Probabilistic Modeling and Estimation of Flow Rate of Sewage Treatment Plant Using Monte Carlo Hybrid Method

نوع مقاله : مقاله پژوهشی

نویسندگان

1 PhD Student, Dept. of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

2 Assist. Prof., Dept. of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

3 Assoc. Prof., Dept. of Civil Engineering, University of Zabol, Zabol, Iran

چکیده

One of the most important results of hydraulic and hydrological modeling of the urban drainage network is hydrograph estimation. Annual journals on hydraulic and hydrological problems, especially in developing countries, are full of missing data, discrete and continuous gaps in most hydrological data, such as inlet flow and other series of flow data. This is due to reasons such as not registering statistics, deleting wrong statistics, or failure of measuring devices. The present paper attempts to investigate the flow rate of wastewater entering the sewage treatment plant (STP) for pumping station planning. The novelty of the research is using the Monte Carlo method, which is one of the simulators of the effect of the uncertainties in the timing and cost of the project, and Fourier series regression model to randomly generate data. These methods were used to simulate a 1-minute time scale of sewage flow data for Zahedan. Considering the construction of the treatment plant in the last decade, this is the first research with this approach in this treatment plant. The results indicate that, this method has been successful in estimating sewage flow data in the hourly and minute intervals. Finally, 270 days of flow data were obtained from a time interval of one minute with two methods of distribution: Lognormal function and a nonlinear Fourier series. Among these two methods, Fourier series’ accuracy was higher in terms of statistical indicators. In this simulation, RMSE, d, CI and EF values for Fourier regression model are 0.29, 0.99, 0.99, and 0.99, respectively.

کلیدواژه‌ها

عنوان مقاله [English]

Probabilistic Modeling and Estimation of Flow Rate of Sewage Treatment Plant Using Monte Carlo Hybrid Method

نویسندگان [English]

  • Jamshid Piri 1
  • Bahareh Pirzadeh 2
  • Behrooz Keshtegar 3
  • Mohammad Givehchi 2
1 PhD Student, Dept. of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2 Assist. Prof., Dept. of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
3 Assoc. Prof., Dept. of Civil Engineering, University of Zabol, Zabol, Iran
چکیده [English]

One of the most important results of hydraulic and hydrological modeling of the urban drainage network is hydrograph estimation. Annual journals on hydraulic and hydrological problems, especially in developing countries, are full of missing data, discrete and continuous gaps in most hydrological data, such as inlet flow and other series of flow data. This is due to reasons such as not registering statistics, deleting wrong statistics, or failure of measuring devices. The present paper attempts to investigate the flow rate of wastewater entering the sewage treatment plant (STP) for pumping station planning. The novelty of the research is using the Monte Carlo method, which is one of the simulators of the effect of the uncertainties in the timing and cost of the project, and Fourier series regression model to randomly generate data. These methods were used to simulate a 1-minute time scale of sewage flow data for Zahedan. Considering the construction of the treatment plant in the last decade, this is the first research with this approach in this treatment plant. The results indicate that, this method has been successful in estimating sewage flow data in the hourly and minute intervals. Finally, 270 days of flow data were obtained from a time interval of one minute with two methods of distribution: Lognormal function and a nonlinear Fourier series. Among these two methods, Fourier series’ accuracy was higher in terms of statistical indicators. In this simulation, RMSE, d, CI and EF values for Fourier regression model are 0.29, 0.99, 0.99, and 0.99, respectively.

