不同进水方式潮汐流人工湿地污染物去除研究
发布时间：2019-01-08 17:54
【摘要】：目前,水污染问题日益严峻,自然湿地自身净化能力有限,人工湿地具有处理效果好、费用低的优势,逐渐成为研究的热点,但是其复氧能力较低,并且受多种因素影响,存在一定的缺陷,因此需要寻求强化人工湿地的技术方法。潮汐流人工湿地(Tidal Flow Constructed Wetland,TF-CW)是一种新型人工湿地生态系统,并且在污染物去除方面受到了广泛的关注。本文为研究TF-CW中污染物去除效果及影响因素,建立潮汐流模拟装置,通过对比不同进水方式下(连续流湿地(A),设闲置/反应时间分别为1:1(B),1:2(C),2:1(D))的模拟装置对污染物的去除效果以及沿程变化规律,并用冗余分析(RDA)筛选出影响其去除效果的主要因子,将各个影响因子输入人工神经网络模型进行各个污染指标出水浓度的训练与验证。得出如下结论:1.A、B、C、D四种进水方式对TN的平均去除率分别为82.41%±4.84%、84.82%±5.09%、86.09%±3.99%、90.23%±3.05%。四种进水方式差异显著(P0.05);A进水方式NH4+-N去除效果与B、C、D差异显著(P0.05),其中D进水方式NH4+-N的去除效果最好,但A对NO3--N的总体去除效果较优;四种进水方式对TP的去除率差异性均不显著(P0.05);闲置/反应时间并不影响TOC(总有机碳)的去除率。四种进水方式下,NH4+-N去除率均在0~15cm深度内最大,随深度增加,去除率下降;NO3--N浓度在0~15cm深度内迅速上升;随处理深度增加,TP浓度逐步降低。TOC浓度处于0~20mg/L间的较低水平,并随深度增加而下降。2.四种进水方式下TF-CW的平均硝化强度差异显著(P0.05),其中A与其他三种潮汐进水方式均差异显著,而D是基质平均硝化强度最大的进水方式;四种模拟装置的基质平均反硝化强度差异性也显著(P0.05),A进水方式反硝化强度最大。TF-CW基质硝化强度与NH4+-N的去除率之间存在明显的正相关性(R2=0.8497,P0.05);反硝化强度与NO3--N的出水浓度呈呈明显负相关关系(R2=0.8448,P0.05)。装置上部0~30cm的处理深度硝化强度最大,反硝化强度则在中部的30~60cm阶段较高。3.RDA分析结果显示TN的去除率的主要影响因子有DO(溶解氧)、RAT(淹没排空比)、ORP(氧化还原电位)、TOC,NH4+-N的主要影响因子有DO、RAT、ORP、Depth(处理深度),NO3--N的主要影响因子有Cond(电导率)、Temp(水温)、Sal(盐度)、p H,TP的主要影响因子有DO、RAT、Time(时间)、Depth。因此在用BP神经网络对TF-CW水体污染物出水浓度进行模拟时选择各指标的主要影响因子作为输入层,污染物指标出水浓度作为输出层,经试错法可得TN、NH4+-N、NO3--N和TP选择的隐含层节点数分别为9,11,12,9。对数据组进行训练的结果显示,BP人工神经网络模型可以有效地预测污染物的出水浓度,模型预测值与实际值存在一定的相关性,也存在较小范围的误差。BP神经网络对各指标的拟合能力TP出水浓度最好,总体R2可达0.90076。对TN、NH4+-N和NO3--N的拟合系数分别为0.67086、0.72854和0.69293。
[Abstract]:At present, the problem of water pollution is becoming more and more serious, the natural wetland's own purification ability is limited, and the artificial wetland has the advantages of good treatment effect and low cost, so it has gradually become the hot spot of research, but its reoxygenation ability is low, and it is influenced by many factors. There are some defects, so it is necessary to seek the technical method to strengthen the constructed wetland. Tidal flow constructed wetland (Tidal Flow Constructed Wetland,TF-CW) is a new constructed wetland ecosystem. In order to study the removal efficiency and influencing factors of pollutants in TF-CW, a tidal flow simulation device was established, and the idle / reaction time of 1:1 (B), was set up by comparing different influent modes (A), of continuous flow wetland). At 1:2 (C), 2:1 (D) simulator, the pollutant removal efficiency and its variation along the path were obtained. The main factors affecting the removal efficiency were screened by redundancy analysis (RDA). The influence factors were inputted into the artificial neural network model to train and verify the effluent concentration of each pollution index. The results are as follows: 1. The average removal rate of TN in the four influent modes is 82.41% 卤4.84% 卤84.82% 卤5.092.92% 卤3.990.23% 卤3.05, respectively. There were significant differences among the four influent modes (P0.05). The removal efficiency of NH4 N in A influent mode was significantly different from that in NH4 D (P0.05). The removal efficiency of NH4 N in D influent mode was the best, but the overall removal efficiency of NO3--N was better in A; There was no significant difference in the removal efficiency of TP among the four influent methods (P0.05), while idle / reaction time had no effect on the removal rate of TOC (total organic carbon). Under four influent conditions, the removal rate of NH4-N was the highest in the depth of 0~15cm, and the removal rate decreased with the increase of the depth, and the concentration of NO3--N increased rapidly in the depth of 0~15cm. With the increase of treatment depth, the concentration of TP gradually decreased, while the concentration of TOC was lower than that of 0~20mg/L, and decreased with the increase of depth. The average nitrification intensity of TF-CW under four influent modes was significantly different (P0.05), among which A was significantly different from the other three tidal influent modes, while D was the influent mode with the highest average nitrification intensity of substrate. The difference of the average denitrification intensity of the four simulators was also significant (P0.05) the denitrification intensity of), A was the largest. There was a significant positive correlation between the nitrification intensity of TF-CW substrate and the removal rate of NH4-N (R2 + 0.8497). P0.05); There was a negative correlation between denitrification intensity and effluent concentration of NO3--N (P 0.05). The treatment depth nitrification intensity of 0~30cm in the upper part of the unit was the highest, while the denitrification intensity was higher in the 30~60cm stage in the middle part of the unit. The results of 3.RDA analysis showed that the main influencing factor of TN removal efficiency was DO (dissolved oxygen), RAT (inundated emptying ratio). ORP (redox potential), DO,RAT,ORP,Depth (treatment depth) and Cond (), Temp (water temperature,), Sal (salinity), p H,) are the main influencing factors of TOC,NH4 N and NO3--N. The main influencing factor of TP is DO,RAT,Time (time), Depth.) Therefore, when BP neural network is used to simulate the effluent concentration of TF-CW pollutants, the main influencing factors of each index are selected as the input layer, the effluent concentration of the pollutants is taken as the output layer, and the TN,NH4 N can be obtained by trial and error method. The number of hidden layer nodes selected by NO3--N and TP is 9 / 11 / 12 / 9 respectively. The results of training the data group show that the BP artificial neural network model can effectively predict the effluent concentration of pollutants, and the predicted value of the model has a certain correlation with the actual value. The BP neural network has the best fit ability for each index, and the overall R2 is 0.90076. The fitting coefficients for TN,NH4 N and NO3--N were 0.67086, 0.72854 and 0.69293, respectively.
