Performance Modeling of the Fenton Process Used as a Single Unit for Treating Raw Textile Effluent

Yıl 2024, Cilt: 39 Sayı: 3, 679 - 693, 03.10.2024

Selman Türkeş , Hakan Güney , Bülent Sarı , Olcayto Keskinkan

https://doi.org/10.21605/cukurovaumfd.1560112

Öz

This study investigates the direct application of the Fenton Oxidation Process (FOP) to untreated textile wastewater, specifically from a woven fabric production facility. Under optimized conditions (pH 3, 0.7 g/L Fe+2, 2 mM H2O2), the process achieved significant removal efficiencies: 81% Chemical Oxygen Demand (COD), 80% Suspended Solids (SS), and 93% color removal. Artificial Neural Networks (ANN) and NARX-ANN were utilized in Matlab R2020a to model FOP performance, employing Levenberg-Marquardt (trainlm) and Scaled Conjugate Gradient (trainscg) algorithms. With a 9-20-3 network topology, the ANN model demonstrated superior predictive capability, achieving an R2 of 0.9843.

Anahtar Kelimeler

Raw wastewater treatment, Fenton process, ANN, NARX-ANN models, Textile industry

Ham Tekstil Atık Sularının Arıtılması İçin Tek Bir Ünite Olarak Kullanılan Fenton Prosesinin Performans Modellemesi

Öz

Bu çalışma, Fenton Oksidasyon Prosesinin (FOP) doğrudan arıtılmamış tekstil atık suyuna uygulanmasını, özellikle dokuma kumaş üretim tesisinden gelen atık suyu hedef alarak incelemektedir. Optimize edilmiş koşullar (pH 3, 0.7 g/L Fe+2, 2 mM H2O2) altında, proses %81 Kimyasal Oksijen İhtiyacı (KOİ), %80 Askıda Katı Madde (AKM) ve %93 renk giderimi gibi önemli verimlilikler sağlamıştır. FOP performansını modellemek amacıyla Matlab R2020a'da Yapay Sinir Ağları (YSA) ve NARX-YSA modelleri, Levenberg-Marquardt (trainlm) ve Ölçeklenmiş Eşlenik Gradyan (trainscg) algoritmaları kullanılarak oluşturulmuştur. 9-20-3 ağ topolojisine sahip YSA modeli, 0.9843 R2 değeri ile yüksek bir tahmin yeteneği göstermiştir.

