基于人工智能的预测模型,用于尾矿和矿山废物的元素发生形式
Chongchong Qi1, Tao Hu1, Jiashuai Zheng1
1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
Environmental research
|February 4, 2024
概括
一个新的贝叶斯优化和随机森林 (RF) 模型快速预测金属和金属化物在尾矿和矿山废物 (TMW) 中的发生. 这种方法有助于评估污染风险,并促进更安全的TMW管理和回收.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 采矿和金工艺产生大量的尾矿和矿山废物 (TMW),导致全球环境污染.
- 评估TMW中金属和金属化物元素的存在对于评估污染至关重要.
- 确定元素发生的传统实验室方法耗时且复杂.
研究的目的:
- 开发一种快速而准确的实证方法,用于确定TMW中的元素发生形式.
- 为TMW分析提出一个结合贝叶斯优化和随机森林 (RF) 方法的预测模型.
主要方法:
- 使用了2376个TMW样本的数据集,将矿物成分,元素性质和总度作为输入特征.
- 一个随机森林 (RF) 模型被训练来预测金属和金属化元素的发生形式的百分比.
- 贝叶斯优化用于微调射频模型,使用相关系数 (R),确定系数和误差指标评估性能.
主要成果:
- 优化的射频模型表现出高的预测准确性,在训练组中达到0.99的R值,在测试组中达到0.965.
- 特性重要性分析确定了元素电负性和总度是预测发生形式中最有影响力的因素.
- 增加的元素电子阴性与减少的残基含量相关,归因于极性增强的可溶性.
结论:
- 开发的射频模型提供了一种快速而准确的方法,用于预测TMW中的金属和金属化物发生形式.
- 这种方法显著减少了测试时间,促进了有效的TMW污染风险评估.
- 这些发现支持改进的TMW管理策略,并促进可持续的回收计划.
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