Skip to main content
SpringerLink
Log in

Synthesis of nano-silica-coated biochar from thermal conversion of sawdust and its application for Cr removal: kinetic modelling using linear and nonlinear method and modelling using artificial neural network analysis

  • Original Article
  • Published:
Biomass Conversion and Biorefinery Aims and scope Submit manuscript

Abstract

In this study, sawdust-based nanocomposite was synthesized from sawdust biochar and surface coating of the biochar was done using silica nanoparticles. Removal of hexavalent chromium (Cr(VI)) was studied using the synthesized nanocomposite. The adsorption capacity of the coated biochar was found as 88.2 mg/g for chromium removal. The mechanism of adsorption process was estimated using both adsorption isotherm and kinetic models. The pseudo-second-order kinetic model was capable of showing the possible mechanism behind the adsorption process. Linear and nonlinear pseudo-second-order model was analysed and the result was compared and error analysis of this study with experimental results was done using correlation coefficient (r2) and chi-square (χ2) test. With higher r2 and low χ2value 0.999 and 0.2947, respectively, the nonlinear form of pseudo-second-order kinetic model was able to describe the adsorption process more successfully than the linear form. Category 1 of the linear form was a good approximation with nonlinear analysis. The ANN model was also developed and the model was validated with the experimental study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig 5

Similar content being viewed by others

References

  1. Abhishek K, Kumar A, Ranjan R, Kumar S (2012) A rainfall prediction model using artificial neural network. In: 2012 IEEE control and system graduate research colloquium. IEEE, pp 82–87. https://doi.org/10.1109/ICSGRC.2012.6287140

  2. Al-Zboon KK (2018) Phosphate removal by activated carbon–silica nanoparticles composite, kaolin, and olive cake. Environ Dev Sustain 20:2707–2724. https://doi.org/10.1007/s10668-017-0012-z

    Article  Google Scholar 

  3. Anupam K, Dutta S, Bhattacharjee C, Datta S (2016) Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon. Desalin Water Treat 57:3632–3641. https://doi.org/10.1080/19443994.2014.987172

    Article  Google Scholar 

  4. Cha JS, Park SH, Jung S-C, Ryu C, Jeon J-K, Shin M-C, Park Y-K (2016) Production and utilization of biochar: a review. J Ind Eng Chem 40:1–15. https://doi.org/10.1016/j.jiec.2016.06.002

    Article  Google Scholar 

  5. Chakraborty V, Sengupta S, Chaudhuri P, Das P (2018) Assessment on removal efficiency of chromium by the isolated manglicolous fungi from Indian Sundarban mangrove forest: Removal and optimization using response surface methodology. Environ Technol Innov 10. https://doi.org/10.1016/j.eti.2018.04.007

  6. Chakraborty V, Das P, Roy PK (2019) Carbonaceous materials synthesized from thermally treated waste materials and its application for the treatment of Strontium metal solution: Batch and optimization using Response Surface Methodology. Environ Technol Innov 15. https://doi.org/10.1016/j.eti.2019.100394

  7. Choi K, Lee S, Park JO, Park JA, Cho SH, Lee SY, Lee JH, Choi JW (2018) Chromium removal from aqueous solution by a PEI-silica nanocomposite. Sci Rep 8:1–10. https://doi.org/10.1038/s41598-018-20017-9

    Article  Google Scholar 

  8. Chowdhury S, Misra R, Kushwaha P, Das P (2011) Optimum sorption isotherm by linear and nonlinear methods for safranin onto alkali-treated rice husk. Bioremediat J 15:77–89. https://doi.org/10.1080/10889868.2011.570282

    Article  Google Scholar 

  9. Chowdhury S, Saha PD (2011) Comparative analysis of linear and nonlinear methods of estimating the pseudo-second-order kinetic parameters for sorption of malachite green onto pretreated rice husk. Bioremediat J 15:181–188. https://doi.org/10.1080/10889868.2011.624140

    Article  Google Scholar 

  10. Chowdhury S, Saha P (2010) Pseudo-second-order kinetic model for biosorption of methylene blue onto tamarind fruit shell: comparison of linear and nonlinear methods. Bioremediat J 14:196–207. https://doi.org/10.1080/10889868.2010.514966

