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Analytical Letters Volume 52, 2019 - Issue 18
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Infrared

Compositional Analysis of Cement Raw Meal by Near-Infrared (NIR) Spectroscopy

Hang XiaoSchool of Control Science and Engineering, Shandong University, Jinan, China; ORCID Iconhttp://orcid.org/0000-0003-1468-5656View further author information
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Zhenfa YangSchool of Control Science and Engineering, Shandong University, Jinan, China; View further author information
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Lei ZhangSchool of Control Science and Engineering, Shandong University, Jinan, China; Correspondence[email protected]
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Dejun FengSuzhou Research Institute, Shandong University, Suzhou, ChinaView further author information
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Faye ZhangSchool of Control Science and Engineering, Shandong University, Jinan, China; View further author information
,
Mingshun JiangSchool of Control Science and Engineering, Shandong University, Jinan, China; View further author information
,
Qingmei SuiSchool of Control Science and Engineering, Shandong University, Jinan, China; View further author information
&
Lei JiaSchool of Control Science and Engineering, Shandong University, Jinan, China; View further author information
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Pages 2931-2937 | Received 04 Apr 2019, Accepted 03 Jun 2019, Published online: 11 Jun 2019
 
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Abstract

A rapid and accurate method is presented to determine CaCO3, SiO2, Fe2O3, and Al2O3 in cement raw meal using near-infrared (NIR) spectroscopy. Multiplicative scatter correction (MSC) was employed to eliminate the scattering signal and partial least squares (PLS) regression was used to build the analysis model. The results demonstrated good performance by this approach for the determination of CaCO3, SiO2, Fe2O3, and Al2O3. NIR spectroscopy exhibits the feasibility to characterize the quality of cement raw meal. Compared with prompt gamma neutron activation analysis (PGNAA) and X-ray fluorescence (XRF), this method is more efficient and safer.

Additional information

Funding

This work was funded by the National Natural Science Foundation of China (No. 11504201), Jiangsu Provincial Natural Science Foundation, China (BK20141222), and Shandong Keypoint Research and Invention Program (2017CXGC0610).

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