回归模型和人工神经网络模型用于预测在含的真水中在UV/单胺消毒过程中从氨基酸中形成尼甲
Bohui Xu1, Xiaoliang Chen2, Changbo Zhang3
1Yellow River Engineering Consulting Co., Ltd., Zhengzhou, 450003, China; Department of Municipal Engineering, Southeast University, Nanjing, 211189, China.
酸甲 (HNMs),有毒的消毒副产品,可以通过优化紫外线/单胺消毒时间来控制. 先进的模型准确预测HNM度和相关的毒性,有助于水安全管理.
科学领域:
- 环境化学环境化学
- 水处理技术水处理技术
- 毒理学 毒理学 毒理学
背景情况:
- 氨基甲 (HNM) 是由氨基酸在富含水中的UV/单胺 (UV/NH2Cl) 处理过程中形成的性消毒副产品.
- 随着消毒时间的延长,HNM度下降,这表明时间优化作为控制策略.
- HNM的毒性和基因毒性与它们的度密切相关,使得它们的控制对水安全至关重要.
研究的目的:
- 开发Halonitromethanes (HNMs) 度和相关毒性的预测模型.
- 评估紫外线/单胺消毒参数和水质对HNM形成的有效性.
- 优化人工神经网络 (ANN) 模型,用于准确的HNM预测和毒性评估.
主要方法:
- 利用特定的紫外线吸收 (SUVA254) 来表征芳香碳,并为HNM度开发了一个单一的预测方程.
- 采用回归模型和人工神经网络 (ANN) 模型,包括反向传播 (BP) 和辐射基函数 (RBF),用于HNM预测.
- 应用主要组件分析 (PCA) 用于数据预处理和遗传算法 (GA) 用于优化ANN模型结构,特别是GA-RBFANN模型.
主要成果:
- 一个方程成功地预测了不同氨基酸和水条件的HNM度,当含有酸盐时,准确度提高.
- 与回归和BP ANN模型相比,GA-RBF ANN模型表现出优异的预测性能 (R2 = 0.90),结构优化和复杂性降低.
- 与HNM相关的细胞毒性和基因毒性与HNM度呈现出强烈的正线性相关性 (R2>0.9),证实度控制是有效的毒性管理.
结论:
- 优化消毒时间是控制UV/NH2Cl处理期间有毒HNMs的有效策略.
- 先进的计算模型,特别是GA优化的RBF ANN,提供HNM形成和毒性的准确预测.
- 通过优化消毒策略控制HNM度对于减轻饮用水中细胞毒性和基因毒性至关重要.
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