Abstract
This study evaluated the COD removal performances of classical-Fenton and photo-Fenton Processes by different prediction models. To optimize both Fenton processes performed in batch reactors, the effects of H2O2 dose, Fe(II) dose, H2O2/Fe(II) rate, and contact time were determined as the independent variables of the prediction models. Besides response surface methodology, three neural networks were used to more reliably and effectively predict the behaviors of dependent variables at different values of relevant parameters. Multi-Layer Perceptron trained by Levenberg–Marquardt (MLP-LM), Multi-Layer Perceptron and Single Multiplicative Neuron models trained by Particle Swarm Optimization algorithm (MLP-PSO; SMN-PSO) were studied. Models’ prediction performances were evaluated by Root-Mean-Square Error (RMSE) and Mean Absolute Percent Error criteria. Regression analysis was applied to determine the performance of the best model. The results from both criteria indicated that SMN-PSO model produced the best predictive results in almost all cases. Moreover, the key process parameters were determined by applying the genetic algorithm to SMN-PSO model outputs. The optimized conditions achieved the optimum removal with over 99% desirability. The optimum Fe(II) dose was determined as 399.99 mg/L in both Fenton processes. H2O2 dose was found as 726.18 and 894.07 mg/L and removal efficiencies were achieved 86.50 and 87.49% for classical Fenton and photo-Fenton, respectively. As a result, it will be possible to simulate and improve the different Fenton processes and determine the optimum process parameters by the obtained data in the treatment of wastewater with similar characteristics without many experiments, which are difficult and costly.
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References
Baird RB, Eaton AD, Rice EW (2017) Standard methods for the examination of water and wastewater, 23rd edn. American Public Health Association, American Water Works Association, Water Environment Federation, Washington, USA
Banerjee P, Dey T et al (2016) Treatment of cosmetic effluent in different configurations of ceramic UF membrane based bioreactor: toxicity evaluation of the untreated and treated wastewater using catfish (Heteropneustes fossilis). Chemosphere 146:133–144. https://doi.org/10.1016/j.chemosphere.2015.12.004
Bautista P, Mohedano AF, Gilarranz MA et al (2007) Application of Fenton oxidation to cosmetic wastewaters treatment. J Hazard Mater 143:128–134. https://doi.org/10.1016/j.jhazmat.2006.09.004
Bautista P, Mohedano AF, Menéndez N et al (2010) Catalytic wet peroxide oxidation of cosmetic wastewaters with Fe-bearing catalysts. Catal Today 151:148–152. https://doi.org/10.1016/j.cattod.2010.01.023
Baştürk E, Alver A (2019) Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches. J Environ Manag 248:109300. https://doi.org/10.1016/j.jenvman.2019.109300
Bom S, Jorge J, Ribeiro HM, Marto J (2019) A step forward on sustainability in the cosmetics industry: a review. J Clean Prod 225:270–290. https://doi.org/10.1016/j.jclepro.2019.03.255
Cagcag Yolcu O, Bas E, Egrioglu E, Yolcu U (2018) Single multiplicative neuron model artificial neural network with autoregressive coefficient for time series modelling. Neural Process Lett 47:1133–1147. https://doi.org/10.1007/s11063-017-9686-3
Cüce H, Aydın Temel F (2021) Classical-Fenton and photo-Fenton oxidation of wastewater arising from cosmetic automobile care products. Environ Prog Sustain Energy 40:1–10. https://doi.org/10.1002/ep.13701
