通过机器学习揭示四环素与生物炭吸附能力背后的驱动器
Pengyan Zhang1,2, Chong Liu1,2, Dongqing Lao3,4
1Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Xinjiang, 843300, China.
Scientific reports
|July 17, 2023
概括
机器学习模型预测四环素在生物炭上的吸附. 随机森林获得了最高的准确性,确定四环素与生物炭的比率是有效废水处理的关键.
科学领域:
- 环境化学环境化学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 废水中的四环素 (TC) 污染对环境构成风险.
- 生物炭 (BC) 是一种有前途的吸附剂,可以去除TC等污染物.
- 了解吸附机制对于有效的废水处理至关重要.
研究的目的:
- 开发一个强大的机器学习模型来预测TC对BC的吸附.
- 确定影响TC吸附效率的关键因素.
- 为了促进从废水中去除TC的最佳BC的选择.
主要方法:
- 使用了四种机器学习算法:随机森林 (RF),梯度增强决策树 (GBDT),极端梯度增强 (XGBoost) 和人工神经网络 (ANN).
- 模拟TC吸附到BC使用295个实验吸附试验的数据.
- 分析的特征的重要性,以确定吸附的驱动因素.
主要成果:
- 射频模型显示了最高的预测准确性 (R2 = 0.9625).
- 具有特定表面积>380cm3·g-1和颗粒大小2.514.0nm的生物炭显示出优异的TC吸附.
- TC-to-BC比率 (权值为0.595) 是最有影响力的因素,其次是度梯度.
结论:
- 成功开发了一种可靠的BCTC吸附的可靠预测模型.
- 该研究强调了BC特性和TC与BC的比重对于有效的TC去除的重要性.
- 这些发现有助于选择适当的BC材料用于TC污染的废水处理.
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