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New explicit formulations for accurate estimation of aeration-related parameters in steady-state completely mixed activated sludge process

Paper Topic: 
Water and Wastewater Treatment

Pages :
140 - 159

Corresponing Author: 
Kaan Yetilmezsoy
Yetilmezsoy K.
Paper ID: 
Paper Status: 
Date Paper Accepted: 
Paper online: 

This paper presents new and explicit equations to estimate aeration-related parameters such as standard oxygen requirement, daily energy consumption and total mass transfer coefficient for the diffused aeration. The proposed formulations are derived for the steady-state completely mixed activated sludge process based on the nonlinear regression analysis by using the Richardson’s extrapolation method and the Levenberg–Marquardt algorithm. The applicability of the proposed models has been investigated for a wide range of thirteen inputs consisting of the fundamental biological, hydraulic, and physical design variables, and tested against a total of 1500 additional computational scenarios. All estimations are proven to be satisfactory with very high determination coefficients (R2) between 0.961–0.965, 0.967–0.972 and 0.980–0.984, respectively, for the prediction of standard oxygen requirement, daily energy consumption and total mass transfer coefficient for diffused aeration. The proposed models offer sufficiently simple and practical mathematical formulations incorporating routinely obtainable parameters, which are readily available for all activated sludge-based treatment plants. Besides eliminating the need for additional time or computational effort typically performed in the theoretical procedure, the developed equations have simple coefficients to be easily used for manual calculations with a hand-held calculator. The statistical results clearly exhibit that the proposed equations are accurate enough to be used in estimation of the studied aeration parameters based on the practical ranges of the corresponding design variables.

completely mixed activated sludge, energy consumption, mass transfer coefficient; nonlinear regression, oxygen requirement, statistical analysis