Cüce H, Aydın Temel F, Cagcag Yolcu O (2021) Modelling and optimization of Fenton processes through neural network and genetic algorithm. Korean J Chem Eng 38:2265–2278. https://doi.org/10.1007/s11814-021-0867-4
de Andrade PM, Dufrayer CR, Ionashiro EY, de Brito NN (2020) The use of metallurgical waste for heterogeneous photo Fenton-Like treatment of cosmetic effluent. J Environ Chem Eng 8:104148. https://doi.org/10.1016/j.jece.2020.104148
Egrioglu E, Aladag CH, Yolcu U et al (2013) Fuzzy time series method based on multiplicative neuron model and membership values. Am J Intell Syst 3:33–39. https://doi.org/10.5923/j.ajis.20130301.05
El-Gohary F, Tawfik A, Mahmoud U (2010) Comparative study between chemical coagulation/precipitation (C/P) versus coagulation/dissolved air flotation (C/DAF) for pre-treatment of personal care products (PCPs) wastewater. Desalination 252:106–112. https://doi.org/10.1016/j.desal.2009.10.016
Elmolla ES, Chaudhuri M, Eltoukhy MM (2010) The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. J Hazard Mater 179:127–134. https://doi.org/10.1016/j.jhazmat.2010.02.068
Fernandes NC, Brito LB, Costa GG et al (2018) Removal of azo dye using Fenton and Fenton-like processes: evaluation of process factors by Box—Behnken design and ecotoxicity tests. Chem Biol Interact 291:47–54. https://doi.org/10.1016/j.cbi.2018.06.003
Friha I, Karray F, Feki F et al (2014) Treatment of cosmetic industry wastewater by submerged membrane bioreactor with consideration of microbial community dynamics. Int Biodeterior Biodegrad 88:125–133. https://doi.org/10.1016/j.ibiod.2013.12.015
Gholizadeh AM, Zarei M, Ebratkhahan M, Hasanzadeh A (2021) Phenazopyridine degradation by electro-Fenton process with magnetite nanoparticles-activated carbon cathode, artificial neural networks modeling. J Environ Chem Eng 9:104999. https://doi.org/10.1016/j.jece.2020.104999
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 13th edn. Addison-Wesley Publishing Company, Boston
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, New York
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, London, England
Jaafarzadeh N, Ahmadi M, Amiri H et al (2012) Predicting Fenton modification of solid waste vegetable oil industry for arsenic removal using artificial neural networks. J Taiwan Inst Chem Eng 43:873–878. https://doi.org/10.1016/j.jtice.2012.05.008
Kennedy J, Eberhart R (1995) Particle Swarm Optimisation. In: Proceedings of IEEE international conference on neural networks. Piscataway,: IEEE Service Center, Perth, Australia, NJ, pp 1942–1948
Lek S, Delacoste M, Baran P et al (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecol Modell 90:39–52. https://doi.org/10.1016/0304-3800(95)00142-5
Li D, Lv R, Si G, You Y (2017) Hybrid neural network-based prediction model for tribological properties of polyamide6-based friction materials. Polym Compos 38:1705–1711. https://doi.org/10.1002/pc.23740
Ma Y, Jiang C, Hou Z, Wang C (2006) The formulation of the optimal strategies for the electricity producers based on the particle swarm optimization algorithm. IEEE Trans Power Syst 21:1663–1671. https://doi.org/10.1109/TPWRS.2006.883676
de Melo ED, Mounteer AH, de Leão LH, S, et al (2013) Toxicity identification evaluation of cosmetics industry wastewater. J Hazard Mater 244–245:329–334. https://doi.org/10.1016/j.jhazmat.2012.11.051
Monsalvo VM, Lopez J, Mohedano AF, Rodriguez JJ (2014) Treatment of cosmetic wastewater by a full-scale membrane bioreactor (MBR). Environ Sci Pollut Res 21:12662–12670. https://doi.org/10.1007/s11356-014-3208-x
Muszyński A, Marcinowski P, Maksymiec J et al (2019) Cosmetic wastewater treatment with combined light/Fe0/H2O2 process coupled with activated sludge. J Hazard Mater 378:120732. https://doi.org/10.1016/j.jhazmat.2019.06.009