Anahtar Kelimeler

Ham atıksu arıtma, Fenton prosesi, YSA, NARX-YSA modelleri, Tekstil endüstrisi

Kaynakça

• 1. ABIT, 2018. Brazilian textile and apparel industry association. Brazilian Textile and Apparel Industry. Brasília, 44.
• 2. Sher, F., Hanif, K., Iqbal, S.Z., Imran, M., 2020. Implications of advanced wastewater treatment: electrocoagulation and electroflocculation of effluent discharged from a wastewater treatment plant. Journal of Water Process Engineering, 33, 101101.
• 3. Alkhagen, M., Samuelsson, Å., Aldaeus, F., Gimåker, M., Östmark, E., Swerin, A., 2015. Roadmap 2015 to 2025. Textile Materials from Cellulose. RISE–Research Institutes of Sweden.
• 4. He, X., Qi, Z., Gao, J., Huang, K., Li, M., Springael, D., Zhang, X.X., 2020. Nonylphenol ethoxylates biodegradation increases estrogenicity of textile wastewater in biological treatment systems. Water Research, 184, 116137.
• 5. Li, Y., Wang, Y., 2019. Double decoupling effectiveness of water consumption and wastewater discharge in china’s textile industry based on water footprint theory. PeerJ, 7, e6937.
• 6. Antczak, A., Greta, M., Kopeć, A., Otto, J., 2019. Characteristics of the textile industry of two Asian powers: China and India. Prospects for Their Further Development on Global Markets. Fibers & Textiles in Eastern Europe.
• 7. Mikac, L., Marić, I., Štefanić, G., Jurkin, T., Ivanda, M., Gotić, M., 2019. Radiolytic synthesis of manganese oxides and their ability to decolorize methylene blue in aqueous solutions. Applied Surface Science, 476, 1086-1095.
• 8. Asgari, G., Shabanloo, A., Salari, M., Eslami, F., 2020. Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/Persulfate: modeling by response surface methodology and artificial neural network. Environmental Research, 184, 109367.
• 9. Jorfi, S., Pourfadakari, S., Kakavandi, B., 2018. A new approach in sono-photocatalytic degradation of recalcitrant textile wastewater using MgO@Zeolite nanostructure under UVA irradiation. Chemical Engineering Journal, 343, 95-107.
• 10. Giwa, A., Yusuf, A., Balogun, H.A., Sambudi, N.S., Bilad, M.R., Adeyemi, I., Curcio, S., 2021. Recent advances in advanced oxidation processes for removal of contaminants from water: a comprehensive review. Process Safety and Environmental Protection.
• 11. Doumic, L.I., Soares, P.A., Ayude, M.A., Cassanello, M., Boaventura, R.A., Vilar, V.J., 2015. Enhancement of a solar photo-fenton reaction by using ferrioxalate complexes for the treatment of a synthetic cotton-textile dyeing wastewater. Chemical Engineering Journal, 277, 86-96.
• 12. Garrido-Cardenas, J.A., Esteban-García, B., Agüera, A., Sánchez-Pérez, J.A., Manzano-Agugliaro, F., 2020. Wastewater treatment by advanced oxidation process and their worldwide research trends. International Journal of Environmental Research and Public Health, 17(1), 170.
• 13. Ma, S., Lee, S., Kim, K., Im, J., Jeon, H., 2021. Purification of organic pollutants in cationic thiazine and azo dye solutions using plasma-based advanced oxidation process via submerged multi-hole dielectric barrier discharge. Separation and Purification Technology, 255, 117715.
• 14. Sampaio, E.F., Rodrigues, C.S., Lima, V.N., Madeira, L.M., 2021. Industrial wastewater treatment using a bubble photo-fenton reactor with continuous gas supply. Environmental Science and Pollution Research, 28(6), 6437-6449.
• 15. Ribeiro, J.P., Marques, C.C., Portugal, I., Nunes, M.I., 2020b. AOX removal from pulp and paper wastewater by fenton and photo-fenton processes: a real case study. Energy Reports, 6, 770-775.
• 16. Liu, R., Chiu, H.M., Shiau, C.S., Yeh, R.Y.L., Hung, Y.T., 2007. Degradation and sludge production of textile dyes by fenton and photo-fenton processes. Dyes and Pigments, 73(1), 1-6.
• 17. Fenton, H.J.H., 1894. LXXIII-oxidation of tartaric acid in presence of iron. Journal of the Chemical Society, Transactions, 65, 899-910.
• 18. Walling, C., 1975. Fenton's reagent revisited. Accounts of Chemical Research, 8(4), 125-131.
