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Study On Characteristics Of Simultaneous Methane Production By Simultaneous Anaerobic Denitrification And The Application Soft-sensing Model

Posted on:2019-05-11Degree:MasterType:Thesis
Country:ChinaCandidate:B H JiFull Text:PDF
GTID:2371330566987253Subject:Environmental engineering
Abstract/Summary:PDF Full Text Request
At present,with the acceleration of industrialization and urbanization in our country,domestic sewage,industrial production wastewater and other pollution sources,which are discharged in large quantities without effective treatment,have caused serious pollution to rivers,soils and groundwater in our country.One of the reasons for the decline of water quality is eutrophication,and nitrogen and chemical oxygen demand are the main pollutants that cause the water quality to deteriorate,and the original function of the lake is degraded.Total nitrogen and chemical oxygen demand are defined as total amount control pollutants in the13th Five-Year Plan of China,with emphasis on reducing total nitrogen and chemical oxygen demand to water ring.The total emissions of the environment.Anaerobic simultaneous denitrification methanogenesis process is to realize simultaneous denitrification and methane production in a single reactor.This process uses carbon source from anaerobic methanogenic wastewater to realize denitrification.It also simplifies the technological process,reduces the number of structures and reduces the cost of treatment,and achieves the purpose of simultaneously removing organic carbon and nitrogen from the wastewater.Therefore,it is of great significance to study the treatment process of anaerobic simultaneous denitrification methanogenic wastewater,which is of great significance to the application of the wastewater treatment process in engineering.The types of carbon sources are one of the main factors affecting the utilization of organic pollutants and nitrate reduction by anaerobic microorganisms.In this study,the coupling characteristics of anaerobic simultaneous denitrification and methane production under different carbon sources were investigated under intermittent test conditions.The fluctuation of ORP and pH values was inversely proportional to the fluctuation of pH value,and the ORP in the reaction system fluctuated violently.The metabolic process of methanogenic bacteria was inhibited in the early stage of the reaction.It was found that the system of sodium propionate as carbon source was more favorable to the rapid progress of denitrification stage.When glucose and starch were carbon source,7.44%and 4.04%of NO3--N in the system was dissimilated NH4+-N.In the late stage of the reaction,methanogenic bacteria with sodium acetate as carbon source could rapidly produce methane.Under the condition of continuous operation of a single reactor,when anaerobic simultaneous denitrification methanation is carried out under different loading conditions,when the ratio of m?COD?/m?NO3--N?exceeds the critical value of 5:1,Then the removal rate of NO3--N in the reaction system decreased and the accumulation of intermediate products represented by NO2--N increased.The violent fluctuation of intermediate products did not completely destroy the subsequent coupling of denitrification and methanogenesis,because the microbial flora in anaerobic granular sludge was distributed in different layers along the vertical line.The structure of the domesticated granular sludge can effectively protect the anaerobic microorganism from no in the central layer.The inhibitory effect of NOx--N was observed.With the increase of influent COD load,the effluent COD concentration increases and the removal rate decreases,and the effluent COD concentration decreases and the removal rate increases with the decrease of m?COD?/m?NO3--N?ratio,which indicates that increasing influent NO3--N concentration can improve the efficiency of COD removal.In the reaction system,the ratio of m?COD?/m?NO3--N?at 5:1 showed that the methanogenic reaction stopped.Because of the sufficient competition of denitrifying bacteria to the reaction matrix and the accumulation of a large number of intermediate products in the denitrification process,the methanogenic bacteria were seriously inhibited.Comprehensive upward increase The removal rate of COD can be improved by the concentration of NO3--N in large influent.When the concentration of COD is less than 10:1,the efficiency of denitrification and decarbonization by simultaneous anaerobic methane production and denitrification is better.Due to the complexity of the influencing factors of continuous running anaerobic and denitrification methanogenesis process,the effluent quality is predicted by intelligent algorithm,and the soft sensor is used to detect the process parameters or effluent parameters of wastewater biochemical treatment in real time.It can improve the monitoring and control level of wastewater treatment process to ensure the stability of effluent quality and sewage treatment efficiency.The BP neural network model is optimized by genetic algorithm,such as slow convergence rate,weak global search ability and so on,which will lead to the inherent deficiency of low precision of prediction results.A water quality prediction model based on GA-BP neural network is established.Improving its prediction accuracy.GA-BP neural network soft sensing The on-line real-time prediction of effluent COD and effluent NO3--N during anaerobic simultaneous denitrification and methane production can be realized by using the quantitative model,and the accuracy of prediction is improved.The maximum relative error absolute value of predicting effluent COD by soft sensor model with better parameter index.GA-BP neural network is 4.99%and mean square.The root error is 2.0451,the mean absolute percentage error is 2.3417%,the correlation coefficient is 0.9134;the absolute value of maximum relative error of NO3--N is 4.97%,the root mean square error is1.9725,the average absolute percentage error is 2.1488%and the correlation coefficient is0.9014.
Keywords/Search Tags:Simultaneous denitrification methane production, Different carbon sources, m(COD)/m(NO3--N)ratio, BP neural networ, Genetic algorithm
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