تخمین TSS خروجی تصفیهخانه فاضلاب اهواز با استفاده از مدل‌های هوشمند
محورهای موضوعی :
آلودگی محیط زیست (آب و فاضلاب)
مجتبی قائدرحمتی
1
,
هادی معاضد
2
,
پروانه تیشه زن
3
1 - کارشناس ارشد مهندسی عمران-محیطزیست،دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران
2 - استاد دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز،اهواز، ایران
3 - استادیار دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران(نویسنده مسوول)
تاریخ دریافت : 1397/04/25
تاریخ پذیرش : 1398/04/26
تاریخ انتشار : 1399/09/01
کلید واژه:
فاضلاب,
مواد جامد معلق,
شبکه عصبی مصنوعی,
استنتاج فازی عصبی تطبیقی,
رگرسیون,
چکیده مقاله :
مقدمه: محدودبودن منابع آب شیرین در جهان، به خصوص در مناطق خشک و نیمه خشک مانند ایران، رویکرد استفاده مجدد از پساب های شهری را اجتناب ناپذیر ساخته است. از مهمترین شاخص های بررسی میزان آلودگی فاضلاب و مقایسه با استاندارد های مختلف جهت بازاستفاده یا تخلیه به منابع آبی TSS می باشد که آزمایشی هزینه بر و زمان بر است. مطالعه حاضر در سال 1395 با هدف تخمین TSS خروجی تصفیه خانه فاضلاب اهواز با استفاده از مدل های هوشمند انجام یافته است.
مواد و روش ها: با توجه به زمان بر و هزینه بر یودن آزمون TSS، در این تحقیق، توانمندی سه مدل رگرسیون خطی چندمتغیره، شبکه عصبی مصنوعی و سیستم استنتاج فازی عصبی تطبیقی جهت تخمین TSS فاضلاب خروجی از تصفیه خانه فاضلاب با استفاده از نرم افزار MATLAB و SPSS 21 بررسی شد. براین اساس ترکیبات مختلفی از پارامترهای کیفی فاضلاب، طی دوره آماری 8 ساله (1394-1387) به عنوان ورودی مدل ها در دو حالت روزانه و ماهانه مورد ارزیابی قرار گرفت.
نتایج: مدل رگرسیون حداکثر ضریب تعیین(R2) برای مراحل آموزش و صحت سنجی را به ترتیب در دوره روزانه 75/0 و 67/0 و در دوره ماهانه 68/0 و 66/0 به دست آورد؛ ریشه میانگین مربعات خطا(RMSE) در این آزمون 033/0 و 025/0 در دوره روزانه و 053/0 و 053/0 در دوره ماهانه، به دست آمد. حداکثرR2 با شبکه عصبی مصنوعی به ترتیب برای مراحل آموزش و صحت سنجی در دوره روزانه 87/0 و 79/0 و در دوره ماهانه 87/0 و 85/0، و RMSE برابر 030/0 و 023/0 در دوره روزانه و 034/0 و 031/0 در دوره ماهانه، به دست آمد. نتایج بیشترین r2 را برای مدل سیستم استنتاج فازی عصبی تطبیقی نشان دادند که در دوره روزانه 91/0 و 83/0 و در دوره ماهانه 89/0 و 87/0، و مقدار RMSE برابر 026/0 و 025/0 در دوره روزانه و 031/0 و 028/0 در دوره ماهانه، به ترتیب برای مراحل آموزش و صحت سنجی بود.
نتیجه گیری: براساس یافته های تحقیق هر سه مدل در تخمین مقدارTSS فاضلاب خروجی کاربرد مناسبی داشتند، اما مدل سیستم استنتاج فازی عصبی تطبیقی به دلیل برازش بهتر و خطای کم تر، مدلی مناسب تر است.
چکیده انگلیسی:
Introduction: The limitation of fresh water resources in the world, especially in arid and semi-arid regions such as Iran, has inevitably led to the reuse of urban wastewater. One of the most important indicators of sewage pollution and comparison with different standards for reuse or discharge to the water resources is TSS. The present study was conducted in 2016 with the aim of estimation of effluent TSS of Ahvaz wastewater treatment plant using inelegant models.
Material and methods: Regard to costly and time-consuming measurement tests of TSS, the capability of multivariate linear regression model, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) was studied to estimate (TSS) in wastewater treatment plant output by MATLAB and SPSS 21 software. Accordingly, various compounds of sewage quality parameters were evaluated during the 8-year statistical period (2008-2015) as input of models in two daily and monthly modes.
Results: The results of the regression model indicated that the maximum R2 for training and verification were 0.75 and 0.67 in daily and 0.68 and 0.66 in monthly period, respectively. The root mean square error (RMSE) in this test was 0.033 and 0.025 in the daily period and 0.053 and 0.053 in the monthly period. The maximum R2 in ANN for training and verification were 0.87 and 0.79 in daily and 0.87 and 0.85 in monthly period, respectively. The RMSE in this test was 0.030 and 0.023 in the daily period and 0.034 and 0.031 in the monthly period. Meanwhile, the maximum R2 in ANFIS for training and verification were 0.91 and 0.83 in daily and 0.89 and 0.87 for monthly period, respectively. The RMSE in this test was 0.026 and 0.025 in the daily period and 0.031 and 0.028 in the monthly period.
Conclusion: The results confirmed the application of three models is appropriate, but the ANFIS was considered as a more appropriate model.
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Ozkaya, B., Demir, A., Bilgili. M.S., 2007. Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors. Journal: Environmental Modelling & Software. Vol. 22, pp. 815-822.
