Modeling studies for the determination of completely mixed, activated sludge reactor volume: Steady-state, empirical and ANN applications

Title:

Modeling studies for the determination of completely mixed, activated sludge reactor volume: Steady-state, empirical and ANN applications

Creator:

Yetilmezsoy, Kaan

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:d5866cd5-2df2-4778-adae-cf667cdf99ad
uuid:d5866cd5-2df2-4778-adae-cf667cdf99ad

Subject:

Activated sludge, completely mixed reactor, steady-state model, empirical model, and artificial neural network

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper presents an empirical model and a three-layer (7:11:1) artificial neural network (ANN) approach for the determination of completely mixed activated sludge reactor volume (CMASRV). CMASRV values were estimated by a new mathematical formulation and a three-layer ANN model for 1,000 different artificial scenarios given in a wide range of seven biological variables. The predicted results obtained from each stochastic approach were compared with the well-known steady state volume model based on mass balance equations. The computational analysis showed that the proposed empirical model and ANN outputs were obviously in agreement with the steady-state volume model and all the predictions proved to be satisfactory with a correlation coefficient of about 0.9989 and 1, respectively. The maximum volume deviations from the steady-state volume equation were recorded as only 7.17% and 6.89% for the proposed model and ANN outputs respectively. In addition to volume comparison, waste sludge mass flow rates (PX), food to mass ratios (F/M), hydraulic retention times (HRTs), volumetric organic loads (LV) and oxygen requirements (ORs) were also compared for each model, and significant points of proposed approaches were evaluated.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:5

Harvested from:

CDK

Metadata only:

false