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Estimation of Tunnel Desilter Sediment Removal Efficiency by ANFIS

  • N. K. TiwariEmail author
  • Parveen Sihag
  • Bhupendra Kishore Singh
  • Subodh Ranjan
  • Krishna Kumar Singh
Research Paper
  • 12 Downloads

Abstract

The tunnel desilter is a simple and economical fluidic device which is the most suitable over other alternative devices for the region if water is abundantly available. The flow mechanism in the tunnel desilter is so complex that it is difficult to estimate the sediment removal efficiency accurately using a conventional regression. Hence, in the present study AI-based techniques, adaptive neurofuzzy interface system (ANFIS) and artificial neural network (ANN), were employed to estimate the sediment removal efficiency of the tunnel desilter using the data-sets collected by conducting the laboratory test. Findings of the sensitivity analysis showed that the size of the sediment was the most significant parameter followed by the concentration in the estimation of removal efficiency. The results of AI-based modeling were also compared with the available conventional predictive regression models, and it was found that the triangular membership function-based ANFIS model outperformed the other considered models. Further, ANN was also found to be giving comparable results.

Keywords

Tunnel desilter Sediment removal efficiency Adaptive neurofuzzy interface system (ANFIS) Artificial neural network (ANN) 

List of Symbols

a

Nondimensional bed layer thickness (2S) relative to depth of flow (D)

C

Sediment concentration (ppm)

D

Flow depth (m)

du

Diameter of under flow outlet which is equal to the width of subtunnel (m)

k

Von Karman’s constant = 0.4

n

Number of observations

Qi

Discharge in inlet channel, i.e., discharge in subtunnel (m3/s)

R

Extraction ratio (%)

S

Sediment size (mm)

\(U_{*}\)

Shear velocity (m/s)

\(U_{*}^{{\prime }}\)

Grain shear velocity (m/s)

V

Mean velocity of flow (m/s)

xi

Observed values

\(\bar{x}\)

Mean observed values

yi

Predicted values

\(\bar{y}\)

Mean predicted values

z

Any depth of water from the bed level (m)

α

Ratio of height of diaphragm slab to depth of water in case of tunnel-type silt ejector

\(\eta\)

Sediment removal efficiency

\(\omega\)

Fall velocity of the sediment particle (m/s)

\(\gamma_{\text{f}}\)

Weight density of fluid (KN/m3)

\(\gamma_{\text{s}}\)

Weight density of sediment (KN/m3)

W

Vertical upward velocity (m/s)

