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[Research on Denitrification Performance of Enhanced Secondary Effluent by Embedded Denitrification Filler and Pilot Application]. | LitMetric

AI Article Synopsis

Article Abstract

The use of an embedded broad-spectrum high-efficiency denitrification filler to treat secondary effluent from municipal wastewater treatment plants can effectively reduce total nitrogen (TN) concentration of the effluent. This study consists of two parts. The D1 stage studies the adaptability of the secondary effluent based on the embedded denitrification, removal effect of total nitrogen, stable working conditions, and backwashing conditions; In the D2 stage, the change in the nitrogen removal performance of the filler under the condition of a year-long stable operation was studied. The variation in the microbial population before and after the operation of the embedded packing was studied by high-throughput sequencing and real-time quantitative PCR detecting system real-time (qPCR). In this research, the embedded denitrification filler had a water temperature of (24±1)℃, pH:7.1, hydraulic retention time (HRT):1 h, and filling rate:10%. Sodium acetate was added to ensure stable operation for seven days. Under adequate carbon source conditions, the filler can adapt to the quality of secondary effluent water and achieve effluent TN<5mg·L. By comparing and studying the effect of different HRT on the removal of filler TN, it is concluded that HRT is 30 min and the filling rate is 10%. After a year of stable operation under 7.2 m·dinfluent conditions, the TN removal rate can reach 90.42%, and the total nitrogen in the effluent can be stabilized below 5 mg·L. In comparison with the backwashing effect, the backwashing strength was 5.2 L·(m·s), and the cycle is three days long. High-throughput sequencing and real-time quantitative PCR analysis results show that the abundance and copy number of denitrifying functional genus in the filler before and after the operation exhibited significant changes, which indicated that the bacteria could achieve good self-growth under embedding conditions.

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http://dx.doi.org/10.13227/j.hjkx.201908053DOI Listing

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