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  • پیش بینی جذب سطحی فنل از فاضلاب با خاک اره به کمک روش‌های هوش-مند

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کد مقاله : 13948 بازدید : 115 صفحه: 37 - 55

10.22034/jest.2019.13948

نوع مقاله: پژوهشی

پیش بینی جذب سطحی فنل از فاضلاب با خاک اره به کمک روش‌های هوش-مند

محورهای موضوعی : مدیریت محیط زیست

محسن کشاورز ترک 1 ( کارشناسی ارشد مهندسی شیمی، دانشگاه علم و صنعت، تهران، ایران. )
احد قائمی 2 ( استادیار، دانشکده مهندسی شیمی، دانشگاه علم و صنعت، تهران، ایران. *(مسوول مکاتبات) )
منصور شیروانی 3 ( دانشیار، دانشکده مهندسی شیمی، دانشگاه علم و صنعت، تهران، ایران. )

تاریخ دریافت : 1394/04/04 تاریخ پذیرش : 1395/05/02 تاریخ انتشار : 1398/02/01

کلید واژه: درصد جذب فنل, خاک اره, شبیه سازی, مدل‌های هوش‌مند, شرایط بهینه,

چکیده مقاله :

زمینه و هدف:حضور فنل و مشتقات آن در آب و فاضلاب به دلیل خطراتی که بر روی سلامت انسان و محیط زیست دارد، به عنوان یک نگرانی عمده محسوب می شود. به دلیل سمی بودن فنل حتی در غلظت های کم و هم چنین به دلیل این که حضور آن در منابع طبیعی آب می تواند سبب شکل گیری ترکیبات جانبی فرآیندهای گندزدایی و اکسیداسیون شود، این ماده یکی از شایع ترین مواد آلی آلاینده آب می باشد. در مطالعه حاضر فرآیند جذب سطحی فنل از فاضلاب توسط جاذب خاک اره با استفاده از روش های هوش مند شبیه سازی شده است. روش بررسی:شیوه های هوش مند شبکه پرسپترون چندلایه، شبکه برپایه توابع شعاعی و ماشین بردار رگرسیونی جهت شبیه سازی استفاده شده است. جهت طراحی ساختار شبکه ها از 125 مجموعه داده تجربی استفاده شده است. معیارهای ارزیابی و توقف شبکه شامل %AARE و R2 می باشند که برای هر سه مدل محاسبه شده است. یافته ها:نتایج نشان داد که مدل ماشین بردار رگرسیونی با داشتن 5132/0 و 979/0 به ترتیب برای %AARE و R2 بهترین مدل می باشد. کلیه مدل ها نتیجه بهتری نسبت به مدل چند جمله ای درجه دوم از خود نشان دادند. مدل ها تطبیق خوبی با داده های تجربی داشتند. بحث و نتیجه گیری: نتایج مدل ها نشان داد که این مدل ها مقدار جذب فنل را با دقت بالا پیش بینی می نماید. هم چنین براساس نتاج مدل ها، پارامترهای بهینه فرآیند شامل، غلظت اولیه فنل 6/127 میلی گرم بر لیتر، مقدار جاذب 84/0 گرم بر لیتر، pH محلول 62/3، زمان جذب 9/146 دقیقه و درصد جذب فنل متناظر 23/91 % به دست آمد.

چکیده انگلیسی:

  Background and Objective: Phenol presence and its derivatives in water and waste water on human health and the environment is one the major concerns. Because of the toxicity of phenol and also because of the presence of even low concentrations in natural resources, water disinfection and oxidation processes can lead to the formation of additional components. This material is one of the most common organic pollutants in water. In this research, adsorption of phenol from wastewater by sawdust was simulated using intelligent techniques. Method: Intelligent techniques including multi-layer Perceptron, radial basis functions network and support vector regression were used. To design the network structure as well as the training and testing of 125 sets of experimental data is used. Performance evaluation criteria and stop network consists of % AARE and, which is used for all three models. Findings: All models compared results showed that the support vector regression with 0.5132 and 0.979, respectively, for %AARE and  is the best model. All models are better results than the quadratic polynomial model showed. Discussion and Conclusion: Models showed good agreement with experimental data. The optimum conditions for the removal of phenol were 127.6 mg/l of initial phenol concentration, 0.84 g/l of adsorbent dose, natural pH value of 3.62 and 146.9 min of contact time, under these conditions the maximum removal efficiency was 91.23%.

