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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach
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Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

机译：分光光度法在线检测饮用水消毒剂：机器学习方法

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摘要

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.
机译：来自水处理厂（WTP）的饮用水的光谱指纹，其特征在于许多光吸收物质，包括有机，硝酸盐，消毒剂和颗粒或浊度。通过将光谱与组合光谱分离，可以更好地实现消毒剂（单氯胺）的检测。在本文中，两个主要焦点是（i）从组合光谱和（ii）的分离从组合的光谱和（ii）评估机器学习算法在单氯胺的实时检测中的应用。支持向量回归（SVR）模型是使用多波长紫外 - 可见（UV-VI）吸光光谱和在线排水量单氯胺残余测量数据开发的。通过使用四种不同的内核函数来评估SVR模型的性能。结果表明，（I）水中的颗粒或浊度对UV-Vis光谱测量有显着影响，并通过使用粒子补偿光谱来实现改善的建模精度; （ii）通过补偿天然有机物质的光谱（NOM）和硝酸盐（NO3）和（iii）核函数的选择大大影响了SVR性能，特别是径向基函数（RBF）似乎是最高执行内核功能。该研究的结果表明，消毒剂残留（单氯胺）可以使用SVR算法实时测量，精密水平为±0.1mg L-1。

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