Volume 6, Issue 1 (9-2021)                   NMCE 2021, 6(1): 63-76 | Back to browse issues page


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Mehrdadi N, Ghasemi M. Modeling of Tehran South Water Treatment Plant Using Neural Network and Fuzzy Logic Considering Effluent and Sludge Parameters. NMCE 2021; 6 (1) :63-76
URL: http://nmce.kntu.ac.ir/article-1-379-en.html
1- Professor, College of Engineering, Faculty of Environment, University of Tehran, Tehran, Iran. , mehrdadi@ut.ac.ir
2- Ph.D. Candidate, College of Engineering, Faculty of Environment, University of Tehran, Tehran, Iran.
Abstract:   (543 Views)
The disposal of sewage with acceptable qualitative characteristics to different acceptor resources is an environmental issue that today's societies face (with). Using the MatLab software, a neural network model, and an adaptive neuro-fuzzy inference system (ANFIS), this study has predicted the qualitative parameters (COD, BOD5, and TSS of the wastewater, along with TS, VS, and SOUR of the sludge) for the south Tehran sewage treatment plant and finally chosen the best models by validating the model and using the defined criteria. Moreover, using these developed models and comparing their results with the available standard values provides a suitable classification to reuse the wastewater and sludge of the south Tehran wastewater treatment plant. The results indicated acceptable errors of both systems, the adaptive neuro-fuzzy inference system and the artificial neural network, in predicting the qualitative characteristics of the sludge and wastewater of the south Tehran sewage treatment plant and the priority of the adaptive neuro-fuzzy inference system over the artificial neural network in estimating the quality of the treated wastewater and sludge.
Full-Text [PDF 985 kb]   (309 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/02/3 | Revised: 2021/03/27 | Accepted: 2021/05/2 | ePublished ahead of print: 2021/05/15

References
1. Qasim, Syed R. "Wastewater treatment plants: planning, design, and operation." Routledge. Quality Parameters, Air, Soil and Water Research (2017): 2 51-59. [DOI:10.1201/9780203734209]
2. Godini, Kazem, Ghasem Azarian, Alireza Kimiaei, Elena Niculina Dragoi, and Silvia Curteanu. "Modeling of a real industrial wastewater treatment plant based on aerated lagoon using a neuro-evolutive technique." Process Safety and Environmental Protection 148 (2021): 114-124. [DOI:10.1016/j.psep.2020.09.057]
3. Muga, Helen E., and James R. Mihelcic. "Sustainability of wastewater treatment technologies." Journal of environmental management 88, no. 3 (2008): 437-447. [DOI:10.1016/j.jenvman.2007.03.008]
4. Neirizi, S. "Reuse of treated sewage, water supply solutions to provide water resources." Journal of water and environment No. 34 (1999): 4-12
5. Erfani Agah. "Evaluating the efficiency of domestic wastewater kept for cultivation of lettuce and tomato." The conference on the environmental aspects of the reusing wastewater in irrigation. Ministry of Energy, iranian Committee on Irrigation and Drainage, (1999): pp. 61-79.
6. Angelakis, A. N., MHF Marecos Do Monte, L. Bontoux, and T. Asano. "The status of wastewater reuse practice in the Mediterranean basin: need for guidelines." Water research 33, no. 10 (1999): 2201-2217. [DOI:10.1016/S0043-1354(98)00465-5]
7. Bouwer, Herman. "Integrated water management: emerging issues and challenges." Agricultural water management 45, no. 3 (2000): 217-228. [DOI:10.1016/S0378-3774(00)00092-5]
8. Tavakkoli, M., Jahani, M., Mahmoudi, Sh. "Management of reuse plans of treated sewage", first edition, Iran Water Resources Management Publications (2011).
9. Rangzan, N., Payandeh, Kh., Landi, A. "Evaluating the quality of wastewater in the accumulation of heavy elements in two plants of Sorghum and clover", the ninth conference on the soil science of Iran, Tehran (2006).
10. Rafat, F., Danesh, Sh., Rajabi, H. "Evaluation of the usability of artificial neural network in predicting the quality of effluent of a semi-mechanical sewage treatment plant", the international conference on environment measurement and modeling, University of Tehran, (2012).
