Coal Calorific Value Detection Technology Based on NIRS-XRF Fusion Spectroscopy

Abstract

Calorific value is an important index for evaluating coal quality, and it is important to achieve the rapid detection of calorific value to improve production efficiency. In this paper, a calorific value detection method based on NIRS-XRF fusion spectroscopy is proposed, which utilizes NIRS to detect organic functional groups and XRF to detect inorganic ash-forming elements in coal. NIRS, XRF and NIRS-XRF fusion spectrum were separately used to establish partial least squares (PLS) regression models for coal calorific value, and better prediction performance was obtained by using fusion spectrum (the determination coefficient of calibration set (R2) was 0.98, the root mean square error of prediction set (RMSEP) was 0.19 MJ/kg, the average relative deviation for prediction (MARDP) was 0.95%). The variable selection is very important for model performance. The effective variables were extracted using Pearson correlation coefficients to further optimize the prediction model, and the evaluation indexes of the optimized model are R2 = 0.99, RMSEP = 0.16 MJ/kg and MARDP = 0.70%. In addition, the repeatability of the proposed method was briefly evaluated. The results show that the proposed method is an effective analysis method to detect the calorific value of coal, which provides a new idea and technique for coal quality detection.