摘要
选取反应时间(t)、水温(T)、pH、总有碳(TOC)、特别紫外吸光度(UV254)、溴离子浓度(Br-)及反应的氯剂量(Cl2)等相关的水质参数,构建了三卤甲烷及卤乙酸两大类消毒副产物的多元线性回归、非线性回归及神经网络预测模型。结果表明,消毒副产物的多元线性回归模型能逐步筛选回归因子,得出影响消毒副产物形成的主要因素及影响程度,各消毒副产物的多元线性回归方程的线性非常显著(p≤0.05);消毒副产物的非线性回归模型能分析预测各种对消毒副产物的影响不呈线性关系的因素;各消毒副产物神经网络预测的判定参数均大于0.83,表明采用神经网络预测消毒副产物的形成可以获得较精确的预测值。
The multivariate linear regression, nonlinear regression and neural network prediction models for two kinds of disinfection by-products, i. e. trihalomethane and haloacetic acids were built with the related water quality parameters selected, including reaction time （ t）, temperature （ T）, pH, TOC, UV254, Br- concentration and C12 dosage. The results showed that multivariate linear regression model of disinfection by-product could screen regression factors gradually. The main factors and the effective degree of disinfection by-products formation were obtained. The linear relationship of multivariate linear regression equation of each disinfection by-products was significant （p≤0.05）. The nonlinear regression model of disinfection by-products could analyze and predict the factors of nonlinear relationship on disinfection by-products influence. The determination parameters of neural network prediction for each disinfection by-products were above 0.83. It indicated that the neural network could obtain more precious prediction value on the disinfection by-products formation.
出处
《供水技术》
2012年第5期27-32,共6页
Water Technology
基金
国家自然科学基金资助项目(41171085)
关键词
饮用水
消毒副产物
模型
drinking water
disinfection by-products
model
