通过测量分子臭氧(O3)和羟基自由基(•OH)的暴露量,可以准确地计算臭氧化对微污染物的减排量(即,取决于[O3]dt和取决于[•OH]dt)。在实际的臭氧化过程中,取决于[O3]dt值可以通过监测过程中的O3衰变来计算。然而,在该领域中,计算281[•OH]dt具有挑战性,这就需要开发模型来从可测量的参数中预测式[·OH]dt。本研究展示了机器学习模型的发展,可以从五个基本输入变量(pH,溶解的有机碳浓度,碱度,温度,和O3剂量)和两个可选的(取决于[O3]dt和瞬时臭氧需求,IOD)。为了开发模型,四种不同的机器学习方法(随机森林,支持向量回归,人工神经网络,和高斯过程回归)使用在130种不同的天然水样品中测量(或确定)的输入和输出变量。结果表明,将281[O3]dt作为输入变量显着提高了预测模型的准确性,总体R2增加0.01-0.09,具体取决于机器学习方法。这表明取决于[O3]dt作为反映溶解有机物的•OH产生特性的关键变量起着至关重要的作用。相反,IOD对预测模型的准确性影响最小。一般来说,基于机器学习的预测模型优于基于作为对照开发的响应面方法的预测模型。值得注意的是,利用高斯过程回归算法的模型在预测模型中显示出最高的确定系数(总体R2=0.91-0.95)。
The abatement of micropollutants by
ozonation can be accurately calculated by measuring the exposures of molecular ozone (O3) and hydroxyl radical (•OH) (i.e., ∫[O3]dt and ∫[•OH]dt). In the actual
ozonation process, ∫[O3]dt values can be calculated by monitoring the O3 decay during the process. However, calculating ∫[•OH]dt is challenging in the field, which necessitates developing models to predict ∫[•OH]dt from measurable parameters. This study demonstrates the development of machine learning models to predict ∫[•OH]dt (the output variable) from five basic input variables (pH, dissolved organic carbon concentration, alkalinity, temperature, and O3 dose) and two optional ones (∫[O3]dt and instantaneous ozone demand, IOD). To develop the models, four different machine learning methods (random forest, support vector regression, artificial neural network, and Gaussian process regression) were employed using the input and output variables measured (or determined) in 130 different natural water samples. The results indicated that incorporating ∫[O3]dt as an input variable significantly improved the accuracy of prediction models, increasing overall R2 by 0.01-0.09, depending on the machine learning method. This suggests that ∫[O3]dt plays a crucial role as a key variable reflecting the •OH-yielding characteristics of dissolved organic matter. Conversely, IOD had a minimal impact on the accuracy of the prediction models. Generally, machine-learning-based prediction models outperformed those based on the response surface methodology developed as a control. Notably, models utilizing the Gaussian process regression algorithm demonstrated the highest coefficients of determination (overall R2 = 0.91-0.95) among the prediction models.