An alternative data driven approach for prediction of thermal discharge initial dilution using tee diffusers

Mixing of heated water discharged from outfalls is an efficient and effective method of waste disposal in coastal areas. Discharging the heated water with large quantities of mass flux generally requires multi-port diffusers. In recent years, using numerical models to predict the plume behavior has received attention from many researchers, who are interested in design of outfalls. This study reports the development and application of an artificial neural network model for prediction of initial dilution of multi-port tee diffusers. Several networks with different structures were trained and tested using error back propagation algorithm. Statistical error measures showed that a three layer network with 9 neurons in the hidden layer is skillful in prediction of initial dilution and the outputs are in good agreement (R = 0.97) with experimental results. Furthermore, the sensitivity analyses showed that the width of the equivalent slot of the diffuser is the most important parameter in the estimation of initial dilution.