使用大型数据集和在线部署,对酸盐离子吸附到生物炭材料的概率预测
Sara Iftikhar1, Rehan Ishtiaq2, Nallain Zahra1
1Environmental Artificial Intelligence Research Group, Islamabad, Pakistan.
Chemosphere
|December 28, 2024
概括
使用机器学习优化从水中去除酸盐. XGBoostLSS准确地预测了吸附能力,确定了诸如有效的生物炭水处理试验条件等关键因素.
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:
- 水体中的酸盐污染是一个重要的环境问题.
- 预测和优化酸盐去除的有效方法至关重要.
- 基于生物炭的材料在吸附酸离子方面表现有前途.
研究的目的:
- 为了预测酸盐 (PO4(III)) 离子对生物炭基材料的吸附能力.
- 通过机器学习来确定酸盐去除的最佳条件.
- 开发一个实用的在线框架来预测酸盐的去除.
主要方法:
- 使用了五种概率机器学习模型:XGBoostLSS,自然梯度提升,贝叶斯NN,概率NN和蒙特卡洛脱落NN.
- 分析了2952个数据点的数据集,其中有16个输入特征.
- 使用SHapely添加式扩展 (SHAP) 来确定特征的重要性.
主要成果:
- 在预测新吸附剂的吸附上,XGBoostLSS获得了最高的R2 (0.95).
- 实验条件 (43%) 对吸附产生了最显著的影响,其次是合成条件 (29%) 和吸附剂特性 (28%).
- 通过使用Ra-LDO吸附剂确定了最大限度的酸盐去除的最佳条件.
结论:
- 机器学习模型,特别是XGBoostLSS,可以准确地预测酸盐对生物炭的吸附.
- 实验参数对于优化酸盐去除效率至关重要.
- 一个基于网络的框架被开发用于水处理的实际应用.
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