حذف کدورت از آب با استفاده از گرافن اکساید به‌عنوان ماده منعقدکننده و مدل‌سازی با شبکه عصبی مصنوعی
محورهای موضوعی :
آلودگی محیط زیست (آب و فاضلاب)
نازیلا رضانیا
1
,
مریم حسنی زنوزی
2
,
مطهره سعادتپور
3
1 - دانشجوی کارشناسی ارشد، گروه آب و محیط زیست، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران.
2 - استادیار، گروه آب و محیط زیست، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران. * (مسوول مکاتبات)
3 - استادیار، گروه آب و محیط زیست، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران.
تاریخ دریافت : 1398/07/15
تاریخ پذیرش : 1399/03/25
تاریخ انتشار : 1400/08/01
کلید واژه:
حذف کدورت,
مواد کلوئیدی,
گرافن اکساید,
شبکه عصبی مصنوعی,
انعقاد و لخته سازی,
چکیده مقاله :
زمینه و هدف: در سال های اخیر، کاربردهای نانومواد با پایه کربنی در عرصه های مختلف نظیر صنعت آب و فاضلاب توسعه یافته است. یکی از این ترکیبات، گرافن اکساید (GO) است که به دلیل دارا بودن ساختار ورقه ای با سطح ویژه بالا و گروه های سطحی متنوع، توجه زیادی را به خود جلب نموده است. در این راستا، هدف اصلی از انجام پژوهش حاضر، بررسی ویژگی های انعقادی گرافن اکساید در حذف کدورت از آب و مدل سازی فرایند با استفاده از شبکه عصبی مصنوعی (ANN) می باشد.روش بررسی: نمونه های کدورت با استفاده از خاک باغچه و آب شهری تهیه و نمونه GO به صورت سوسپانسیون غلیظ خریداری گردید. آزمایش های جارتست برای بررسی تاثیر pH، غلظت GO، کدورت اولیه، زمان ته نشینی و سایر پارامترها بر بازدهی حذف کدورت، انجام شد. به منظور شبیه سازی فرایند، از شبکه عصبی مصنوعی پرسپترون استفاده شد.یافته ها: تحت شرایط pH اسیدی و نیز با افزایش غلظت GO از 5/2 تا40 میلی گرم بر لیتر، بازدهی حذف کدورت افزایش یافت. اما تغییرات کدورت اولیه اثر مشخص و مستقلی بر بازدهی فرایند نداشت. بخش عمده حذف کدورت در 10 دقیقه ابتدای ته نشینی رخ داد و سرعت ته نشینی لخته ها با افزایش غلظت GO و کاهش pH به شدت افزایش یافت. در مدل‌سازی با ANN، مقادیر ضریب تعیین (R2) و ضریب همبستگی (R) میان مقادیر واقعی و مقادیر پیش بینی شده حذف کدورت برای داده های آزمون به ترتیب برابر با 9492/0 و 9740/0 به دست آمد که نشان دهنده کارایی خوب مدل بود.بحث و نتیجه گیری: نانو ماده گرافن اکساید کارایی بالایی در حذف کدورت از آب نشان داد. پارامترهای pH و غلظت منعقدکننده، به عنوان مهمترین پارامترهای کنترل کننده فرآیند تشخیص داده شد. مدل داده کاوی شبکه عصبی از عملکرد خوبی برای پیش بینی بازدهی حذف کدورت با استفاده از گرافن اکساید برخوردار بود.
چکیده انگلیسی:
Background and Objective: In recent years, applications of carbon-based nanomaterials have been developed in various fields such as water and wastewater industry. One of these compounds is graphene oxide (GO), which has attracted a lot of attention due to its high specific surface two-dimensional structure and various surface groups. In this regard, the main purpose of this study is to investigate the coagulation properties of graphene oxide in removing turbidity from water and modeling the process using artificial neural network (ANN).Material and Methodology: The samples were prepared by using garden soil and tap water and the GO was purchased in the form of suspension. Jar tests were performed to assess the influence of pH, GO dosage, initial turbidity, settling time and other parameters on the turbidity removal efficiency. In order to simulate the process, Perceptron neural network was used.Findings: Under acidic pH conditions and with increasing the GO dosage from 2.5 mg/L to 40 mg/L, the removal efficiency increased considerably. However, the initial turbidity did not show a clear effect on the process performance. Much of the turbidity removal occurred within the first 10 minutes of the settling time and the flocs’ exhibited higher settling rates at higher GO dosages and acidic pH condition. According to the results obtained from the created ANN model, the coefficient of determination (R2) and the correlation coefficient (R) between the observed and predicted values of the test data were 0.9492 and 0.974, respectively, which reveal the model’s high capability in predicting the process results.Discussion and Conclusion: GO showed high capability in turbidity removal from water. The pH and GO dosage were recognized as the process controller parameters. The ANN data mining model showed good performance in predicting process efficiency.
منابع و مأخذ:
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Aboubaraka, A.E., Aboelfetoh, E.F., Ebeid, E.M., 2017. Coagulation effectiveness of graphene oxide for the removal of turbidity from raw surface water. Chemosphere, Vol. 181, pp. 738-746.
Altaher, H., 2012. The use of chitosan as a coagulant in the pre-treatment of turbid sea water. Journal of hazardous materials, 233, pp. 97-102.
Qasim, S.R., Motley, E.M., Zhu, G., 2000. Water works engineering: planning, design, and operation. Prentice Hall PTR.
Metcalf, I.N.C., 2003. Wastewater engineering; treatment and reuse. McGraw-Hill.
Reynolds, T.D., Richards, P.A., 1995. Unit operations and processes in environmental engineering. PWS Publishing Company.
