| Summary: | Enhanced biological phosphorus removal (EBPR) facilities achieve low effluent phosphorus (P) levels (below 1 g P.m-3) for long periods of time. However, these facilities are often affected by unpredictable upsets that increase their operational costs and reduce the potential to recover P from downstream processes. Thereby, these facilities need to have access to reliable tools, capable of dynamically predicting EBPR performance and diagnosing plant upsets. To address this need, a novel integrated metabolic activated sludge model, the META-ASM, was developed with a robust single set of default parameters to describe the activity of the key organisms and processes relevant to EBPR systems. The advances regarding EBPR mechanisms investigated over the last twenty years were integrated in the META-ASM model to overcome various shortcomings of existing EBPR models. Special attention is given to the effect of operational conditions on the competition between polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), along with the capability of PAOs and GAOs to denitrify, the metabolic shifts as a function of storage polymer concentration for each group, the role of these polymers in endogenous processes, and a better description of the fermentation process. The model was calibrated and validated against 34 data sets describing different EBPR dynamics obtained from bench-scale batch tests inoculated with lab-scale enriched PAO-GAO cultures and full- scale sludge from different EBPR facilities. The overall strong correlations obtained between the predicted and the measured EBPR profiles demonstrated that this new model reduces calibration efforts and is capable of predicting the microbial and chemical transformations over a wide range of operational and environmental conditions, supporting the robustness of the unique default parameter set that was generated. A performance comparison between META-ASM and literature models also demonstrated that existing models require extensive parameter changes and have limited predictive power to describe different EBPR dynamics. The capacity of the META-ASM model to describe the long-term performance of a full-scale 3- stage Phoredox (A2/O) EBPR system and to be used as an operational diagnostic tool was evaluated in a 1336-day long-term dynamic simulation, while its performance was compared with the ASM-inCTRL model, a version based on the Barker & Dold model. Overall, the META-ASM provided a better description of PAOs active biomass and storage polymers and was a more powerful operational diagnostic tool for plant upsets. Viable troubleshooting scenarios were simulated to mitigate the upsets caused by the high aerobic hydraulic retention times (HRTs) and low organic loading rates (OLRs) of the plant. This thesis demonstrates that the META-ASM model is a powerful operational diagnostic tool for EBPR systems, capable of predicting plant upsets, optimising performance and evaluating new process designs.
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