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

Journal of Environmental Health Science Engineering

Zahra Bagheri¹  Majid Ehteshami²  Siamak Boudaghpour²  Majid Bagheri²  Seyed Ahmad Mirbagheri² 

[1] Department and Faculty of Basic Sciences, PUK University, Kermanshah, Iran;Department of Civil Engineering, K.N. Toosi University of Technology, Vanak square, Tehran, Iran

关键词: Radial basis function;    Artificial neural network;    Treatment efficiency;    Submerged membrane bioreactor;    Combined wastewater;   

Others  :  1161196
DOI  :  10.1186/s40201-015-0172-4

 received in 2015-02-13, accepted in 2015-03-01,  发布年份 2015

【 摘 要 】

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, View MathML"> 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, View MathML"> 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.

【 授权许可】

   
2015 Mirbagheri et al.; licensee BioMed Central.

【 图 表 】

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