منابع و مأخذ:


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  1. Shen, S., Chang, Z. and Liu, H., 2006. Three-liquid-phase extraction systems for separation of phenol and p-nitrophenol from wastewater. Separation and Purification Technolgy, Vol. 49(3), pp. 217–222.
    2.
  2. Jiang, H., Fang, Y., Fu, Y. and Guo, Q.-X., 2003. Studies on the extraction of phenol in wastewater. Journal of Hazardous Materials,  Vol. 101(2), pp. 179–190.
    3.
  3. Asgri, G. and Ramabandi, B., 2012. Study of phenol adsorption from wastewater using pumice modified by Mg/Cu biometallic particles. Irannian Journal of Health, Vol. 1(4), pp. 20–30.
    4.
  4. Asgari, G., Sidmohammadi, A., Ebrahimi, A., Gholami, Z. and Hosseinzadeh, E., 2010. Study on phenol removing by using modified zolite (Clinoptilolite) with FeCl3 from aqueous solutions. Journal Health System Resarch, Vol. 89, pp. 848–857.
    5.
  5. Bazrafshan, E. and Heidarinezhad, F., 2011. Phenol removal from aqueous solutions using Pistachio hull ash as a low cost adsorbent. Journal of Sabzevar University of Medical Science,  Vol. 20(2), pp. 142–153.
    6.
  6. Khosravi, R.and Fazlzadeh, M., 2013. Investigation of Phenol Adsorption from Aqueous Solution by Carbonized Service Bark and Modified-Carbonized Service Bark by ZnO. Journal of Health, Vol. 4(1), pp. 21–30.
    7.
  7. Diyanati, R., Yousefi, Z., Yazdani Cherati, J. and Balarak, D., 2013. Investigating Phenol Absorption from Aqueous Solution by Dried Azolla. Journal Mazandaran University Medical Science, Vol. 22(2), pp. 13–20.
    8.
  8. Daraei, H., Manshouri, M. and Yazdanbakhsh, A. R., 2010. Removal of phenol from aqueous solution using ostrich feathers ash. Mazandaran University Medical Sciences, Vol. 20(79), pp. 81–87.
    9.
  9. Ghaneian M. T. and Ghanizadeh, G., 2009. Application of enzymatic polymerization process for the removal of phenol from synthetic wastewater.
    10.
  10. Irannian Journal of Health and Environment, Vol. 2(1), pp. 46–55.
    11.
  11. Sanati, A. M., Bahramifar, N., Mehrabani, Z. and Younesi, H., 2012. Lead Removal from Aqueous Solution Using Date-Palm Leaf Ash in Batch System. Journal Water & Wastewater. Vol. 4, pp. 51–58.
    12.
  12. Manshouri, M., Daraei, H. and Yazdanbakhsh, A., 2010. Determining the Optimum Parameters of Phenol Removal from Industrial Effluents by Using Ostrich Feathers. Jundishapur Scientific Medical. Vol. 11(5), pp. 457–466.
    13.
  13. Turan, N. G., Mesci, B. and Ozgonenel, O., 2011. Artificial neural network (ANN) approach for modeling Zn(II) adsorption from leachate using a new biosorbent. Chemical Engineering Journal, Vol. 173(1), pp. 98–105.
    14.
  14. Ahmaruzzaman, M., 2008. Adsorption of phenolic compounds on low-cost adsorbents: A review. Advances In Colloid And Interface Science, Vol. 143, pp. 48–67.
    15.
  15. Piuleac, C. G., Rodrigo, M. A. and Can, P., 2010. Environmental Modelling & Software Ten steps modeling of electrolysis processes by using neural networks. Envionmental Modelling & Software, Vol. 25, pp. 74–81.
    16.
  16. Vajedi M. and Shahhosseini, S., 2011. Modeling of Activated Sludge Process Using Sequential Adaptive Neuro-fuzzy Inference System. Journal Water and Wastewater, Vol. 4, pp. 108–111.