11. Hasanlu, H. "Simulation of the performance of the industrial sewage treatment using the hybrid method of artificial neural network and analysis technique of major components". M.Sc. Thesis in environmental engineering, The field of Water and sewage Faculty of environment in Tehran University
12. Oliaei, A., Banejhad, H. Samadi, M., Rahmani, A. and Saghi, H. "Evaluation of the efficiency of artificial neural network in predicting the qualitative properties (DO and BOD5) of the water in a river located in Darreh Morad Beyg, Hamedan". Water and sewage knowledge research journal, 20(3). (2010): 199-210.
13. Zoghi, M., J., and Saeedi, M. "Estimation of the amount of leachate in the waste disposal centers using the artificial neural network". Water and sewage scientific-research journal, 22(1). (2011): 76-84.
14. Gholabi, M., Karami, B. "Simulation and prediction of water qualitative parameters using artificial neural network, fuzzy-neural method, and statistical regression (case study: Karoun River, Khouzestan Province)", The ninth international conference on river engineering, Shahid Chamran University, (2012).
15. Aghdarnejhad, A., Asadi, S., and Parhizghar, M. "A comparison of the efficiency of artificial neural network and regression models to predict the total dissolved solids (TDS): Karoun River (Steel bridge station)." National Conference on environment sciences and engineering, Shahid Chamran University, (2014).
16. Daghbandan, A., Ali Taleshi, F., and Yaghubi, M. "A comparison of the multi-objective GMDH neural networks and Bayesian Belief Network in predicting the turbidity of treated water (case study: The large water treatment plant of Gilan)", Water and sewage scientific-research journal 27(2) (2014): 71-83.
17. Asadi, S., Aghdarnejhad, A., and Albaji, M. "Determining the water quality along the river using artificial neural network (case study: Karoun River)". National Conference on environment sciences and engineering, Shahid Chamran University, (2014).
18. Gholami, F., Ebrahimpour, R., And Amirifard, A. "Predicting the sludge return rate using artificial neural networks, Case Study: Torbat Heydariyeh sewage treatment plant", The journal of Water and sewage, 4 (2014): 99-110
19. Vajedi, Mahsa, and Shahrokh Shahhosseini. "Modeling of Activated Sludge Process Using Sequential Adaptive Neuro-fuzzy Inference System." Journal of Water and Wastewater; Ab va Fazilab (in persian) 25, no. 4 (2014): 108-111.
20. Najah, A., A. El-Shafie, O. A. Karim, O. Jaafar, and Amr H. El-Shafie. "An application of different artificial intelligences techniques for water quality prediction." International Journal of Physical Sciences 6, no. 22 (2011): 5298-5308. [DOI:10.1007/s00521-012-0940-3]
21. Zhu, Jiabao, Jim Zurcher, Ming Rao, and Max QH Meng. "An on-line wastewater quality predication system based on a time-delay neural network." Engineering Applications of Artificial Intelligence 11, no. 6 (1998): 747-758. [DOI:10.1016/S0952-1976(98)00017-7]
22. Gontarski, C. A., P. R. Rodrigues, M. Mori, and L. F. Prenem. "Simulation of an industrial wastewater treatment plant using artificial neural networks." Computers & Chemical Engineering 24, no. 2-7 (2000): 1719-1723. [DOI:10.1016/S0098-1354(00)00449-X]
23. Chen, W. C., Ni-Bin Chang, and Wen K. Shieh. "Advanced hybrid fuzzy-neural controller for industrial wastewater treatment." Journal of environmental engineering 127, no. 11 (2001): 1048-1059. [DOI:10.1061/(ASCE)0733-9372(2001)127:11(1048)]
24. Oliveira-Esquerre, K. P., M. Mori, and R. E. Bruns. "Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis." Brazilian Journal of Chemical Engineering 19 (2002): 365-370. [DOI:10.1590/S0104-66322002000400002]
25. Hong, Yoon-Seok Timothy, Michael R. Rosen, and Rao Bhamidimarri. "Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis." Water research 37, no. 7 (2003): 1608-1618. [DOI:10.1016/S0043-1354(02)00494-3]
