Application of S-transform-based nonlinear processing for accurate LIBS quantitative analysis of iron ore slurry

Area of Science:

• Analytical Chemistry
• Materials Science
• Mineral Processing

Background:

• Real-time elemental analysis of iron ore slurry is vital for quality control and process optimization.
• Laser-induced breakdown spectroscopy (LIBS) offers online elemental monitoring but faces challenges from matrix and self-absorption effects.
• Linear data processing methods in LIBS limit analytical precision for complex samples like iron ore slurry.

Purpose of the Study:

• To develop a novel nonlinear data processing approach for enhancing LIBS accuracy in iron ore slurry analysis.
• To integrate spectral distance variable selection (SDVS) and S-transform (ST) nonlinear processing with partial least squares regression (PLS).
• To evaluate the proposed SDVS-ST-PLS model against conventional linear and nonlinear LIBS analysis methods.

Main Methods:

• Implementation of a feature selection unit using the spectral distance variable selection (SDVS) method.
• Incorporation of a nonlinear processing unit utilizing the S-transform (ST).
• Development of a partial least squares regression (PLS) model, combining SDVS and ST for LIBS data analysis.

Main Results:

• The proposed SDVS-ST-PLS model demonstrated significantly improved performance compared to linear models (Raw-PLS, SDVS-PLS) and other nonlinear models (ST-PLS, SDVS-ANN).
• Nonlinear processing effectively mitigated matrix and self-absorption effects inherent in LIBS measurements of iron ore slurry.
• The enhanced accuracy facilitated the successful application of the LIBS slurry analyzer in industrial mineral flotation processes.

Conclusions:

• The integration of SDVS and S-transform nonlinear processing offers a robust solution for improving LIBS accuracy in complex industrial applications.
• The developed SDVS-ST-PLS model provides a more precise method for real-time Fe content monitoring in iron ore slurry.
• This advanced LIBS analysis approach enhances mineral processing efficiency and concentrate quality evaluation.