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基于机器学习的Fe单一/复合物修改生物炭的Cd吸附预测:可控制制剂的应用

Xin Xiang¹, Dongmei Jia¹, Zongzheng Yang²

¹College of Marine and Environmental Sciences, Tianjin University of Science & Technology, Tianjin, 300457, China.

Environmental research

|November 28, 2024

概括

机器学习模型,包括随机森林,准确地预测铁改性生物炭的吸附能力. 这使得可针对性地制备功能性生物炭,以有效处理废水.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
计算化学计算化学

背景情况:

水中的污染对环境和健康构成重大风险.
生物炭是一种可持续材料,显示出重金属修复的潜力.
为增强吸附量量身定制生物炭特性,需要高效的预测工具.

研究的目的:

开发和比较机器学习模型 (ANN和RF),用于预测Fe修饰生物炭上的吸附.
确定影响生物炭吸附能力的关键结构和化学因素.
为了指导高性能Fe修饰生物炭的目标合成,以去除.

主要方法:

人工神经网络 (ANN) 和随机森林 (RF) 模型的构建和评估.
用SHAP (夏普利添加式扩展) 分析来确定特征的重要性.
来自各种原料的Fe-Ca改性生物炭 (FeCa-BC) 的实验合成和表征.
使用实验数据和GUI工具箱验证ML模型预测.
使用FTIR,XRD和XPS进行吸附机制分析.

主要成果:

与ANN相比,射频模型显示出更高的预测性能 (R2 = 0.98).
生物炭的结构特征是影响吸附的最重要因素 (55.44%).
根据元素组成 (C,Fe,O,H,N) 和pH值确定了最佳的吸附条件.
实验FeCa-BC验证了射频模型的预测准确性 (R2 = 0.89).
开发的GUI工具箱实现了低于5%的预测错误.

结论:

机器学习,特别是射频,是预测和优化用于重金属吸附的生物炭性能的强大工具.
基于ML洞察力的Fe-修饰生物炭的向合成可以显著提高去除效率.
表面复杂化,降水和离子交换是FeCa-BC上吸附的关键机制.
这项研究为数据驱动的功能生物炭用于环境修复的设计提供了一个框架.

关键词:

生物炭的特征是生物炭的特征.是的组成部分.可控制的制备方法在Fe的修改过程中,Fe被修改了.机器学习是机器学习.

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