Extensive studies on improving anammox performance have taken place for decades with particular focuses on its operational and environmental factors, but such parameter-based optimization is difficult, because of the sheer number of possible combinations and multidimensional arrays of these factors. Utilizing machine-learning algorithm and published anammox data, Bayesian nonparametric general regression (BNGR) was applied to identify the possible governing variable(s) from among 11 operating and environmental parameters: reactor type, mixing type, working volume, hydraulic retention time, temperature, influent pH, nitrite, ammonium, nitrate concentration, nitrogen loading rate, and organic concentration. The results showed that working volume is a key but oft-overlooked governing parameter. By integrating the BNGR findings with computational fluid dynamics simulation, which assessed mixing properties, it became feasible to conclude that working volume and mixing intensity co-regulated flow fields in reactors and had a significant influence on anammox performance. Furthermore, this study experimentally validated how mixing intensity affected performance, and specific input power (x), a parameter that conjugates both working volume and mixing intensity, was used to establish the relationship with ammonium removal rate (NH4+-N RR, y) y = 49.90x+1.97 (R2 = 0.94). With specific input power increased from 3.4 × 10−4 to 2.6 × 10−2 kW m−3, the ammonium removal rate exhibited a rise from 1.8 to 3.2 mg L−1h−1. Following, a relationship among input power-working volume-nitrogen removal rate was also established with a view to determining the design variables for anammox reactor. Consequently, the study highlighted the necessity to consider the working volume-mixing intensity correlation when optimizing the anammox process.

中文翻译：

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机器学习揭示了厌氧氨氧化优化中被忽视的工作体积和混合强度的结合


几十年来，人们一直在进行关于提高厌氧氨氧化性能的广泛研究，特别关注其操作和环境因素，但这种基于参数的优化是困难的，因为这些因素的可能组合和多维数组的数量庞大。利用机器学习算法和已发布的厌氧氨氧化数据，应用贝叶斯非参数一般回归 （BNGR） 从 11 个操作和环境参数中确定可能的控制变量：反应器类型、混合类型、工作体积、水力停留时间、温度、进水 pH 值、亚硝酸盐、铵、硝酸盐浓度、氮负载速率和有机物浓度。结果表明，工作体积是一个关键但经常被忽视的控制参数。通过将 BNGR 研究结果与评估混合特性的计算流体动力学模拟相结合，可以得出结论，工作体积和混合强度共同调节反应器中的流场，并对厌氧氨氧化性能产生重大影响。此外，本研究通过实验验证了混合强度如何影响性能，并使用比输入功率 （x） （一个共轭工作体积和混合强度的参数）来建立与铵去除率 （NH4+-N RR， y） y = 49.90x+1.97 （R2 = 0.94） 的关系。随着比输入功率从 3.4 × 10-4 增加到 2.6 × 10-2 kW m-3，铵态氮去除率从 1.8 mg L-1h-1 上升到 3.2 mg L-1h-1。接下来，还建立了输入功率-工作容积-氮去除率之间的关系，以确定厌氧氨氧化反应器的设计变量。 因此，该研究强调了在优化厌氧氨氧化工艺时考虑工作体积-混合强度相关性的必要性。