1st International Conference on Environment, Technology, and Management, Niğde, Türkiye, 27 - 29 Haziran 2019, cilt.1, sa.1, ss.147-156
Performance of artificial
neural network (ANN) in predicting efficiency of discrete settling process was
evaluated in this study. For this purpose, a layered settling approach was used
in a primary sedimentation tank and suspended solids (SS) removal efficiencies
were calculated for various operating conditions by solving the mass balance
equations. The removal efficiencies from the layered settling calculations were
employed for training and validating the ANN. The results showed that an ANN
with the logistic function satisfactorily predicts SS removal efficiency in
discrete settling process at a given set of influent flowrate, split ratio,
particle density, particle diameter, and water temperature. ANN predicted the
SS removal efficiencies with mean square errors as low as 0.0064%.