  11. Das P, Banerjee P, Rathour R, Misra R (2015) Assessment on linear and non-linear analysis for the estimation of pseudo-second-order kinetic parameters for removal of dye using graphene nanosheet. Desalin Water Treat 56:502–508. https://doi.org/10.1080/19443994.2014.937759

    Article  Google Scholar 

  12. Dubey S, Gusain D, Sharma YC (2016) Kinetic and isotherm parameter determination for the removal of chromium from aqueous solutions by nanoalumina, a nanoadsorbent. J Mol Liq 219:1–8. https://doi.org/10.1016/j.molliq.2016.01.021

    Article  Google Scholar 

  13. Fu F, Han W, Cheng Z, Tang B (2016) Removal of hexavalent chromium from wastewater by acid-washed zero-valent aluminum. Desalin Water Treat 57:5592–5600. https://doi.org/10.1080/19443994.2015.1006259

    Article  Google Scholar 

  14. Gusain D, Srivastava V, Sillanpää M, Sharma YC (2016) Kinetics and isotherm study on adsorption of chromium on nano crystalline iron oxide/hydroxide: linear and nonlinear analysis of isotherm and kinetic parameters. Res Chem Intermed 42:7133–7151. https://doi.org/10.1007/s11164-016-2523-x

    Article  Google Scholar 

  15. Ho Y-S (2006) Second-order kinetic model for the sorption of cadmium onto tree fern: a comparison of linear and non-linear methods. Water Res 40:119–125. https://doi.org/10.1016/j.watres.2005.10.040

    Article  Google Scholar 

  16. Ihsanullah, Abbas A, Al-Amer AM, Laoui T, Al-Marri MJ, Nasser MS, Khraisheh M, Atieh MA (2016) Heavy metal removal from aqueous solution by advanced carbon nanotubes: critical review of adsorption applications. Sep Purif Technol 157:141–161. https://doi.org/10.1016/j.seppur.2015.11.039

    Article  Google Scholar 

  17. Kalidhasan S, Santhana Krishna Kumar A, Rajesh V, Rajesh N (2016) The journey traversed in the remediation of hexavalent chromium and the road ahead toward greener alternatives—a perspective. Coord Chem Rev 317:157–166. https://doi.org/10.1016/j.ccr.2016.03.004

    Article  Google Scholar 

  18. Kavitha B, Sarala Thambavani D (2016) Kinetics, equilibrium isotherm and neural network modeling studies for the sorption of hexavalent chromium from aqueous solution by quartz/feldspar/wollastonite. RSC Adv 6:5837–5847. https://doi.org/10.1039/C5RA22851D

    Article  Google Scholar 

  19. Kumar KV, Porkodi K, Rondon RLA, Rocha F (2008) Neural network modeling and simulation of the solid/liquid activated carbon adsorption process. Ind Eng Chem Res 47:486–490. https://doi.org/10.1021/ie071134p

    Article  Google Scholar 

  20. Lin J, Wang L (2009) Comparison between linear and non-linear forms of pseudo-first-order and pseudo-second-order adsorption kinetic models for the removal of methylene blue by activated carbon. Front Environ Sci Eng China 3:320–324. https://doi.org/10.1007/s11783-009-0030-7

    Article  Google Scholar 

  21. Mohammadi F, Yavari Z, Rahimi S, Hashemi M (2019) Artificial neural network modeling of Cr(VI) biosorption from aqueous solutions. J Water Chem Technol 41:219–227. https://doi.org/10.3103/S1063455X19040039

    Article  Google Scholar 

  22. Moussout H, Ahlafi H, Aazza M, Maghat H (2018) Critical of linear and nonlinear equations of pseudo-first order and pseudo-second order kinetic models. Karbala Int J Mod Sci 4:244–254. https://doi.org/10.1016/j.kijoms.2018.04.001

    Article  Google Scholar 

  23. Pakade VE, Tavengwa NT, Madikizela LM (2019) Recent advances in hexavalent chromium removal from aqueous solutions by adsorptive methods. RSC Adv 9:26142–26164. https://doi.org/10.1039/c9ra05188k

