MODELING STUDIES FOR THE DETERMINATION OF COMPLETELY MIXED ACTIVATED SLUDGE REACTOR VOLUME: STEADY-STATE, EMPIRICAL AND ANN APPLICATIONS

Abstract

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 (P-X), food to mass ratios (F/M), hydraulic retention times (HRTs), volumetric organic loads (L-V) and oxygen requirements (ORs) were also compared for each model, and significant points of proposed approaches were evaluated.