A PLS model based on dominant factor for coal analysis using laser-induced breakdown spectroscopy

Area of Science:

• Analytical Chemistry
• Geochemistry
• Spectroscopy

Background:

• Coal elemental analysis is crucial for resource evaluation and combustion control.
• Laser-induced breakdown spectroscopy (LIBS) offers rapid elemental analysis but faces challenges with heterogeneous samples like coal.
• Sample heterogeneity and laser-induced plasma effects cause spectral fluctuations, impacting accuracy.

Purpose of the Study:

• To develop an improved method for accurate elemental concentration measurement in bituminous coal using LIBS.
• To address spectral fluctuations and enhance calibration quality in LIBS analysis of coal.
• To refine partial least squares (PLS) modeling for precise carbon (C) concentration determination.

Main Methods:

• Utilized 33 bituminous coal samples for LIBS analysis.
• Investigated spectral data normalization techniques, comparing whole spectral area vs. segmental spectral area normalization.
• Developed a novel partial least squares (PLS) model incorporating a dominant factor approach for carbon concentration, including non-linear inter-element effects and residual correction.

Main Results:

• Segmental spectral area normalization significantly improved measurement precision and accuracy compared to whole spectral area normalization.
• The novel PLS model, utilizing a dominant factor and accounting for non-linearities, reduced RMSEP to 4.47% (from 5.52%) and RMSEC&P to 2.92% (from 3.60%).
• The proposed model maintained a high coefficient of determination (R² = 0.999), demonstrating robustness across a wider carbon concentration range.

Conclusions:

• The developed dominant factor-based PLS model offers a more robust and accurate method for determining carbon concentration in coal using LIBS.
• Improved normalization strategies and advanced modeling techniques are key to overcoming challenges in LIBS analysis of heterogeneous materials.
• This approach enhances the reliability of LIBS for coal elemental analysis, with potential applications in resource management and environmental monitoring.