Skip to main content
SpringerLink
Log in

Saw dust-derived activated carbon in different impregnation ratios and its application in de-fluoridation of waste water using IT2FLC and RSM

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

Abstract

The era of continuous advancement in science and technology leads to an increase in global pollution in the aquatic environment. Due to the presence of emerging contaminants, water bodies pose a serious threat toward human beings as well as other aquatic animals. Among all the contaminants, there is a significant amount of fluoride reported in many literature studies. In this work, activated carbon was synthesized from saw dust. Saw dust was a low-cost material obtained as a byproduct of the various woodworking operations. In this investigation, we have developed a comparative study on Interval Type 2 Fuzzy Logic System (IT2FLS) and Response Surface Methodology (RSM) by which we can predict the removal of fluoride. Here, Absorbent dose, Contact time, and temperate are the input parameters in an imprecise data set. The comparative batch study was conducted for determining the influence of process parameters on adsorption. A comparative study on the adsorption model and the adsorption kinetics was also done. The mechanism of fluoridation adsorption is further analyzed from a thermodynamics point of view. For economical feasibility, the regeneration study was conducted with one of the samples where it showed the reduction in de-fluoridation efficiency by 28.39% in five cycles. Our objective is to apply different soft computing tools, IT2FLS and RSM approach, to predict the removal of fluoride from the wastewater treatment process. The prediction capability of both methods was compared on the basis of the coefficient of determination (R2). The R2 value for IT2FLS was 0.993 and for RSM was 0.98. It can be concluded from the result that the IT2FLS has a more prominent prediction result than RSM.

Graphical Abstract

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Campos LMA, Moura HOMA, Cruz AJG et al (2020) Response surface methodology (RSM) for assessing the effects of pretreatment, feedstock, and enzyme complex association on cellulose hydrolysis. Biomass Conv Bioref DOI: https://doi.org/10.1007/s13399-020-00756-4

  2. Okwu MO, Samuel SD, Otanocha OB, Tartibu LK, Omoregbee HO, Mbachu VM (2020) Development of ternary models for prediction of biogas yield in a novel modular biodigester: a case of fuzzy Mamdani model (FMM), artificial neural network (ANN), and response surface methodology (RSM). Biomass Conversion and Biorefinery

  3. Omilakin RO, Ibrahim AP, Sotunde B et al (2020) Process modeling of solvent extraction of oil from Hura crepitans seeds: adaptive neuro-fuzzy inference system versus response surface methodology. Biomass Conv Bioref https://doi.org/10.1007/s13399-020-01080-7

  4. Lenin Sundar M, Kalyani G, Gokulan R et al (2021) Comparative adsorptive removal of Reactive Red 120 using RSM and ANFIS models in batch and packed bed column. Biomass Conv Bioref https://doi.org/10.1007/s13399-021-01444-7

  5. Lotfy HR, Roubík H (2021) Water purification using activated carbon prepared from agriculture waste — overview of a recent development

  6. Junkeon AJ, Chang D (2016) Fuzzy-based HAZOP study for process industry. J Hazard Mater 317:303–311

    Article  Google Scholar 

  7. Alagumuthu G, Rajan M (2010) Kinetic and equilibrium studies on fluoride removal by zirconium (IV): Impregnated groundnut shell carbon. Hem Ind 64:295–304

    Article  Google Scholar 

  8. Alagumuthu G, Veeraputhiran V, Venkataraman R (2011) Fluoride sorption using Cynodondactylonbased activated carbon. Hem Ind 65:23–35

    Article  Google Scholar 

  9. Alagumuthu G, Rajan M (2010) Equilibrium and kinetics of adsorption of fluoride onto zirconium impregnated cashew nut shell carbon. Chem Eng J 158:451–457

    Article  Google Scholar 

  10. Chen N, Zhang Z, Feng C, Li M, Zhu D, Sugiura N (2011) Studies on fluoride adsorption of iron-impregnated granular ceramics from aqueous solution. Mater Chem Phys 125:293–298

    Article  Google Scholar 

  11. Cui H, Qian Y, An H, Sun C, Zhai J, Li Q (2012) Electrochemical removal of fluoride from water by PAOA modified carbon felt electrodes in a continuous flow reactor. Water Res 46:3943–3950

    Article  Google Scholar 

  12. Raji C, Anirudhan TS (1997) Chromium (VI) adsorption by sawdust carbon, kinetics and equilibrium. Indian J Chem Technol 4:228–236

    Google Scholar 

  13. Das N, Pattanaik P, Das R (2005) Defluoridation of drinking water using activated titanium rich bauxite. J Colloid Interface Sci 292:1–10

    Article  Google Scholar 

  14. Daifullah AA, Yakout SM, Elreefy SA (2007) Adsorption of fluoride in aqueous solutions using KMno4-modified activated carbon derived from steam pyrolysis of rice straw. J Hazard Mater 147:633–643

