Abstract：
The objective of this study is to develop a mechanistic model to predict the long-term dynamic performance of High-Rate Activated Sludge (HRAS) process, including the removal of carbon (COD), nitrogen (N), and phosphorus (P). The model was formulated with inspiration from Activated Sludge Models No. 1 and 3 (ASM1 and ASM3) to incorporate essential mechanisms, such as adsorption and storage substrate, specific to HRAS systems. A stepwise protocol was followed for calibration with dynamic data from a pilot-scale HRAS plant. Sensitivity analysis identified influential model parameters, including maximum specific growth rate (μ), growth yield (YH), storage yield (YSTO), storage rate (kSTO), decay rate (b), and half saturation of the readily biodegradable substrate for growth (KS1). The calibrated model achieved prediction efficiencies above the normalized Mean Absolute Error (MAE) of 70 % for mixed liquor suspended solids (MLSS), total chemical oxygen demand (TCOD), soluble COD (SCOD), particulate COD (XCOD), total nitrogen (TN), ammonia nitrogen (SNH), total phosphorus (TP), soluble TP (STP), and particulate TP (XTP). Uncertainty analysis revealed that SCOD was underestimated. Based on the dynamic profiles of uncertainty bands and observed data, there is potential for improving the estimation of dynamic behavior in STP. The observed discrepancies may be attributed to variations in wastewater characteristics during the monitoring period, particularly concerning the phosphorus (P) fractions of the readily biodegradable substrate (SS) and soluble inerts (SI), which were not considered as dynamically changing parameters in the model.
摘要：
本研究的目的是建立一个机制模型来预测高速率活性污泥（HRAS）过程的长期动态性能，包括去除碳(COD)，氮(N),和磷（P）。该模型是在活性污泥模型No.1和3(ASM1和ASM3)纳入基本机制，如吸附和储存基质，特定于HRAS系统。遵循逐步方案，使用中试规模的HRAS工厂的动态数据进行校准。灵敏度分析确定了有影响的模型参数，包括最大比生长率(μ)，生长产量(YH)，存储产量(YSTO)，存储速率(kSTO)，衰减率(b)，和半饱和的易于生物降解的生长基质（KS1）。校准模型实现了超过70%的标准化平均绝对误差（MAE）的混合液体悬浮固体（MLSS）的预测效率，总化学需氧量（TCOD），可溶性COD(SCOD),颗粒COD(XCOD),总氮(TN)，氨氮(SNH)，总磷（TP），可溶性TP（STP），和微粒TP(XTP)。不确定性分析表明，SCOD被低估了。根据不确定带的动态剖面和观测数据，有可能改善STP中动态行为的估计。观察到的差异可能归因于监测期间废水特性的变化，特别是关于易于生物降解的底物（SS）和可溶性惰性物质（SI）的磷（P）部分，这些参数在模型中不被视为动态变化的参数。