Naumczyk J, Bogacki J, Marcinowski P, Kowalik P (2014) Cosmetic wastewater treatment by coagulation and advanced oxidation processes. Environ Technol (U K) 35:541–548. https://doi.org/10.1080/09593330.2013.808245
Oller I, Malato S, Sánchez-Pérez JA (2011) Combination of advanced oxidation processes and biological treatments for wastewater decontamination—A review. Sci Total Environ 409:4141–4166. https://doi.org/10.1016/j.scitotenv.2010.08.061
Paździor K, Bilińska L, Ledakowicz S (2019) A review of the existing and emerging technologies in the combination of AOPs and biological processes in industrial textile wastewater treatment. Chem Eng J 376:120597. https://doi.org/10.1016/j.cej.2018.12.057
Puyol D, Monsalvo VM, Mohedano AF et al (2011) Cosmetic wastewater treatment by upflow anaerobic sludge blanket reactor. J Hazard Mater 185:1059–1065. https://doi.org/10.1016/j.jhazmat.2010.10.014
Radwan M, Gar Alalm M, Eletriby H (2018) Optimization and modeling of electro-Fenton process for treatment of phenolic wastewater using nickel and sacrificial stainless steel anodes. J Water Process Eng 22:155–162. https://doi.org/10.1016/j.jwpe.2018.02.003
Rumelhart E, Hinton G, Williams R (1986) Learning internal representations by error propagation. The M.I.T. Press, Cambridge, pp 318–362
Sabour MR, Amiri A (2017) Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate. Waste Manag 65:54–62. https://doi.org/10.1016/j.wasman.2017.03.048
Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceeding of the 1999 Congress Evoluationary Computation CEC 1999 3:1945–1950. https://doi.org/10.1109/CEC.1999.785511
Talwar S, Verma AK, Sangal VK (2019) Modeling and optimization of fixed mode dual effect (photocatalysis and photo-Fenton) assisted Metronidazole degradation using ANN coupled with genetic algorithm. J Environ Manage 250:109428. https://doi.org/10.1016/j.jenvman.2019.109428
Tolba A, Gar Alalm M, Elsamadony M et al (2019) Modeling and optimization of heterogeneous Fenton-like and photo-Fenton processes using reusable Fe3O4-MWCNTs. Process Saf Environ Prot 128:273–283. https://doi.org/10.1016/j.psep.2019.06.011
Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. Harvard University, Cambridge
Wiliński PR, Marcinowski PP, Naumczyk J, Bogacki J (2017) Pretreatment of cosmetic wastewater by dissolved ozone flotation (DOF). Desalin Water Treat 71:95–106. https://doi.org/10.5004/dwt.2017.20552
Yadav RN, Kalra PK, John J (2007) Time series prediction with single multiplicative neuron model. Appl Soft Comput J 7:1157–1163. https://doi.org/10.1016/j.asoc.2006.01.003
Yolcu U, Egrioglu E, Bas E et al (2019) Probabilistic forecasting, linearity and nonlinearity hypothesis tests with bootstrapped linear and nonlinear artificial neural network. J Exp Theor Artif Intell 33:383–404. https://doi.org/10.1080/0952813X.2019.1595167
Zarei M, Khataee AR, Ordikhani-Seyedlar R, Fathinia M (2010) Photoelectro-fenton combined with photocatalytic process for degradation of an azo dye using supported TiO2 nanoparticles and carbon nanotube cathode: Neural network modeling. Electrochim Acta 55:7259–7265. https://doi.org/10.1016/j.electacta.2010.07.050
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Cüce, H., Cagcag Yolcu, O. & Aydın Temel, F. Combination of ANNs and heuristic algorithms in modelling and optimizing of Fenton processes for industrial wastewater treatment. Int. J. Environ. Sci. Technol. 20, 6065–6078 (2023). https://doi.org/10.1007/s13762-022-04664-0
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DOI: https://doi.org/10.1007/s13762-022-04664-0