In this study,an up-flow anaerobic sludge blanket(UASB) reactor was applied to treat the high salinity wastewater from heavy oil production process.At a HRT of ≥24 h,the COD removal reached as high as 65.08% at an in...In this study,an up-flow anaerobic sludge blanket(UASB) reactor was applied to treat the high salinity wastewater from heavy oil production process.At a HRT of ≥24 h,the COD removal reached as high as 65.08% at an influent COD ranging from 350mg/L to 640mg/L.An average of 74.33% oil reduction was also achieved in the UASB reactor at an initial oil concentration between 112mg/L and 205mg/L.These results indicated that this heavy oil production related wastewater could be degraded efficiently in the UASB reactor.Granular sludge was formed in this reactor.In addition,two models,built on the back propagation neural network(BPNN) theory and linear regression techniques were developed for the simulation of the UASB system performance in the oily wastewater biodegradation.The average error of COD and oil removal was-0.65% and 0.84%,respectively.The results indicated that the models built on the BPNN theory were wellfitted to the detected data,and were able to simulate and predict the removal of COD and oil by the UASB reactor.展开更多
基金the support provided by the Research & Technology Development Project of China National Petroleum Corporation (06A0302)Postdoctor Innovation Funds in Shandong Province (201002039)the Fundamental Research Funds for the Central Universities (27R1204023A)
文摘In this study,an up-flow anaerobic sludge blanket(UASB) reactor was applied to treat the high salinity wastewater from heavy oil production process.At a HRT of ≥24 h,the COD removal reached as high as 65.08% at an influent COD ranging from 350mg/L to 640mg/L.An average of 74.33% oil reduction was also achieved in the UASB reactor at an initial oil concentration between 112mg/L and 205mg/L.These results indicated that this heavy oil production related wastewater could be degraded efficiently in the UASB reactor.Granular sludge was formed in this reactor.In addition,two models,built on the back propagation neural network(BPNN) theory and linear regression techniques were developed for the simulation of the UASB system performance in the oily wastewater biodegradation.The average error of COD and oil removal was-0.65% and 0.84%,respectively.The results indicated that the models built on the BPNN theory were wellfitted to the detected data,and were able to simulate and predict the removal of COD and oil by the UASB reactor.
