基于近红外光谱检测技术的水泥生料成分含量检测研究

近红外光谱检测技术已经成功应用于水泥生料成分的快速检测,但我国水泥企业在生产水泥生料时所用原材料品种不一,使用不同的原材料进行生产时对近红外光谱建模带来一定影响.为了研究不同原料生产的水泥生料近红外光谱建模差异,对不同地区水泥生产线所生产的水泥生料进行建模研究.选取两个不同地区水泥生产线的水泥生料样本各95份和82份,...

Research on Detection of Cement Raw Material Content Based on Near-Inf rared Spectroscopy

Near-infrared spectroscopy has been successfully applied to the rapid detection of cement raw meal composition, but Chinese cement companies use different raw materials when producing cement raw meal. The use of different raw materials for production has a certain impact on near-infrared spectroscopy modeling. In order to study the difference of near-infrared spectral modeling of cement raw meal produced in different regions, this paper studies the modeling of cement raw meal produced by cement production lines in different regions. 95 and 82 samples of cement raw materials from cement production lines in two different regions were selected respectively, 80 and 67 samples were selected as calibration sets, and 15 samples were selected as verification sets. Firstly, the samples from the two cement production lines are repeatedly loaded and tested three times, and the average spectrum is taken as the near-infrared spectrum of the samples. Then the near-infrared spectra of cement raw materials produced in two different regions are then pretreated by the S-G smoothing method. The partial least squares regression algorithm is used to establish the detection model, and the comparison shows that there are certain differences in the near-infrared spectra of cement raw meal in the two regions, and the accuracy of the model established by the same method is quite different. Using the CARS band selection method, the near-infrared spectra of two kinds of cement raw materials were selected. The near-infrared spectra bands of SiO₂，Al₂O₃，Fe₂O₃ and CaO samples from production line 1 retained 85, 89, 55 and 67 variables respectively from 3113 variables. The near infrared spectral bands of SiO₂，Al₂O₃，Fe₂O₃ and CaO in the cement raw meal samples of production line 2 are 51, 55, 55 and 55 variables respectively retained from 3113 variables, and the retained bands are different. Finally, the near-infrared spectrum detection models of SiO₂，Al₂O₃，Fe₂O₃ and CaO in cement raw materials in the two regions were established respectively. Through comparison, it is found that the selected wave bands are different when the raw materials are different, and the prediction effect of the detection model is good. The RMSEP (predicted root mean square error) of production line I SiO₂，Al₂O₃，Fe₂O₃ and CaO detection models are 0.109, 0.053, 0.034 and 0.185 respectively, while the RMSEP (predicted root mean square error) of production line Ⅱ SiO₂，Al₂O₃，Fe₂O₃ and CaO detection models are 0.084, 0.024, 0.023 and 0.184 respectively. The results show that when the raw materials of cement raw materials change or the place of production is different, the cement raw materials cannot be detected only by the modified model, but the near-infrared spectral modeling needs to be carried out again, and the spectral band selection will also change. Using the band selection method to select the band of near-infrared spectrum of cement raw meal can improve the model accuracy of the detection model.