International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
Vol. 7, No. 1, 2010, pp. 29-36
Bioline Code: st10004
Full paper language: English
Document type: Research Article
Document available free of charge
International Journal of Environment Science and Technology, Vol. 7, No. 1, 2010, pp. 29-36
© Copyright 2010 - Center for Environment and Energy Research and Studies (CEERS)
An alternative data driven approach for prediction of thermal discharge initial dilution using tee diffusers|
Etemad-Shahidi, A.; Zoghi, M. J. & Saeedi, M.
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.
Mixing; Neural network; Ocean outfalls; Thermal discharge
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