Modeling of Methylene Blue Adsorption on the Surfactant-Modified Tea Waste by the Artificial Neural Network

Document Type : Research paper

Authors

1 Assistant Prof. (Mathematics) Fouman Faculty of Engineering, College of Engineering, University of Tehran

2 Fouman Faculty of Engineering, College of Engineering, University of Tehran

3 Assistant Prof. in Chemical Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran

4 MSc Student, Chemical Engineering (Separation Processes), Fouman Faculty of Engineering, College of Engineering, University of Tehran

Abstract

The adsorption process modeling of Methylene Blue dye (MB+) on the modified tea waste (TW) with anionic surfactant (SDS) was performed by the artificial neural network. The FTIR and EDS analyses were used to investigate the presence of SDS molecules on the adsorbent surface and it was determined that the SO3- anions help to MB+ adsorption by the ion exchange mechanism. The adsorption isotherm data were fitted to Langmuir and Freundlich isotherm equations and the Langmuir adsorption capacity (Qmax) was calculated respectively, equal to 124.3 and 156.2 mg/g for TW and STW. It was observed that the adsorption kinetics of MB follows the pseudo-second-order kinetic model. The findings of this investigation suggest that physical adsorption also plays a role in controlling the sorption rate of MB. The feedforward ANN model with 5 input parameters and one hidden layer having 15 neurons was designed to predict the adsorptive behavior of sorbents, and it was observed that the model is able to predict the MB removal percentage for the test data series with great R2>0.97.

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References

 
[1] C. Nunez, A. S. Garcez, I. T. Kato, T. M. Yoshimura, L. Gomes, M. S. Baptista, M. S. Ribeiro (2014) “Effects of ionic strength on the antimicrobial photodynamic efficiency of methylene blue”, Photochemical and Photobiological Sciences, 13, 595–602.
##
[2] V. K. Gupta, S., suhas (2009) “Application of low-cost adsorbents for dye removal: A review”, Journal of Environmental Management, 90, 2313–2342.
##
[3] B. H. Hameed (2009) “Spent tea leaves: A new non-conventional and low-cost adsorbent for removal of basic dye from aqueous solutions”, Journal of Hazardous Materials, 161, 753-759.
##
[4] N. Nasuha, B. H. Hameed, A. T. Mohd Din (2010) “Rejected tea as a potential low-cost adsorbent for the removal of methylene blue”, Journal of Hazardous Materials, 175, 126–132.
##
[5] S. Hokkanen, A. Bhatnagar, M. Sillanpää (2016) “A review on modification methods to cellulose-based adsorbents to improve adsorption capacity”, Water Research, 91, 156-173.
##
[6] M. T. Yagub, T. K. Sen, S. Afroze, H. M. Ang (2014) “Dye and its removal from aqueous solution by adsorption: A review”, Advances in Colloid and Interface Science, 209, 172–184.
##
[7] A. M. Ghaedi, A. Vafaei (2017) “Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review”, Advances in Colloid and Interface Science, 245, 20–39.
##
[8] M. Tanzifi, S. H. Hosseini, A. Dehghani Kiadehi, M. Olazar, K. Karimipour, R. Rezaiemehr, I. Ali (2017) “Artificial neural network optimization formethyl orange adsorption onto polyaniline nano-adsorbent: Kinetic, isotherm and thermodynamic studies”, Journal of Molecular Liquids, 244, 189–200.
##
[9] P. Assefi, M. Ghaedi, A. Ansari, M. H. Habibi, M. S. Momeni (2014) “Artificial neural network optimization for removal of hazardous dye Eosin Y from aqueous solution using Co2O3-NP-AC: Isotherm and kinetics study”, Journal of Industrial Engineering Chemistry, 20, 2905-2913.
##
[10] H. Karimi, M. Ghaedi (2014) “Application of artificial neural network and genetic algorithm to modeling and optimization of removal of methylene blue using activated carbon”, Journal of Industrial and Engineering Chemistry, 20: 2471–2476.
##
[11] A. A. Babaei, A. Khataee, E. Ahmadpour, M. Sheydaei, B. Kakavandi, Z. Alaee (2016) “Optimization of cationic dye adsorption on activated spent tea: equilibrium, kinetics, thermodynamic and artificial neural network modeling”, Korean Journal of Chemical Engineering, 33:1352–1361.
##
[12] N. Zeinali, M. Ghaedi, G. Shafie (2014) “Competitive adsorption of methylene blue and brilliant green onto graphite oxide nano particle following: derivative spectrophotometric and principal component-artificial neural network model methods for their simultaneous determination”, Journal of Industrial and Engineering Chemistry, 20:3550–3558.
##
[13] A. Ebrahimian Pirbazari, B. Fakhari Kisom, M. Ghamangiz Khararoodi (2015) “Anionic surfactant-modified rice straw for removal of methylene blue from aqueous solution”, Desalination and Water Treatment, 65, 1-15.
##
[14] N. G. Turan, B. Mesci, O. Ozgonenel (2011) “Artificial neural network (ANN) approach for modeling Zn(II) adsorption from leachate using a new biosorbent”, Chemical Engineering Journal, 173, 98–105.
##
[15] R. Gnanasambandam, A. Protor (2000) “Determination of pectin degree of esterification by diffuse reflectance Fourier transform infrared spectroscopy - quantitative evaluation of uronic acid and acetamidodeoxyhexose moieties”, Food Chemistry, 68, 327–332.
##
[16] D. Sidiras, F. Batzias, E. Schroeder, R. Ranjan, M. Tsapatsis (2011) “Dye adsorption on autohydrolyzed pine sawdust in batch and fixed-bed system”, Chemical Engineering Journal, 171, 883- 896.
##
[17] K. S. W. Sing, D. H. Everett, R. A. W. Haul, L. Moscou, R. A. Pierotti (1985) “Reporting physisorption data for gas/solid systems with special reference to the determination of surface area and porosity”,  Pure and Applied Chemistry, 57, 603–619.
##
[18] A. Ariapad, M. A. Zanjanchi, M. Arvand (2012) “Efficient removal of anionic surfactant using partial template-containing MCM-41”, Desalination, 284, 142- 149.
##
[19] A. Ebrahimian Pirbazari, N. Ramazani Pargami, N. Ashja, MS. Emami (2015) “Surfactant-coated Tea Waste: Preparation, Characterization and its Application for Methylene Blue Adsorption from Aqueous Solution”, Journal of Environmental & Analytical Toxicology, 5, 1–11.
##
[20] N. Asasian, T. Kaghazchi (2012) “Comparison of dimethyl disulfide and carbon disulfide in sulfurization of activated carbons for producing mercury adsorbents”, Industrial and Engineering Chemistry Research, 51, 12046-12057.
##
[21] L. Bulgariu, M. Răţoi, D. Bulgariu, M. Macoveanu (2008) “Equilibrium study of Pb(II) and Hg(II) sorption from aqueous solution by moss peat”, Environmental and Engineering Management Journal, 7, 511-516.
##
Journal of Separation Science and Engineering
Volume 11, Issue 1
July 2019
Pages 41-52

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