Abstract
The present study is aimed to model and optimize the electrocoagulation (EC) process with five important parameters for the decolorization of Reactive Black B (RBB) from simulated wastewater. A multivariate approach, response surface methodology (RSM) together with central composite design (CCD) is used to optimize process parameters such as pH (5–9), electrode gap (0.5–2.5 cm), current density (2.08–10.41 mA/cm2), process time (10–30 min), and initial dye concentration (100–500 mg/l). The predicted percentage decolorization of dye is obtained as 97.21% at optimized conditions: pH (6.8), gapping (1.3 cm), current density (8.32 mA/cm2), time (23 min), and initial dye concentration (200 mg/L), which is very close to experimental percent decolorization (98.41%). The statistical analysis of variance (ANOVA) is performed to evaluate the quadratic model (RSM), and shows good fit of experimental data with coefficient of determination R2 >0.93. An Artificial Neural Network (ANN) is also used to predict the percentage decolorization and gives overall 94.96% which shows performance accuracy between the predicted and actual value of decolorization. The additional considerations of operating cost and current efficiency are also taken care to show the efficacy of EC process with mathematical tool. The sludge characteristics are determined by FE-SEM/EDX.
Acknowledgment
The Authors would like to thank TEQIP-II for required financial support and centre for interdisciplinary research, MNNIT Allahabad (India) for providing the necessary analysis facilities.
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Rafi, F, Fraeankalin, W, Cerniglia, C. Azoreductase activity of anaerobic bacteria isolated from human intestinal microflora. Appl Environ Microbiol 1990;56:2146.10.1128/aem.56.7.2146-2151.1990Search in Google Scholar PubMed PubMed Central
2. Hao, OJ, Kim, H, Chiang, P-C. Decolorization of wastewater. Crit Rev Environ Sci Technol 2000;30:449–505. https://doi.org/10.1080/10643380091184237.10.1080/10643380091184237Search in Google Scholar
3. Ramalho, PA, Cardoso, MH, Cavaco-Paulo, A, Ramalho, MT. Characterization of azo reduction activity in a novel ascomycete yeast strain. Appl Environ Microbiol 2004;70:2279–88. https://doi.org/10.1128/aem.70.4.2279-2288.2004.10.1128/AEM.70.4.2279-2288.2004Search in Google Scholar PubMed PubMed Central
4. Holkar, CR, Jadhav, AJ, Pinjari, DV, Mahamuni, NM, Pandit, AB. A critical review on textile wastewater treatments: possible approaches. J Environ Manag 2016;182:351–66. https://doi.org/10.1016/j.jenvman.2016.07.090.10.1016/j.jenvman.2016.07.090Search in Google Scholar PubMed
5. Sürme, Y, Yılmaz, RF, Kayakırılmaz, K. Removal of textile dye Lanaset Red G from waters by electrochemical degradation and spectrophotometric determination. Desalin Water Treat 2015;53:524–9. https://doi.org/10.1080/19443994.2013.839395.10.1080/19443994.2013.839395Search in Google Scholar
6. Mishra, S, Maiti, A. The efficacy of bacterial species to decolourise reactive azo, anthroquinone and triphenylmethane dyes from wastewater: a review. Environ Sci Pollut R 2018;25:8286–314. https://doi.org/10.1007/s11356-018-1273-2.10.1007/s11356-018-1273-2Search in Google Scholar PubMed
7. Mishra, S, Mohanty, P, Maiti, A. Bacterial mediated bio-decolourization of wastewater containing mixed reactive dyes using jack-fruit seed as co-substrate: Process optimization. J Clean Prod 2019;235:21–33. https://doi.org/10.1016/j.jclepro.2019.06.328.10.1016/j.jclepro.2019.06.328Search in Google Scholar
