Algal-bacterial shortcut nitrogen removal model with seasonal light variations

Sahand Iman Shayan, Steve Youssef, P. van der Steen, Qiong Zhang, S. Ergas
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Abstract

The algal–bacterial shortcut nitrogen removal (ABSNR) process can be used to treat high ammonia strength wastewaters without external aeration. However, prior algal–bacterial SNR studies have been conducted under fixed light/dark periods that were not representative of natural light conditions. In this study, laboratory-scale photo-sequencing batch reactors (PSBRs) were used to treat anaerobic digester sidestream under varying light intensities that mimicked summer and winter conditions in Tampa, FL (USA). A dynamic mathematical model was developed for the ABSNR process, which was calibrated and validated using data sets from the laboratory PSBRs. The model elucidated the dynamics of algal and bacterial biomass growth under natural illumination conditions as well as transformation processes for nitrogen species, oxygen, organic and inorganic carbon. A full-scale PSBR with a 1.2 m depth, a 6-day hydraulic retention time (HRT) and a 10-day solids retention time (SRT) was simulated for treatment of anaerobic digester sidestream. The full-scale PSBR could achieve >90% ammonia removal, significantly reducing the nitrogen load to the mainstream wastewater treatment plant. The dynamic simulation showed that ABSNR process can help wastewater treatment facilities meet stringent nitrogen removal standards with low energy inputs.
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具有季节性光照变化的藻类-细菌捷径脱氮模型
藻类-细菌短程脱氮(ABSNR)工艺可用于处理高氨氮废水,而无需外部曝气。然而,之前的藻类-细菌短程脱氮研究都是在固定的光照/黑暗时间段内进行的,并不能代表自然光照条件。在本研究中,实验室规模的光序批式反应器(PSBRs)被用来处理厌氧消化池的侧流,光照强度的变化模拟了美国佛罗里达州坦帕市夏季和冬季的光照条件。针对 ABSNR 过程开发了一个动态数学模型,并利用实验室 PSBR 的数据集对该模型进行了校准和验证。该模型阐明了自然光照条件下藻类和细菌生物量的动态生长过程,以及氮物种、氧气、有机碳和无机碳的转化过程。模拟了一个深度为 1.2 米、水力停留时间(HRT)为 6 天、固体停留时间(SRT)为 10 天的全规模 PSBR,用于处理厌氧消化池的侧流。全规模 PSBR 的氨氮去除率大于 90%,大大降低了主流污水处理厂的氮负荷。动态模拟结果表明,ABSNR 工艺可以帮助污水处理设施以较低的能源投入达到严格的脱氮标准。
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