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建模优化和应用改性饮用水污泥,以高效地去除

Zelin Jing¹, Ying Liu¹, Liwenze He²

¹Faculty of Environment Science and Engineering, Southwest Jiaotong University, Chengdu, 611756, People's Republic of China.

Journal of environmental management

|September 18, 2025

概括

机器学习优化饮用水污泥 (DWS) 修改以去除,实现高效率和低成本. 这种以数据为导向的方法为控制水温缩水提供了一个可持续的解决方案.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
数据科学数据科学数据科学

背景情况:

污染导致水的环氧化,需要先进的清除技术.
吸附是去除的关键方法,避免二次污染.
饮用水处理污泥 (DWS) 是一种用于吸附剂的低成本材料,但其修改效率低.

研究的目的:

开发一种以数据为导向的方法,以优化DWS修改以提高去除效果.
评估机器学习模型来预测修改后的DWS的性能.
使用优化修改参数设计一种新型吸附剂.

主要方法:

利用来自14个来源的657个数据集来训练和评估六个机器学习模型.
开发了一种改进的混合编码遗传算法 (HEGA),用于对修改参数的反向优化.
使用渐变增强决策树 (GBDT) - HEGA优化准备的-层双氧化物 (Ca-Al LDOs).

主要成果:

GBDT模型实现了最高的预测准确性 (R2 = 0.992).
优化的Ca-Al LDO证明了96.81%的酸盐去除,达到污水度低于中国的III级地表水标准.
在实际湖水测试中保持了高去除效率 (87.09%),每公斤去除的成本低至0.8美元.

结论:

GBDT-HEGA框架为优化环境流程提供了一种高效,数据驱动的方法.
开发的Ca-Al LDO提供了一个可行的,低成本的解决方案,用于水处理中先进的去除.
这种方法克服了传统的试错效率低下,显示了控制水缩的重大实际价值.

关键词:

在DWS中使用DWS.在GBDT-海牙.低成本的吸收剂是一种吸收剂.模型的解释性模型的解释性修改优化优化修改优化清除的方法是去除.

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