Author/Authors :
MIRBAGHERI، AHMAD نويسنده , , Bagheri، Seyyed Majid نويسنده Department of Physiology, Shahid Sadoughi University of Medical Sciences, Yazd, Iran , , Boudaghpour، Siamak نويسنده 1Department of Civil Engineering, K.N. Toosi University of Technology, Vanak Square, Tehran, Iran , , Ehteshami، Majid نويسنده Department of Environmetal Engineering, School of Civil Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran , , Bagheri، Zahra نويسنده Department of Epidemiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran ,
Abstract :
Treatment process models are efficient tools to assure proper operation and better control of wastewater treatment
systems. The current research was an effort to evaluate performance of a submerged membrane bioreactor (SMBR)
treating combined municipal and industrial wastewater and to simulate effluent quality parameters of the SMBR
using a radial basis function artificial neural network (RBFANN). The results showed that the treatment efficiencies
increase and hydraulic retention time (HRT) decreases for combined wastewater compared with municipal and
industrial wastewaters. The BOD, COD, NH?
4 ?N and total phosphorous (TP) removal efficiencies for combined
wastewater at HRT of 7 hours were 96.9%, 96%, 96.7% and 92%, respectively. As desirable criteria for treating
wastewater, the TBOD/TP ratio increased, the BOD and COD concentrations decreased to 700 and 1000 mg/L,
respectively and the BOD/COD ratio was about 0.5 for combined wastewater. The training procedures of the
RBFANN models were successful for all predicted components. The train and test models showed an almost perfect
match between the experimental and predicted values of effluent BOD, COD, NH?
4 ?N and TP. The coefficient of
determination (R2) values were higher than 0.98 and root mean squared error (RMSE) values did not exceed 7% for
train and test models.
