Performance evaluation and modeling of a submerged membrane bioreactor treating combined municipal and industrial wastewater using radial basis function artificial neural networks

Treatment process models are efficient tools to assure proper operation and better control of wastewater treatmentsystems. 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 smbrusing a radial basis function artificial neural network (rbfann). the results showed that the treatment efficienciesincrease and hydraulic retention time (hrt) decreases for combined wastewater compared with municipal andindustrial wastewaters. the bod, cod, nh?4 ?n and total phosphorous (tp) removal efficiencies for combinedwastewater at hrt of 7 hours were 96.9%, 96%, 96.7% and 92%, respectively. as desirable criteria for treatingwastewater, 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 therbfann models were successful for all predicted components. the train and test models showed an almost perfectmatch between the experimental and predicted values of effluent bod, cod, nh?4 ?n and tp. the coefficient ofdetermination (r2) values were higher than 0.98 and root mean squared error (rmse) values did not exceed 7% fortrain and test models.