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Experimental Study On The Treatment Of Low-pollution Water By Biological Contact Oxidation Process

Posted on:2021-04-11Degree:MasterType:Thesis
Country:ChinaCandidate:X X HanFull Text:PDF
GTID:2381330614459597Subject:Architecture and civil engineering
Abstract/Summary:PDF Full Text Request
With the improvement of people's requirements for the quality of the water environment,low-pollution water treatment gradually attracts people's attention.Low-pollution water has the characteristics of wide variety,large water volume,widely exist and high nitrogen content.If untreated low-pollution water is discharged directly,it will lead to eutrophication and destroy the ecological balance of the receiving water.Therefore,it is urgent to carry out research on the treatment process of the low-pollution water.Based on this background,this paper adopts the biological contact oxidation?BCO?process to treat low-pollution water,which has the advantages of area saving,less capital construction cost,low treatment cost and high organic load.The main research contents and results are as follows:The BCO reactor was started by artificial inoculation.The influent concentration of the reactor was adjusted from high concentration to low concentration,and the microorganism in the reactor was cultured and acclimated.Rapid biofilm formation was successfully realized in a short time.After the biofilm was formed,typical low-pollution water with low carbon-to-nitrogen ratio?C/N??NH4+-N was the main form of nitrogen?was treated by using the BCO reactor.Effects of aeration mode,hydraulic retention time?HRT?,and ratio of air to water?A/W?on the removal of chemical oxygen demand were investigated,and the analysis of microbial community structure was also conducted.Results showed that when the A/W ratio was 6:1 and the HRT was 9 h,better removal effect was achieved for the operation mode of intermittent influent and intermittent aeration(influent 3 min,stirring 40 min,aeration 320 min,settling 180 min,94.50%,83.38%,and 74.23%,respectively.While for the operation mode of continuous influent and continuous aeration,when the HRT was 9 h and the A/W ratio was 6:1,and 45.45%,respectively.Results of microbial community analysis showed that Ignavibacterium,Pirellula,Gemmata,and Candidatus Hydrogenedens with anaerobic ammonia oxidation?Anammox?function existed in the dominant populations;the sum of relative abundances of the four bacteria in the intermittent aeration BCO reactors were higher than those in the continuous aeration BCO reactors.The sum of relative abundances of Defluviimonas,Hyphomicrobium,and Longilinea with denitrification function were also higher in the intermittent aeration BCO reactors.Therefore,the denitrification efficiency of BCO reactor was higher for the operation mode of intermittent influent and intermittent aeration.The effect of carbon sources?sodium acetate,methanol,glucose,sodium acetate+methanol?1:1,calculated by COD?mixed carbon source,sodium acetate+glucose?1:1,calculated by COD?mixed carbon source?and C/N?2.0,4.5,7.0?on the treatment of low-pollution water?water volume mainly from the effluent of sewage treatment plant,NO3--N was the main form of nitrogen?was investigated.The results showed that when the carbon source was sodium acetate,the denitrification effect of BCO reactor was the best for the low-pollution river water?water volume mainly from effluent of sewage treatment plant?,and the denitrification efficiency increased with the increase of carbon nitrogen ratio.When the A/W was 6:1,HRT was12 h,the C/N was 4.5,the carbon sources were methanol,glucose,sodium acetate+methanol?1:1,calculated by COD?mixed carbon source and sodium acetate+glucose?1:1,calculated by COD?mixed carbon source,the treatment effect of low-pollution river water by the BCO process was poor,the effluent TN and TP concentrations could not meet the Class V of the Environmental quality standards for surface water?GB 3838-2002,China??EQSSW?.When the carbon source was sodium acetate and the C/N was 2.0,the treatment effect of BCO reactor on the low-pollution river water was poor,and the effluent TN and part of TP concentration could not meet the Class V of EQSSW.When the carbon source was sodium acetate and the C/N was4.5,better treatment effect was observed for the low-pollution river water,and the TP could meet the Class V of EQSSW.The results of microbial community structure analysis showed that the relative abundances of denitrifying bacteria and anammox bacteria were lower in BCO reactor when carbon source was methanol and glucose,and C/N was 4.5.When carbon source was sodium acetate and C/N was 2.0,the relative abundances of anammox bacteria in BCO reactor were higher,and the relative abundances of denitrifying bacteria were lower.When the carbon source was sodium acetate and C/N was 4.5,the relative abundances of anammox and denitrifying bacteria in BCO reactor were higher.When the carbon source was sodium acetate and C/N was4.5,it was more favorable for the reproduction of anammox and denitrifying bacteria.Under this condition,better denitrification effect was observed for the BCO reactor?TN removal rate?86.8%?.An artificial neural network?ANN?model was established for the treatment of NO3--N,NO2--N,TN and TP in the effluent were predicted.The data in this experiment were used as training samples and test samples of ANN model,and L-M algorithm was used to train the samples.Mean square error?MSE?and linear regression coefficient?R2?were used to evaluate the accuracy of the ANN model.The results showed that when BCO process was used to treat typical low-pollution river water,Back Propagation?BP?ANN model could accurately predict the concentration of each pollutant?MSE=0.001?1.269,R2=0.877?0.992?.When BCO process was used to treat the low-pollution water?water volume mainly from the effluent of sewage treatment plant?,However,at this time,the BP ANN model could accurately predict the concentration ofCODCr,NO3--N,TN and TP?MSE=0.0022.544,R2=0.9360.992?.
Keywords/Search Tags:Low-pollution water, Biological contact oxidation, Intermittent aeration, Microbial community structure, Artificial neural network model
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