微动力曝气技术应用于黑臭水体治理的参数优化研究

为了得到微动力曝气技术在黑臭水体治理过程中优化后的条件参数及预测模型,以曝气量、曝气时长和曝气位置为自变量,脱氮效果(氨氮消除时长和总氮削减率)为响应变量,根据Design-expert设置了17组试验。在试验过程中考虑内源污染释放等因素,然后以试验数据为基础,结合响应面模型分析,研究得出优化后的条件参数和预测模型。研究结果表明:两个响应面模型调整后的拟合度分别为0.99和0.96,预测模型的拟合度为0.95和0.69,具有良好的拟合度;优化后的条件参数曝气量为1 L/min、曝气时长12 h/d和曝气位置位于上覆水中部;此试验组下的氨氮消除时长和总氮削减率分别为5.50 d±0.00 d和51.84%±1.14%,与预测模型的预测值相比较,两者标准差分别为0.00%和0.98%。最终得出结论:优化条件参数后的微动力曝气技术应用于黑臭水体治理可以取得较好脱氮效果,模型可以较准确地预测水体修复效果。

Study on parameter optimization for application of micro-dynamic aeration technology to treatment of black-odor water

In order to obtain the optimized condition parameters and prediction models for the micro-dynamic aeration technology in the process of black-odor water treatment，17 groups of tests are set in accordance with Design-expert by taking aeration amount，aeration duration and aeration position as the independent variables and nitrogen removal effect ( ammonium removal duration and total nitrogen removal rate) as the response variables. During the test，the factors of endogenous pollution releasing， etc. are considered，and then the optimized condition parameters and the prediction models are obtained from the study in combination with the relevant response surface model based on the test data. The study result shows that the adjusted fitting degrees of two response surface models are 0. 99 and 0. 96，while the fitting degrees of the prediction models are 0. 95 and 0. 69，of which all have better fitting degrees. The optimized condition parameter aeration amount is 1 L/min with the aeration duration of 12 h /d and the aeration position is situated at the mid-part of the overlying water. The ammonium removal duration and total nitrogen removal rate of this group of tests are 5. 50 d ± 0. 00 d and 51. 84% ± 1. 14% respectively; if compared with the predicting values from the prediction model，the standard deviations between both of them are 0. 00% and 0. 98% respectively. It is finally concluded that better nitrogen removal effect can be obtained from applying the micro-dynamic aeration technology after optimizing the condition parameters to black-odor water treatment; for which the models can more accurately predict the restoration effect of the water body concerned.