The Research on the Deposition Regularity of Suspended Particles in Storm Sewer

  • Cuiyun Liu
  • Shuai Tan
  • Xiaohua Zhang
  • Jinpeng Yu
  • Yanhua Xu
  • Yonghai Xu


The deposition process of suspended particles in storm sewer has been simulated to explore the depositional regularity of storm sewer under different conditions of pipelines and suspended particles. The maximum deposition position, maximum deposition rate, and average deposition velocity of suspended particles have been calculated according to the mathematical models. The results show that the different conditions have a great influence on the deposition process in the pipeline. The higher concentration, the larger fullness and particle size or the smaller flow velocity and pipe slope, the more serious the deposition in the front section of the pipeline. The mathematical models show that the five factors have different effects on the maximum deposition position, maximum deposition rate, and average deposition velocity in the pipeline. When the concentration, particle size, and fullness increase, the maximum deposition position of suspended particles is removed forward. When the concentration and flow velocity increases, the maximum deposition rate tends to decrease, while it rises with the increase of particle size. In the case of high concentration or large particle size, the average deposition velocity of the suspended particles is larger, and it rises first and then decreases when flow velocity increases. Under the test conditions, when the flow velocity is in the range of 0.3–0.35 m/s, the average deposition velocity reaches the maximum.


Storm sewer Suspended particles Deposition position Deposition rate Deposition velocity 


Funding Information

This research was supported by the Natural Science Foundation of the Jiangsu Province in China (BK20150959), National College Students Innovation and Entrepreneurship Training Program (201710291051), and Yang Jinfeng plan of Yangzhou in Jiangsu Province (YZLYJFJH2016YB098).


