The effect of bioreactor configurations on the dynamics of aeration modelling was investigated by incorporating three different correlations from the literature to estimate α-factors into the aeration model. Estimated air flow rates using the three correlations were then validated against experimental data obtained from pilot sequencing batch reactors (SBRs). Two identical SBRs were operated in parallel; one received raw wastewater and the other received primary treated wastewater. The validated aeration model was then used to evaluate aeration dynamics in different bioreactor configurations, both for nitrification only and nitrification/denitrification, with the three different correlations. The current study is the first to investigate the validity of the aforementioned correlations using various bioreactor configurations and to establish that the bioreactor configuration not only impacts spatial and temporal biological oxygen demands as currently understood but also oxygen transfer efficiency. The first correlation based on the real-time bioreactor soluble chemical oxygen demand (sCOD) was able to predict the temporal measured air flow rate in the pilot SBRs. The second correlation based on the influent COD overestimated the air flow rates as it considered the impact of the influent loading rates on the α-factor and overlooked the improvement in α-factor due to biodegradation. The third correlation based on MLSS concentrations underestimated the air flow rates at the beginning of the aeration cycle as it ignored the impact of influent loading rates on the α-factor and considered only the insignificant change in MLSS during the aeration cycle. In terms of bioreactor configuration, the model-based analysis showed that the first correlation is suitable for designing SBR, plug flow reactor (PFR), step-feed PFR, and completely mixed stirred reactor (CSTR) systems, and the third correlation is suitable for designing CSTRs and membrane bioreactors (MBRs), while the second correlation was not accurate in any of the reactors modelled. When nitrification was targeted, the CSTR reduced aeration energy by 44 %–49 % compared to the PFR, and 41 %–43 % when both nitrification and denitrification were targeted. Compared to the plug-flow reactor, the step-feed PFR reduced aeration energy by 9 % when nitrification only was targeted. However, when pre-denitrification was added, both systems showed the same aeration energy consumption. Pre-denitrification reduced organic loadings to aeration tanks and decreased aeration energy by 22 %, 11 %, 15 %, and 14 % in PFR, CSTR, PFR step feed and MBR systems, respectively.

通过将文献中的三种不同相关性结合到曝气模型中来估计 α 因子，研究了生物反应器配置对曝气建模动力学的影响。然后根据从中试顺序间歇反应器 (SBR) 获得的实验数据验证使用三个相关性估计的空气流速。两台相同的 SBR 并行运行；一个接收未经处理的废水，另一个接收经过初级处理的废水。然后使用经过验证的曝气模型来评估不同生物反应器配置中的曝气动力学，包括仅硝化和硝化/反硝化，具有三种不同的相关性。目前的研究是第一个使用各种生物反应器配置调查上述相关性的有效性，并确定生物反应器配置不仅影响当前理解的空间和时间生物氧气需求，而且影响氧气转移效率。基于实时生物反应器可溶性化学需氧量 (sCOD) 的第一个相关性能够预测试点 SBR 中的时间测量空气流速。基于进水 COD 的第二个相关性高估了空气流速，因为它考虑了进水负载率对 α 因子的影响，而忽略了由于生物降解而导致的 α 因子改善。第三个基于 MLSS 浓度的相关性低估了曝气循环开始时的空气流速，因为它忽略了进水加载速率对 α 因子的影响，只考虑了曝气循环期间 MLSS 的微不足道的变化。在生物反应器配置方面，基于模型的分析表明，第一个关联式适用于设计 SBR、活塞流反应器 (PFR)、分步进料 PFR 和完全混合搅拌反应器 (CSTR) 系统，第三个关联式适用于设计用于设计 CSTR 和膜生物反应器 (MBR)，而第二个相关性在任何建模的反应器中都不准确。当以硝化为目标时，与 PFR 相比，CSTR 降低了 44%–49% 的曝气能量，当以硝化和反硝化为目标时，曝气能量降低了 41%–43%。与活塞流反应器相比，当仅以硝化为目标时，分步进料 PFR 将曝气能量降低了 9%。然而，当加入预脱氮时，两个系统都表现出相同的曝气能耗。在 PFR、CSTR、PFR 分步进料和 MBR 系统中，预反硝化减少了曝气池的有机负荷，并将曝气能量分别降低了 22%、11%、15% 和 14%。