在Undaria pinnatifida中优化Cu2 +吸附预测,使用机器学习和异热模型
Haoran Chen1, Rui Zhang1, Xiaohan Qu1
1College of Materials & Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Journal of hazardous materials
|April 12, 2025
概括
这项研究结合了传统模型和机器学习 (ML) 来预测Undaria pinnatifida藻类对铜 (Cu2+) 的吸附. ML提高了预测准确度,确定初始度是有效去除重金属的关键.
科学领域:
- 环境科学 环境科学
- 生物技术是生物技术.
- 吸附科学 吸附科学
背景情况:
- 藻类是重金属修复的有效生物吸收剂.
- 传统的吸附模型在预测藻类生物吸附能力方面存在局限性.
- Undaria pinnatifida在去除铜 (Cu2+) 的潜力未得到充分利用.
研究的目的:
- 整合机器学习 (ML) 与传统模型来预测Undaria pinnatifida的Cu2+吸附.
- 为了确定藻类生物活性化合物和Cu2+吸附之间的关系.
- 确定影响Cu2+吸附的关键因素,以优化环境修复策略.
主要方法:
- 通过Undaria pinnatifida部分的Cu2+吸附的实验性确定.
- 使用弗洛伊德利希,伪二阶和热力学模型建模吸附.
- ML回归算法 (CatBoost) 的应用和比较,用于预测.
- 使用Shapley和部分依赖图的特征重要性分析.
主要成果:
- 罗宁含量与Cu2+吸附能力正相关.
- 吸附行为最好由弗洛伊德利希和伪二阶模型描述,表明多层吸附.
- CatBoost ML模型实现了高预测精度 (R2=0.9883).
- 最初的Cu2+度被确定为影响吸附的最重要因素.
结论:
- 机器学习和传统模型的整合为预测藻类重金属吸附提供了一个强大的方法.
- Undaria pinnatifida,特别是其罗宁含量,显示出Cu2+补救的巨大潜力.
- ML促进了准确的预测,提高了藻在环境污染控制中的有针对性的利用.
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