在臭氧化过程中使用不同的机器学习方法与臭氧衰变动力学参数预测基暴露
Dongwon Cha1, Sanghun Park2, Min Sik Kim3
1School of Chemical and Biological Engineering, Institute of Chemical Process (ICP), and Institute of Engineering Research, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
Water research
|July 14, 2024
概括
机器学习模型准确地预测了使用随时可用的水参数在臭氧化过程中的基暴露 (∫[•OH]dt). 整合分子臭氧暴露 (∫[O3]dt) 显著提高了微污染物减排预测的准确性.
科学领域:
- 环境化学环境化学
- 水处理技术水处理技术
- 预测建模预测建模
背景情况:
- 通过臭氧化减轻微污染物依赖于精确测量分子臭氧 (O3) 和基 (•OH) 暴露.
- 对基暴露 (∫[•OH]dt) 的现场测量具有挑战性,需要预测模型.
- 预测基暴露的现有方法需要开发,以便实际应用.
研究的目的:
- 在臭氧化过程中开发和评估用于预测基暴露 (∫[•OH]dt) 的机器学习模型.
- 确定影响基暴露预测的关键水质参数.
- 将不同机器学习算法的性能与传统方法进行比较.
主要方法:
- 开发了四种机器学习模型:随机森林,支持向量回归,人工神经网络和高斯过程回归.
- 使用了五个基本输入变量 (pH,溶解有机碳,性,温度,O3剂量) 和两个可选变量 (∫[O3]dt,瞬间臭氧需求).
- 经过训练和验证的模型使用来自130个自然水样的数据.
主要成果:
- 将分子臭氧暴露 (∫[O3]dt) 作为输入变量显著提高了预测准确性 (R2增加0.01-0.09).
- 高斯过程回归模型实现了最高的确定系数 (总R2 = 0.91-0.95).
- 即时臭氧需求对预测模型准确性的影响很小.
结论:
- 机器学习模型,特别是高斯过程回归,可以准确地预测臭氧化过程中基暴露的情况.
- 分子臭氧暴露 (∫[O3]dt) 是一个关键的预测指标,反映了溶解有机物中的OH-产生特性.
- 这些模型为在现实水处理场景中估计基暴露提供了实用解决方案.
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