Using Experimental and Artificial Intelligence Techniques for Prediction of the Sedimentation at Lateral Intakes

Volume 7, Issue 1, February 2022     |     PP. 1-26      |     PDF (1458 K)    |     Pub. Date: January 20, 2022
DOI: 10.54647/cebc56057    96 Downloads     5546 Views  

Author(s)

Ahmed, S.T., Faculty of Engineering, Cairo University, Jiza, Egypt
M. Soliman, Faculty of Engineering, Cairo University, Jiza, Egypt

Abstract
The main use of the lateral intakes is water conversion from a main channel into a branch channel for different purposes such as water distribution network, irrigation and cooling systems at thermal power plants. The flow structure at the intake is very complicated due to sediment diversions. The intake usually installed making a right angle with the main channel flow direction, it have been noticed that the right-angle intakes produce a large sediment diversion and reduce flow rate inside the intake. Thus, the optimum angle of an intake is very important to minimize sediment volume at the intake channel and to maximize flow rate. Therefore, a physical model utilized to investigate the different obtuse angle of the intake. Five different intake angles (110°, 130°, 145°, 160° and 170°) investigated for the lateral intake with different flow ratios. For further investigation sensitivity and parametric analysis using LSL and ANN-based models applied to experimental results. Analysis of experimental results showed that the intake angle 160° gives better results compared to other intake angles. In addition, the results indicated that the separation zone vanishes at the highest flow discharge ratio at both the intake angle of 160° and 170°.LSL results found that the diverted sediment Svr is most sensitive to the intake angle with 55.3% influence, followed by the discharge ratio with about 40.2% and by the width ratio with about 4.5%. Two ANN-based models were developed using the experimental results combinations from the most influential parameters on the sediment ratio diverted to the lateral intake (Svr) and contraction coefficient (Cc). The ANN models for predicting Svr and Cc performed well with an average R2 of 0.98. The error measures, RMSE shows low values.
From analysis, the optimum lateral intake angle is 164°. Also, the intake angles range 150 to 170 we can neglect effect the width ratios.

Keywords
lateral intake, intake angles, sediment volume, sediment diversion, ANN-based models.

Cite this paper
Ahmed, S.T., M. Soliman, Using Experimental and Artificial Intelligence Techniques for Prediction of the Sedimentation at Lateral Intakes , SCIREA Journal of Civil Engineering and Building Construction . Volume 7, Issue 1, February 2022 | PP. 1-26. 10.54647/cebc56057

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