모델링 및 최적화 기법은 하수처리장의 고도처리와 제어 및 최적 운전의 요구가 커져감에 따라 오염물질의 제거 효율을 평가하고 분석하기 위해 사용되고 있다. 본 연구는 activated sludge model(ASM)에 기반하여 하수처리장 설계, 모델링, 공정 ...
모델링 및 최적화 기법은 하수처리장의 고도처리와 제어 및 최적 운전의 요구가 커져감에 따라 오염물질의 제거 효율을 평가하고 분석하기 위해 사용되고 있다. 본 연구는 activated sludge model(ASM)에 기반하여 하수처리장 설계, 모델링, 공정 최적화를 목적으로 시작되었으며, 이 연구는 호흡률측정법을 통해 ASMs에 사용되는 하수의 구성 성분과 동역학 및 화학양론적 매개변수를 추정하는 단계, 모델과 실제 값의 에러를 최소화하는 ASM 모델 보정단계, 하수처리공정의질소와 인, 그리고 chemical oxygen demand(COD)의 제거효율을 동시에 최적화 시키는 다중목적함수에 기반 한 다중 목적최적화 단계로 구성된다. 마지막으로 연구를 통해 개발된 통합 시스템을 실 규모 하수처리장의 적용하여 그 적용성을 평가하였다.
모델링 및 최적화 기법은 하수처리장의 고도처리와 제어 및 최적 운전의 요구가 커져감에 따라 오염물질의 제거 효율을 평가하고 분석하기 위해 사용되고 있다. 본 연구는 activated sludge model(ASM)에 기반하여 하수처리장 설계, 모델링, 공정 최적화를 목적으로 시작되었으며, 이 연구는 호흡률측정법을 통해 ASMs에 사용되는 하수의 구성 성분과 동역학 및 화학양론적 매개변수를 추정하는 단계, 모델과 실제 값의 에러를 최소화하는 ASM 모델 보정단계, 하수처리공정의질소와 인, 그리고 chemical oxygen demand(COD)의 제거효율을 동시에 최적화 시키는 다중목적함수에 기반 한 다중 목적최적화 단계로 구성된다. 마지막으로 연구를 통해 개발된 통합 시스템을 실 규모 하수처리장의 적용하여 그 적용성을 평가하였다.
A model-based study has been common part of study in the field of wastewater treatment plants (WWTPs). Activated sludge models (ASMs) developed by International Water Association (IWA) have been successfully used to describe the nutrient removal dynamics in a variety of WWTPs. In general the charact...
A model-based study has been common part of study in the field of wastewater treatment plants (WWTPs). Activated sludge models (ASMs) developed by International Water Association (IWA) have been successfully used to describe the nutrient removal dynamics in a variety of WWTPs. In general the characteristics of influent and organic biomass are not well known. In addition, some stoichiometric and kinetic parameters of ASMs have to be calibrated properly to improve the models’accuracy. However, it is not easy to determine the influent composition and ASM model parameters when modeling full-scale WWTP. The aim of this study is to develop a systematic methodology for ASM model calibration and optimization. An integrated protocol using ASM1 and ASM2d models of influent characterization, model calibration, and process optimization, is suggested and validated in a full-scale membrane bioreactor(MBR) plant.
The respirometry is well known as an appropriate method for the analysis of wastewater composition and activated biomass. The organic components of ASM1 and ASM2d models were estimated by a respiration test, while nitrogen and phosphorus components were estimated by standard method. Five key parameters of ASM model (YH, YA, , bH, iXB) related to oxygen consumption of biomass were estimated by the respiration test.
In the parameter calibrations step, key parameters of ASM model were selected by sensitivity analysis. After selecting these key parameters, the multiple response surface method(MRSM) wasused to estimate optimal values of key ASM parameters. Thus, model errors for effluent in ASM1 and ASM2d are minimized under estimated parameter values.
Then, the process optimization using the calibrated ASM models was performed. For ASM1 model, maximization of COD and TN removal was performed. For ASM2d model, a maximization of TN and TP removal was performed. Before process optimization, four key operational variables of MBR (DO set-point of oxic reactor, internal recycle flow rate, waste sludge flow rate and frequency of backwash) were analyzed and selected. After selecting key operational variables, optimal values of each operational variable were determined by using multi-objective genetic algorithm. The results of ASM1 optimization show that TN removal efficiency is increased, while COD removal efficiency is not increased due to low C/N ratio. To increase the COD and TN removal efficiencies, a strategy of an external carbon addition is suggested. For ASM2d optimization, both TN and TP removal efficiencies are increased by 3% and 2%, respectively.
Because this study is a methodology development, we expect that it could be useful to model, and optimize wastewater treatment plants.
A model-based study has been common part of study in the field of wastewater treatment plants (WWTPs). Activated sludge models (ASMs) developed by International Water Association (IWA) have been successfully used to describe the nutrient removal dynamics in a variety of WWTPs. In general the characteristics of influent and organic biomass are not well known. In addition, some stoichiometric and kinetic parameters of ASMs have to be calibrated properly to improve the models’accuracy. However, it is not easy to determine the influent composition and ASM model parameters when modeling full-scale WWTP. The aim of this study is to develop a systematic methodology for ASM model calibration and optimization. An integrated protocol using ASM1 and ASM2d models of influent characterization, model calibration, and process optimization, is suggested and validated in a full-scale membrane bioreactor(MBR) plant.
The respirometry is well known as an appropriate method for the analysis of wastewater composition and activated biomass. The organic components of ASM1 and ASM2d models were estimated by a respiration test, while nitrogen and phosphorus components were estimated by standard method. Five key parameters of ASM model (YH, YA, , bH, iXB) related to oxygen consumption of biomass were estimated by the respiration test.
In the parameter calibrations step, key parameters of ASM model were selected by sensitivity analysis. After selecting these key parameters, the multiple response surface method(MRSM) wasused to estimate optimal values of key ASM parameters. Thus, model errors for effluent in ASM1 and ASM2d are minimized under estimated parameter values.
Then, the process optimization using the calibrated ASM models was performed. For ASM1 model, maximization of COD and TN removal was performed. For ASM2d model, a maximization of TN and TP removal was performed. Before process optimization, four key operational variables of MBR (DO set-point of oxic reactor, internal recycle flow rate, waste sludge flow rate and frequency of backwash) were analyzed and selected. After selecting key operational variables, optimal values of each operational variable were determined by using multi-objective genetic algorithm. The results of ASM1 optimization show that TN removal efficiency is increased, while COD removal efficiency is not increased due to low C/N ratio. To increase the COD and TN removal efficiencies, a strategy of an external carbon addition is suggested. For ASM2d optimization, both TN and TP removal efficiencies are increased by 3% and 2%, respectively.
Because this study is a methodology development, we expect that it could be useful to model, and optimize wastewater treatment plants.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.