W

Width of channel

References

  1. Ansari MA, Athar M (2013) Artificial neural networks approach for estimation of sediment removal efficiency of vortex settling basins. ISH J Hydraul Eng 19(1):38–48CrossRefGoogle Scholar
  2. Athar M, Kothyari UC, Garde RJ (2002) Sediment removal efficiency of vortex chamber type sediment extractor. J Hydraul Eng 128(12):1051–1059CrossRefGoogle Scholar
  3. Athar M, Kothyari UC, Garde RJ (2003) Distribution of sediment concentration in the vortex chamber type sediment extractor. J Hydraul Res 41(4):427–438CrossRefGoogle Scholar
  4. Atkinson E (1994a) Vortex-tube sediment extractors. I: trapping efficiency. J Hydraul Eng 120(10):1110–1125CrossRefGoogle Scholar
  5. Atkinson E (1994b) Vortex-tube sediment extractors. II: design. J Hydraul Eng 120(10):1126–1138CrossRefGoogle Scholar
  6. Atkinson E, Lawrence P (1984) A quantitative design method for tunnel type sediment extractors. In: Fourth congress, Asian and Pacific Division, Indian Association for Hydraulic Research, Chiang Mai-Thailand, pp 77–81Google Scholar
  7. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, OxfordzbMATHGoogle Scholar
  8. Blench T (1952) Discussion of model and prototype studies of sand traps, by RL Parshall. Trans ASCE 117:213Google Scholar
  9. Cui ZD, Tang YQ, Yan XX, Yan CL, Wang HM, Wang JX (2010) Evaluation of the geology-environmental capacity of buildings based on the ANFIS model of the floor area ratio. Bull Eng Geol Environ 69(1):111–118CrossRefGoogle Scholar
  10. Curi KV, Esen II, Velioglu SG (1979) Vortex type solid liquid separator. Progr Water Technol 7(2):183–190Google Scholar
  11. Dashtbozorgi S, Asareh A (2015) Study of the rate of sediment trapping and water loss in the vortex tube structure at different placement angles. J Sci Res Dev 2(5):104–110Google Scholar
  12. Dhillon GS, Aggarwal RK, Kotwal AN (1977) Model prototype conformity study of sediment ejectors on Upper Bari Doab Hydel Channel. In: Proceedings, 46th Research Session of CBIP, 3, 47–56Google Scholar
  13. Dongre NB (2002) Settling basin design. M.Tech. thesis, Department of Civil Engineering, Indian Institute of Technology, Roorkee, IndiaGoogle Scholar
  14. Fischer MM (1998) Computational neural networks: a new paradigm for spatial analysis. Environ Plan A 30(10):1873–1891CrossRefGoogle Scholar
  15. Garde RJ, Kothyari UC (2004) Sediment management in hydroelectric projects. In: Proceeding of ninth international symposium on river sedimentation, Tsinghua University Press, Yichang (China), pp 19–28Google Scholar
  16. Garde RJ, Pande PK (1976) Use of sediment transport concepts in design of tunnel-type sediment excluders. ICID Bull 25(2):101–111Google Scholar
  17. Garde RJ, Raju KGR, Sujudi AWR (1990) Design of settling basins. J Hydraul Res 28(1):81–91CrossRefGoogle Scholar
  18. Gautam SR (2005) Computer aided design of tunnel type silt ejector. M.E. thesis of Civil Engineering in Hydraulics and Flood Control Engineering, Delhi College of Engineering University of Delhi, DelhiGoogle Scholar
  19. Irrigation and Power Research Institute (IPRI) (1989) Design Norms for Vortex Settling Basin. Report no. HY/R/17/89–90, Amritsar, PunjabGoogle Scholar
  20. IS: 6004 (1980) Criteria for Hydraulic design of Sediment Ejector for Irrigation and Power Channels. Indian standard Institution, Manak Bhawan, 9, Bahadur Shah Zafar Marg, New-DelhiGoogle Scholar
  21. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  22. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. IEEE Trans Autom Control 42(10):1482–1484CrossRefGoogle Scholar
  23. Kothyari UC, Pande PK, Gahlot AK (1994) Design for tunnel-type sediment excluder. J Irrig Drain Eng 120(1):36–47CrossRefGoogle Scholar
  24. Kumar M, Ranjan S, Tiwari NK, Gupta R (2018a) Plunging hollow jet aerators-oxygen transfer and modelling. ISH J Hydraul Eng 24(1):61–67CrossRefGoogle Scholar
  25. Kumar M, Tiwari NK, Ranjan S (2018b) Prediction of oxygen mass transfer of plunging hollow jets using regression models. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1435311 Google Scholar
  26. Lawrence P, Sanmuganathan K (1981) Field verification of vortex tube design method. In: Proceedings of the South-East Asian regional symposium on problems of soil erosion and sedimentation, held at Asian Institute of Technology, January 27–29, 1981/edited by T. Tingsanchali, H. Eggers. The Institute, BangkokGoogle Scholar
  27. Mashauri DA (1986) Modelling of vortex settling chamber for primary clarification of water. PhD thesis, Tampere University of Technology, Tampere University of Tampere, FinlandGoogle Scholar
