Application of artificial intelligent approaches for the efficiency and energy consumption of a novel sonocatalyst

Document Type : Original Article

Authors

1 Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran

2 Department of Environmental Health, Faculty of Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 Lecturer, Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran

4 Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

5 School of Mechanical Engineering, Yeungnam University, Gyeongsan, Republic of Korea

Abstract

The sonocatalytic activity of nano-sized ZnO powder was studied via the degradation of the Direct Blue 71 azo dye. The nano-sized ZnO powder that was selected was the same as that which was synthesized and characterized in our previous study. The influences of six operational parameters including the initial pH, the initial concentration, the dose of sonocatalyst, the ultrasound frequency, the ultrasound power, and the process time were investigated on the basis of process efficiency and energy consumption. The design of experiments was applied and the experiments were conducted according to the design. The experiments were carried out in a batch reactor. The experimentally obtained dye removal percent (DR%) and the energy consumption per mass ranged from 0.03–100 and 0.19–1273 (wh/g), respectively. The data were used for modelling by using reduced quadratic multiple regression models and the artificial neural network (ANN). Multi-objective optimization of DR% and EPM was applied by using the genetic algorithm (GA) over the outperformed ANN models. The mineralization was studied using total organic carbon analysis. The study indicated promising results in the application of both the novel sonocatalyst and the Artificial Intelligent Approaches.

Keywords

References

  1. Wang J, Jiang Z, Zhang Z, Xie Y, Wang X, Xing Z, et al. Sonocatalytic degradation of acid red B and rhodamine B catalyzed by nano-sized ZnO powder under ultrasonic irradiation. Ultrasonics sonochemistry. 2008;15(5):768-774.
  2. Wang J, Guo B, Zhang X, Zhang Z, Han J, Wu J. Sonocatalytic degradation of methyl orangein the presence of TiO2 catalysts and catalytic activity comparison of rutile and anatase. Ultrasonics sonochemistry. 2005;12(5):331-337.
  3. Turner C, Van Hook A. The effect of ultrasonic irradiation on the formation of colloidal sulfur and ice. Journal of colloid science. 1950;5(4):315-316.
  4. Mahvi AH, Maleki A. Photosonochemical degradation of phenol in water. Desalination and Water Treatment. 2010;20(1-3):197-202.
  5. Wang J, Jiang Z, Zhang L, Kang P, Xie Y, Lv Y, et al. Sonocatalytic degradation of some dyestuffs and comparison of catalytic activities of nano-sized TiO2, nano-sized ZnO and composite TiO2/ZnO powders under ultrasonic irradiation. Ultrasonics sonochemistry. 2009;16(2):225-231.
  6. Daraei H, Maleki A, Mahvi AH, Zandsalimi Y, Alaei L, Gharibi F. Synthesis of ZnO  nanosono-catalyst for degradation of reactive dye focusing on energy consumption: operational parameters influence, modeling, and optimization. Desalination and Water Treatment. 2014;52(34-36):6745-6755.
  7. Maleki A, Safari M, Shahmoradi B, Zandsalimi Y, Daraei H, Gharibi F. Photocatalytic degradation of humic substances in aqueous solution using Cu-doped ZnO nanoparticles under natural sunlight irradiation. Environmental Science and Pollution Research 2015;22(21):16875-16880.
  8. Srikanth CK, Jeevanandam P. Effect of anion on the homogeneous precipitation of  precursors and their thermal decomposition to zinc oxide. J Alloy Compd. 2009;486(1-2):677-684.
  9. Hatami T, Rahimi M, Daraei H, Heidaryan E, Alsairafi AA. PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system. Korean Journal of Chemical Engineering. 2012:1-11.
  10. Maleki A, Daraei H, Khodaei F, Bayazid-Aghdam K, Rezaee R, Naghizadeh A. Investigation of potato peel-based bio-sorbent efficiency in reactive dye removal: Artificial neural network modeling and genetic algorithms optimization. Journal of Advances in Environmental Health Research. 2013;1(1):21-28.
  11. Maleki A, Daraei H, Shahmoradi B, Razee S, Ghobadi N. Electrocoagulation efficiency and energy consumption probing by artificial intelligent approaches. Desalination and Water Treatment. 2014;52(13-15):2400-2411.
  12. Hatami T, Rahimi M, Daraei H, Heidaryan E, Alsairafi AA. PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system. Korean Journal of Chemical Engineering. 2012;29(5):657-667.
  13. Cheng Z, Quan X, Xiong Y, Yang L, Huang Y. Synergistic degradation of methyl orange in an ultrasound intensified photocatalytic reactor. Ultrasonics sonochemistry.  2012;19(5):1027-1032.
  14. Wang J, Sun W, Zhang Z, Zhang X, Li R, Ma T, et al. Sonocatalytic degradation of methyl parathion in the presence of micron-sized and nano-sized rutile titanium dioxide catalysts and comparison of their sonocatalytic abilities. Journal of Molecular Catalysis A: Chemical. 2007;272(1):84-90.
  15. Eren Z, Ince NH. Sonolytic and sonocatalytic degradation of azo dyes by low and high frequency ultrasound. Journal of hazardous materials. 2010;177(1):1019-1024.
Journal of Advances in Environmental Health Research
Volume 4, Issue 4
November 2016
Pages 213-218

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