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.

中文翻译：

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机器学习方法

来自水处理厂（WTP）的饮用水的光谱指纹图谱的特征在于许多吸光物质，包括有机物，硝酸盐，消毒剂以及颗粒或浊度。通过将其光谱与组合光谱分离，可以更好地实现对消毒剂（一氯胺）的检测。本文的两个主要重点是（i）从组合光谱中分离一氯胺光谱，以及（ii）评估机器学习算法在实时检测一氯胺中的应用。支持向量回归（SVR）模型是使用多波长紫外可见（UV-Vis）吸收光谱和在线安培一氯胺残留测量数据开发的。通过使用四个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，并且通过使用颗粒补偿光谱可以提高建模精度；（ii）通过补偿天然有机物（NOM）和硝酸盐（NO₃）和（iii）内核功能的选择极大地影响了SVR性能，尤其是径向基函数（RBF）似乎是性能最高的内核功能。该研究的结果表明，可以使用SVR算法实时测量消毒剂残留（一氯胺），精度为±0.1 mg L ^-1。