  28. Moradi A, Hasoonizade H, Kashkuli HA, Jahromi HM, Sedghi H (2013) Investigation of the effect of vortex tube structure with 60 and 90 degree angles on Sedimentation entrance trap efficiency to intakes at 180-degree bend location. Int J Agric Crop Sci 5(23):2885–2889Google Scholar
  29. Orak SJ, Asareh A (2015) Effect of gradation on sediment extraction (trapping) efficiency in structures of vortex tube with different angles. WALIA J 31(S4):53–58Google Scholar
  30. Pal M, Singh NK, Tiwari NK (2012) M5 model tree for pier scour prediction using field dataset. KSCE J Civil Eng 16(6):1079–1084CrossRefGoogle Scholar
  31. Pal M, Singh NK, Tiwari NK (2013) Pier scour modelling using random forest regression. ISH J Hydraul Eng 19(2):69–75CrossRefGoogle Scholar
  32. Parsaie A, Haghiabi AH, Saneie M, Torabi H (2018) Prediction of energy dissipation of flow over stepped spillways using data-driven models. Iran J Sci Technol Trans Civ Eng 42(1):39–53CrossRefGoogle Scholar
  33. Paul TC, Sayal SK, Sakhuja VS, Dhillon GS (1991) Vortex settling chamber design considerations. J Hydrol Eng ASCE 117(2):172–189CrossRefGoogle Scholar
  34. Raju KR, Kothyari UC, Srivastava S, Saxena M (1999) Sediment removal efficiency of settling basins. J Irrig Drain Eng 125(5):308–314CrossRefGoogle Scholar
  35. Robinson AR (1962) Vortex tube and trap. Trans ASCE 127:391–433Google Scholar
  36. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation (No. ICS-8506). California University, San Diego La Jolla Inst for Cognitive ScienceGoogle Scholar
  37. Saxena M (1996) Effect of flushing on efficiency of settling basins. M.E. thesis, Department of Civil Engineering, University of Roorkee, Roorkee (UP)Google Scholar
  38. Schalkoff RJ (1992) Pattern classification: statistical, structural and neural approachesGoogle Scholar
  39. Schrimpf W (1991) Discussion of design of settling basins by RJ Garde, KG Ranga Raju and AWR Sujudi. J Hydraul Res IAHR 29(1):136–142CrossRefGoogle Scholar
  40. Sihag P, Tiwari NK, Ranjan S (2017a) Modelling of infiltration of sandy soil using Gaussian process regression. Model Earth Syst Environ 3(3):1091–1100CrossRefGoogle Scholar
  41. Sihag P, Tiwari NK, Ranjan S (2017b) Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2017.1381861 Google Scholar
  42. Sihag P, Tiwari NK, Ranjan S (2018) Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1439776 Google Scholar
  43. Singh KK (1987) Experimental study of settling basins. M.E. thesis, Department of Civil Engineering, University of Roorkee, Roorkee UP, IndiaGoogle Scholar
  44. Singh BK (2016) Study of sediment extractor doctoral thesis. National Institute of Technology, KurukshetraGoogle Scholar
  45. Singh KK, Pal M, Ojha CSP, Singh VP (2008) Estimation of removal efficiency for settling basins using neural networks and support vector machines. J Hydrol Eng 13(3):146–155CrossRefGoogle Scholar
  46. Singh BK, Tiwari NK, Singh KK (2016) Support vector regression based modeling of trapping efficiency of silt ejector. J Indian Water Resour Soc 36(1):41–49Google Scholar
  47. Solomatine DP, Xue Y (2004) M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. J Hydrol Eng 9(6):491–501CrossRefGoogle Scholar
  48. Srivastava S (1997) Effect of flushing on the efficiency of settling basins. M.E. thesis, Department of Civil Engineering, University of Roorkee, Roorkee, UP, IndiaGoogle Scholar
  49. Sugeno M, Takagi T (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132zbMATHGoogle Scholar
  50. Takagi T, Sugeno M (1993) Fuzzy identification of systems and its applications to modeling and control. In: Readings in fuzzy sets for intelligent systems, pp 387–403Google Scholar
  51. Tiwari NK, Sihag P, Ranjan S (2017) Modeling of infiltration of soil using adaptive neuro-fuzzy Inference system (ANFIS). J Eng Technol Educ 11(1):13–21Google Scholar
  52. Tiwari NK, Sihag P, Kumar S, Ranjan S (2018) Prediction of trapping efficiency of vortex tube ejector. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1441752 Google Scholar
  53. UPIRI (1975) Sediment excluders and ejectors design monograph (45-H1-6)Google Scholar
  54. Uppal HL (1966) Sediment control in river and canal, CBIP (India), Publication Number-79Google Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Institute of TechnologyKurukshetraIndia
  2. 2.Defence Research and Development OrganisationNew DelhiIndia

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