Skip to main content
Log in

Comparative adsorptive removal of Reactive Red 120 using RSM and ANFIS models in batch and packed bed column

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

Abstract

The present study investigated the removal efficiency of Reactive Red 120 (RR120) using biochar produced from a seaweed Ulva reticulata using the predictive models namely Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The experiments were conducted in both batch and continuous processes, and the coded variables namely biochar dose, pH, Initial RR120 concentration, and temperature were studied for batch process, and the coded variables namely sorbent depth, solute flowrate, and initial RR120 concentration were studied for the continuous process. The correlation coefficient of the RSM and ANFIS for the batch process was obtained as 0.9977 and 0.9999. Similarly, for the continuous process, the correlation coefficient of 0.9979 and 0.9997 was obtained for RSM and ANFIS. Further statistical error analysis was conducted to find the goodness of the model with the experimental values. A comparison study was arrived based on the cluster analysis of experimental, RSM, and ANFIS models. The results concluded that the ANFIS model was superior to RSM in the prediction of the removal efficiency of the Reactive Red 120.

Graphical abstract

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Abdolali A, Guo WS, Ngo HH, Chen SS, Nguyen NC, Tung KL (2014) Typical lignocellulosic wastes and by-products for biosorption process in water and wastewater treatment: a critical review. Bioresour Technol 160:57–66. https://doi.org/10.1016/j.biortech.2013.12.037

    Article  Google Scholar 

  2. Anirudhan TS, Ramachandran M (2015) Adsorptive removal of basic dyes from aqueous solutions by surfactant modified bentonite clay (organoclay): kinetic and competitive adsorption isotherm. Process Saf Environ Prot 95:215–225. https://doi.org/10.1016/j.psep.2015.03.003

    Article  Google Scholar 

  3. Fegousse A, El Gaidoumi A, Miyah Y et al (2019) Pineapple bark performance in dyes adsorption: optimization by the central composite design. J Chemother 2019:1–11. https://doi.org/10.1155/2019/3017163

    Article  Google Scholar 

  4. Franca AS, Oliveira LS, Ferreira ME (2009) Kinetics and equilibrium studies of methylene blue adsorption by spent coffee grounds. Desalination. 249:267–272. https://doi.org/10.1016/j.desal.2008.11.017

    Article  Google Scholar 

  5. Yagub MT, Sen TK, Afroze S, Ang HM (2014) Dye and its removal from aqueous solution by adsorption: a review. Adv Colloid Interf Sci 209:172–184

    Article  Google Scholar 

  6. Ferreira AM, Coutinho JAP, Fernandes AM, Freire MG (2014) Complete removal of textile dyes from aqueous media using ionic-liquid-based aqueous two-phase systems. Sep Purif Technol 128:58–66. https://doi.org/10.1016/j.seppur.2014.02.036

    Article  Google Scholar 

  7. Gupta N, Kushwaha AK, Chattopadhyaya MC (2016) Application of potato (Solanum tuberosum) plant wastes for the removal of methylene blue and malachite green dye from aqueous solution. Arab J Chem 9:S707–S716. https://doi.org/10.1016/j.arabjc.2011.07.021

    Article  Google Scholar 

  8. Saleh TA, Gupta VK (2012) Photo-catalyzed degradation of hazardous dye methyl orange by use of a composite catalyst consisting of multi-walled carbon nanotubes and titanium dioxide. J Colloid Interface Sci 371:101–106. https://doi.org/10.1016/j.jcis.2011.12.038

    Article  Google Scholar 

  9. Kharat DS (2015) Preparing agricultural residue-based adsorbents for removal of dyes from effluents - A review. Brazilian J Chem Eng 20(1):1–12. https://doi.org/10.1590/0104-6632.20150321s00003020

  10. Lehmann J, Joseph S (2009) Biochar for environmental management: an introduction. In: Biochar for Environmental Management: Science and Technology. Earthscan, London, pp 1–12. https://doi.org/10.4324/9781849770552

  11. Liu Y, Liu YJ (2008) Biosorption isotherms, kinetics and thermodynamics. Sep Purif Technol 61(3):229–242. https://doi.org/10.1016/j.seppur.2007.10.002

  12. Beesley L, Moreno-Jiménez E, Gomez-Eyles JL, Harris E, Robinson B, Sizmur T (2011) A review of biochars’ potential role in the remediation, revegetation and restoration of contaminated soils. Environ Pollut 159:3269–3282

    Article  Google Scholar 

  13. Moghaddam MG, Khajeh M (2011) Comparison of response surface methodology and artificial neural network in predicting the microwave-assisted extraction procedure to determine zinc in fish muscles. Food Nutr Sci 02:803–808. https://doi.org/10.4236/fns.2011.28110

    Article  Google Scholar 

  14. Isoda N, Rodrigues R, Silva A, Gonçalves M, Mandelli D, Figueiredo FCA, Carvalho WA (2014) Optimization of preparation conditions of activated carbon from agriculture waste utilizing factorial design. Powder Technol 256:175–181. https://doi.org/10.1016/j.powtec.2014.02.029

