Assessment of Cu (II) removal from an aqueous solution by raw Gundelia tournefortii as a new low-cost biosorbent: Experiments and modelling

Shandi, Saba Golshan; Ardejani, Faramarz Doulati; Sharifi, Fereydoun

doi:10.1016/j.cjche.2018.12.027

Cited by 14 publications

(6 citation statements)

References 61 publications

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“…This comparison demonstrates that the Cu(II) adsorption process in MPP is following a pseudo-second order mechanism and its adsorption rate is controlled by the chemical adsorption of Cu(II) within the MPP. This conclusion is in agreement with that reported by Shandi et al [21], who found that the Cu(II) biosorption by Gundelia tournefortii is more precisely described using a pseudo-second-order model than a pseudo-first-order model. Furthermore, these authors highlighted that this chemical adsorption involves complexation and ion-exchange mechanisms [21].…”

Section: Adsorption Kineticssupporting

confidence: 93%

Assessment of Cu(II) Removal from Aqueous Solutions by Modified Pomelo Peels: Experiments and Modelling

et al. 2023

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In this study, low-cost pomelo peel wastes were used as a bio-sorbent to remove copper ions (e.g., Cu(II)) from aqueous solutions. Prior to testing its Cu(II) removal capability, the structural, physical and chemical characteristics of the sorbent were examined by scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectroscopy, and Brunauer–Emmett–Teller (BET) surface area analysis. The impacts of the initial pH, temperature, contact time and Cu(II) feed concentration on the Cu(II) biosorption using modified pomelo peels were then assessed. Thermodynamic parameters associated to the biosorption clearly demonstrate that this biosorption is thermodynamically feasible, endothermic, spontaneous and entropy driven. Furthermore, adsorption kinetic data were found to fit very well with the pseudo-second order kinetics equation, highlighting that this process is driven by a chemical adsorption. Finally, an artificial neural network with a 4:9:1 structure was then established for describing the Cu(II) adsorption using modified pomelo peels with R2 values close to 0.9999 and to 0.9988 for the training and testing sets, respectively. The results present a big potential use of the as-prepared bio-sorbent for the removal of Cu(II), as well as an efficient green technology for ecological and environmental sustainability.

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Section: Adsorption Kineticssupporting

confidence: 93%

Assessment of Cu(II) Removal from Aqueous Solutions by Modified Pomelo Peels: Experiments and Modelling

et al. 2023

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“…The maximum value of Cu(II) separated from the solution is 43 mg/g of SNPs, indicating good applicability of the developed adsorbent for real case scenarios. [ 56 ]…”

Section: Resultsmentioning

confidence: 99%

“…The Langmuir isotherm should have been treated as an ideal condition for adsorption. [56] In comparison, the specialty of the isotherm is the value of R L . The R L calculated from the Langmuir isotherm verifies the feasibility of the adsorption process.…”

Section: Adsorption Isothermmentioning

confidence: 99%

“…The maximum value of Cu(II) separated from the solution is 43 mg/g of SNPs, indicating good applicability of the developed adsorbent for real case scenarios. [56] 4 | CONCLUSIONS SNPs were synthesized through the robust ultrasound assisted acid hydrolysis process. By process optimization, the synthesized SNPs show a smaller particle size distribution at a low acid concentration (0.25 M) (50-80 nm).…”

Section: Adsorption Isothermmentioning

confidence: 99%

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Cu(II) ions removal from wastewater using starch nanoparticles (SNPs): An eco‐sustainable approach

et al. 2022

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The complex structured starch particles were reduced to the nanoscale size range through hydrolysis utilizing low concentration acid assisted by ultrasound irradiation. The synthesized starch nanoparticles (SNPs) were characterized by transmission electron microscopy (TEM), Fourier‐transform infrared (FTIR), and X‐ray diffraction (XRD) techniques. The synthesized SNPs possessed surface activated entities, as many cationic functional groups were confirmed through the FTIR spectrum. Also, these SNPs were effectively utilized to separate heavy Cu metal ions from the synthetic ion solution. The SNPs were characterized using field emission scanning electron microscope (FESEM), X‐ray photoelectron spectroscopy (XPS), and Brunauer–Emmett–Teller (BET) analysis for the surface modification after the adsorption process. The weak electrostatic interaction between the SNP surface and Cu ion was confirmed by the XPS spectrum and energy‐dispersive X‐ray spectroscopy. The maximum efficiency of Cu ions removal was about 93% at an optimal pH 5 and 25 mg/ml dosage. The adsorption equilibrium was obtained in 60 min. The nitrogen isotherm BET analysis of SNPs after adsorption shows a higher specific surface area of 18.552 m2/g, attributed to the interaction and presence of Cu ions on the SNP surface. The process feasibility was validated by the Langmuir isotherm model. The process exhibits pseudo‐second‐order adsorption kinetics and follows the Langmuir isotherm. The RL predicted by the Langmuir isotherm mechanism is 0.017, implying favourable adsorption. The process is reproducible and allows for the separation of heavy metal ions from the wastewater through biosorption effectively.

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“…Among them, the ANN model provides a superior nonlinear ability to learn from a variety of complicated systems. Several studies on the adsorption capacity of heavy metal ions, such as Pb(II), Ni(II), Co(II), Cr(VI), Cu(II), and Zn(II), based on the ANN model have been reported [7,[10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

Jiao,

Jacquemin,

Zhang

et al. 2023

Molecules

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It is very well known that traditional artificial neural networks (ANNs) are prone to falling into local extremes when optimizing model parameters. Herein, to enhance the prediction performance of Cu(II) adsorption capacity, a particle swarm optimized artificial neural network (PSO-ANN) model was developed. Prior to predicting the Cu(II) adsorption capacity of modified pomelo peels (MPP), experimental data collected by our research group were used to build a consistent database. Then, a PSO-ANN model was established to enhance the model performance by optimizing the ANN’s weights and biases. Finally, the performances of the developed ANN and PSO-ANN models were deeply evaluated. The results of this investigation revealed that the proposed hybrid method did increase both the generalization ability and the accuracy of the predicted data of the Cu(II) adsorption capacity of MPPs when compared to the conventional ANN model. This PSO-ANN model thus offers an alternative methodology for optimizing the adsorption capacity prediction of heavy metals using agricultural waste biosorbents.

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Assessment of Cu (II) removal from an aqueous solution by raw Gundelia tournefortii as a new low-cost biosorbent: Experiments and modelling

Cited by 14 publications

References 61 publications

Assessment of Cu(II) Removal from Aqueous Solutions by Modified Pomelo Peels: Experiments and Modelling

Assessment of Cu(II) Removal from Aqueous Solutions by Modified Pomelo Peels: Experiments and Modelling

Cu(II) ions removal from wastewater using starch nanoparticles (SNPs): An eco‐sustainable approach

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

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