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一种基于可变辐射基函数神经网络的带度补偿的COD测量方法.

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  • 1Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fujian, 350005, China. senlin16888@fjirsm.ac.cn.

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概括

这项研究引入了一种新的方法,用于测量废水中的化学氧气需求 (COD),使用紫外线可见光谱法. 该技术有效地弥补了度,提高了COD分析的准确性和速度.

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科学领域:

  • 环境科学 环境科学
  • 分析化学 分析化学
  • 频谱学是一种光谱学.

背景情况:

  • 紫外可见 (UV-vis) 光谱法通常用于测量污水中的化学氧气需求 (COD).
  • 现有的UV-vis方法难以消除度的影响,影响测量准确度.

研究的目的:

  • 开发一种新的,准确和高效的方法来使用紫外线光谱测量废水COD.
  • 为了应对光谱COD分析中度干扰的挑战.

主要方法:

  • 一个新的度补偿算法,利用主要成分分析 (PCA) 来识别特征波长.
  • 开发一种度补偿模型,以消除度带来的光谱干扰.
  • 在COD度测量中应用可变辐射基函数 (VRBF) 神经网络.

主要成果:

  • 与传统的部分最小平方回归相比,拟议的方法显著提高了确定系数 (R2) 从0.27到0.88.
  • 平方根平均偏差从5.56降至1.69,表明精度提高.
  • 通过VRBF神经网络的方法,比改进的包装和MLP算法更快,更直接,更准确的COD测量.

结论:

  • 新的UV-vis光谱法与度补偿和VRBF神经网络为污水COD测量提供了一种优越的方法.
  • 这种方法通过有效处理度来克服现有技术的局限性.
  • 这些发现表明,实时和可靠的水质监测有望取得进展.