|
|
|
|
|
|
| Research on Detection of Cement Raw Material Content Based on Near-Infrared Spectroscopy |
| HUANG Bing1, WANG Xiao-hong2, JIANG Ping2* |
1. