Modelling and optimization of Fenton processes through neural network and genetic algorithm

Hüseyin Cüce; Fulya Aydın Temel; Ozge Cagcag Yolcu

Korean Journal of Chemical Engineering, Vol.38, No.11, 2265-2278, November, 2021

DOI10.1007/s11814-021-0867-4 Export Citation

Modelling and optimization of Fenton processes through neural network and genetic algorithm

Hüseyin Cüce, Fulya Aydın Temel^{1, †}, and Ozge Cagcag Yolcu²

Department of Geomatic Engineering, Faculty of Engineering, Giresun University, Giresun, Turkey
¹Department of Environmental Engineering, Faculty of Engineering, Giresun University, Giresun, 28200, Turkey
²Department of Statistics, Faculty of Sciences and Arts, Marmara University, İstanbul, 34722, Turkey

E-mail:

Response surface methodology (RSM), multi-layer perceptron trained by Levenberg-Marquardt (MLPLM); multi-layer perception and Sigma-Pi neural networks trained by particle swarm optimization (PSO) were used to effectively and reliably predict the performance of Classical-Fenton and Photo-Fenton processes. H2O2 doses, Fe(II) doses, and H2O2/Fe(II) rates were determined as independent variables in batch reactors. The performance of models was compared by using RMSE and MAE error criteria. The performance of models was also evaluated in terms of some properties of regression analysis and scatter that showed high linear relationship between the predictions of SPPSO and the actual removal values. As a distinctive aspect of this study, SPNN trained by PSO was used for the first time in the literature in this area and the best predictive results for almost all cases were generated. Moreover, the genetic algorithm (GA) was applied for SP-PSO model results to determine the optimum values of the study. According to the results of GA, under the optimum conditions Photo-Fenton processes had higher performance in each experiment. Thereby, SP-PSO produced satisfactory prediction results without the need for any additional experiments in the case that experimental designs are difficult or costly for wastewater treatment.

Keywords:Fenton;Multilayer Perceptron;Sigma Pi Neural Network;Particle Swarm Optimization;Genetic Algorithm;Response Surface Methodology

[References]

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[2] Jefferson B, Laine A, Parsons S, Stephenson T, Judd S, Urban Water, 1(4), 285 (2000)

[3] Friedler E, Hadari M, Desalination, 190(1-3), 221 (2006)

[4] Terechova EL, Zhang G, Chen J, Sosnina NA, Yang F, J. Environ. Chem. Eng., 2(4), 2111 (2014)

[5] Mostafazadeh AK, Benguit AT, Carabin A, Drogui P, Brien E, J. Water Process Eng., 28, 277 (2019)

[6] Patil VV, Gogate PR, Bhat AP, Ghosh PK, Sep. Purif. Technol., 239, 116594 (2020)

[7] Ciabatti I, Cesaro F, Faralli L, Fatarella E, Tognotti F, Desalination, 245(1-3), 451 (2009)

[8] Moura AGL, Centurion VB, Okada DY, Motteran F, Delforno TP, Oliveira VM, Varesche MBA, J. Environ. Manage., 251, 109495 (2019)

[9] Dimoglo A, Sevim-Elibol P, Dinc O, Gokmen K, Erdogan H, J. Water Process Eng., 31, 100877 (2019)

[10] Ge JT, Qu JH, Lei PJ, Liu HJ, Sep. Purif. Technol., 36(1), 33 (2004)

[11] Choobar BG, Shahmirzadi MAA, Kargari A, Manouchehri M, J. Environ. Chem. Eng., 7(2), 103030 (2019)

[12] Huang AK, Veit MT, Juchen PT, Goncalves GDC, Palacio SM, Cardoso CDO, J. Environ. Chem. Eng., 7(4), 103226 (2019)

[13] Sumisha A, Arthanareeswaran G, Thuyavan YL, Ismail AF, Chakraborty S, Ecotoxicol. Environ. Saf., 121(2004), 174 (2015)

[14] Turkay O, Barisci S, Sillanpaa M, J. Environ. Chem. Eng., 5(5), 4282 (2017)

[15] Kim TH, Park C, Yang JM, Kim S, J. Hazard. Mater., 112(1-2), 95 (2004)

[16] Li HY, Li YL, Xiang LJ, Huang QQ, Qiu JJ, Zhang H, Sivaiah MV, Baron F, Barrault J, Petit S, Valange S, J. Hazard. Mater., 287, 32 (2015)

[17] Fernandes NC, Brito LB, Costa GG, Taveira SF, Cunha-Filho MSS, Oliveira GAR, Marreto RN, Chem. Biol. Interact., 291, 47 (2018)

[18] Ertugay N, Acar FN, Arab. J. Chem., 10, S1158 (2017)

[19] Poyatos JM, Munio MM, Almecija MC, Torres JC, Hontoria E, Osorio F, Water. Air. Soil Pollut., 205(1-4), 187 (2010)

[20] Emami F, Tehrani-Bagha AR, Gharanjig K, Menger FM, Desalination, 257(1-3), 124 (2010)

[21] Pazdzior K, Bilinska L, Ledakowicz S, Chem. Eng. J., 376, 120597 (2019)

[22] Yolcu U, Jin Y, Egrioglu E, 2016 IEEE Symp. Ser. Comput. Intell. SSCI 2016 (2017).

[23] Yolcu U, Egrioglu E, Bas E, Yolcu OC, Dalar AZ, J. Exp. Theor. Artif. Intell., 33(3), 383 (2021)

[24] Yolcu OC, Bas E, Egrioglu E, Yolcu U, Neural Process. Lett., 47(3), 1133 (2018)

[25] Easturk E, Alver A, J. Environ. Manage., 248, 109300 (2019)

[26] Elmolla ES, Chaudhuri M, Eltoukhy MM, J. Hazard. Mater., 179(1-3), 127 (2010)

[27] Radwan M, Alalm MG, Eletriby H, J. Water Process Eng., 22, 155 (2018)

[28] Sabour MR, Amiri A, Waste Manag., 65, 54 (2017)

[29] Talwar S, Verma AK, Sangal VK, J. Environ. Manage., 250, 109428 (2019)

[30] Tolba A, Alalm MG, Elsamadony M, Mostafa A, Afify H, Dionysiou DD, Process Saf. Environ. Prot., 128, 273 (2019)

[31] Gholizadeh AM, Zarei M, Ebratkhahan M, Hasanzadeh A, J. Environ. Chem. Eng., 9, 104999 (2021)

[32] Jaafarzadeh N, Ahmadi M, Amiri H, Yassin MH, Martinez SS, J. Taiwan Inst. Chem. Eng., 43(6), 873 (2012)

[33] Baird RB, Eaton AD, Rice EW, Standard methods for the examination of water and wastewater, 23rd Ed., Washington, DC (2017).

[34] Werbos PJ, The roots of backpropagation, John Wiley & Sons, New York (1974).

[35] Shin Y, Gosh J, IJCNN-91-Seattle International Joint Conference on Neural Networks, 1, 13 (1991).

[36] Kennedy J, Eberhart R, Proceedings of IEEE international conference on neural networks, Australia, 1942 (1995).

[37] Holland JH, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, England (1992).

[38] Goldberg DE, Genetic algorithms in search, optimization and machine learning 13th Ed. Edition, United States (1989).