    Article  Google Scholar 

  24. Ramazanpour Esfahani A, Hojati S, Azimi A, Farzadian M, Khataee A (2015) Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: impact of operational parameters and artificial neural network modeling. J Taiwan Inst Chem Eng 49:172–182. https://doi.org/10.1016/j.jtice.2014.11.011

    Article  Google Scholar 

  25. Rout S, Kumar A, Ravi PM, Tripathi RM (2015) Pseudo second order kinetic model for the sorption of U (VI) onto soil: a comparison of linear and non-linear methods. 6:145–154. https://doi.org/10.6088/ijes.6017

  26. Sen S, Nandi S, Dutta S (2018) Application of RSM and ANN for optimization and modeling of biosorption of chromium(VI) using cyanobacterial biomass. Appl Water Sci 8:148–112. https://doi.org/10.1007/s13201-018-0790-y

    Article  Google Scholar 

  27. Singha B, Bar N, Das SK (2014) The use of artificial neural networks (ANN) for modeling of adsorption of Cr(VI) ions. Desalin Water Treat 52:415–425. https://doi.org/10.1080/19443994.2013.813682

    Article  Google Scholar 

  28. Sinha K, Chowdhury S, Saha PD, Datta S (2013) Modeling of microwave-assisted extraction of natural dye from seeds of Bixa orellana (Annatto) using response surface methodology (RSM) and artificial neural network (ANN). Ind Crop Prod 41:165–171. https://doi.org/10.1016/j.indcrop.2012.04.004

    Article  Google Scholar 

  29. Sinha K, Saha PD, Datta S (2012) Response surface optimization and artificial neural network modeling of microwave assisted natural dye extraction from pomegranate rind. Ind Crop Prod 37:408–414. https://doi.org/10.1016/j.indcrop.2011.12.032

    Article  Google Scholar 

  30. Sutherland C, Chittoo BS, Venkobachar C (2019) Application of ANN predictive model for the design of batch adsorbers - equilibrium simulation of Cr(VI) adsorption onto activated carbon. Open Civ Eng J 13:69–81. https://doi.org/10.2174/1874149501913010069

  31. Yildiz S (2018) Artificial neural network approach for modeling of Ni(II) adsorption from aqueous solution by peanut shell. Ecol Chem Eng S 25:581–604. https://doi.org/10.1515/eces-2018-0039

    Article  Google Scholar 

  32. Zhang J, Shang T, Jin X, Gao J, Zhao Q (2015) Study of chromium(<scp>vi</scp>) removal from aqueous solution using nitrogen-enriched activated carbon based bamboo processing residues. RSC Adv 5:784–790. https://doi.org/10.1039/C4RA11016A

    Article  Google Scholar 

  33. Zhang X, Zhang L, Li A (2018) Eucalyptus sawdust derived biochar generated by combining the hydrothermal carbonization and low concentration KOH modification for hexavalent chromium removal. J Environ Manag 206:989–998. https://doi.org/10.1016/j.jenvman.2017.11.079

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to the Science and Engineering Research Board, Department of Science and Technology, for the financial assistance (Ref: 2018/EEQ/001309).

Author information

Authors and Affiliations

  1. School of Water Resources Engineering, Jadavpur University, Kolkata, India

    Vijoyeta Chakraborty

  2. Department of Chemical Engineering, Jadavpur University, Kolkata, India

    Papita Das

  3. School of Advanced Studies in Industrial Pollution Control Engineering, Jadavpur University, Kolkata, India

    Papita Das

Authors
  1. Vijoyeta Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Papita Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Papita Das.

Ethics declarations

Conflict of interest

The authors do not have any conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakraborty, V., Das, P. Synthesis of nano-silica-coated biochar from thermal conversion of sawdust and its application for Cr removal: kinetic modelling using linear and nonlinear method and modelling using artificial neural network analysis. Biomass Conv. Bioref. 13, 821–831 (2023). https://doi.org/10.1007/s13399-020-01024-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13399-020-01024-1

Keywords

Search

Navigation