• 19. Zhang, H., Choi, H.J., Huang, C.P., 2005. Optimization of fenton process for the treatment of landfill leachate. Journal of Hazardous Materials, 125(1-3), 166-174.
• 20. Rodrigues, C.S., Neto, A.R., Duda, R.M., de Oliveira, R.A., Boaventura, R.A., Madeira, L.M., 2017. Combination of chemical coagulation, photo-fenton oxidation and biodegradation for the treatment of vinasse from sugar cane ethanol distillery. Journal of Cleaner Production, 142, 3634-3644.
• 21. Barros, V.G., Rodrigues, C.S.D., Botello-Suarez, W.A., Dudu, R.M., Oliveira, R.A., Silva,E.S., Faria, J.L., Boaventura, R.A.R., Madeira, L.M., 2020. Treatment of biodigested coffee processing wastewater using fenton’s oxidation and coagulation/flocculation. Environmental Pollution, 259, 113796.
• 22. Yu, X., Somoza-Tornos, A., Graells, M., Pérez-Moya, M., 2020. An experimental approach to the optimization of the dosage of hydrogen peroxide for fenton and photo-fenton processes. Science of the Total Environment, 743, 140402.
• 23. Ribeiro, J.P., Marques, C.C., Portugal, I., Nunes, M.I., 2020a. Fenton processes for AOX removal from a kraft pulp bleaching industrial wastewater: optimization of operating conditions and cost assessment. Journal of Environmental Chemical Engineering, 8(4), 104032.
• 24. Silva, L.G., Moreira, F.C., Cechinel, M.A.P., Mazur, L.P., de Souza, A.A.U., Souza, S.M.G.U., Vilar, V.J., 2020. Integration of fenton's reaction based processes and cation exchange processes in textile wastewater treatment as a strategy for water reuse. Journal of Environmental Management, 272, 111082.
• 25. Elmolla, E.S., Chaudhuri, M., Eltoukhy, M.M., 2010. The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the fenton process. Journal of Hazardous Materials, 179(1-3), 127-134.
• 26. Radwan, M., Alalm, M.G., Eletriby, H., 2018. Optimization and modeling of electro-fenton process for treatment of phenolic wastewater using nickel and sacrificial stainless steel anodes. Journal of Water Process Engineering, 22, 155-162.
• 27. Talwar, S., Verma, A.K., Sangal, V.K., 2019. Modeling and optimization of fixed mode dual effect (photocatalysis and photo-fenton) assisted metronidazole degradation using ANN coupled with genetic algorithm. Journal of Environmental Management, 250, 109428.
• 28. Gholizadeh, A.M., Zarei, M., Ebratkhahan, M., Hasanzadeh, A., 2021. Phenazopyridine degradation by electro-fenton process with magnetite nanoparticles-activated carbon cathode, artificial neural networks modeling. Journal of Environmental Chemical Engineering, 9(1), 104999.
• 29. Baştürk, E., Alver, A., 2019. Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches. Journal of Environmental Management, 248, 109300.
• 30. Mohammadi, F., Bina, B., Karimi, H., Rahimi, S., Yavari, Z., 2020. Modeling and sensitivity analysis of the alkylphenols removal via moving bed biofilm reactor using artificial neural networks: comparison of levenberg marquardt and particle swarm optimization training algorithms. Biochemical Engineering Journal, 161, 107685.
• 31. Ahmad, Z.U., Yao, L., Islam, F., Zappi, M.E., Gang, D.D., 2020. The use of artificial neural network (ANN) for modeling the adsorption of sunset yellow onto neodymium-modified ordered mesoporous carbon. Chemosphere, 256, 127081.
• 32. Bousalah, D., Zazoua, H., Boudjemaa, A., Benmounah, A., Bachari, K., 2020. Degradation of indigotine food dye by fenton and photo-fenton processes. International Journal of Environmental Analytical Chemistry, 1-14.
• 33. MathWorks, 2020. Matlab deep learning toolbox release 2020a. Natick, Massachusetts, United States. License Number, 968398.
• 34. APHA, 2017. Standard methods for the examination of water and wastewater (23rd ed.). American Public Health Association. Washington DC. ISSN, 55-1979.
• 35. Levenberg, K., 1944. A Method for the solution of certain nonlinear problems. Q. Appl. Math., 2, 164-168.