Šćiban, M., Klašnja, M., Antov, M., Škrbić, B., 2009. Removal of water turbidity by natural coagulants obtained from chestnut and acorn. Bioresource technology. Vol. 100, No. 24, pp. 6639-6643.
Yang, Z., Yan, H., Yang, H., Li, H., Li, A., Cheng, R., 2013. Flocculation performance and mechanism of graphene oxide for removal of various contaminants from water. Water research, Vol. 47, No. 4, pp. 3037-3046.
Banihashemi, A., Alavi Moghaddam, M.R., Maknoun, R., Nikazar, M., 2008. Lab-scale study of water turbidity removal using aluminum inorganic polymer. Water and Wastewater, Vol. 19, No. 2, pp. 82-86. (In Persian)
Divakaran, R., Pillai, V.S., 2002. Flocculation of river silt using chitosan, Water Research, 36, No. 9, pp. 2414-2418.
Huang, X., Zhao, Y., Gao, B., Sun, S., Wang, Y., Li, Q., 2016. Polyacrylamide as coagulant aid with polytitanium sulfate in humic acid-kaolin water treatment: Effect of dosage and dose method. Journal of the Taiwan Institute of Chemical Engineers, Vol. 64, pp. 173-179.
Zhu, G., Zheng, H., Zhang, Z., Tshukudu, T., Zhang, P., Xiang, X., 2011. Characterization and coagulation–flocculation behavior of polymeric aluminum ferric sulfate (PAFS). Chemical Engineering Journal, Vol. 178, pp. 50-59.
Park, S., Ruoff, R.S., 2009. Chemical methods for the production of graphenes. Nature nanotechnology, Vol. 4, No. 4, pp. 217-244.
Simate, G.S., Iyuke, S.E., Ndlovu, S., Heydenrych, M., 2012. The heterogeneous coagulation and flocculation of brewery wastewater using carbon nanotubes. Water research, Vol. 46, No. 4, pp. 1185-1197.
Chen, L., Hu, P., Zhang, L., Huang, S., Luo, L., Huang, C., 2012. Toxicity of graphene oxide and multi-walled carbon nanotubes against human cells and zebrafish. Science China Chemistry, Vol. 55, No. 10, pp. 2209-2216.
Bieroza, M., Baker, A., Bridgeman, J., 2011. Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: an operational tool for improved drinking water treatment. Environmetrics, Vol. 22, No. 3, pp. 256-270.
Hernández, J.A., Romero, R.J., Juárez, D., Escobar, R.F., Siqueiros, J., 2009. A neural network approach and thermodynamic model of waste energy recovery in a heat transformer in a water purification Desalination, Vol. 243, No. 1-3, pp. 273-285.
Najah, A., Elshafie, A., Karim, O.A., Jaffar, O., 2009. Prediction of Johor River water quality parameters using artificial neural networks. European Journal of Scientific Research, Vol. 28, No. 3, pp. 422-435.
Nasr, M.S., Moustafa, M.A., Seif, H.A., El Kobrosy, G., 2012. Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT. Alexandria Engineering Journal, Vol. 51, No. 1, pp. 37-43.
Khairi, M.T.M., Ibrahim, S., Yunus, M.A.M., Faramarzi, M., Yusuf, Z., 2016. Artificial neural network approach for predicting the water turbidity level using optical tomography. Arabian Journal for Science and Engineering, Vol. 41, No. 9, pp. 3369-3379.
Yang, R., Li, H., Huang, M., Yang, H., Li, A., 2016. A review on chitosan-based flocculants and their applications in water treatment. Water research, Vol. 95, pp. 59-89. Huang, C., Chen, Y., 1996. Coagulation of colloidal particles in water by chitosan. Journal of Chemical Technology & Biotechnology: International Research in Process, Environmental and Clean Technology, Vol. 66, No. 3, pp. 227-232.22. Bina, B., Mahdinezhad, M., Nikaein, M., Movahedian Attar. H., 2009. Effectiveness of chitosan as natural coagulant aid in treating turbid waters. Iranian Journal of Environmental Health Science & Engineering, Vol. 6, No.4, pp. 247-252.23. Hu, C.Y., Lo, S.L., Chang, C.L., Chen, F.L., Wu, Y.D., Ma, J.L., 2013. Treatment of highly turbid water using chitosan and aluminum salts. Separation and Purification Technology, Vol, 104, pp. 322-326.24. Zoqi, M.J., Jafari, M.A., 2014. A Neural Network Model for Prediction of Tri-Halo-Methane Concentration in Drinking Water. Journal of Environmental Science and Technology, Vol. 16, No. 3, pp. 1-10. (In Persian)25. Kennedy, M.J, Gandomi, A.H, Miller, C.M., 2015. Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal. Journal of Environmental Chemical Engineering. Vol. 3, No. 4, pp. 2829-2838.26. Sharma, A., 2000. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1-A strategy for system predictor identification. Journal of Hydrology, Vol. 239, No. (1-4), pp. 232-239.
Holmes, M., Reeve, P., Pestana, C., Chow, C., Newcombe, G., West, J., 2015. Zeta potential measurement for water treatment coagulation control. SA Water, Adelaide.
Liu, G., Han, K., Ye, H., Zhu, C., Gao, Y., Liu, Y., Zhou, Y., 2017. Graphene oxide/triethanolamine modified titanate nanowires as photocatalytic membrane for water treatment. Chemical Engineering Journal, Vol. 320, pp.74-80.
Tan, P., Bi, Q., Hu, Y., Fang, Z., Chen, Y., Cheng, J., 2017. Effect of the degree of oxidation and defects of graphene oxide on adsorption of Cu2+ from aqueous solution. Applied Surface Science, Vol. 423, pp. 1141-1151.
