Coal origin identification based on visible-infrared spectroscopy and attention networks

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-08-08 DOI:10.1016/j.chemolab.2025.105501

Jingyi Liu , Ba Tuan Le , Thai Thuy Lam Ha

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引用次数: 0

Abstract

Coal origin identification is a crucial process in the coal industry, which is important in ensuring coal quality and optimizing supply chain management. However, due to the diversity of coal mine resources and the increasing market demands for quality, coal origin identification has become more complex. This study proposes a coal origin identification method based on spectroscopy and advanced machine learning techniques with deep attention networks. Through an improved model architecture and optimization strategy, the method achieves efficient classification and precise recognition of coal samples. This method uses the attention network as the core to fully explore the potential spectral features in coal samples. Experimental results show that compared with traditional methods, this method has achieved significant improvements in multiple key indicators, verifying its superior performance and application potential. This study not only provides an efficient and reliable solution for coal origin identification, but also provides important support for the intelligent and precise development of the coal industry.

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基于可见红外光谱和注意网络的煤源识别

煤炭产地识别是煤炭行业的关键环节，对保证煤炭质量、优化供应链管理具有重要意义。然而，由于煤矿资源的多样性和市场对质量要求的不断提高，煤炭产地鉴定变得更加复杂。本文提出了一种基于光谱学和先进机器学习技术以及深度注意网络的煤源识别方法。该方法通过改进模型结构和优化策略，实现了煤样的高效分类和精确识别。该方法以注意力网络为核心，充分挖掘煤样中潜在的光谱特征。实验结果表明，与传统方法相比，该方法在多个关键指标上取得了显著改进，验证了其优越的性能和应用潜力。该研究不仅为煤炭产地识别提供了高效可靠的解决方案，而且为煤炭工业的智能化、精细化发展提供了重要支撑。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.