Intelligent non-destructive measurement of coal moisture via microwave spectroscopy and chemometrics

Abstract

The rapid and non-destructive measurement of coal moisture content is essential in the coal industry for production, transportation and utilization purposes. Existing measurement methods have still drawbacks, such as being time-consuming, producing destructive samples and yielding unstable outcomes. To address these issues, this paper explored the utilization of broadband microwave spectrum for intelligent coal moisture measurement. A multi-type outliers detection method based on the Monte-Carlo cross-validation (MCCV) strategy was used to prevent masking effect of microwave spectra. In order to effectively extract microwave spectral features and establish correlations with coal moisture, a novel neural network model, UC-PLSR, is proposed by combining U-Net, Convolutional Block Attention Module (CBAM) and Partial Least Squares Regression (PLSR) algorithm. Furthermore, a design scheme/case of a microwave measurement device for coal moisture was presented, offering guidance for the development of rapid coal moisture measurement instruments or on-site measurement systems. Experimental results demonstrated that the proposed model outperformed traditional chemometrics methods, achieving superior prediction accuracy and generalization capability with $R^2$ = 0.8756, MAE = 1.2523 and RMSE=1.6560.