通过机器学习对固体样本火焰发射光谱的矿物学分析
Adam R Bernicky1, Boyd Davis2, Milen Kadiyski3
1Department of Chemistry, Queen's University, 90 Bader Lane, Kingston, Ontario K7L 3N6, Canada.
Analytical chemistry
|November 22, 2024
概括
一个新的人工神经网络 (ANN) 准确地使用火焰光学发射光谱学 (OES) 分析铜矿样本. 这种先进的方法精确量化了元素含量,并识别了矿物质,超过了传统的模型.
科学领域:
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 热金铜炼厂分析固体矿石样本.
- 精确的元素和矿物学分析对于过程优化至关重要.
- 传统的光谱分析方法可能是复杂和耗时的.
研究的目的:
- 开发和验证一个人工神经网络 (ANN) 用于分析固体预缩矿石样本.
- 提高铜矿石分析中元素和矿物识别的准确性和效率.
- 将ANN的性能与传统的非线性部分最小平方 (PLS) 模型进行比较.
主要方法:
- 使用专门的火焰光学发射光谱 (OES) 系统进行样本分析.
- 开发和优化了一个人工神经网络 (ANN),每层有10个隐藏层和40个节点.
- 使用训练有素的ANN. 分类了超过8500个复杂的光谱.
主要成果:
- 该ANN量化元素含量准确度高于1.5质量%.
- 该ANN识别了普遍存在的矿物质,准确度高于2.5质量%.
- 火焰温度的确定不确定性<3K,粒子大小在2μm以内.
- 该ANN显著优于非线性部分最小正方形匹配模型.
结论:
- 开发的ANN为铜矿样品的定量分析提供了一种卓越的方法.
- 这种人工智能驱动的方法提高了金炼中的元素和矿物学表征的精度.
- 该研究表明了先进的机器学习技术在工业化学分析中的潜力.
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