26. Pai, Tzu-Yi, Y. P. Tsai, H. M. Lo, C. H. Tsai, and C. Y. Lin. "Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent." Computers & Chemical Engineering 31, no. 10 (2007): 1272-1281. [DOI:10.1016/j.compchemeng.2006.10.012]
27. Hamed, Maged M., Mona G. Khalafallah, and Ezzat A. Hassanien. "Prediction of wastewater treatment plant performance using artificial neural networks." Environmental Modelling & Software 19, no. 10 (2004): 919-928. [DOI:10.1016/j.envsoft.2003.10.005]
28. Mjalli, Farouq S., S. Al-Asheh, and H. E. Alfadala. "Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance." Journal of Environmental Management 83, no. 3 (2007): 329-338. [DOI:10.1016/j.jenvman.2006.03.004]
29. Hanbay, Davut, Ibrahim Turkoglu, and Yakup Demir. "Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks." Expert systems with applications 34, no. 2 (2008): 1038-1043. [DOI:10.1016/j.eswa.2006.10.030]
30. Raman, Bai V., Reinier Bouwmeester, and S. Mohan. "Fuzzy logic water quality index and importance of water quality parameters." Air, Soil and Water Research 2 (2009): ASWR-S2156. [DOI:10.4137/ASWR.S2156]
31. Nasr, Mahmoud S., Medhat AE Moustafa, Hamdy AE Seif, and Galal El Kobrosy. "Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT." Alexandria engineering journal 51, no. 1 (2012): 37-43. [DOI:10.1016/j.aej.2012.07.005]
32. Güçlü, Dünyamin, and Şükrü Dursun. "Artificial neural network modelling of a large-scale wastewater treatment plant operation." Bioprocess and biosystems engineering 33, no. 9 (2010): 1051-1058. [DOI:10.1007/s00449-010-0430-x]
33. Kotti, Irini P., Georgios K. Sylaios, and Vassilios A. Tsihrintzis. "Fuzzy logic models for BOD removal prediction in free-water surface constructed wetlands." Ecological engineering 51 (2013): 66-74. [DOI:10.1016/j.ecoleng.2012.12.035]
34. Mahdipour, A., Shokuhian, M. "Evaluation of the effect of input sewage parameters on the prediction accuracy of output wastewater using the sensitivity analysis based on artificial neural networks." The seventh international conference on civil engineering, University of Zahedan, Sistan and Baluchestan Province (2013).
35. Akilandeswari, S., and B. Kavitha. "Determination of biochemical oxygen demand by adaptive neuro fuzzy inference system." Advances in Applied Science Research 4, no. 1 (2013): 101-104.
36. Agyemang, Emmanuel Okoh, Esi Awuah, Lawrence Darkwah, Richard Arthur, and Gabriel Osei. "Water quality assessment of a wastewater treatment plant in a Ghanaian Beverage Industry." International Journal of Water Resources and Environmental Engineering 5, no. 5 (2013): 272-279.
37. Kiurski-Milosević, Jelena Ž., Mirjana B. Vojinović-Miloradov, and Nebojša M. Ralević. "Fuzzy model for determination and assessment of groundwater quality in the city of Zrenjanin, Serbia." Hemijska industrija 69, no. 1 (2015): 17-28. [DOI:10.2298/HEMIND131215016K]
38. Priya, K. L. "A Fuzzy Logic Approach for Irrigation Water Quality Assessment: A Case Study of Karunya Watershed, India. J Hydrogeol Hydrol Eng 2: 1." of 8 (2013): 2.
39. Zareh, H., Bayat, M., and Bayat, J. "The use of artificial neural network in evaluating the Ekbatan sewage treatment plant", Journal of environmental studies 38(3) (2012): 85-98.
40. Shokri, S., Asghari Moghaddam, A., and Nadiri, A. "Evaluating the efficiency of Tabriz wastewater treatment plant using different fuzzy systems", The national conference of environmental research center Shahid Mofatteh Faculty, Hameda. (2013).
41. Modarresi, S., and Mirbagheri, A. "Evaluation of the data-based models in sewage treatment.", The first national conference on civil engineering and sustainable development of Iran The center of solutions to achieve sustainable development in Tehran, (2014).
