ANFIS BASED APPROACH TO PREDICT SEDIMENT REMOVAL EFFICIENCY OF VORTEX SETTLING BASIN

M.A. ANSARI, M. DANISH, A. HUSSAIN, H.M. AZAMATHULLA

Abstract


The intricate flow dynamics within a vortex settling basin (VSB) make it challenging to establish a generalized regression model for accurately estimating sediment removal efficiency. Therefore, this study proposes an alternative approach using the Adaptive Neuro Fuzzy Inference System (ANFIS) to predict the sediment removal efficiency of VSB. The model is developed based on a comprehensive and reliable database sourced from literature, encompassing a wide range of hydraulic and geometrical variables from laboratory and field studies. The sediment removal efficiency of the VSB is modelled as a function of five key variables: water abstraction ratio, depth ratio, width ratio, diameter ratio, and particle Reynolds number. Training and testing data are extracted from laboratory and field datasets in various reputable references. Numerical tests reveal that ANFIS yields more accurate VSB removal efficiency predictions than previous empirical approaches. Sensitivity analysis further indicates that the particle Reynolds number exerts a more significant influence on sediment removal efficiency than the other independent parameters. This ANFIS-based approach offers an enhanced understanding and prediction capability for the complex processes occurring within a VSB.

Keywords


ANFIS, Vortex, Settling basin

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References


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