8. Katheresan, V, Kansedo, J, Lau, SY. Efficiency of various recent wastewater dye removal methods: A review. J Enivorn Chem Eng 2018;6:4676–97. https://doi.org/10.1016/j.jece.2018.06.060.10.1016/j.jece.2018.06.060Search in Google Scholar
9. Ozyonar, F, Muratcobanoglu, H, Gokkus, O. Taguchi approach for color removal using electrocoagulation with different electrode connection types. Fresenius Environ Bull 2017;26:7600–7.Search in Google Scholar
10. Ozyonar, F, Aksoy, S. Removal of salicylic acid from aqueous solutions using various electrodes and different connection modes by electrocoagulation. Int J Electrochem Sci 2016;11:3680–96. https://doi.org/10.20964/110454.10.20964/110454Search in Google Scholar
11. Alinsafi, A, Khemis, M, Pons, M, Leclerc, J, Yaacoubi, A, Benhammou, A, et al. Electro-coagulation of reactive textile dyes and textile wastewater. Chem Eng Process 2005;44:461–70. https://doi.org/10.1016/s0255-2701(04)00153-9.10.1016/j.cep.2004.06.010Search in Google Scholar
12. Szpyrkowicz, L, Juzzolino, C, Kaul, SN. A Comparative study on oxidation of disperse dyes by electrochemical process, ozone, hypochlorite and fenton reagent. Water Res 2001;35:2129–36. https://doi.org/10.1016/s0043-1354(00)00487-5.10.1016/S0043-1354(00)00487-5Search in Google Scholar
13. Mollah, MY, Morkovsky, P, Gomes, JA, Kesmez, M, Parga, J, Cocke, DL. Fundamentals, present and future perspectives of electrocoagulation. J Hazard Mater 2004;114:199–210. https://doi.org/10.1016/j.jhazmat.2004.08.009.10.1016/j.jhazmat.2004.08.009Search in Google Scholar PubMed
14. Gautam, K, Kamsonlian, S, Kumar, S. Removal of Reactive Red 120 dye from wastewater using electrocoagulation: optimization using multivariate approach, economic analysis, and sludge characterization. Separ Sci Technol 2019:1–15. https://doi.org/10.1080/01496395.2019.1677713.10.1080/01496395.2019.1677713Search in Google Scholar
15. Box, GE, Draper, NR. Empirical model-building and response surfaces. New York: Wiley; 1987.Search in Google Scholar
16. Chung, WJ, Chun, SY, Kim, SS, Chang, SW. Photocatalytic removal of tetracycline using TiO2/Ge composite optimized by response surface methodology (RSM). J Ind Eng Chem 2016;36:320–5. https://doi.org/10.1016/j.jiec.2016.02.022.10.1016/j.jiec.2016.02.022Search in Google Scholar
17. Fattahi, M, Kazemeini, M, Khorasheh, F, Rashidi, A. Kinetic modeling of oxidative dehydrogenation of propane (ODHP) over a vanadium–graphene catalyst: application of the DOE and ANN methodologies. J Ind Eng Chem 2014;20:2236–47. https://doi.org/10.1016/j.jiec.2013.09.056.10.1016/j.jiec.2013.09.056Search in Google Scholar
18. Ozyonar, F. Optimization of operational parameters of electrocoagulation process for real textile wastewater treatment using Taguchi experimental design method. Desalin Water Treat 2016;57:2389–99. https://doi.org/10.1080/19443994.2015.1005153.10.1080/19443994.2015.1005153Search in Google Scholar
19. Somayajula, A, Asaithambi, P, Susree, M, Matheswaran, M. Sonoelectrochemical oxidation for decolorization of Reactive Red 195. Ultrason Sonochem 2012;19:803–11. https://doi.org/10.1016/j.ultsonch.2011.12.019.10.1016/j.ultsonch.2011.12.019Search in Google Scholar PubMed
20. Amani-Ghadim, A, Aber, S, Olad, A, Ashassi-Sorkhabi, H. Optimization of electrocoagulation process for removal of an azo dye using response surface methodology and investigation on the occurrence of destructive side reactions. Chem Eng Proc 2013;64:68–78. https://doi.org/10.1016/j.cep.2012.10.012.10.1016/j.cep.2012.10.012Search in Google Scholar