  1. Ab Ghani, A., & Azamathulla, H. M. (2011). Gene-expression programming for sediment transport in sewer pipe systems. Journal of Pipeline Systems Engineering and Practice, 2(3), 102–106.CrossRefGoogle Scholar
  2. Ab-Ghani, A. (1993). Sediment transport in sewers. University of Newcastle, Upon Tyne.Google Scholar
  3. Ashley, R., Crabtree, B., Fraser, A., et al. (2003). European research into sewer sediments and associated pollutants and processes. Journal of Hydraulic Engineering-Asce, 129(4), 267–275.CrossRefGoogle Scholar
  4. Aytek, A., & Kisi, O. (2008). A genetic programming approach to suspended sediment modeling. Journal of Hydrology, 351(3–4), 288–298.CrossRefGoogle Scholar
  5. Azamathulla, H. M., Ab Ghani, A., & Fei, S. Y. (2012). ANFIS-based approach for predicting sediment transport in clean sewer. Applied Soft Computing, 12, 1227–1230.CrossRefGoogle Scholar
  6. Banasiak, R., Verhoeven, R., De, S. R., et al. (2005). The erosion behavior of biologically active sewer sediment deposits: observations from a laboratory study. Water Research, 39(20), 5221–5231.CrossRefGoogle Scholar
  7. Becouze-Lareure, C., Thiebaud, L., Bazin, C., et al. (2016). Dynamics of toxicity within different compartments of a peri-urban river subject to combined sewer overflow discharges. Science of the Total Environment, 539(1), 503–514.CrossRefGoogle Scholar
  8. Bersinger, T., Le, H. I., Bareille, G., et al. (2015). Assessement of erosion and sedimentation dynamic in a combined sewer network using online turbidity monitoring. Water Science and Technology, 72(8), 1375–1382.CrossRefGoogle Scholar
  9. Butler, D., May, R., & John, A. (2003). Self-cleansing sewer design based on sediment transport principles. Journal of Hydraulic Engineering, 192(4), 276–280.CrossRefGoogle Scholar
  10. Carnacina, I., Larrarte, F., & Leonardi, N. (2017). Acoustic measurement and morphological features of organic sediment deposits in combined sewer networks. Water Research, 112(4), 279–290.CrossRefGoogle Scholar
  11. Caviedes-Voullième, D., Morales-Hernández, M., Juez, C., et al. (2017). Two-dimensional numerical simulation of bed-load transport of a finite-depth sediment layer: applications to channel flushing. Journal of Hydraulic Engineering, 143(9), 04017034.CrossRefGoogle Scholar
  12. Ebtehaj, I., & Bonakdari, H. (2013). Evaluation of sediment transport in sewer using artificial neural network. Engineering Applications of Computational Fluid Mechanics, 7(3), 382–392.CrossRefGoogle Scholar
  13. Ebtehaj, I., & Bonakdari, H. (2016a). A nonlinear simulation method based on a combination of multilayer perceptron and decision trees for predicting non-deposition sediment transport. Water Science & Technology, 16(5), 1198–1206.Google Scholar
  14. Ebtehaj, I., & Bonakdari, H. (2016b). Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water Journal, 13(5), 499–510.CrossRefGoogle Scholar
  15. Ebtehaj, I., Bonakdari, H., & Shamshirband, S. (2016a). Extreme learning machine assessment for estimating sediment transport in open channels. Engineering with Computers, 32(4), 691–704.CrossRefGoogle Scholar
  16. Ebtehaj, I., Bonakdari, H., Shamshirband, S., et al. (2016b). A combined support vector machine-wavelet transform model for prediction of sediment transport in sewer. Flow Measurement and Instrumentation, 47(3), 19–27.CrossRefGoogle Scholar
  17. Guerineau, H., Dorner, S., Carriere, A., et al. (2014). Source tracking of leaky sewers: a novel approach combining fecal indicators in water and sediments. Water Research, 58(7), 50–61.CrossRefGoogle Scholar
  18. Jalliffier-Verne, I., Heniche, M., Madoux-Humery, A. S., et al. (2016). Cumulative effects of fecal contamination from combined sewer overflows: management for source water protection. Journal of Environmental Management, 174(6), 62–70.CrossRefGoogle Scholar
  19. Jin, P. K., Wang, B., Jiao, D., et al. (2015). Characterization of microflora and transformation of organic matters in urban sewer system. Water Research, 84(11), 112–119.CrossRefGoogle Scholar
  20. Launay, M. A., Dittmer, U., & Steinmetz, H. (2016). Organic micropollutants discharged by combined sewer overflows—characterisation of pollutant sources and stormwater-related processes. Water Research, 104(11), 82–92.CrossRefGoogle Scholar
  21. Li, H. Y., Mei, H. R., & Xu, B. P. (2011). Investigation and analysis of storm sewer sediments in Beijing. China Water& Wastewater, 27(6), 36–39 (in Chinese).Google Scholar
  22. Liu, Y., Ni, B. J., Ganigue, R., et al. (2015). Sulfide and methane production in sewer sediments. Water Research, 70(2), 350–359.CrossRefGoogle Scholar
  23. Madoux-Humery, A., Dorner, S., Sauve, S., et al. (2016). The effects of combined sewer overflow events on riverine sources of drinking water. Water Research, 92(4), 218–227.CrossRefGoogle Scholar
  24. Memarian, H., & Balasundram, H. S. (2012). Comparison between multi-layer perceptron and radial basis function networks for sediment load estimation in a tropical watershed. Journal of Water Resource and Protection, 4(10), 870–876.CrossRefGoogle Scholar
  25. Ota, J.J., Perrusquía, G. (2011). Particle velocity and sediment transport at limit deposition in sewers. 12th International Conference on Urban Drainage, Porto Alegre/Brazil, 11–16 September.Google Scholar
  26. Rodriguez, J. P., McIntyre, N., Diaz-Granados, M., et al. (2012). A database and model to support proactive management of sediment-related sewer blockages. Water Research, 46(15), 4571–4586.CrossRefGoogle Scholar
  27. Shahsavari, G., Arnaud-Fassetta, G., & Campisano, A. (2017). A field experiment to evaluate the cleaning performance of sewer flushing on non-uniform sediment deposits. Water Research, 118(7), 59–69.CrossRefGoogle Scholar
  28. Tang, X., Chen, W. B., & Li, H. Z. (2013). Study on characteristics of sediments in urban sewer system and its desilting mode. Urban Roads Bridges & Flood Control, 3, 106–110 (in Chinese).Google Scholar
  29. Zhang, W., Yu, J., & Li, W. (2012). Research on the present situation of drainage pipeline in Guangzhou. Water & Wastewater Engineering, 38(7), 147–150 (in Chinese).Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cuiyun Liu
    • 1
    • 2
  • Shuai Tan
    • 1
  • Xiaohua Zhang
    • 1
  • Jinpeng Yu
    • 1
  • Yanhua Xu
    • 2
  • Yonghai Xu
    • 3
  1. 1.College of Urban ConstructionNanjing Tech UniversityNanjingChina
  2. 2.Jiangsu Key Laboratory of Industrial Water-Conservation and Emission ReductionNanjing Tech UniversityNanjingChina
  3. 3.Jiangsu Hanjian GroupYangzhouChina

Personalised recommendations