    Article  Google Scholar 

  15. Emmanuel KA, Ramaraju KA, Rambabu G, Veerabhadra Rao A (2008) Removal of fluoride from drinking water with activated carbons prepared from HNO3 activation-A comparative study. Rasayan J Chem 1:802–818

    Google Scholar 

  16. Evbuomwari BO, Agbede AM, Atuka MM (2013) A Comparative Study of the physico-chemical properties of Activated Carbon from oil palm waste. Int J Sci Eng Invest 2(19):75–79

    Google Scholar 

  17. Hanumantharao Y, Kishore M, Ravindhranath K (2011) Preparation and development of adsorbent carbon from Acaciafarnesianafordefluoridation. Int J Plant Anim Environ Sci 1:209–223

    Google Scholar 

  18. Hernandez-Montoya V, Ramirez-Montoya LA, Bonilla-Petriciolet A, Montes-Moran MA (2012) Optimizing the removal of fluoride from water using new carbons obtained by modification of nut shell with a calcium solution from egg shell. Biochem Eng J 62:1–7

    Article  Google Scholar 

  19. Jolly SS, Singh ID, Prasad S, Sharma R, Singh BM, Mathur OC (1969) An Epidemiological Study of EndemicFluorosis in Punjab. Indian J Med Res 57:1333–1346

    Google Scholar 

  20. Jun TY, Arumugam SD, Latip NHA, Abdulla AM, Latif PA (2010) Effect of activation temperature and Heating Duration on physical characteristics of activated carbon prepared from agriculture waste. Environment Asia 3 (special issue):143– 148

    Google Scholar 

  21. Karthikeyan G, Siva Ilango S (2007) Fluoride sorption using MorringaIndica-based activated carbon.Iran. J Enviton Health Sci Eng 4:21–28

    Google Scholar 

  22. Li YH, Wang S, Cao A, Zhao D, Zhang X, Xu C, Luan Z, Ruan D, Liang J, Wu D, Wei B (2001) Adsorption of Fluoride from Water by Amorphous Alumina Supported on Carbon Nanotubes. Chem Phys Lett 350:412–416

    Article  Google Scholar 

  23. ShirzadSiboni M, Samarghandi MR, Azizian S, Kim WG, Lee SM (2011) The removal of Hexavalent chromium from aqueous solutions using modified holly sawdust: Equilibrium and kinetics studies. Environ Eng Res 16(2):55–60

    Article  Google Scholar 

  24. Reddy DR (2009) Neurology of endemic skeletal fluorosis, vol 57

  25. Rodriguez-Reinoso F, Lopez-Gonzalez J, De D, Berenguer C (1982) Activated carbons from almond shells -1:Preparation and characterization by nitrogen adsorption. Carbon 20(6):513– 518

    Article  Google Scholar 

  26. Divyanshi S (2011) Removal of heavy metals from water and wastewater using agricultural and industrial by-products as adsorbents. Asian J Res Chem 4:30–36

    Google Scholar 

  27. Short HE, Pandit CG, Raghvachari TNS (1937) Endemic fluorosis in nellore district of south india. Indian Medical Gazettiar 72:396–400

    Google Scholar 

  28. Jana DK, Roy K, Dey S (2018) Comparative assessment on lead removal using micellar-enhanced ultrafiltration (MEUF) based on a type-2 fuzzy logic and response surface methodology, vol 207, pp 28–41

    Google Scholar 

  29. Lounici H, Addour L, Belhocine D, Grib H, Nicolas S, Bariou B (1997) Study of a new technique for fluoride removal from water. Desalination 114:241–251

    Article  Google Scholar 

  30. Swaina SK, Patnaikb T, Singha VK, Jhaa U, Patelc RK, Dey RK (2011) Kinetics, equilibrium and thermodynamic aspects ofremoval of fluoride from drinking water using meso-structured zirconium phosphate. ChemEng J 171:1218–1226

    Google Scholar 

  31. Siddique R, Singh M, Mehta S, Belarbi R (2020) Utilization of treated saw dust in concrete as partial replacement of natural sand. J Cleaner Product 261:121226, 0959–6526. https://doi.org/10.1016/j.jclepro.2020.121226

    Article  Google Scholar 

  32. Bell MC, Ludwig TG (1970) The supply of fluoride to man: ingestion from water. In: Fluorides and human health, WHO monograph series. World Health Organization, Geneva, p 59

  33. Yan XR, Song KX, Wang JP, Hu LC, Yang ZH (1998) Preparation of CeO2-TiO2/SiO2 and Its Removal Properties for Fluoride Ions. J Rare Earths 16:279–280

    Google Scholar 

  34. Rahmanian B, Pakizeha M, Esfandyari M, Heshmatnezhad F, Maskooki A (2011) Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF). J Hazardous Mater 192:585–592

    Article  Google Scholar 

  35. Jana DK, Das B, Maiti M (2014) Multi-item partial backlogging inventory models over random planning horizon in random fuzzy environment. Appl Soft Comput 21:12–27

    Article  Google Scholar 

  36. Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821

    Article  Google Scholar 

  37. Castillo O, Melin P (2008) Type-2 fuzzy logic: Theory and applications, studies in fuzziness and soft computing. Springer, Berlin, p 223