کلیدواژه‌ها [English]

  • Probability Distribution
  • Monte Carlo
  • Lognormal
  • Fourier Series
  • Wastewater Inflow

مراجع

Akan, A. O. & Houghtalen, R. J. 2003. Urban hydrology, hydraulics, and stormwater quality: engineering applications and computer modeling, John Wiley & Sons.
Alizadeh, A. 2013. The Principles of Applied Hydrology, Imam Reza (AS) University, Mashhad. (In Persian)
Asmar, N. H. 2016. Partial differential equations with Fourier series and boundary value problems, Courier Dover Publications.
Basser, H., Cheraghi, R., Karami, H., Ardeshir, A., Amirmojahedi, M., Akib, S. et al. 2015. Modeling sediment transport around a rectangular bridge abutment. Environmental Fluid Mechanics, 15, 1105-1114.
Bhaduri, B. 2004. Urban hydrology, hydraulics, and stormwater quality: engineering applications and computer modeling. Journal of the American Water Resources Association, 40, 1667.
Chow, V. T. 2010. Applied Hydrology, Tata McGraw-Hill Education.
Chen, H. L. & Rao, A. R. 2003. Linearity analysis on stationary segments of hydrologic time series. Journal of Hydrology, 277, 89-99.
Cho, W. K. T. & Liu, Y. Y. 2018. Sampling from complicated and unknown distributions: Monte Carlo and Markov Chain Monte Carlo methods for redistricting. Physica A: Statistical Mechanics and its Applications, 506, 170-178.
Fan, Y., Lu, W., Miao, T., An, Y., Li, J. & Luo, J. 2020. Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty. Environmental Science and Pollution Research, 1-13.
Hattab, N., Hambli, R., Motelica-Heino, M. & Mench, M. 2013. Neural network and Monte Carlo simulation approach to investigate variability of copper concentration in phytoremediated contaminated soils. Journal of Environmental Management, 129, 134-142.
Keshtegar, B., Heddam, S. & Hosseinabadi, H. 2019. The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river. Environmental Earth Sciences, 78, 34.
Keshtegar, B., Piri, J. & Kisi, O. 2016. A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Computers and Electronics in Agriculture, 127, 120-130.
Khosravi, M., Salajegheh, A. & Mahdavi, M. 2012. Comparing chow regression and stochastic method for annual peak flood (case study: central Alborz region). Journal of Iran- Water Resources Research, 8(1), 43-50. (In Persian).
Kosugi, K. I. 1994. Three‐parameter lognormal distribution model for soil water retention. Water Resources Research, 30, 891-901.
Lee, S. 2006. Dynamic planning and control methodology: understanding and managing iterative error and change cycles in large-scale concurrent design and construction projects. Massachusetts Institute of Technology Pub.
Lu, D., Ricciuto, D. & Evans, K. 2018. An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method. Advances in Water Resources, 113, 223-235.
Lu, D., Zhang, G., Webster, C. & Barbier, C. 2016. An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations. Water Resources Research, 52, 9642-9660.
Nj, A. 2011. Prevalence and risk factors of helicobacter pylori and intestinal protozoal infections among sewage workers in mansoura, Egypt. Egyptian Journal of Occupational Medicine, 35, 67-81.
Norouzi Khatiri, K., Niksokhan, M. H. & Sarang, A. 2020. Choosing various likelihood functions on uncertainty assessment in groundwater simulation-optimization model. Water Supply, 20, 737-750.
Piri, J. & Kisi, O. 2015. Modelling solar radiation reached to the Earth using ANFIS, NN-ARX, and empirical models (case studies: Zahedan and Bojnurd stations). Journal of Atmospheric and Solar-Terrestrial Physics, 123, 39-47.
Rad, I. Z. & Ardeshir, A. 2015. A multi-objective approach towards urban drainage development prioritization and project definition structure. Journal of Hydroinformatics, 17, 959-976.
Shirvani Moghaddam, M., Shirvani Moghaddam, K., Abolhasani, M. M., Farhangi, M., Barsari, V. Z., Liu, H. et al. 2019. Towards a green and self-powered internet of things using piezoelectric energy harvesting. IEEE Access, 7, 94533-94556.
Smith, E. 2001. Pollutant concentrations of stormwater and captured sediment in flood control sumps draining an urban watershed. Water Research, 35, 3117-3126.
Zahedan, W. T. P. 2018. Sewage collection network, water and wastewater research and design consultant engineers. Zahedan wastewater treatment plant. (In Persian)
مجله آب و فاضلاب
دوره 31، شماره 6 - شماره پیاپی 130
بهمن و اسفند 1399
صفحه 60-69

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