    17.
  17. Dakhil, I. H., 2013. Removal Of Phenol From Industrial Wastewater Using Sawdust. International Journal of Engineering and Science, vol. 3, pp. 25–31.
    18.
  18. Aghav, R. M., Kumar, S. and Mukherjee, S. N., 2011. Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents. Journal of hazardous materials, Vol. 188, pp. 67–77.
    19.
  19. Hassoun, M. H., 1996. Fundamentals of artificial neural networks. Proceedings of the IEEE, Vol. 84, pp. 906.
    20.
  20. Asl, S. H., Ahmadi, M., Ghiasvand, M. and Katal, R., 2013. Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA). Journal of Industrial and Engineering Chemistry, Vol. 19, pp. 1044–1055.
    21.
  21. He, Y., Xu, Y., Geng, Z. and Zhu, Q., 2015. Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network. Chinese Journal of Chemical  Engineering, Vol. 23, pp. 138–145.
    22.
  22. Xi, J., Xue, Y., and Shen, Y., 2013. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols. Food Chemstry, Vol. 141, pp. 320–326.
    23.
  23. Turan, N. G., Mesci, B. and Ozgonenel, O., 2011. The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice. Chemical Engineering Journal, Vol. 171, pp. 1091–1097.
    24.
  24. Chen, Y., Xu, J., Yang, B., Zhao, Y. and He, W., 2012. A novel method for prediction of protein interaction sites based on integrated RBF neural networks. Computers in Biology and Medicine, Vol. 42, pp. 402–407.
    25.
  25. Mahmoodzadeh Vaziri B. and Shahsavand, A., 2013. Analysis of supersonic separators geometry using generalized radial basis function (GRBF) artificial neural networks. Journal of Natural Gas Science and Engineering, Vol. 13, pp. 30–41.
    26.
  26. Dehghani, A., Piri, M., Hesam, M. and Dehghani, N., 2010. Estimation of Daily Pan Evaporation By Using MLP, RBf and Recuurent Neural Networks. Journal of  Soil and Water Conservation, pp. 49–68.
    27.
  27. Eskandari, A., Noori, R. A. and Kiaghadi, A., 2009. Development model based on artificial neural network and support vector machine for predicting biochemical oxygen demand during 5 days. Journal of Ecology, pp. 34 – 46.
    28.
  28. B. Zhao, “Modeling pressure drop coefficient for cyclone separators: A support vector machine approach,” Chem. Eng. Sci., vol. 64, no. 19, pp. 4131–4136, Oct. 2009.
    29.
  29. Nandi, S., Badhe, Y., Lonari, J., Tambe, S. S. and Kulkarni, B. D., 2004. Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst. Chemical Engineering Journal, Vol. 97, pp. 115–129.
    30.
  30. Gandhi A. B. and Joshi, J. B., 2010. Estimation of heat transfer coefficient in bubble column reactors using support vector regression. Chemical Engineering Journal, Vol. 160, pp. 302–310.
    31.
  31. Alves, J. C. L., Henriques, C. B. and Poppi, R. J., 2012. Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system. Fuel, Vol. 97, pp. 710–717.
    32.
  32. Zaidi, S., 2012. Development of support vector regression (SVR) -based model for prediction of circulation rate in a vertical tube thermosiphon reboiler. Chemical Engineering Science, Vol. 69, pp. 514–521.
    33.

 

 

 



 

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