Prediction of Aeration Efficiency in Tilting Flume Equipped with Screens Using AI Techniques

Abstract This study investigates the innovative use of perforated screens as an aeration method in an open channel wastewater treatment system. Although screens are typically used in primary treatment to eliminate solids, their use in aeration has not yet been investigated. To evaluate this potential, 189 experimental tests were conducted using acrylic screens with different perforations/jets, such as circular, triangular, and square. Input parameters such as number of jets, discharge, angle of inclination of tilting flume ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="alpha" display="inline" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> ), hydraulic radius of each geometry perforated on screens, Froude number, and shape factor (SF) were used to predict aeration efficiency ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="normal upper E 20" display="inline" overflow="scroll"> <mml:msub> <mml:mi mathvariant="normal">E</mml:mi> <mml:mn>20</mml:mn> </mml:msub> </mml:math> ) using three artificial intelligence techniques, including artificial neural network (ANN), random forest, and support vector machine with Pearson VII kernel. With a correlation coefficient of 0.9635, ANN outperformed the other developed models. Sensitivity analysis revealed that SF was the most important component affecting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="normal upper E 20" display="inline" overflow="scroll"> <mml:msub> <mml:mi mathvariant="normal">E</mml:mi> <mml:mn>20</mml:mn> </mml:msub> </mml:math> . The dependability of the results was further supported by the great accuracy ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper R squared equals 0.9116" display="inline" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>0.9116</mml:mn> </mml:math> ) of a mathematical model that used the same variables. The results lay the groundwork for further study and field-scale validation of this innovative aeration technique.

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Publication Details

Journal
Journal of Environmental Engineering
Published
2026-08-03
DOI
https://doi.org/10.1061/joeedu.eeeng-8330
Primary Topic
Hydraulic flow and structures
Type
article
Field-Weighted Citation Impact
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article

Prediction of Aeration Efficiency in Tilting Flume Equipped with Screens Using AI Techniques

Parveen Sihag, Mohindra Singh Thakur, Diksha Puri
Journal of Environmental Engineering
Hydraulic flow and structures
article

Prediction of Aeration Efficiency in Tilting Flume Equipped with Screens Using AI Techniques

Parveen Sihag, Mohindra Singh Thakur, Diksha Puri
article en

Abstract

Abstract This study investigates the innovative use of perforated screens as an aeration method in an open channel wastewater treatment system. Although screens are typically used in primary treatment to eliminate solids, their use in aeration has not yet been investigated. To evaluate this potential, 189 experimental tests were conducted using acrylic screens with different perforations/jets, such as circular, triangular, and square. Input parameters such as number of jets, discharge, angle of inclination of tilting flume ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="alpha" display="inline" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> ), hydraulic radius of each geometry perforated on screens, Froude number, and shape factor (SF) were used to predict aeration efficiency ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="normal upper E 20" display="inline" overflow="scroll"> <mml:msub> <mml:mi mathvariant="normal">E</mml:mi> <mml:mn>20</mml:mn> </mml:msub> </mml:math> ) using three artificial intelligence techniques, including artificial neural network (ANN), random forest, and support vector machine with Pearson VII kernel. With a correlation coefficient of 0.9635, ANN outperformed the other developed models. Sensitivity analysis revealed that SF was the most important component affecting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="normal upper E 20" display="inline" overflow="scroll"> <mml:msub> <mml:mi mathvariant="normal">E</mml:mi> <mml:mn>20</mml:mn> </mml:msub> </mml:math> . The dependability of the results was further supported by the great accuracy ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper R squared equals 0.9116" display="inline" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>0.9116</mml:mn> </mml:math> ) of a mathematical model that used the same variables. The results lay the groundwork for further study and field-scale validation of this innovative aeration technique.

Journal of Environmental EngineeringVol. 152(10)
Chandigarh University (IN), Marwadi University (IN), Shoolini University (IN)
Clean water and sanitation
Openalex Percentile: Top 14%
Hydraulic flow and structures
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