使用机器学习和实验室生成的数据,预测浮出水中的铜回收
José Benítez1, Víctor Flores1, Sergio Curilef2
1Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte, Avenida Angamos 0610, Antofagasta 1240000, Chile.
Chaos (Woodbury, N.Y.)
|September 12, 2025
概括
机器学习模型,特别是人工神经网络 (ANN),可以显著改善浮动过程中的铜回收. 这一进步通过优化效率和降低成本,有助于可持续采矿.
科学领域:
- 金工程 金工程 金工程
- 数据科学数据科学数据科学
- 可持续采矿 可持续采矿
背景情况:
- 由于高品质铜矿石的减少,需要先进的开采技术.
- 漂浮过程对于铜回收至关重要,但对操作参数敏感.
- 优化是高效,成本效益和环保的铜矿开采的关键.
研究的目的:
- 研究机器学习 (ML) 技术的应用,以优化铜漂浮.
- 评估四个ML算法的预测性能:随机森林,支持向量机,K-means集群和人工神经网络 (ANN).
- 提高铜回收效率并支持可持续的采矿实践.
主要方法:
- 利用实验室规模漂浮系统的实验数据.
- 训练并验证了四个ML算法,以预测铜回收.
- 用包括预测准确性和概率选择措施在内的指标来评估模型性能.
主要成果:
- 人工神经网络 (ANN) 显示出最高的预测准确率为98.69%.
- ANN有效地模拟了关键漂浮过程变量之间的复杂非线性相互作用.
- 不平衡和度测量验证了ANN模型的强大性能.
结论:
- ML,特别是ANN,显示出优化铜漂浮工艺的巨大潜力.
- 准确预测铜回收的情况,可以提高效率,降低运营成本.
- 这些发现有助于铜开采技术的可持续发展.
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