Regression analysis and artificial intelligence for removal of methylene blue from aqueous solutions using nanoscale zero-valent iron
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This study attempted to investigate the adsorption of methylene blue (MB) onto nanoscale zero-valent iron (nZVI) from aqueous solutions and to determine the correlation between experimental factors and dye removal efficiency. The adsorption mechanism was discussed in combination with results obtained from transmission electron microscopy, X-ray diffraction, and scanning electron microscopy. Results indicated that at pH 6, nZVI dosage 10 g L−1, initial MB concentration 10 mg L−1, temperature 30 °C, and stirring rate 150 rpm, the equilibrium time was 30 min achieving a removal efficiency of approximately 100%. The adsorption data of MB fitted well to Freundlich isotherm (r2: 0.9358) and pseudo-second-order kinetic model (r2: 0.9976). Response surface methodology (RSM) was developed to visualize the effects of independent factors on the adsorption efficiency. The curves of pH, stirring rate, and reaction time were quadratic linear concave up, whereas nZVI dosage attained a linear up plot. Additionally, artificial neural network (ANN) with a structure of 6–10–1 was used to predict the MB removal efficiency. It was revealed that the ANN model (r2: 0.9313) was more accurate than the RSM model (r2: 0.6316) in describing the adsorption of MB onto nZVI. Sensitivity analysis using the connection weights method showed that the reaction time was the most influential parameter with a relative importance of 22.77%. These advanced modeling techniques could be applied to maximize the performance of nZVI for treating dye-contaminated water under different environmental conditions.
KeywordsAdsorption Artificial neural network Cationic dye Iron nanoparticles Isotherms and kinetics
This research was supported by the Egyptian Housing and Building National Research Center (HBRC), Environmental Engineering Program, Zewail City of Science and Technology.
Compliance with ethical standards
The authors declare that they have no conflict of interest.
- Agarwal S, Tyagi I, Gupta VK, Ghasemi N, Shahivand M, Ghasemi M (2016) Kinetics, equilibrium studies and thermodynamics of methylene blue adsorption on Ephedra strobilacea saw dust and modified using phosphoric acid and zinc chloride. J Mol Liq 218:208–218. https://doi.org/10.1016/j.molliq.2016.02.073 CrossRefGoogle Scholar
- Demuth H, Beale M, Hagan M (2007) Neural network toolbox 5: users guide. The MathWorks Inc, NatickGoogle Scholar
- Djenouhat M, Hamdaoui O, Chiha M, Samar MH (2008) Ultrasonication-assisted preparation of water-in-oil emulsions and application to the removal of cationic dyes from water by emulsion liquid membrane Part 2. Permeation and stripping. Sep Purif Technol 63:231–238. https://doi.org/10.1016/j.seppur.2008.05.005 CrossRefGoogle Scholar
- Freundlich HMF (1906) Over the adsorption in solution. J Phys Chem 57:385–470Google Scholar
- Garson GD (1991) Interpreting neural network connection weights. Artif Intell Expert 6(4):47–51Google Scholar
- Hu XJ, Wang JS, Liu YG, Li X, Zeng GM, Bao ZL, Zeng XX, Chen AW, Long F (2011) Adsorption of chromium (VI) by ethylenediamine-modified cross-linked magnetic chitosan resin: isotherms, kinetics and thermodynamics. J Hazard Mater 185(1):306–314. https://doi.org/10.1016/j.jhazmat.2010.09.034 CrossRefGoogle Scholar
- Lagergren S (1898) Zur theorie der sogenannten adsorption gelöster stoffe, Kungliga Svenska Vetenskapsakademiens. Handlingar 24(4):1–39Google Scholar
- Petala E, Dimos K, Douvalis A, Bakas T, Tucek J, Zbořil R, Karakassides MA (2013) Nanoscale zero-valent iron supported on mesoporous silica: characterization and reactivity for Cr(VI) removal from aqueous solution. J Hazard Mater 261:295–306. https://doi.org/10.1016/j.jhazmat.2013.07.046 CrossRefGoogle Scholar
- Rahimi S, Poormohammadi A, Salmani B, Ahmadian M, Rezaei M (2016) Comparing the photocatalytic process efficiency using batch and tubular reactors in removal of methylene blue dye and COD from simulated textile wastewater. J Water Reuse Desalin 6(4):574–582. https://doi.org/10.2166/wrd.2016.190 CrossRefGoogle Scholar
- Ranasinghe RATM, Jaksa MB, Kuo YL, Pooya Nejad F (2017) Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results. JRMGE 9:340–349Google Scholar