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使用机器学习模型预测基于色素的浮性重金属去除

Zaher Mundher Yaseen^1,2, Ziaul Haq Doost¹, Rauf Khan¹

¹Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

|October 20, 2025

概括

机器学习模型准确地预测了使用基托基花剂从废水中去除重金属的情况. 升级梯度增强回归器 (HGBR) 在联合金属去除方面表现出强的性能,有助于环境监测.

科学领域:

环境科学环境科学
水处理技术水处理技术
计算化学计算化学

背景情况:

重金属污染对环境和公共健康构成重大风险.
有效的废水处理需要精确的监测和修复策略.
基于酸盐的花剂 (CBF) 显示出重金属去除的前景.

研究的目的:

评估新的机器学习 (ML) 模型,以预测使用CBF的重金属 (HM) 清除效率.
评估梯度增强回归器 (GBR),历史梯度增强回归器 (HGBR),随机森林回归器 (RFR) 和极端梯度增强回归器 (XGBR) 的性能.
通过结合K-means集群标签来提高ML模型的准确性.

主要方法:

开发了四个ML模型 (GBR,HGBR,RFR,XGBR) 使用484个花试验的数据集.
包括K-means集群标签作为改进模型学习的额外功能.
测试的模型预测了 (Cd2+),铜 (Cu2+), (Ni2+), (Pb2+) 和 (Zn2+) 的去除.

主要成果:

HGBR模型在组合的HM移除中表现出优异的性能 (R2 = 0.94/0.97用于测试/培训).
所有模型都实现了高精度的单个金属去除,特别是 (Ni2+).
在单个金属测试中,GBR模型的错误率最低.

结论:

由于其强大的概括能力,HGBR模型是环境监测的可靠工具.
ML模型显示了优化废水处理中的HM清除过程的巨大潜力.
未来的工作重点应该是将这些模型集成到实时监测系统中,并探索更广泛的环境应用.

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