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用多任务神经网络预测煤灰中的稀土元素度.

Yu Song1,2, Yifan Zhao1, Alex Ginella1

  • 1Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab) 5731B Boelter Hall, Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA. yusong@ucla.edu.

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概括

本研究引入了一种机器学习模型,用于预测煤灰中的稀土元素 (REE) 度,从而能够有效地选这种有价值的资源. 该方法使用散装组合,为REE提取提供了比传统分析方法更快的替代方案.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 环境科学 环境科学
  • 数据科学数据科学数据科学

背景情况:

  • 对稀土元素 (REEs) 的需求不断增长,需要可持续的采购策略.
  • 煤灰是可再生能源的潜在二次来源,但高效的提取受到昂贵和耗时的分析所阻碍.
  • 准确和快速的选方法对于识别富含REE的煤灰原料至关重要.

研究的目的:

  • 开发一种机器学习模型,使用易于测量的散装成分来预测煤灰中的REE含量.
  • 为了提高效率并降低确定适合回收 REE 的煤灰来源的成本.
  • 调查多任务学习和转移学习的使用,以提高模型性能和适应性.

主要方法:

  • 开发一个多任务神经网络,同时预测各种REE的度.
  • 使用煤灰的散装成分数据作为机器学习模型的输入特征.
  • 转移学习的应用,使模型适应来自不同来源的煤灰.

主要成果:

  • 与单一任务模型相比,多任务神经网络显示出更好的准确性和降噪.
  • 确定了关键数据模式,以有效选具有高REE度的煤灰.
  • 转移学习成功地提高了模型对各种煤灰样本的适应性.

结论:

  • 机器学习为预测煤灰中的REE含量提供了可行和高效的解决方案.
  • 拟议的模型有助于快速识别可靠的煤灰来源,以实现可持续的可再生能源开采.
  • 这种方法支持REE替代供应链的发展,减少对传统采矿的依赖.