21. Mishra, S, Maiti, A. Seasonal dynamics of phytoplankton population and water quality in Bidoli reservoir. Environ Monit Assess 2019;191:766. https://doi.org/10.1007/s10661-019-7185-x.10.1007/s10661-019-7185-xSearch in Google Scholar PubMed
22. Aleboyeh, A, Daneshvar, N, Kasiri, M. Optimization of C. I. Acid Red 14 azo dye removal by electrocoagulation batch process with response surface methodology. Chem Eng Process 2008;47:827–32. https://doi.org/10.1016/j.cep.2007.01.033.10.1016/j.cep.2007.01.033Search in Google Scholar
23. Daneshvar, N, Khataee, A, Djafarzadeh, N. The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C. I. Basic Yellow 28 by electrocoagulation process. J Hazard Mater 2006;137:1788–95. https://doi.org/10.1016/j.jhazmat.2006.05.042.10.1016/j.jhazmat.2006.05.042Search in Google Scholar PubMed
24. Murugan, AA, Ramamurthy, T, Subramanian, B, Kannan, CS, Ganesan, M. Electrocoagulation of Textile Effluent: RSM and ANN Modeling. Int J Chem React Eng 2009;7:1–14. https://doi.org/10.2202/1542-6580.1942.10.2202/1542-6580.1942Search in Google Scholar
25. Nourouzi, MM, Chuah, T, Choong, TS. Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction. Water Sci Technol 2011;63:984–94. https://doi.org/10.2166/wst.2011.280.10.2166/wst.2011.280Search in Google Scholar PubMed
26. Mook, W, Aroua, M, Szlachta, M, Lee, C. Optimisation of Reactive Black 5 dye removal by electrocoagulation process using response surface methodology. Water Sci Technol 2017;75:952–62. https://doi.org/10.2166/wst.2016.563.10.2166/wst.2016.563Search in Google Scholar PubMed
27. Rauf, M, Marzouki, N, Körbahti, BK. Photolytic decolorization of Rose Bengal by UV/H2O2 and data optimization using response surface method. J Hazard Mater 2008;159:602–9. https://doi.org/10.1016/j.jhazmat.2008.02.098.10.1016/j.jhazmat.2008.02.098Search in Google Scholar PubMed
28. Thirugnanasambandham, K, Sivakumar, V, Prakash Maran, J. Evaluation of an electrocoagulation process for the treatment of bagasse-based pulp and paper industry wastewater. Environ Prog Sustain Energy 2015;34:411–19. https://doi.org/10.1002/ep.12001.10.1002/ep.12001Search in Google Scholar
29. Roosta, M, Ghaedi, M, Daneshfar, A, Sahraei, R, Asghari, A. Optimization of the ultrasonic assisted removal of methylene blue by gold nanoparticles loaded on activated carbon using experimental design methodology. Ultrason Sonochem 2014;21:242–52. https://doi.org/10.1016/j.ultsonch.2013.05.014.10.1016/j.ultsonch.2013.05.014Search in Google Scholar PubMed
30. Maran, JP, Sivakumar, V, Thirugnanasambandham, K, Sridhar, R. Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L. Alex Eng J 2013;52:507–16. 10.1016/j.aej.2013.06.007Search in Google Scholar
31. Karimi, H, Ghaedi, M. Application of artificial neural network and genetic algorithm to modeling and optimization of removal of methylene blue using activated carbon. J Ind Eng Chem 2014;20:2471–6. https://doi.org/10.1016/j.jiec.2013.10.028.10.1016/j.jiec.2013.10.028Search in Google Scholar
32. Mirsoleimani-azizi, SM, Amooey, AA, Ghasemi, S. Salkhordeh-Panbechouleh, S. Modeling the removal of Endosulfan from aqueous solution by electrocoagulation process using artificial neural network (ANN). Ind Eng Chem Res 2015;54:9844–9. https://doi.org/10.1021/acs.iecr.5b02846.10.1021/acs.iecr.5b02846Search in Google Scholar