42. Rafat, F., Danesh, Sh., and Rajabi, H. "Evaluation and comparison of the capability of ordinary artificial neural network and neural network optimized with the genetic algorithm to predict the effluent quality of the semi-mechanical sewage treatment plants." The tenth international conference on civil engineering, University of Tabriz, (2015).
43. Dacewicz, Ewa. "Waste assessment decision support systems used for domestic sewage treatment." Journal of Water Process Engineering 31 (2019): 100885. [DOI:10.1016/j.jwpe.2019.100885]
44. Pisa, Ivan, Antoni Morell, Jose Lopez Vicario, and Ramon Vilanova. "ANN-based Internal Model Control strategy applied in the WWTP industry." In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1477-1480. IEEE, 2019. [DOI:10.1109/ETFA.2019.8868241]
45. Yogeswari, M. K., K. Dharmalingam, and P. Mullai. "Implementation of artificial neural network model for continuous hydrogen production using confectionery wastewater." Journal of environmental management 252 (2019): 109684. [DOI:10.1016/j.jenvman.2019.109684]
46. Pai, Tzu-Yi, T. J. Wan, S. T. Hsu, T. C. Chang, Y. P. Tsai, C. Y. Lin, H. C. Su, and L. F. Yu. "Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent." Computers & Chemical Engineering 33, no. 7 (2009): 1272-1278. [DOI:10.1016/j.compchemeng.2009.02.004]
47. Yel, Esra, and Sukran Yalpir. "Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach." procedia computer science 3 (2011): 659-665. [DOI:10.1016/j.procs.2010.12.110]
48. Wan, Jinquan, Mingzhi Huang, Yongwen Ma, Wenjie Guo, Yan Wang, Huiping Zhang, Weijiang Li, and Xiaofei Sun. "Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system." Applied Soft Computing 11, no. 3 (2011): 3238-3246. [DOI:10.1016/j.asoc.2010.12.026]
49. Pai, T. Y., P. Y. Yang, S. C. Wang, M. H. Lo, C. F. Chiang, J. L. Kuo, H. H. Chu et al. "Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality." Applied Mathematical Modelling 35, no. 8 (2011): 3674-3684. [DOI:10.1016/j.apm.2011.01.019]
50. Belhaj, Dalel, Ikram Jaabiri, Nesrine Turki, Chafai Azri, Monem Kallel, and H. Ayadi. "Descriptive and multivariable analysis of the water parameters quality of Sfax sewage treatment plant after rehabilitation." IOSR Journal of Computer Engineering (IOSR-JCE) 16 (2014): 81-91. [DOI:10.9790/0661-16178191]
51. "Reuse of treated sewage in agriculture", The Iranian National Committee on Irrigation and Drainage. (2001).
52. Asano, Takashi, Franklin Burton, and Harold Leverenz. Water reuse: issues, technologies, and applications. McGraw-Hill Education, 2007.
53. Jebelli, J. "Solutions to reduce the adverse effects of agricultural wastewater", The conference on the environmental effects of agricultural wastewater on the surface water and groundwater The national committee of irrigation and drainage. (2001).
54. Technologies, and Applications, Metcalf & Eddy, Inc. McGraw Hill, 1st edition, NewYork, USA2
55. Karamouz, M., Kerachian, R. "Qualitative planning and management of water resource systems" Publications of Amirkabir University of Technology, (2014).
56. Alborzi, M. "Getting familiar with neural networks", Scientific Publications Institute of Sharif University, (2004).
57. Zadeh, L. A. 1965. "Fuzzy sets as a basic for theory of possibility", Fuzzy Sets and Systems, 1(1), pp. 3-29 [DOI:10.1016/0165-0114(78)90029-5]
58. Kia, M., (2014). "Neural networks in Matlab", Qian academic publications, Iran. (In Persian)
59. Nourani, V. and Salehi, K. "Modeling the rainfall-runoff using adaptive neuro-fuzzy inference system and comparing them with other artificial neural networks used in its." The fourth international conference on civil engineering, University of Tehran, (2008).

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