The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regressi...The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.展开更多
Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) method...Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error(ARE),relative standard deviation(RSD) and root mean squared error of prediction(RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%,RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.展开更多
X-ray fluorescence (XRF) analysis utilizes particle size which is resulted from milling of a material. The milling ensures uniform and fine grained powder. The finer and more uniform the particle size is, the better t...X-ray fluorescence (XRF) analysis utilizes particle size which is resulted from milling of a material. The milling ensures uniform and fine grained powder. The finer and more uniform the particle size is, the better the result and easier it is for material quality control. To ensure uniformity in particle size and finer powder, a comparative analysis was conducted with different grinding aids and pressed pellet method was used in obtaining analysis results. Pressed pellets of cement raw meal sample milled with different grinding aids (graphite, aspirin and lithium borate) were subjected to XRF. Graphite produced better particle size uniformity with a corresponding standard deviation that made quality control of raw meal easier and better than aspirin and lithium borate.展开更多
基金Funded by the National Natural Science Foundation of China (No. 62073153)The Major Scientific and Technological Innovation Projects in Shandong Province (No.2019JZZY010448)The Key Research and Development Plan of Shandong Province of China (No.2019GSF109018)。
文摘The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.
基金supported by National Natural Science Foundation of China (Grant Nos. 61505223, 41775128)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. Y03RC21124)+1 种基金the External Cooperation Program of Chinese Academy of Sciences (Grant No. GJHZ1726)the project of China State Key Lab. of Power System (Grant Nos. SKLD18KM11, SKLD18M12)
文摘Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error(ARE),relative standard deviation(RSD) and root mean squared error of prediction(RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%,RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.
文摘X-ray fluorescence (XRF) analysis utilizes particle size which is resulted from milling of a material. The milling ensures uniform and fine grained powder. The finer and more uniform the particle size is, the better the result and easier it is for material quality control. To ensure uniformity in particle size and finer powder, a comparative analysis was conducted with different grinding aids and pressed pellet method was used in obtaining analysis results. Pressed pellets of cement raw meal sample milled with different grinding aids (graphite, aspirin and lithium borate) were subjected to XRF. Graphite produced better particle size uniformity with a corresponding standard deviation that made quality control of raw meal easier and better than aspirin and lithium borate.
