机器学习在紫外法测定硝酸盐氮浓度中的应用

Application of Machine Learning in Determination of Nitrate Nitrogen Based on Ultraviolet Spectrophotometry

紫外分光光度法(UV法)由于较传统化学方法具有效率高操作简便、无二次污染且可现场原位测试等优点,近些年来被广泛应用到水质参数的测试中。硝酸盐氮是工业废水中的主要污染物之一。基于UV法测量水体中硝酸盐氮浓度的标准方法是分别测量水样在220nm和275nm处的吸光度,然后用275nm处的吸光度对220nm处的吸光度进行校正,进而绘制出校正后的吸光度与硝酸盐氮浓度的标准曲线。然而,当硝酸盐氮浓度升高时,标准法所采用的朗伯比尔定律的线性关系以及不同物质吸光度叠加的线性不能很好地满足,在实际的实验测试中也发现,很难建立硝酸盐氮在220nm处的吸收模型。为了克服单波长或双波长方法的缺陷,将硝酸盐氮吸收峰范围的各个波长的吸光特性引入到模型的建立之中。同时,为了降低模型的复杂度,在建立模型之前先对吸光度数据进行主成分分析,将输入数据的维度数从107压缩到4,然后对压缩后的数据使用局部加权线性回归法建模,该吸收模型对于训练样本和测试样本都有较好的预测结果,且能够适应高浓度时吸光度与浓度的非线性关系,测量上限可达几百mg·L^(-1)。另外,此方法的原理和流程也适用于其他水质参数吸收模型的建立。

Ultraviolet spectrophotometry has been widely applied in determination of water quality parameters because of its advantagous properties compared to chemical method, such as high efficiency, easy operation and non-secondary pollutions. Nitrate nitrogen is one of maior pollutants in waste water. The standard ultraviolet spectrophotometry used to determinate the concen- tration of nitrate nitrogen in water is firstly to determinate the absorbances at wavelength 220 and 275 nm, which is used to cali- brate the former, followed by the plotting of standard curve. While the linear equation described by Lambert-Beer＇s law and the linearity in the superposition of absorbances of various substance, on which the standard ultraviolet spectrophotometry based, are not fitted well anymore with the increase of concentration. In addition, it was found to be difficult to construct absorption model of nitrate solution at wavelength 220 nm in experiment. To overcome the disadvantages in single-wavelength or double wavalengths spectrophotometry, the absorbances at the wavelength that covered by the absorption peak are introduced into the construction of the model and to avoid the increase of the model complexification resulted by the introduction of more wave- lengths, we run the principal components analysis on the original absorbances data. The data with dimensions compressed from 107 down to 4 after process construct the absorbance model using locally weighted linear regression. Good performance were achieved in both training samples set and test samples set using this model and it was able to deal with the non-linear relation between the absorbance and concentration thus raised the upper range limit concentrations of nitrate nitrogen up to hundreds mg· L 1 from 4 mg · L-1 defined in the standard method. Meanwhile the principle and procedure of this analytical method are suit able for the absorhance model construction of other solutions.