Studies On Model And Simulation Of The Activated Sludge Biological Nitrogen Removal Process

Mathematical modeling and simulation are valuable tools in various aspects of the wastewater treatment practice,such as optimization of the plant operation,planning and design of new facilities,or development of new treatment processes.Based on the activated sludge model No.1(ASM1)developed by International Water Association,the experimental methods for characterizing wastewater composition and parameters of ASM1 were firstly determined.Then the sensitivity analysis of components and parameters of ASM1 were discussed and parameters estimation of ASM1 by the genetic algorithm has been extensively investigated.Finally,the activated sludge process model was introduced to simulate carbon and nitrogen removal in a 4-stages step-feed biological nutrient removal pilot plant.In order to determine the key parameters and components of ASM1,sensitivity analysis was adopted based on COST wastewater treatment plant benchmark.The results showed that the most sensitive one is heterotrophic yield Y_H among 5 stoichiometric parameters.At the same time three other parameters,b_H,μ_A and k_h have high sensitivity in 14 kinetic parameters. The soluble inert organic matter S_I is sensitive to effluent COD,slowly biodegradable substrate X_S is sensitive to suspended solids in effluent,ammonia S_NHand Slowly biodegradable substrate X_S are sensitive to ammonia and total nitrogen in effluent.Genetic algorithm adopts the least absolute difference between experimental results as the fitness function.To confirm the applicability of the method,parameters were estimated using the proposed methods under the COST simulation benchmark.Simultaneous estimation of three or four parameters is made and error is less than 5%.The genetic algorithm can accomplish the complicated parameter estimation of the activated Sludge model perfectly and effectively in contrast manual parameter estimation.The characteristics of municipal wastewater in xi'an Beishiqiao sewage treatment plant were analyzed with proposed methods.Total COD consists of 4.1%of S_I,17.6%of S_S,51.6% of X_S,and 13.28%of X_I,the total nitrogen consists of 64.94%of S_NH,7.35%of S_ND,and 27.71%of X_ND.The average heterotrophic yield coefficient Y_H is 0.72 in xi'an Beishiqiao sewage treatment plant,and is 0.697 in a four stages pilot plant of step-feed biological nutrient removal by batch OUR respirometric experiment.The relationship between particulate components of the ASM1 and SS in the following clarifier should be transformed by a transformation coefficient f_COD-SS,which is 0.808gCOD/gMLSS for xi'an Beishiqiao sewage treatment plant,and 0.777gCOD/gMLSS for the 4-stage step-feed biological nutrient removal pilot plant.The value of the decay rate of the heterotrophic biomass b_H at 25℃is 0.612d^-1in xi'an Beishiqiao sewage treatment plant.In the 4-stage step-feed biological nutrient removal pilot plant,model calibration with estimated parameters and experimental data showed a good agreement within 9.3%error range.Using the genetic algorithm,it is possible to estimate active sludge model parameters efficiently.Application of the genetic algorithms to optimize influent flow distribution in a 4-stage pilot plant of step-feed biological nutrient removal system was discussed.The objective function of optimization was designed to minimize the difference between estimated and required effluent concentrations.The optimized parameter for influent distribution ratios are 37.2%,27.4%,23.2%and 12.2%respectively.Good performance was achieved with the optimized influent distribution ratios.In conclusion,activated sludge process modeling and simulation offer an important theoretical fundamental and a convenient approach to get more detailed understandings to the dynamic behaviors and characteristics of activated sludge system.It is an effective tool to research on the more reasonable and optimal process control strategies.It may hopefully promote the design and management of wastewater treatment system up to a new scientific and technological level in order to reach more strict environmental objectives more economically.