• 36. Yu, H., Wilamowski, B.M., 2011. Industrial electronics handbook. Levenberg-Marquadt Training.
• 37. Khaki, M., Yusoff, I., Islami, N., 2015. Application of the artificial neural network and neuro‐fuzzy system for assessment of groundwater quality. Clean–Soil, Air, Water, 43(4), 551-560.
• 38. Alsumaiei, A.A., 2020. A nonlinear autoregressive modeling approach for forecasting groundwater level fluctuation in urban aquifers. Water, 12(3), 820.
• 39. Bishop, C.M., 1995. Neural networks for pattern recognition. Oxford University Press. ISBN:978 0-19-853864-6.
• 40. Di Nunno, F., Granata, F., 2020. Groundwater level prediction in apulia region using NARX neural network. Environmental Research, 190, 110062.
• 41. Møller, M.F., 1993. A scaled conjugate gradient algorithm for fast supervised learning [J]. Neural Networks, 6(4), 525-534.
• 42. Sharma, B., Venugopalan, K., 2014. Comparison of neural network training functions for hematoma classification in brain CT images. IOSR J. Comput. Eng, 16(1), 31-35.
• 43. Chitsazan, M., Rahmani, G., Neyamadpour, A., 2015. Forecasting groundwater level by artificial neural networks as an alternative approach to groundwater modeling. Journal of the Geological Society of India, 85(1), 98-106.
• 44. Du, Y.C., Stephanus, A., 2018. Levenberg-marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor. Sensors, 18(7), 2322.
• 45. Jawad, J., Hawari, A.H., Zaidi, S., 2020. Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux. Desalination, 484, 114427.
• 46. Zhang, T., Barthorpe, R.J., Worden, K., 2020. On treed gaussian processes and piecewise-linear NARX modelling. Mechanical Systems and Signal Processing, 144, 106877.
• 47. Bararpour, S.T., Feylizadeh, M.R., Delparish, A., Qanbarzadeh, M., Raeiszadeh, M., Feilizadeh, M., 2018. Investigation of 2-nitrophenol solar degradation in the simultaneous presence of K2S2O8 and H2O2: using experimental design and artificial neural network. Journal of Cleaner Production, 176, 1154-1162.
• 48. Kalantary, R.R., Moradi, M., Pirsaheb, M., Esrafili, A., Jafari, A.J., Gholami, M., Dragoi, E.N., 2019. Enhanced photocatalytic inactivation of E. coli by natural pyrite in presence of citrate and EDTA as effective chelating agents: experimental evaluation and kinetic and ANN models. Journal of Environmental Chemical Engineering, 7(1), 102906.
• 49. Mousavi, S.A., Vasseghian, Y., Bahadori, A., 2020. Evaluate the performance of fenton process for the removal of methylene blue from aqueous solution: experimental, neural network modeling and optimization. Environmental Progress & Sustainable Energy, 39(2).
• 50. Roudi, A.M., Kamyab, H., Chelliapan, S., Ashokkumar, V., Kumar, A., Yadav, K.K., Gupta, N., 2020. Application of response surface method for total organic carbon reduction in leachate treatment using fenton process. Environmental Technology & Innovation, 19, 101009.
• 51. Göde, J.N., Hoefling Souza, D., Trevisan, V., Skoronski, E., 2019. Application of the fenton and fenton-like processes in the landfill leachate tertiary treatment. Journal of Environmental Chemical Engineering, 7, 103352.
• 52. Bello, M.M., Raman, A.A.A., Asghar, A., 2020. Activated carbon as carrier in fluidized bed reactor for fenton oxidation of recalcitrant dye: oxidation-adsorption synergy and surface interaction. Journal of Water Process Engineering, 33, 101001.
• 53. Xing, L., Kong, M., Xie, X., Sun, J., Wei, D., Li, A., 2020. Feasibility and safety of papermaking wastewater in using as ecological water supplement after advanced treatment by fluidized-bed fenton coupled with large-scale constructed wetland. Science of the Total Environment, 699, 134369.
• 54. Masalvad, S.K.S., Sakare, P.K., 2020. Application of photo-fenton process for treatment of textile congo-red dye solution materials today, Proceedings.
• 55. Wu, C., Chen, W., Gu, Z., Li, Q., 2021. A review of the characteristics of fenton and ozonation systems in landfill leachate treatment. Science of the Total Environment, 762, 143131.
