Investigating the Prediction of the Amount of Coagulant Injection in the Drinking Water Purification Process Using Fuzzy Regression Analysis (Case Study: Mashhad Water Treatment Plant No.3)

Document Type : Applied Article

Authors

• Zeynab Vakili ¹
• Ebrahim Alamatian ²
• Mohamad Zanguee ³

¹ MSc of Environmental Engineering, Khavaran Institute of Higher Education, Mashhad, Iran

² Assistant Professor, Khavaran Institute of Higher Education, Mashhad, Iran

³ Ph.D. Student in Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

10.22067/jwsd.v10i4.2305-1240

Abstract

Surface waters have various impurities. Aggregation of particles and their transformation from dispersed and fine states to coarse state is done by a process called coagulation process. This process is one of the basic processes in drinking water treatment plants. The purpose of this research is to provide a suitable relationship for determining the amount of chlorophric coagulant injection in the coagulation and flocculation process using fuzzy regression in drinking water treatment plant No. 3 of Mashhad. Temperature, pH, turbidity, electrical conductivity and TDS of raw and purified water have been used as primary data to determine the appropriate equation to predict the amount of coagulant injection in the purification process. Appropriate coefficients for different linear, power, exponential and quadratic models were determined in two types of least squares and regression. According to the results obtained in this research, the exponential-regression model with RMSE equal to 0.68. It has been introduced as a desirable model.

Keywords

• Fuzzy regression
• Coagulant
• Drinking Water
• Coagulation

Main Subjects

• Water Quality, Recycling and Wastewater

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References

شرکت مهندسین مشاور سروآب. (1392). گزارش مطالعات تصفیه‌خانه شماره 3 آب مشهد. مشهد. ایران.
زنگویی، حسین، دلنواز، محمد، و اسداله فردی، غلام‌رضا. (۱۳۹۵). مدل‌سازی فرآیند انعقاد و لخته‌سازی توسط روش‌های استنتاج عصبی- فازی تطبیقی، شبکه‌های عصبی مصنوعی و رگرسیون فازی. مهندسی عمران مدرس، ۱۶(۳)، ۷۳-۸۵. URL: http://mcej.modares.ac.ir/article-16-2112-fa.html
قائدرحمتی، مجتبی، معاضد، هادی، و تیشه‌زن، پروانه. (1399). تخمین TSS خروجی تصفیه‌خانه فاضلاب اهواز با استفاده از مدل‌های هوشمند. مجله علوم و تکنولوژی محیط زیست، 22(9)، 251-267. https://doi.org/10.22034/JEST.2019.37718.4377 
Bagastyo, A.Y., Nurhayati, E., Manah, S.P.H., Iswari, A.A.W.R., Yulikasari, A., Warmadewanthi, I.D.A.A. & Lin, T.F. (2023). The role of aeration and pre-chlorination prior to coagulation-flocculation process in water treatment: A laboratory and field research in Indonesia. Case Studies in Chemical and Environmental Engineering, 7, 100352. https://doi.org/10.1016/j.cscee.2023.100352
Jiang, J.Q. (2015). The role of coagulation in water treatment. Current Opinion in Chemical Engineering, 8, 36-44. https://doi.org/10.1016/j.coche.2015.01.008
Khedher, M., Awad, J., Donner, E., Drigo, B., Fabris, R., Harris, M., Braun, K. & Chow, C.W. (2023). Using the Flocculation Index to optimise coagulant dosing during drinking water treatment. Journal of Water Process Engineering, 51, 103394. https://doi.org/10.1016/j.jwpe.2022.103394
Moreira, V.R., Guimaraes, R.N., Moser, P.B., Santos, L.V., de Paula, E.C., Lebron, Y.A., Silva, A.F.R., Casella, G.S. & Amaral, M.C. (2023). Restrictions in water treatment by conventional processes (coagulation, flocculation, and sand-filtration) following scenarios of dam failure. Journal of Water Process Engineering, 51, 103450. https://doi.org/10.1016/j.jwpe.2022.103450
Nadiri A., Shokri S., Tsai F. & Moghaddam A. (2018). Prediction of Effluent Quality Parameters of a Wastewater Treatment Plant Using a Supervised Committee Fuzzy Logic Model. Journal of Cleaner Production, 180, 539-549.  https://doi.org/10.1016/j.jclepro.2018.01.139
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, 35(8), 3674-3684. https://doi.org/10.1016/j.apm.2011.01.019
Ruan, J., Zhang, C., Li, Y., Li, P., Yang, Z., Chen, X., & Zhang, T. (2017). Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor. Journal of Environmental Management, 187, 550–559. https://doi.org/10.1016/j.jenvman.2016.10.056
Tanaka, H. (1987). Fuzzy data analysis by possibility linear models, Fuzzy Sets and Systems, 24(3), 363- 375. https://doi.org/10.1016/0165-0114(87)90033-9
Zabaleta, A., Pascual Fernandez, P., Prados-Castilloc, J., and Castro-Pardo, M. (2022). Constructing fuzzy composite indicators to support water policy entrepreneurship, Sustainable Technology and Entrepreneurship, 1(3), 100022. https://doi.org/10.1016/j.stae.2022.100022
Zadeh, L.A. & Aliev, R.A. (2018). Fuzzy logic theory and applications: part I and part II. World Scientific Publishing. https://doi.org/10.1142/10936