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Environmental Quality Management through Parshall Flume Aeration Efficiency Modelling

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The dissolved oxygen content in surface waters is one of the vital indicators for human water quality usage as well as the aquatic plant and animal environmental life sustainability. Parshall flumes are one of the important ejector devices that are successfully used for oxygen requirement satisfying in various irrigation, wastewater, and ecosystems. However, the present study aimed to manage and improve various waterworks aeration efficiency through integrated modeling of experimental and analytical analysis as well as their operation conditional parameters for the Parshall flumes configuration. On the basis of the experiment work data sets run results, the principal component regression (PCR), partial least squares (PLS), and ridge regression (RR) techniques are used to develop the required aeration efficiency prediction models for such aerators by interrelating the impact of Parshall flumes characteristics and configurations, as well as various water flow rates on aeration efficiency. The predictive models developed in the study were statistically compared to the experimental data. The comparison confirms a good reliability and high accuracy. Considering the proposed aeration models, the optimum design of the new Parshall flumes can be successfully facilitated.
Rocznik
Strony
124--130
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
  • Civil Engineering Department, Canadian International College, El Sheikh Zayed, Giza, Egypt
Bibliografia
  • 1. Thornton, C.I., Smith, B.A., Abt, S.R., Robeson, M.D. 2009. Supercritical flow measurement using a small parshall flume. Journal of Irrigation and Drainage Engineering, 135(5), ASCE. DOI: 10.1061/(ASCE) IR.1943-4774.0000014.
  • 2. Hamed, M.A.R. 2022. Configuration influence in relation to fluid flow of venturi system. Environmental Quality Management, Wiley, 1–6. DOI:10.1002/tqem.21896
  • 3. Al Ba’ba’, H. 2017. A study of optimum aeration efficiency of a lab-scale air-diffused system. Water and Environment Journal, Wiley, 432–439. DOI:10.1111/wej.12261
  • 4. Preul, H.C., Holler, A.G. 2017. Reaeration through low dams in the Ohio River”. In: Proceedings of the 24th Industrial Waste Conference-Part Two, Purdue University, Lafayette, Indiana.
  • 5. Avery, S.T., Novak, P. 1978. Oxygen transfer at hydraulic structures. Journal of the Hydraulics Division, 104(11), 1521–1540. https://doi.org/10.1061/JYCEAJ.0005100
  • 6. Markofsky, M., Kobus, H. 2017. Unified presentation of weir-aeration data.
  • 7. Wormleaton, P.R., Tsang, C.C. 2000. Aeration performance of rectangular planform labyrinth weirs. Journal of Environmental Engineering, 126(5), 456–465. https://doi.org/10.1061/(ASCE)0733-9372126:5(456)
  • 8. Gulliver, J.S., Thene, J.R., Rindels, A.J. 1990. Indexing gas transfer in self-aerated flows. Journal of environmental engineering, 116(3), 503–523.
  • 9. Sangeeta, S.B.H.S.A., Sharafati, A., Sihag, P., Al-Ansari, N., Kwok-Wing Chau K.W. 2021. Machine learning model development for predicting aeration efficiency through Parshall flume. Engineering Applications of Computational Fluid Mechanics, 15(1), 889–901. DOI: 10.1080/19942060.2021.1922314
  • 10. Sihaga, P., Dursunb, O.F., Sammenc, S.S., Malikd, A., Chauhane, A. 2021. Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models. Engineering Applications of Computational Fluid Mechanics, 15(1), 21(8), 2021. DOI: 10.2166/ws.2021.161 http://creativecommons.org/licenses/by/4.0
  • 11. Stock, J., Watson, M. 2002. Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97, 1167–1179.
  • 12. Helland, I. 1990. Partial least squares regression and statistical models. Scandinavian Journal of Statistics, 17, 97–114.
  • 13. Parsaie, A., Najafian, S., Shamsi, Z. 2016. Predictive modeling of discharge of flow in compound open channel using radial basis neural network”, Model. Earth Syst. Environ. https://doi.org/10.1007/s40808-016-0207-6.
  • 14. Tikhamarine, Y., Malik, A., Souag-Gamane, D., Kisi, O. 2020. Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration. Environ”, Sci. Pollut. Res., 27, 30001–30019. https://doi.org/10.1007/s11356-020-08792-3
  • 15. Tao, H., Diop, L., Bodian, A., Djaman, K., Ndiaye, P.M., Yaseen, Z.M. 2018 Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso. Agric. Water Manag. 208, 140–151. https://doi.org/10.1016/j.agwat.2018.06.018
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0f58b2e8-6e03-4e18-a5cd-25b8f1adf217
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