• 56. Zhai, J., Ma, H., Liao, J., Rahaman, M.H., Yang, Z., Chen, Z., 2018. Comparison of fenton, ultraviolet–fenton and ultrasonic–fenton processes on organics and colour removal from pre-treated natural gas produced water. International Journal of Environmental Science and Technology, 15(11), 2411-2422.
• 57. Pliego, G., Zazo J.A., Garcia-Muñoz, P., 2015. Trends in the intensification of the fenton process for wastewater treatment: an overview. Crit Rev Environ Sci Technol, 45, 2611-2692.
• 58. Tamimi, M., Qourzal, S., Barka, N., Assabbane, A., Ait-Ichou, Y., 2008. Methomyl degradation in aqueous solutions by fenton's reagent and the photo-fenton system. Separation and Purification Technology, 61(1), 103-108.
• 59. Abedinzadeh, N., Shariat, M., Monavari, S.M., Pendashteh, A., 2018. Evaluation of color and COD removal by fenton from biologically (SBR) pre-treated pulp and paper wastewater. Process Safety and Environmental Protection, 116, 82-91.
• 60. Sevimli, M.F., Deliktacs, E., Sahinkaya, S., Güçlü, D., 2014. A comparative study for treatment of white liquor by different applications of fenton process. Arab. J. Chem. 7, 1116-1123.
• 61. ZDHC Programme, 2016. Zero discharge of hazardous chemicals programme. Textile industry wastewater quality guideline. Literature Review. Revision 1, 1-84.
• 62. Brink, A., Sheridan, C.M., Harding, K.G., 2011. The fenton oxidation of biologically treated paper and pulp mill effluents: performance and kinetic study. Process Saf. Environ Prot., 107, 206-215.
• 63. Askarniya, Z., Sadeghi, M.T., Baradaran, S., 2020. Decolorization of congo red via hydrodynamic cavitation in combination with fenton’s reagent. Chemical Engineering and Processing-Process Intensification, 150, 107874.
• 64. Gadekar, M.R., Ahammed, M.M., 2019. Modeling dye removal by adsorption onto water treatment residuals using combined response surface methodology-artificial neural network approach. Journal of Environmental Management, 231, 241-248.
• 65. Besliu-Ionescu, D., Talpeanu, D.C., Mierla, M., Muntean, G.M., 2019. On the prediction of geoeffectiveness of CMEs during the ascending phase of SC24 using a logistic regression method. Journal of Atmospheric and Solar-Terrestrial Physics, 193, 105036.
• 66. Ghaedi, A.M., Karamipour, S., Vafaei, A., Baneshi, M.M., Kiarostami, V., 2019. Optimization and modeling of simultaneous ultrasound-assisted adsorption of ternary dyes using copper oxide nanoparticles immobilized on activated carbon using response surface methodology and artificial neural network. Ultrasonics Sonochemistry, 51, 264-280.
• 67. Koçak, Y., Şiray, G.Ü., 2021. New activation functions for single layer feedforward neural network. Expert Systems with Applications, 164, 113977.
• 68. Erdem, F., 2019. S. cerevisiae ile Remazol Sarı (RR) giderimine yapay sinir ağı (YSA) Yaklaşımı. Uludağ University J. Fac. Eng. 24(2), 289-298.
• 69. Huo, S., Necas, D., Zhu, F., Chen, D., An, J., Zhou, N., Ruan, R., 2021. Anaerobic digestion wastewater decolorization by H2O2-enhanced electro-fenton coagulation following nutrients recovery via acid tolerant and protein-rich chlorella production. Chemical Engineering Journal, 406, 127160.
• 70. Yu, R.F., Chen, H.W., Cheng, W.P., Hsieh, P.H., 2009. Dosage control of the fenton process for color removal of textile wastewater applying ORP monitoring and artificial neural networks. Journal of Environmental Engineering, 135(5), 325-332.
• 71. ASCE., 2000. Task committee on application of artificial neural networks in hydrology. J. Hydrol. Eng. 5(2). 115-123.
• 72. Yetkin, M., Kim, Y., 2019. Time series prediction of mooring line top tension by the NARX and volterra model. Applied Ocean Research, 88, 170-186.
• 73. Roghanchi, P., Kocsis, K.C., 2019. Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input (NARX) algorithm. International Journal of Mining Science and Technology, 29(2), 255-262.