    Book  Google Scholar 

  38. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  39. Sugeno M (1985) Industrial applications of fuzzy control. Amsterdam, New York, NY USA: North-Holland. Sole distributors for the U.S.A. and Canada, Elsevier Science Publishing company

  40. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7:1–13

    Article  MATH  Google Scholar 

  41. Liu Y, Liu YJ (2008) Biosorption isotherms, kinetics and thermodynamics. Sep Purif Technol 61:229–242

    Article  Google Scholar 

  42. Liu Y, Xu H (2007) Equilibrium, thermodynamics and mechanisms of Ni2+ biosorption by aerobic granules. Biochem Eng J 35:174–182

    Article  Google Scholar 

  43. Yu Liu Y (2009) Is the free energy change of adsorption correctly calculated. J Chem Eng Data 541981-1985

  44. Mijanur S, Seikha R, Castillo O (2021) Type-2 intuitionistic fuzzy matrix games based on a new distance measure: Application to biogas-plant implementation problem. Appl Soft Comput 106:107357

    Article  Google Scholar 

  45. Ontiveros-Robles E, Patricia Melin P, Oscar Castillo O (2021) An efficient high-order α-plane aggregation in general type-2 fuzzy systems using newton-cotes rules. Int J Fuzzy Syst 23(4):1102–1121

    Article  MATH  Google Scholar 

  46. Mittal K, Jain A, Kacprzyk J (2019) A comprehensive review on type 2 fuzzy logic applications: Past, present and future. Eng Appl Artif Intell 95:103916 x

    Article  Google Scholar 

  47. Olivas F, Valdez F, Castillo O (2019) Interval type-2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm. Inf Sci 476:159–175

    Article  Google Scholar 

  48. Castillo O, Amador-Angulo L (2018) A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design. Inf Sci 460-461:476–496

    Article  Google Scholar 

  49. Akyuz E (2016) Quantitative Human error assessment during abandon ship procedures in maritime transportation. Ocean Eng 120:2129. https://doi.org/10.1016/j.oceaneng.2016.05.017

    Article  Google Scholar 

  50. Mendel JM (2000) Uncertainty, fuzzy logic, and signal processing. Signal Process J 80:913–933

    Article  MATH  Google Scholar 

  51. Wu D, Tan W (2005) Type-2 FLC modeling capability analysis. In: Proceeding of the 2005 IEEE international conference on fuzzy systems, pp 242–247

  52. Karnik NN, Mendel JM (1998) Type-2 fuzzy logic systems: type-reduction SMC’98 Conference Proceedings. 1998 IEEE International Conference on Syst. man, and Cyber, vol 2. IEEE, Piscataway, pp 2046–2051

    Google Scholar 

  53. Mendel JM, John RB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10 (2):117–127

    Article  Google Scholar 

  54. Wu H, Mendel JM (2002) Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 10(5):622–639

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Chemical Engineering Department, Jadavpur University, India, West Bengal Pollution Control Board, India, and Haldia Institute of Technology, India. All authors were thankful to Dr. Priyanka Dey, Mr. Biman Mondal for her support to improve language of this paper.

Author information

Authors and Affiliations

  1. School of AppliedScience & Humanities, Haldia Institute of Technology, Purba Midnapur, 721657, West Bengal, Haldia, India

    Dipak Kumar Jana & Sudipta Roy

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

    Swapnila Roy

  3. Department of Chemical Engineering, Haldia Institute of Technology, Haldia, Pin, 721657, India

    Samyabrata Bhattacharjee

  4. Faculty of Business And Management, Brno University of Technology, Kolejni 2906/4, Kralovo Pole, 612 00, Brno, Czech Republic

    Petr Dostal

Authors
  1. Dipak Kumar Jana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swapnila Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samyabrata Bhattacharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Petr Dostal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sudipta Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipak Kumar Jana.

Additional information

Publisher’s Note

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

Highlights 1. Prediction the % removal of fluoride from waste water by the active carbon derived from saw dust has been studied in imprecise environments. 2. Interval type-2 fuzzy logic controller has been used to control three essential parameters. 3. In activated carbon (AC) syntheses, the magnesium chloride (MgCl2) is used as activated agent in different impregnation ratio (IR) upon thermal treatment. 4. Comparative analysis was conducted in different experimental boundaries to estimate the influence of different process factor on fluoride removal percentage using soft computing techniques, IT2FLC and Response Surface Methodology. 5. Desirability study, Regeneration study successfully carried out. 6. Validity of the proposed model is done with the help of Statistical Approach.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jana, D.K., Roy, S., Bhattacharjee, S. et al. Saw dust-derived activated carbon in different impregnation ratios and its application in de-fluoridation of waste water using IT2FLC and RSM. Biomass Conv. Bioref. 13, 12021–12041 (2023). https://doi.org/10.1007/s13399-021-02014-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13399-021-02014-7

Keywords

Search

Navigation