在凝固-超过程中揭示了五种被忽视的微塑料的去除行为:来自实验和预测建模的见解
Guanyu Zhou1, Guijing Chen2, Peng Tang2
1State Key Laboratory of Hydraulics and Mountain River Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China; Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin 644000, PR China.
Journal of hazardous materials
|March 11, 2025
概括
这项研究结合了实验和人工神经网络 (ANN) 建模,有效地从饮用水中去除微塑料. 据ANN模型准确地预测了去除,揭示了水处理过程中的关键机制.
科学领域:
- 环境科学 环境科学
- 水处理技术 水处理技术
- 材料科学 材料科学 材料科学
背景情况:
- 微塑料污染对饮用水安全构成重大风险.
- 对于水处理中的微塑料有效的去除策略至关重要,但研究不足.
- 有限的研究存在于微塑料去除的综合实验和建模方法.
研究的目的:
- 研究5种微塑料在凝固-超过程中的去除行为和机制.
- 开发和验证人工神经网络 (ANN) 模型,以预测微塑料清除效率.
- 为了阐明水处理中微塑料去除过程中的潜在化学相互作用.
主要方法:
- 使用凝固和超过方法去除微塑料的实验分析.
- 开发和优化用于预测的人工神经网络 (ANN) 模型.
- 使用X射线光电子谱学和2D相关谱学的去除机制的表征.
- 理论计算以了解与超膜的界面相互作用.
主要成果:
- 凝固消除了37.0-56.0%的微塑料;超过实现了几乎完全的去除.
- 优化的ANN模型显示出高预测准确度 (R2值为0.9972-0.9987).
- 确定了关键机制,包括Al物种,结,π-π相互作用,疏水吸引力和酸相互作用.
结论:
- 结合实验和ANN建模,有效阐明了水处理中的微塑料去除.
- 了解这些机制为优化饮用水净化过程提供了宝贵的见解.
- 这项研究为评估和改进微塑料清除技术提供了一个强大的框架.
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