Sin Gürkan, Vanrolleghem Peter A
BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure Links 653, B-9000 Gent, Belgium.
Water Res. 2007 Aug;41(15):3345-58. doi: 10.1016/j.watres.2007.03.029. Epub 2007 May 11.
Recently a model was introduced to interpret the respirometric (OUR) -titrimetric (Hp) data obtained from aerobic oxidation of different carbon sources in view of calibration of Activated Sludge Model No.1 (ASM1). The model requires, among others, the carbon dioxide transfer rate (CTR) to be relatively constant during aerobic experiments. As CTR is an inherently nonlinear process, this assumption may not hold for certain experimental conditions. Hence, we extended the model to describe the nonlinear CTR behavior. A simple calibration procedure of the CO2 model was developed only using titrimetric data. The identifiable parameter subset of this model when using titrimetric data only contained the first equilibrium constant of the CO2 dissociation, pK1, the initial aqueous CO2 concentration, C(Tinit) and the nitrogen content of biomass, i(NBM). The extended model was then successfully applied to interpret typical data obtained from respirometric-titrimetric measurements with a nonlinear CO2 stripping process. The parameter estimation results using titrimetric data were consistent with the results estimated using respirometric data (OUR) alone or combined OUR and Hp data, thereby supporting the validity of the dynamic CO2 model and its calibration approach. The increased range of applicability and accurate utilization of the titrimetric data are expected to contribute particularly to the improvement of calibration of ASM models using batch experiments.
最近引入了一个模型,旨在根据1号活性污泥模型(ASM1)的校准来解释从不同碳源的好氧氧化中获得的呼吸测量(OUR)-滴定测量(Hp)数据。该模型尤其要求在好氧实验期间二氧化碳转移速率(CTR)相对恒定。由于CTR是一个本质上非线性的过程,这一假设在某些实验条件下可能不成立。因此,我们扩展了该模型以描述非线性CTR行为。仅使用滴定数据开发了一种简单的CO2模型校准程序。仅使用滴定数据时,该模型的可识别参数子集仅包含CO2解离的第一个平衡常数pK1、初始水溶液CO2浓度C(Tinit)和生物质的氮含量i(NBM)。然后,扩展模型成功应用于解释通过非线性CO2汽提过程的呼吸测量-滴定测量获得的典型数据。使用滴定数据的参数估计结果与仅使用呼吸测量数据(OUR)或结合OUR和Hp数据估计的结果一致,从而支持了动态CO2模型及其校准方法的有效性。滴定数据适用范围的扩大和准确利用预计将特别有助于改进使用批次实验对ASM模型的校准。