Rapid Detection of Cement Raw Meal Composition Based on Near Infrared Spectroscopy

Abstract

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 SiO₂, Al₂O₃, Fe₂O₃ 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.

Keywords

near infrared spectroscopy / cement raw meal / band selection / detection model

References

[1] ASTM International. Standard Test Methods for Chemical Analysis of Hydraulic Cement[J]. ASTM C114-00, 2018, 01

[2] M A Elbagermia, A I Alajtala, M Alkerzab. Chemical Analysis of Available Portland Cement in Libyan Market Using X-ray Fluorescence[J]. International Journal of Chemical, 2014 (8): 73–75

[3] P Stutzman, A Heckert. Performance Criteria for Chemical Analysis of Hydraulic Cements by X-ray Fluorescence Analysis[J]. Advances in Civil Engineering Materials, 2014 (3): 434–453

[4] J P Reboucas, J J R Rohwedder, C Pasquini. Near Infrared Emission Spectroscopy for Rapid Compositional Analysis of Portland Cements[J]. Anal. Chim. Acta, 2018 (1 024): 136–144

[5] M C A Costa, M A Morgano, M M C Ferreira, et al. Quantification of Mineral Composition of Brazilian Bee Pollen by Near Infrared Spectroscopy and PLS Regression[J]. Food Chem., 2019 (273): 85–90

[6] Wang L J, Yang Y Y. Purification and Noise Elimination of Near Infrared Spectrum in Rapid Detection of Milk Components Concentration by Using Principal Component Weight Resetting[J]. Acta Optica Sinica, 2017, 37(10): 1 030 003

[7] Machado J C, Faria M A, Ferreira I, et al. Varietal Discrimination of Hop Pellets by Near and Mid Infrared Spectroscopy[J]. Talanta, 2017, 180

[8] Schlegel L B, Schubert-Zsilavecz M, Abdel-Tawab M. Quantification of Active Ingredients in Semi-solid Pharmaceutical Formulations by Near Infrared Spectroscopy[J]. Journal of Pharmaceutical & Biomedical Analysis, 2017, 142: 178-189.

[9] Zhang L, Zhang R B. Fast Detection of Inorganic Phosphorus Fractions and Their Phosphorus Contents in Soil Based on Near Infrared Spectroscopy[J]. Chemical Engineering Transactions, 2015, 46: 1 405-1 410.

[10] Lim H H, Cheon E, Lee D H, et al. Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near Infrared Imaging[J]. Network Weekly News, 2020, 20(6): 1-15.

[11] Hang X, Yang Z, Zhang L, et al. Compositional Analysis of Cement Raw Meal by Near Infrared (NIR) Spectroscopy[J]. Analytical Letters, 2019, 52(18): 2 931-2 937.

[12] Yang Z F, Hang X, Zhang L, et al. Fast Determination of Oxides Content in Cement Raw Meal Using NIR-Spectroscopy and Backward Interval PLS With Genetic Algorithm[J]. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy, 2019, 223: 117 327-117 333.

[13] Deng B C, Yun Y H, Liang Y Z, et al. A Bovel Variable Selection Approach That Relatively Optimizes Variable Space Using Weighted Binary Matrix Sampling[J]. Analyst, 2014, 139(19): 4 836

[14] B C Deng, Y H Yun, P Ma, C, et al. A New Method for Wavelength Interval Selection That Intelligently Optimizes The Locations, Widths and Combinations of the Intervals[J]. Analyst, 2015 (140): 1876–1885

[15] Gao H Y, Mao H P, Zhang X D. Determination of Lettuce Nitrogen Content Using Spectroscopy With Efficient Wavelength Selection and Extreme Learning Machine[J]. Zemdirbyste, 2015, 102(1): 51-58.

[16] Y Liu, Y Wang, Z Xia, et al. Rapid Determination of Phytosterols by NIRS and Chemometric Methods[J]. Spectrochim. Acta A Mol. Biomol. Spectrosc, 2019 (211): 336–341