Response surface modelling of the biosorption of Zn(II) and Pb(II) onto Micropogonias undulatus scales: Box–Behnken experimental approach

Abstract

In this study, the scales of Micropogonias undulatus fish were investigated as precursors for the development of low-cost biosorbent for the removal of Pb(II) and Zn(II) from aqueous media. The biosorbent was characterised using Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and X-ray diffractometry (XRD). Adsorption parameters (temperature, biosorbent dosage and contact time) were optimised using response surface methodology Box-Behnken experimental design. The optimal factors for Zn(II) removal by croaker fish scale were 145.5 min, 7.01 g/L biosorbent dosage, 30 °C and pH 5.4. The optimal factors for the removal of Pb(II) were 179.3 min, 6.61 g/L biosorbent dosage, 20 °C and pH 3.4. The numerical optimisation revealed that the optimal removal efficiency for Zn(II) and Pb(II) sorption is 96.45% and 98.76%, respectively. The biosorption of both heavy metals was best fit to Freundlich isotherm and pseudo-second-order kinetic models. Thermodynamics studies revealed that the biosorption process was exothermic and spontaneous. Keywords Biosorption • Croaker • Fish scales • Heavy metals • Box-Behnken Abbreviations AAS Atomic absorption spectrophotometry ANOVA Analysis of variance BBD Box-Behnken design Df Degree of freedom DOE Design of experiments FS Fish scales FTIR Fourier transform infrared spectroscopy Rpm Rounds per minute RSM Response surface methodology SEM Scanning electron microscopy XRD X-ray diffractometry List of symbols α Temkin isotherm constant (equilibrium binding constant) (L/mol) Ǻ Armstrong b Temkin constant related to the adsorption heat β Constant related to adsorption energy (mol 2 /J 2) C Constant of the thickness of boundary layer C e Equilibrium concentration (mg/L) C i Initial concentration (mg/L) C L Equilibrium concentration in the liquid phase C S Equilibrium concentration in the solid phase K F Freundlich constant (mg/g) K E Equilibrium constant K 1 Rate constant of the pseudo-first-order model (min −1) K 2 Rate constant of the pseudo-second-order model (gm g −1 min −1) K dif Intra-particle diffusion rate constant (mg/ gmin 0.5) K L Langmuir constant related to affinity (l/g) n Freundlich constant q e Amount of chemical species removed at equilibrium (mg/g) q max Maximum metal uptake (mg/g)

Removal of lead from aqueous solutions using three biosorbents of aquatic origin with the emphasis on the affective factors

“…In other words, the peak shifts and disappearances observed after the adsorption can be considered strong evidence for adsorption of Pb 2+ on the surface of the biosorbents 85 , 86 . The presence of similar functional groups as well as their shifts after Pb adsorption on the surface of different aquatics-based biosorbents were also reported by some other researchers 4 – 6 , 27 , 29 , 39 , 87 .…”

Performance of geopolymer as adsorbent on desulphurization of heavy gas oil

“…[ 51–53 ] The negative value of entropy proves that adsorption of sulphur poses a lesser degree of freedom or decreases in the randomness of adsorbed species at the solid–solution interface. [ 51,54,55 ] The negative values of enthalpy and Gibbs free energy indicate that ADS on the geopolymer is exothermic and spontaneous, respectively. [ 51,52,54 ]…”

“…[ 51,54,55 ] The negative values of enthalpy and Gibbs free energy indicate that ADS on the geopolymer is exothermic and spontaneous, respectively. [ 51,52,54 ]…”

Auto-correlation robustness of factorial designs and GAMS in studying the effects of process variables in a dual-objective adsorption system

“…ANOVA implies a statistical algorithm that splits the overall disparity in a set of data into component items concerning specific variation sources with the aim of hypotheses testing on the model variables and parameters (Alam et al 2016;Amosa et al 2016c;Turan and Ozgonenel 2013). In this study, the statistical significance of the variation in mean square ratio due to the residual errors and regression of the mean square are appraised with the application of ANOVA (Ighalo and Eletta 2020). It is observed that some of the control variables showed significant effects (i.e., p ˂ 0.05) while some of them are not significant (i.e., p ˃ 0.05)-see Tables 5 and 6.…”