Advanced Multi-Parameter Prediction of Coal Quality Using LIBS and Ensemble Machine Learning Techniques.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Machine Learning

Background:

• Accurate coal quality assessment is crucial for efficient combustion and reduced emissions in power plants.
• Traditional coal analysis methods can be time-consuming and labor-intensive.
• Developing rapid and reliable methods for coal analysis is an ongoing challenge.

Purpose of the Study:

• To develop a Laser-Induced Breakdown Spectroscopy (LIBS)-based framework for predicting key coal quality parameters.
• To integrate advanced machine learning techniques for enhanced predictive accuracy.
• To provide a fast and efficient alternative for routine coal quality analysis.

Main Methods:

• Utilized Laser-Induced Breakdown Spectroscopy (LIBS) for elemental and molecular analysis of coal.
• Applied spectral preprocessing techniques, including outlier removal and baseline correction.
• Developed predictive models using machine learning algorithms, notably the Least Squares Support Vector Machine (LS-SVM).

Main Results:

• The LIBS-based framework successfully predicted key coal quality parameters: elemental carbon, ash content, volatile matter, total sulfur, and calorific value.
• The Least Squares Support Vector Machine (LS-SVM) model achieved a high accuracy, with an R² of 0.9940 for elemental carbon prediction.
• The proposed method demonstrated reliability and efficiency in rapid coal quality analysis.

Conclusions:

• The integrated LIBS and machine learning approach offers a robust solution for real-time coal quality monitoring.
• This framework has the potential to significantly improve the optimization of combustion processes and emission control in coal-fired power plants.
• The study highlights the applicability of advanced analytical techniques for intelligent industrial monitoring and control systems.