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Genetic Algorithm Modeling for Photocatalytic Elimination of Impurity in Wastewater

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1037))

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Abstract

The existence of C.I. Acid Yellow 23 (AY23) in water causes a great danger to people and society. Here, we suggest an advanced technique which predicts the photochemical deletion of AY23. The genetic algorithm (GA) technique is suggested in order to predict the photocatalytic removal of AY23 by implementing the Ag-TiO\(_{2}\) nanoparticles provided under appropriate conditions.

In order to evaluate the proposed method, a total of 100 data are utilized which are arbitrarily divided into two: 80 samples in order to train the model as well as 20 samples in order to test the model. Experimental outcomes reveals that the suggested technique is efficient for photocatalytic elimination of impurity in wastewater.

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References

  1. Chagas, E.P., Durrant, L.R.: Decolorization of azo dyes by Phanerochate chrysosporium and Pleurotus sajorcaju. Enzyme Microbial Technol. 29(1), 473–477 (2001)

    Article  Google Scholar 

  2. Fan, C.T., Wang, Y.K., Huang, C.R.: Heterogeneous information fusion and visualization for a large-scale intelligent video surveillance system. IEEE Trans. Syst. Man Cybern. Syst. 47, 593–604 (2016)

    Article  Google Scholar 

  3. Jafari, R., Razvarz, S.: Solution of fuzzy differential equations using fuzzy sumudu transforms. Math. Comput. Appl. 23, 1–15 (2018)

    MathSciNet  MATH  Google Scholar 

  4. Jafari, R., Yu, W.: Uncertainty nonlinear systems control with fuzzy equations, pp. 2885–2890 (2015)

    Google Scholar 

  5. Jafari, R., Yu, W.: Artificial neural network approach for solving strongly degenerate parabolic and Burgers-Fisher equations. In: 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (2015). https://doi.org/10.1109/ICEEE.2015.7357914

  6. Jafari, R., Yu, W.: Uncertain nonlinear system control with fuzzy differential equations and Z-numbers. In: 18th IEEE International Conference on Industrial Technology, Canada, pp. 890–895 (2017). https://doi.org/10.1109/ICIT.2017.7915477

  7. Jafari, R., Yu, W., Li, X.: Solving fuzzy differential equation with Bernstein neural networks. In: IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary, pp. 1245–1250 (2016)

    Google Scholar 

  8. Razvarz, S., Jafari, R., Gegov, A., Yu, W., Paul, S.: Neural network approach to solving fully fuzzy nonlinear systems. In: Fuzzy Modeling and Control Methods Application and Research, pp. 45–68. Nova Science Publisher, Inc., New York (2018). ISBN: 978-1-53613-415-5

    Google Scholar 

  9. Razvarz, S., Jafari, R., Granmo, O.-C., Gegov, A.: Solution of dual fuzzy equations using a new iterative method. In: Proceedings of the 10th Asian Conference on Intelligent Information and Database Systems. Lecture Notes in Artificial Intelligence (subseries of LNCS), pp. 245–255. Springer (2018)

    Google Scholar 

  10. Shirvani Ardekani, P., Karimi, H., Ghaedi, M., Asfaram, A., Kumar Purkait, M.: Ultrasonic assisted removal of methylene blue on ultrasonically synthesized zinc hydroxide nanoparticles on activated carbon prepared from wood of cherry tree: experimental design methodology and artificial neural network. J. Mol. Liq. 229, 114–124 (2017)

    Article  Google Scholar 

  11. Mazaheri, H., Ghaedi, M., Ahmadi Azqhandi, M.H., Asfaram, A.: Application of machine/statistical learning, artificial intelligence and statistical experimental design for the modeling and optimization of methylene blue and Cd(II) removal from a binary aqueous solution by natural walnut carbon. Phys. Chem. Chem. Phys. 19, 11299–11317 (2017)

    Article  Google Scholar 

  12. Chakraborty, P., Das, S., Roy, G.G., Abraham, A.: On convergence of the multi-objective particle swarm optimizers. Inf. Sci. 181, 1411–1425 (2011)

    Article  MathSciNet  Google Scholar 

  13. El-Wakeel, A.S., Hassan, F., Kamel, A., Abdel-Hamed, A.: Optimum tuning of pid controller for a permanent magnet brushless DC motor. Int. J. Electr. Eng. Technol. 4, 53–64 (2013)

    Google Scholar 

  14. Fathi, V., Montazer, G.A.: An improvement in RBF learning algorithm based on PSO for real time applications. Neurocomputing. 111, 169–176 (2013)

    Article  Google Scholar 

  15. Khajeh, M., Kaykhaii, M., Sharafi, A.: Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water sample. J. Ind. Eng. Chem. (2013). https://doi.org/10.1016/j.jiec.2013.01.033

    Article  Google Scholar 

  16. Eberhart, R.C., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann, San Diego (2001)

    Google Scholar 

  17. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  18. Chen, C.L.P., Zhang, T., Tam, S.Ch.: A novel evolutionary algorithm solving optimization problems. In: IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, USA (2014)

    Google Scholar 

  19. Niknam, T., Taherian Fard, E., Pourjafarian, N., Rousta, A.: An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Eng. Appl. Artif. Intell. 24, 306–317 (2011)

