A recently reported stable and efficient EBPR system at high temperatures around 30 °C has led to characterization of kinetic and stoichiometric parameters of the Activated Sludge Model no. 2d (ASM2d). Firstly, suitable model parameters were selected by identifiability analysis. Next, the model was calibrated and validated. ASM2d was found to represent the processes well at 28 and 32 °C except in polyhyroxyalkanoate (PHA) accumulation of the latter. The values of the kinetic parameters for PHA storage (q PHA), polyphosphate storage (q PP) and growth (μ PAO) of polyphosphate-accumulating organisms (PAOs) at 28 and 32 °C were found to be much higher than those reported by previous studies. Besides, the value of the stoichiometric parameter for the requirement of polyphosphate for PHA storage (Y PO4) was found to decrease as temperature rose from 28 to 32 °C. Values of two other stoichiometric parameters, i.e. the growth yield of heterotrophic organisms (Y H) and PAOs (Y PAO), were high at both temperatures. These calibrated parameters imply that the extremely active PAOs of the study were able to store PHA, store polyphosphate and even utilize PHA for cell growth. Besides, the parameters do not follow the Arrhenius correlation due to the previously reported unique microbial clade at 28 and 32 °C, which actively performs EBPR at high temperatures.
Recently reported kinetic and stoichiometric parameters of the Activated Sludge Model no. 2d (ASM2d) for high‐temperature EBPR processes suggested that the absence of glycogen in the model contributed to underestimation of PHA accumulation at 32 °C. Here, two modified ASM2d models were used to further explore the contribution of glycogen in the process. The ASM2d‐1G model incorporated glycogen metabolism by PAOs (polyphosphate‐accumulating organisms), while the ASM2d‐2G model further included processes by GAOs (glycogen‐accumulating organisms). These models were calibrated and validated using experimental data at 32 °C. The ASM2d‐1G model supported the hypothesis that the excess PHA was attributed to glycogen, but remained inadequate to capture the dynamics of glycogen without considering GAOs activities. The ASM2d‐2G model performed better, but it was challenging to calibrate as it often led to wash‐out of either PAOs or GAOs. Associated hurdles are highlighted and additional efforts in calibrating ASM2d‐2G more effectively are proposed.
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