33. Ozyonar, F, Karagozoglu, B. Investigation of technical and economic analysis of electrocoagulation process for the treatment of great and small cattle slaughterhouse wastewater. Desalin Water Treat 2014;52:74–87. https://doi.org/10.1080/19443994.2013.787373.10.1080/19443994.2013.787373Search in Google Scholar
34. Ozyonar, F, Karagozoglu, B. Operating cost analysis and treatment of domestic wastewater by electrocoagulation using aluminum electrodes. Pol J Environ Stud 2011;20:173.Search in Google Scholar
35. Gautam, K, Kumar, S, Kamsonlian, S. Decolourization of reactive dye from aqueous solution using electrocoagulation: kinetics and isothermal study. Z Phys Chem 2019;233:1447–68. https://doi.org/10.1515/zpch-2017-1044.10.1515/zpch-2017-1044Search in Google Scholar
36. Bhatti, MS, Kapoor, D, Kalia, RK, Reddy, AS, Thukral, AK. RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: multi objective optimization using genetic algorithm approach. Desalination 2011;274:74–80. https://doi.org/10.1016/j.desal.2011.01.083.10.1016/j.desal.2011.01.083Search in Google Scholar
37. Virkutyte, J, Rokhina, E, Jegatheesan, V. Optimisation of Electro-Fenton denitrification of a model wastewater using a response surface methodology. Bioresour Technol 2010;101:1440–6. https://doi.org/10.1016/j.biortech.2009.10.041.10.1016/j.biortech.2009.10.041Search in Google Scholar
38. Ölmez, T. The optimization of Cr(VI) reduction and removal by electrocoagulation using response surface methodology. J Hazard Mater 2009;162:1371–8. https://doi.org/10.1016/j.jhazmat.2008.06.017.10.1016/j.jhazmat.2008.06.017Search in Google Scholar
39. Ghalwa, N, Saqer, A, Farhat, N. Removal of Reactive Red 24 dye by clean electrocoagulation process using iron and aluminum electrodes. J Chem Eng Process Technol 2016;7:269. http://dx.doi.org/10.4172/2157-7048.1000269.10.4172/2157-7048.1000269Search in Google Scholar
40. Racyte, J, Rimeika, M, Bruning, H. pH effect on decolorization of raw textile wastewater polluted with reactive dyes by advanced oxidation with UV/H2O2. Environ Protect Eng 2009;35:167–78.Search in Google Scholar
41. Şengil, İA, Özacar, M. The decolorization of CI Reactive Black 5 in aqueous solution by electrocoagulation using sacrificial iron electrodes. J Hazard Mater 2009;161:1369–76. https://doi.org/10.1016/j.jhazmat.2008.04.100.10.1016/j.jhazmat.2008.04.100Search in Google Scholar
42. Daneshvar, N, Khataee, A, Ghadim, AA, Rasoulifard, M. Decolorization of C.I. Acid Yellow 23 solution by electrocoagulation process: Investigation of operational parameters and evaluation of specific electrical energy consumption (SEEC). J Hazard Mater 2007;148:566–72. https://doi.org/10.1016/j.jhazmat.2007.03.028.10.1016/j.jhazmat.2007.03.028Search in Google Scholar
43. Ahmad, A, Hameed, B. Effect of preparation conditions of activated carbon from bamboo waste for real textile wastewater. J Hazard Mater 2010;173:487–93. https://doi.org/10.1016/j.jhazmat.2009.08.111.10.1016/j.jhazmat.2009.08.111Search in Google Scholar
44. Pandian, PS, Selvan, SS, Subathira, A, Saravanan, S. Optimization of aqueous two phase extraction of proteins from litopenaeus vannamei waste by response surface methodology coupled multi-objective genetic algorithm. Chem Prod Process Model 2019;15:1–10. 10.1515/cppm-2019-0034Search in Google Scholar
45. Basheer, I, Hajmeer, M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 2000;43:3–31. https://doi.org/10.1016/s0167-7012(00)00201-3.10.1016/S0167-7012(00)00201-3Search in Google Scholar