【学位授予单位】：中国林业科学研究院
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：X703
[Abstract]:At present, the problem of water pollution is becoming more and more serious, the natural wetland's own purification ability is limited, and the artificial wetland has the advantages of good treatment effect and low cost, so it has gradually become the hot spot of research, but its reoxygenation ability is low, and it is influenced by many factors. There are some defects, so it is necessary to seek the technical method to strengthen the constructed wetland. Tidal flow constructed wetland (Tidal Flow Constructed Wetland,TF-CW) is a new constructed wetland ecosystem. In order to study the removal efficiency and influencing factors of pollutants in TF-CW, a tidal flow simulation device was established, and the idle / reaction time of 1:1 (B), was set up by comparing different influent modes (A), of continuous flow wetland). At 1:2 (C), 2:1 (D) simulator, the pollutant removal efficiency and its variation along the path were obtained. The main factors affecting the removal efficiency were screened by redundancy analysis (RDA). The influence factors were inputted into the artificial neural network model to train and verify the effluent concentration of each pollution index. The results are as follows: 1. The average removal rate of TN in the four influent modes is 82.41% 卤4.84% 卤84.82% 卤5.092.92% 卤3.990.23% 卤3.05, respectively. There were significant differences among the four influent modes (P0.05). The removal efficiency of NH4 N in A influent mode was significantly different from that in NH4 D (P0.05). The removal efficiency of NH4 N in D influent mode was the best, but the overall removal efficiency of NO3--N was better in A; There was no significant difference in the removal efficiency of TP among the four influent methods (P0.05), while idle / reaction time had no effect on the removal rate of TOC (total organic carbon). Under four influent conditions, the removal rate of NH4-N was the highest in the depth of 0~15cm, and the removal rate decreased with the increase of the depth, and the concentration of NO3--N increased rapidly in the depth of 0~15cm. With the increase of treatment depth, the concentration of TP gradually decreased, while the concentration of TOC was lower than that of 0~20mg/L, and decreased with the increase of depth. The average nitrification intensity of TF-CW under four influent modes was significantly different (P0.05), among which A was significantly different from the other three tidal influent modes, while D was the influent mode with the highest average nitrification intensity of substrate. The difference of the average denitrification intensity of the four simulators was also significant (P0.05) the denitrification intensity of), A was the largest. There was a significant positive correlation between the nitrification intensity of TF-CW substrate and the removal rate of NH4-N (R2 + 0.8497). P0.05); There was a negative correlation between denitrification intensity and effluent concentration of NO3--N (P 0.05). The treatment depth nitrification intensity of 0~30cm in the upper part of the unit was the highest, while the denitrification intensity was higher in the 30~60cm stage in the middle part of the unit. The results of 3.RDA analysis showed that the main influencing factor of TN removal efficiency was DO (dissolved oxygen), RAT (inundated emptying ratio). ORP (redox potential), DO,RAT,ORP,Depth (treatment depth) and Cond (), Temp (water temperature,), Sal (salinity), p H,) are the main influencing factors of TOC,NH4 N and NO3--N. The main influencing factor of TP is DO,RAT,Time (time), Depth.) Therefore, when BP neural network is used to simulate the effluent concentration of TF-CW pollutants, the main influencing factors of each index are selected as the input layer, the effluent concentration of the pollutants is taken as the output layer, and the TN,NH4 N can be obtained by trial and error method. The number of hidden layer nodes selected by NO3--N and TP is 9 / 11 / 12 / 9 respectively. The results of training the data group show that the BP artificial neural network model can effectively predict the effluent concentration of pollutants, and the predicted value of the model has a certain correlation with the actual value. The BP neural network has the best fit ability for each index, and the overall R2 is 0.90076. The fitting coefficients for TN,NH4 N and NO3--N were 0.67086, 0.72854 and 0.69293, respectively.
【学位授予单位】：中国林业科学研究院
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：X703
【相似文献】
相关期刊论文 前7条
1 关宗山,安洪儒;棒(线)冷却装置的水力学研究[J];冶金设备;1981年05期
2 周健;窦艳艳;何强;栗静静;陈W，
本文编号：2404937
本文链接：https://www.wllwen.com/kejilunwen/huanjinggongchenglunwen/2404937.html
最近更新
教材专著
