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Updated: May 14, 2026

Quantitative Analysis by Thermogravimetry-Mass Spectrum Analysis for Reactions with Evolved Gases

Quantitative Analysis by Thermogravimetry-Mass Spectrum Analysis for Reactions with Evolved Gases

Published on: October 29, 2018

[Improving precision in coal moisture detection using wavelet transform].

Hao Jia¹, Qiang Fu, Chan-Juan Han

¹State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China. jiahaocl@tju.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|February 8, 2013

Summary

This summary is machine-generated.

Near-infrared (NIR) spectroscopy improves coal moisture analysis. Wavelet decomposition enhances spectral data, leading to more precise and stable detection of moisture content in coal.

Related Experiment Videos

Last Updated: May 14, 2026

Quantitative Analysis by Thermogravimetry-Mass Spectrum Analysis for Reactions with Evolved Gases

Quantitative Analysis by Thermogravimetry-Mass Spectrum Analysis for Reactions with Evolved Gases

Published on: October 29, 2018

Area of Science:

Analytical Chemistry
Spectroscopy
Materials Science

Context:

Coal moisture content significantly impacts its economic value and energy efficiency.
Accurate and rapid coal moisture detection is crucial for industrial applications.
Existing methods for coal moisture analysis have limitations in precision and comparability.

Purpose:

To enhance the precision of coal moisture analysis using Near-Infrared (NIR) spectroscopy.
To investigate optimized spectrum pretreatment and wavelength selection techniques.
To validate the robustness and potential of improved methods for coal moisture detection.

Summary:

This study optimized Near-Infrared (NIR) spectroscopy for coal moisture analysis, achieving high precision (R-square of 0.995, RMSEC of 0.06%, RMSEP of 0.27%).
Wavelet decomposition and reconstruction were identified as superior methods for noise reduction and baseline correction of spectral data (1300-2400 nm) prior to Partial Least Squares (PLS) modeling.
Stability experiments confirmed the robust potential of the optimized approach for precise coal moisture detection.

Impact:

The findings offer a more accurate, cost-effective, and non-invasive method for determining coal moisture content.
Improved coal moisture analysis can lead to better resource utilization and energy efficiency in the coal industry.
The developed methodology provides a robust framework for advancing spectroscopic techniques in materials analysis.