硫自养反硝化对含盐水体脱氮及其动力学模型

    Nitrate removal and kinetic model of saline water by sulfur-based autotrophic denitrification

    • 摘要: 以闭合循环养殖系统去除硝酸盐为目的,研究了填料床硫自养反硝化反应器对含盐水体的NO3--N去除效果及动力学特性。结果表明,反应器对NO3--N浓度为22.5~368 mg/L的含盐水体具有良好的反硝化性能。(29±1)℃条件下,进水NO3--N负荷0.052~1.088 kg/(m3·d)为最适进水负荷范围,NO3--N去除率大于95%,出水NO2--N浓度小于1 mg/L。进水NO3--N负荷2.171 kg/(m3·d)时,达到最大NO3--N体积负荷去除率,为1.65 kg/(m3·d)。动力学研究结果表明反应器填料表面生物膜对污染物NO3--N的去除呈半级反应速率关系,反应器单位体积半级动力学常数K1/2v为7.84~ 8.5 mg1/2/(L1/2·h)。建立的动力学模型采用该值的计算结果可以预测出水NO3--N的浓度,预测值与实际值采用统计软件SAS 8.0做方差分析表明,Pr>F值分别为0.9732和 0.8845,模型预测值与实际值无显著性差异。

       

      Abstract: The investigation of nitrate removal in recirculating aquaculture system as a purpose, autotrophic denitrification of nitrate removal and kinetic characteristics for saline synthetic wastewater were carried out in a sulfur packed-bed reactor. The experimental results show that nitrate is effectively removed when the influent NO3--N concentration is in the range of 22.5~368 mg/L. The optimal influent loading rate in order to ensure that the removal efficiency of nitrate was higher than 95% and nitrite accumulation occurred in effluent below 1 mg/L was in the range of 0.052~1.088 kg/(m3·d) when the water temperature was (29±1)℃. The maximum nitrate volumetric removal rate of 1.65 kg/(m3·d) was achieved at an influent loading rate of 2.171 kg/(m3·d) with obvious nitrite accumulation in effluent reaching 28.69 mg/L. The kinetic experimental results show that the NO3--N removal rate in the reactor can be described by a half-order kinetic model for biofilms. It has been found that the half-order reaction rate constant per unit reactor volume K1/2v is 7.84~8.5 mg1/2/(L1/2·h) and it can be successfully applied in the kinetic model for the prediction of effluent NO3--N concentration. The two groups’ predictive values and actual values were analyzed by using SAS 8.0 software for ANOVA analysis. The ANOVA analysis indicates that the corresponding Pr>F values are 0.9732 and 0.8845, which proved that no significant differences exist between the predictive values and actual values of the model.

       

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