46. Seyedi, ZS, Zahraei, Z, Kashi, FJ. Decolorization of Reactive Black 5 and Reactive Red 152 azo dyes by new haloalkaliphilic bacteria isolated from the textile wastewater. Curr Microbiol 2020;77:2084–92. https://doi.org/10.1007/s00284-020-02039-7.10.1007/s00284-020-02039-7Search in Google Scholar PubMed
47. Saba, B, Christy, AD, Park, T, Yu, Z, Li, K, Tuovinen, OH. Decolorization of Reactive Black 5 and Reactive Blue 4 dyes in microbial fuel cells. Appl Biochem 2018;186:1017–33. https://doi.org/10.1007/s12010-018-2774-7.10.1007/s12010-018-2774-7Search in Google Scholar PubMed
48. Nabil, GM, El-Mallah, NM, Mahmoud, ME. Enhanced decolorization of reactive black 5 dye by active carbon sorbent-immobilized-cationic surfactant (AC-CS). J Ind Eng Chem 2014;20:994–1002. https://doi.org/10.1016/j.jiec.2013.06.034.10.1016/j.jiec.2013.06.034Search in Google Scholar
49. Sadhu, SP, Ruparelia, J, Patel, UD. Homogeneous photocatalytic degradation of azo dye Reactive Black 5 using Fe(III) ions under visible light. Environ Technol 2020:1–8. https://doi.org/10.1080/09593330.2020.1782995.10.1080/09593330.2020.1782995Search in Google Scholar PubMed
50. Lim, C, Bay, H, Kee, T, Zaiton, AM, Ibrahim, Z. Decolourisation of Reactive Black 5 using Paenibacillus sp. immobilised onto macrocomposite. J Bioremediat Biodegrad 2012;3:1–4. http://dx.doi.org/10.4172/2155-6199.S1-004.10.4172/2155-6199.S1-004Search in Google Scholar
51. Al-Tohamy, R, Sun, J, Fareed, MF, Kenawy, E-R, Ali, SS. Ecofriendly biodegradation of Reactive Black 5 by newly isolated Sterigmatomyces halophilus SSA1575, valued for textile azo dye wastewater processing and detoxification. Sci Rep 2020;10:1–16. https://doi.org/10.1038/s41598-020-69304-4.10.1038/s41598-020-69304-4Search in Google Scholar PubMed PubMed Central
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Editorial special section: selected extended papers from an International Conference on Energy and Environmental Technologies for Sustainable Development (CHEM-CONFLUX20)
- Research Articles
- Model based control strategies to control voltage of Proton Exchange Membrane Fuel Cell
- Nested control loop configuration for a three stage biological wastewater treatment process
- Energy saving in batch distillation for separation of ternary zeotropic mixture integrated with vapor recompression scheme: dynamics and control
- Pyrolysis of corn cob: physico-chemical characterization, thermal decomposition behavior and kinetic analysis
- Decolorization of Reactive Black B from wastewater by electro-coagulation: optimization using multivariate RSM and ANN
- Numerical simulation of the effect of baffle cut and baffle spacing on shell side heat exchanger performance using CFD
Articles in the same Issue
- Frontmatter
- Editorial
- Editorial special section: selected extended papers from an International Conference on Energy and Environmental Technologies for Sustainable Development (CHEM-CONFLUX20)
- Research Articles
- Model based control strategies to control voltage of Proton Exchange Membrane Fuel Cell
- Nested control loop configuration for a three stage biological wastewater treatment process
- Energy saving in batch distillation for separation of ternary zeotropic mixture integrated with vapor recompression scheme: dynamics and control
- Pyrolysis of corn cob: physico-chemical characterization, thermal decomposition behavior and kinetic analysis
- Decolorization of Reactive Black B from wastewater by electro-coagulation: optimization using multivariate RSM and ANN
- Numerical simulation of the effect of baffle cut and baffle spacing on shell side heat exchanger performance using CFD