Estimation of Tunnel Desilter Sediment Removal Efficiency by ANFIS

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.