Calibration and application of large-scale LIBS project based on transfer learning in online quantitative analysis of coal

Abstract

In this paper, four identical online LIBS coal quality analysis systems were developed, and these systems were installed on four production lines as 1, 2, 3, and 4, respectively, to perform real-time detection and statistical analysis of the moisture (Mad), ash content (Ad), sulfur (St), and calorific value (Q) in raw coal blocks. To calibrate the four devices efficiently, we proposed the transfer learning method and part of the parameters locked in the PLS model. The proposed method is time-saving and not require extensive sample preparation, making LIBS measurement suitable for real-time monitoring. Comparing to the traditional laboratory, the result of online measurement based on LIBS system improve the sampling frequence about 3600 times, and the measurement accuracy has not decreased, the mean absolute error (MAE) of moisture, ash content, sulfur and calorific value less than 0.55 wt.%, 1.50 wt.%, 0.10 wt.% and 1.0 MJ/kg respectively. The implementation of these systems on actual production lines has provided a robust framework for continuous monitoring and quality control, leading to significant operational improvements and cost savings. This method has greatly enhanced the efficiency of LIBS device calibration, laying the foundation for the large-scale application of LIBS online systems and opening up new application scenarios for LIBS.