    Article  Google Scholar 

  15. Deb A, Debnath A, Saha B (2020) Ultrasound-aided rapid and enhanced adsorption of anionic dyes from binary dye matrix onto novel hematite/polyaniline nanocomposite: response surface methodology optimization. Appl Organomet Chem. https://doi.org/10.1002/aoc.5353

  16. Ohale PE, Uzoh CF, Onukwuli OD (2017) Optimal factor evaluation for the dissolution of alumina from Azaraegbelu clay in acid solution using RSM and ANN comparative analysis. South African J Chem Eng 24:43–54. https://doi.org/10.1016/j.sajce.2017.06.003

    Article  Google Scholar 

  17. Gonzalez del Cerro RT, Subathra MSP, Manoj Kumar N et al (2020) Modelling the daily reference evapotranspiration in semi-arid region of South India: a case study comparing ANFIS and empirical models. Inf Process Agric 8:173–184. https://doi.org/10.1016/j.inpa.2020.02.003

    Article  Google Scholar 

  18. Robic A, Rondeau-Mouro C, Sassi JF, Lerat Y, Lahaye M (2009) Structure and interactions of ulvan in the cell wall of the marine green algae Ulva rotundata (Ulvales, Chlorophyceae). Carbohydr Polym 77:206–216. https://doi.org/10.1016/j.carbpol.2008.12.023

    Article  Google Scholar 

  19. Kumar M, Gokulan R, Sujatha S, Shanmuga Priya SP, Praveen S, Elayaraja S (2021) Biodecolorization of Reactive Red 120 in batch and packed bed column using biochar derived from Ulva reticulata. Biomass Convers Biorefin. https://doi.org/10.1007/s13399-020-01268-x

  20. Zaghloul MS, Hamza RA, Iorhemen OT, Tay JH (2020) Comparison of adaptive neuro-fuzzy inference systems (ANFIS) and support vector regression (SVR) for data-driven modelling of aerobic granular sludge reactors. J Environ Chem Eng 8:103742. https://doi.org/10.1016/j.jece.2020.103742

    Article  Google Scholar 

  21. Jaafari J, Yaghmaeian K (2019) Response surface methodological approach for optimizing heavy metal biosorption by the blue-green alga chroococcus disperses. Desalin Water Treat 142:225–234. https://doi.org/10.5004/dwt.2019.23406

    Article  Google Scholar 

  22. Mazaheri H, Ghaedi M, Ahmadi Azqhandi MH, Asfaram A (2017) Application of machine/statistical learning, artificial intelligence and statistical experimental design for the modeling and optimization of methylene blue and Cd(ii) removal from a binary aqueous solution by natural walnut carbon. Phys Chem Chem Phys 19:11299–11317. https://doi.org/10.1039/c6cp08437k

    Article  Google Scholar 

  23. Taran M, Aghaie E (2015) Designing and optimization of separation process of iron impurities from kaolin by oxalic acid in bench-scale stirred-tank reactor. Appl Clay Sci 107:109–116. https://doi.org/10.1016/j.clay.2015.01.010

    Article  Google Scholar 

  24. Sodeifian G, Sajadian SA, SaadatiArdestani N (2016) Evaluation of the response surface and hybrid artificial neural network-genetic algorithm methodologies to determine extraction yield of Ferulago angulata through supercritical fluid. J Taiwan Inst Chem Eng 60:165–173. https://doi.org/10.1016/j.jtice.2015.11.003

    Article  Google Scholar 

  25. Pilkington JL, Preston C, Gomes RL (2014) Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua. Ind Crop Prod 58:15–24. https://doi.org/10.1016/j.indcrop.2014.03.016

    Article  Google Scholar 

  26. Vijayaraghavan K, Yun YS (2008) Competition of Reactive red 4, Reactive orange 16 and Basic blue 3 during biosorption of Reactive blue 4 by polysulfone-immobilized Corynebacterium glutamicum. J Hazard Mater 153:478–486. https://doi.org/10.1016/j.jhazmat.2007.08.079

    Article  Google Scholar 

  27. Aksu Z, Çaǧatay ŞŞ (2006) Investigation of biosorption of Gemazol Turquise Blue-G reactive dye by dried Rhizopus arrhizus in batch and continuous systems. Sep Purif Technol 48:24–35. https://doi.org/10.1016/j.seppur.2005.07.017

    Article  Google Scholar 

  28. Joglekar A, May A (1987) Product excellence through design of experiments. Cereal Foods World 32:857–868

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Mallaian Lenin Sundar or Ravindiran Gokulan.

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

Lenin Sundar, M., Kalyani, G., Gokulan, R. et al. Comparative adsorptive removal of Reactive Red 120 using RSM and ANFIS models in batch and packed bed column. Biomass Conv. Bioref. 13, 5843–5859 (2023). https://doi.org/10.1007/s13399-021-01444-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13399-021-01444-7

Keywords

Navigation