| 逆流湍动床短程硝化反应器的运行性能及基质抑制动力学模型 |
| 摘要点击 3656 全文点击 2886 投稿时间：2010-01-20 修订日期：2010-07-12 |
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| 中文关键词 短程硝化 逆流湍动床 工艺性能 基质抑制 模型 生物脱氮 |
| 英文关键词 nitritation inverse turbulent bed (ITB) process performance substrate inhibition model biological nitrogen removal |
| DOI 10.13227/j.hjkx.20110134 |
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| 中文摘要 |
| 采用模拟含氨废水和逆流湍动床(inverse turbulent bed，ITB)反应器研究了短程硝化工艺的运行性能及基质抑制动力学模型. 结果表明，采用“预挂膜”和“快速排泥”的联合挂膜方法，以及“低浓度，高负荷”的启动策略，可在20 d将ITB短程硝化反应器NH+4-N负荷升至0.59 kg·(m3·d)-1，实现快速启动. 在进水NH+4-N浓度为700 mg·L-1，水力停留时间(HRT)为3 h，NH+4-N负荷为5.60 kg·(m3·d)-1时，该反应器NH+4-N去除速率最高可达4.25 kg·(m3·d)-1. NO-2-N最高生产率可达3.70 kg·(m3·d)-1. 在Haldane、 Edwards、 Aiba、 Luong模型中，Haldane和Aiba模型更适用于对短程硝化的基质抑制动力学行为的数学模拟. 当进水NH+4-N浓度为630 mg·L-1，HRT为8 h，NH+4-N负荷为1.89 kg·(m3·d)-1时，在Haldane模型中，最大氨氧化速率rmax为1.84 kg·(m3·d)-1，抑制动力学常数KIH为97.4 mg·L-1，半饱和常数Km为0.188 mg·L-1，取得最大氨氧化速率时的游离氨浓度为4.28 mg·L-1；在Aiba模型中，测得rmax为1.83 kg·(m3·d)-1，抑制动力学常数KIA为114 mg·L-1. |
| 英文摘要 |
| The performance of a nitritation inverse turbulent bed (ITB) reactor was tested and the substrate inhibition kinetics characteristics of the reactor were analyzed. The results showed that a rapid reactor startup could be realized within 20 d with a strategy that combined the biofilm attachment method named “precoating carrier treatment” and “rapid suspending sludge discharge”, with the feeding strategy named “low strength, high load”. When operated at a hydraulic retention time of 3 h and influent NH+4-N of 700 mg·L-1, corresponding to a nitrogen loading rate of 5.60 kg·(m3·d)-1, a maximum NH+4-N removal rate of 4.25 kg·(m3·d)-1 was observed. The maximum NO-2-N production rate was as high as 3.70 kg·(m3·d)-1. Four inhibition kinetic models (Haldane, Edwards, Aiba and Luong) were analyzed through non-linear regression to represent the inhibitions caused by substrate of nitritation process and the parameters of models were gained, which were rmax of 1.84 kg·(m3·d)-1, KIH of 97.4 mg·L-1 and Km of 0.188 mg·L-1 for Haldane model, and rmax of 1.83 kg·(m3·d)-1 and KIA of 114 mg·L-1for Aiba model. It was proposed that Haldane and Aiba models well fitted the process data harvested in the ITB reactor. |