Karamooz, M., Tabesh, M., Nazeef, S., Moridi, A., 2005. Applications of artificial neural network and
Neuro-fuzzy inference system to predict pressure of water pipe networks. Journal of water and wastewater. Vol.53, pp.3-14. (persian)
Tabesh, M., Dini. M., 2009. Forecasting daily urban water demand using artificial neural networks, a case study of Tehran urban water. Journal of water and wastewater. Vol.1, pp. 84-95 (persion)
Zare Abyaneh, h. Bayat Varkeshi, M., Bayat Varkeshi, j., 2012. Application of Artificial Neural Networks in Evaluation of Ekbatan Wastewater Treatment Plant. Journal of Ecology. Vol. 38(3), pp.98-85. (persion).
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 (1), pp. 37-43.
Shokri, S., Asghari Moghadam, A., Nadiri. A., 2013. Evaluation of efficiency of Tabriz wastewater treatment plant using different fuzzy systems. National Conference on Environmental Research of Iran. Shahid Mofatheh Faculty. Hamedan. Pages: 19 (persion).
Jang, J.S.R., Sun, C.T., Mizutani. E., 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall International. New Jersey.
Vagedi, M., Shah Hoseini, Sh., 2014. Acoustic sludge process modeling by comparative fuzzy-inductive inference system. Journal of water and wastewater. Vol.4, pp. 111-108. (persion)
Wan, J., Huang, M., Ma, Y., Guo, W., Wang, Y., Zhang HSun, X., 2011. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system. Applied Soft Computing. Vol.11 (3), pp. 3238-3246.
Pai, T. Y., Yang, P. Y., Wang, S. C., Lo, M. H., Chiang, C. F., Kuo, J. LChang, Y. H., 2011. Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Applied Mathematical Modelling. Vol.35 (8), pp. 3674-3684.
Pai, T. Y., Wan, T. J., Hsu, S. T., Chang, T. C., Tsai, Y. P., Lin, C. Y. Yu, L. F., 2009. Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent. Computers & Chemical Engineering. Vol.33 (7), pp.1272-1278.
Rafat Motaali, F., Danesh, Sh., Rajabi Mashhadi. H., 2014. Evaluation and management of semi-mechanical treatment plants by predicting their waste effluent quality by genetic algorithm optimized neural network model. 5th National Conference on Water, Wastewater and Waste. Tehran. pages: 14.(persion)
Hanbay, D., Turkoglu, I., Demir, Y., 2008. Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks. Expert Systems with Applications. Vol.34 (2), pp.1038-1043.
Belhaj, D., Jaabiri, I., Turki, N., Azri, C., Kallel, M., Ayadi. H., 2014. Descriptive and multivariable analysis of the water parameters quality of Sfax sewage treatment plant after rehabilitation. Journal of Computer Engineering. Vol.16 (1), pp. 81-91.
Solgi, A., Radmanesh, F., Zarei, H., Nourani. V., 2014. Hybrid Models Performance Assessment to Predict Flow of Gamasyab River. International journal of Advanced Biological and Biomedical Research. Vol. 2(5), pp. 1837-1846.
Riad, S., Mania, J., Bouchaou, L., Najjar. Y., 2004. Rainfall-runoff model usingan artificialneural network approach. Mathematical and Computer Modelling. Vol. 40(7–8), pp. 839-846.
Fathi, P. Mohammadi, Y., Homaii, M., 2009. Intelligent Modeling Time Series Monthly Input to Sanandaj Vahdat Dam. Journal of Soil and Water (Science and Technology of Agriculture). Vol.(1) 23, pp. 220-209.(persion)
Nourani, V., Komasi. M., 2013. A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. Journal of Hydrology, Vol.490, pp.41-55.
Asadi, S., Shahrabi. J., Abbaszadeh, P., Tabanmehr. S., 2013. A New Hybrid Artificial Neural Networks for Rainfall–Runoff Process Modeling. Neurocomputing. Pp. 05-23.
Haghdadi, N., Zarei-Hanzaki, A., Khalesian, A.R., Abedi. H.R., 2013. Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy. Materials & Design. Vol. 49, pp. 386-391.
Hamada, M., Zaqoot, H.A., Jreiban,A.A., 2018. Application of artificial neural networks for the prediction of Gaza wastewater treatment plant performance-Gaza strip, Journal of Applied Research in Water and Wastewater, 5 (1),pp. 399-406.
Turkmenler, H. and Pala, M., 2017. Performance assessment of advanced biological wastewater treatment plants using artificial neural networks. International journal of engineering technologies. Vol. (3):3; pp.151-156.
Chin, W.W., 1998. The partial Least squares approach to structural equation modeling. Mosern methods for business research. Vol.295 (2), pp. 295-336.
Mjalli, F. S., Al-Asheh, S., Alfadala, H. E., 2007. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. Journal of Environmental Management. Vol. 83(3), pp. 329-338.
Mehdi Pour, A., Shokohian, M., 2012. Investigating the Effect of Input Sewage Parameters on Estimation accuracy of Outlet TSS Using Artificial Neural Networks Based Sensitivity Analysis. 7th Civil Engineering Congress. University of Sistan and Baluchestan. Zahedan. pages:8.(persion)
Shokri, S., Nadiri. A., Asghari Moghadam, A., 2014. Evaluation of wastewater treatment efficiency of Tabriz using artificial intelligence models. Journal of Echology, 40(4), pp.827-844(persion)
Akilandeswari, S., Kavitha. B., 2013. Determination of biochemical oxygen demand by adaptive neuro fuzzy inference system. Journal: Pelagia Research Library. Vol.4, pp.101-104.
