Influence of Bulk Density on Near-Infrared Spectrum Detection of Cement Raw Materials and Compensation Methods

Abstract

Near-infrared (NIR) spectrum detection technology is used widely, with broad application prospects in detecting the composition of cement raw meal. However, the onsite cement production environment is relatively complex, noting that the output needs to be adjusted continuously according to the production requirements. Furthermore, the sampling volume is changing continuously, affecting the online detection results. This paper examines the influence of sample bulk density on a NIR spectrum detection model of cement raw meal by establishing two different models of calibration set samples. The model I samples have the same bulk density, but the model II samples have a bulk density that changes. After a smoothing preprocessing of the spectral data and band selection, a detection model was established by partial least squares regression. A comparison of the prediction results of the two groups of models revealed a 19.10%, 17.65%, 20.37%, and 27.40% increase in the coefficients of determination (R2) of SiO2, Al2O3, Fe2O3, and CaO, respectively, in model II compared to model I. The experimental results show that the variation of bulk density leads to specific errors in the prediction results of the model.