基于变压器的深度学习模型用于重金属离子对生物炭基吸附剂的吸附能力预测
Zeeshan Haider Jaffari1, Ather Abbas2, Chang-Min Kim3
1Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea.
Journal of hazardous materials
|October 22, 2023
概括
机器学习准确地预测了通过生物炭吸附剂去除重金属 (HM). 使用FT变压器优化条件确定了有效处理废水的关键因素.
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:
- 从废物中提取的生物炭有效地从废水中去除重金属 (HM).
- 预测生物炭吸附能力 (qe) 是很困难的,因为不同的特性和条件.
研究的目的:
- 在生物炭上开发HM吸附的准确预测模型.
- 使用机器学习 (ML) 和深度学习 (DL) 识别影响吸附能力的重要因素.
主要方法:
- 使用1518个数据点构建并比较各种ML和DL模型.
- 使用递归特征消除来识别14个重要的输入特征.
- 使用FT转换器,一个深度学习模型,用于预测和SHAP分析特征的重要性.
主要成果:
- FT变压器实现了最高的精度 (R2=0.98) 和最低的误差 (RMSE=0.296,MAE=0.145).
- 吸附条件是影响重金属去除的最关键因素 (72.12%).
- 优化条件涉及特定的吸附剂负载,初始度和香皮生物炭的pH值.
结论:
- 机器学习,特别是FT变压器,提供了一种可靠的方法来预测生物炭吸附能力.
- 了解关键影响因素可以优化基于生物炭的废水处理过程.
- 这项研究为设计有效的生物碳吸附剂用于环境修复提供了一个框架.
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