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Extensions to modeling aerobic carbon degradation using combined respirometric-titrimetric measurements in view of activated sludge model calibration

机译:鉴于活性污泥模型的校准,扩展了使用组合呼吸-滴定法测量需氧碳降解的模型

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摘要

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 CO_2 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 CO_2 dissociation, pK_1, the initial aqueous CO_2 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 CO_2 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 CO_2 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.
机译:最近,鉴于校准1号活性污泥(ASM1),引入了一种模型来解释从不同碳源的好氧氧化获得的呼吸(OUR)-滴定(Hp)数据。除其他外,该模型要求在有氧实验中二氧化碳的传输速率(CTR)必须相对恒定。由于CTR是固有的非线性过程,因此该假设可能不适用于某些实验条件。因此,我们扩展了模型以描述非线性CTR行为。仅使用滴定数据开发了CO_2模型的简单校准程序。使用滴定数据时,该模型的可识别参数子集仅包含CO_2离解的第一平衡常数pK_1,初始含水CO_2浓度C_(Tinit)和生物质的氮含量i_(NBM)。然后将扩展模型成功地用于解释通过非线性CO_2汽提过程从呼吸滴度测量获得的典型数据。使用滴定数据的参数估计结果与使用单独的呼吸数据(OUR)或组合的OUR和Hp数据估计的结果一致,从而支持动态CO_2模型及其校准方法的有效性。滴定数据的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处理实验对ASM模型的校准。

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