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基于变压器的深度学习模型用于用激光诱导分解光谱测量稀土矿石中的La,Ce和Nd的量化

Jiaxing Yang¹, Shijie Li¹, Zhao Zhang²

¹Frontiers Science Center for Rare Isotopes, Lanzhou University, Lanzhou, 730000, China.

|March 24, 2025

概括

这项研究引入了先进的深度学习模型,即iTBi-LIBS和iTBi-RF-LIBS,用于精确地对矿石中的稀土元素如,和进行定量分析. 这些模型通过克服激光诱导分解光谱学的挑战,显著提高了准确性.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

稀土元素 (La,Ce,Nd) 对于高科技应用至关重要.
稀土矿石的实时定量分析对于高效的炼油至关重要.
激光诱导分解光谱 (LIBS) 是一个强大的工具,但面临着矩阵效应和光谱重叠的挑战.

研究的目的:

开发和评估新的深度学习模型,用于精确的定量分析稀土矿石中的La,Ce和Nd.
解决传统LIBS分析的局限性,特别是矩阵效应和光谱干扰.
将拟议模型的性能与现有分析方法进行比较.

主要方法:

开发一个iTransformer-双向长短期记忆 (iTBi) 深度学习算法用于LIBS定量分析.
将 iTBi 模型与随机森林 (RF) 算法集成,以创建一个 iTBi-RF-LIBS 组合模型.
使用35个稀土矿石样本进行验证,其中La,Ce和Nd的度各不相同.

主要成果:

该iTBi-LIBS模型实现了高校准系数 (R2 ≥0.983) 和低预测误差 (MAEP,RMSEP) 的La,Ce和Nd.
iTBi-RF-LIBS组合模型进一步提高了准确性,R2值达到0.996,并减少了MAEP和RMSEP.
两种拟议模型都显示出与单变量分析,BiLSTM,RF和反向传播神经网络模型相比具有显著优势.

结论:

在LIBS分析中,iTBi-LIBS和iTBi-RF-LIBS模型有效地减轻了矩阵效应和光谱重叠干扰.
这些深度学习方法为确定稀土矿石中的La,Ce和Nd度提供了可行和精确的技术途径.
开发的模型代表了关键稀土元素的定量分析的重大进步.

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