    Article  Google Scholar 

  20. Pothiya, S., Ngamroo, I., Kongprawechnon, W.: Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints. Energy Convers. Manag. 49, 506–516 (2008)

    Article  Google Scholar 

  21. Yousefi, M., Darus, A.N., Mohammadi, H.: An imperialist competitive algorithm for optimal design of plate-fin heat exchangers. Int. J. Heat Mass Transfer 55, 3178–3185 (2012)

    Article  Google Scholar 

  22. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary Computation, Singapore, pp. 4661–4667 (2007)

    Google Scholar 

  23. Azamuthullah, H.M., Ghani, A., Zakaria, N.A., Chang, C.K., Abu Hassan, Z.: Genetic programming approach to predict sediment concentration for Malaysian rivers. Int. J. Ecol. Econ. Stat. 16, 53–64 (2010)

    Google Scholar 

  24. Guven, A., Aytek, A.: New approach for stage-discharge relationship: gene expression programming. J. Hydro. Eng. 14, 812–820 (2009)

    Article  Google Scholar 

  25. Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, New York (2008)

    MATH  Google Scholar 

  26. Thomas, B.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York (1996)

    MATH  Google Scholar 

  27. Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  28. Chang, Y., Erera, A.L., White, C.C.: Risk assessment of deliberate contamination of food production facilities. IEEE Trans. Syst. Man Cybern. Syst. 47, 381–393 (2015)

    Article  Google Scholar 

  29. Jafari, R., Razvarz, S.: Solution of fuzzy differential equations using fuzzy Sumudu transforms. In: IEEE International Conference on Innovations in Intelligent Systems and Applications, pp. 84–89 (2017)

    Google Scholar 

  30. Jafari, R., Razvarz, S., Gegov, A.: A new computational method for solving fully fuzzy nonlinear systems. In: Computational Collective Intelligence, ICCCI 2018. Lecture Notes in Computer Science, vol. 11055, pp. 503–512. Springer, Cham (2018)

    Chapter  Google Scholar 

  31. Jafari, R., Razvarz, S., Gegov, A., Paul, S.: Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, Nottingham, UK, 5–7 September 2018. Advances in Intelligent Systems and Computing, vol. 840, pp. 66–107. Springer (2018)

    Google Scholar 

  32. Jafari, R., Razvarz, S., Gegov, A., Paul, S., Keshtkar, S.: Fuzzy Sumudu transform approach to solving fuzzy differential equations with Z-numbers. In: Advanced Fuzzy Logic Approaches in Engineering Science, pp. 18–48. IGI Global, Hershey (2018). https://doi.org/10.4018/978-1-5225-5709-8.ch002

  33. Jafari, R., Yu, W.: Fuzzy modeling for uncertainty nonlinear systems with fuzzy equations. Math. Probl. Eng. 2017 (2017). https://doi.org/10.1155/2017/8594738

    Article  MathSciNet  Google Scholar 

  34. Liang, G., Lan, X., Wang, J., Wang, J., Zheng, N.: A limb-based graphical model for human pose estimation. IEEE Trans. Syst. Man Cybern. Syst. 48, 1080–1092 (2016)

    Article  Google Scholar 

  35. Razvarz, S., Jafari, R.: Experimental study of AL2O3 nanofuids on the thermal efficiency of curved heat pipe at different tilt angle. J. Nanomater. 1–7 (2018)

    Article  Google Scholar 

  36. Razvarz, S., Jafari, R.: Experimental study of Al2O3 nanofluids on the thermal efficiency of curved heat pipe at different tilt angle. In: 2nd International Congress on Technology Engineering and Science (ICONTES), Malaysia (2016)

    Google Scholar 

  37. Razvarz, S., Jafari, R., Yu, W.: Numerical solution of fuzzy differential equations with Z-numbers using fuzzy Sumudu transforms. Adv. Sci. Technol. Eng. Syst. J. (ASTESJ) 3, 66–75 (2018)

    Article  Google Scholar 

  38. Razvarz, S., Vargas-Jarillo, C., Jafari, R., Gegov, A.: Flow control of fluid in pipelines using PID controller. IEEE Access 7, 25673–25680 (2019)

    Article  Google Scholar 

  39. Yukalov, V.I., Sornette, D.: Quantitative predictions in quantum decision theory. IEEE Trans. Syst. Man Cybern. Syst. 48, 366–381 (2016)

    Article  Google Scholar 

  40. Kasiri, M.B., Aleboyeh, H., Aleboyeh, A.: Modeling and optimization of heterogeneous photo-fenton process with response surface methodology and artificial neural networks. Environ. Sci. Technol. 42, 7970–7975 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway

    Raheleh Jafari & Morten Goodwin

  2. Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico

    Sina Razvarz & Wen Yu

  3. School of Computing, University of Portsmouth, Buckingham Building, Portsmouth, PO1 3HE, UK

    Alexander Gegov & Mo Adda

Authors
  1. Raheleh Jafari
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  2. Sina Razvarz
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  3. Wen Yu
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  4. Alexander Gegov
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  5. Morten Goodwin
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  6. Mo Adda
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Corresponding author

Correspondence to Raheleh Jafari .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Jafari, R., Razvarz, S., Yu, W., Gegov, A., Goodwin, M., Adda, M. (2020). Genetic Algorithm Modeling for Photocatalytic Elimination of Impurity in Wastewater. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_17

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