摘要
为了改善消毒副产物(DBPs)预测模型的预测力和广谱性,提出了一种根据DBPs浓度与各运行指标参数的关系,结合DBPFP测定值预测配水管网中DBPs浓度的方法,并确定DBPs浓度与消毒时间之间的定量关系。根据试验现象和他人的研究成果提出了一系列假设,并据此建立了基于反应动力学的DBPs生成消耗模型;然后以实测数据进行回归分析,确定模型中的待定参数;最后以DBPs生成消耗模型建立DBPs预测模型,并以检测数据对其进行检验和改善。结果表明,回归分析DBPs生成消耗模型预测待定参数时稳定性均较好,且公共参数预测结果比较接近;除CHCl_3和DCAA以外其他DBPs生成消耗模型回归分析的确定性系数(R^2)均大于0.90;使用DBPs预测模型预测配水管网中DBPs浓度时存在按固定比例缩小的情况,引入修正量k进行修正后其预测能力较好。
To improve the predictive ability and broad-spectrum suitability of DBPs prediction models, a method to predict DBPs concentrations in water distribution network was put forward according to the relationship between DBPs concentrations and operation parameters, combined with DBPFP detec- tion value. The main content of the research was to determine the quantitative relationship between DBPs concentrations and disinfection time. A series of hypothesis were raised according to experimental phe- nomena and others' research findings, by which the DBPs formation-consumption model based on reac- tion kinetic was built. Then regression analysis was conducted with the actual monitoring data to deter- mine the unknown parameters in the model. Lastly, the DBPs prediction model was built based on the DBPs formation-consumption model, and was tested and revised with the actual monitoring data. The re- suits showed that it was very stable to predict the unknown parameters by regression analysis of DBPs formarion-consumption models, and the public parameters obtained were close to each other. The certainty coefficients of DBPs formation-consumption models by regression analysis except CHC13 and DCAA were higher than 0.90. Since the concentrations of DBPs in water distribution network predicted by the DBPs prediction model were reduced to a fixed proportion compared with their actual values, a correction pa- rameter k was used to optimize the model. The optimized model could predict DBPs concentrations well.
出处
《中国给水排水》
CAS
CSCD
北大核心
2017年第15期42-46,51,共6页
China Water & Wastewater
基金
国家水体污染控制与治理科技重大专项(2014ZX07405002)
关键词
消毒副产物
预测模型
生成消耗模型
消毒副产物生成潜能
disinfection byproduct
prediction model
formation-consumption model
disin-fection formation potential