated using the diffusion-chemisorption model while equilibrium data were best represented by the Sips isotherm. The dominant transport mechanism was attributed to intraparticle diffusion while the dominant attachment mechanism was chemical sorption. A predictive model was successfully developed using an artificial neural network (ANN) and optimized using a genetic algorithm (GA). The accuracy of the ANN-GA prediction was validated by laboratory experiments, which revealed a residual error of 1.3% and thus underscores the applicability of the model. This new biosorbent exhibited a remarkable affinity for the heavy metal ion and compared well to other reported biosorbents in the literature.
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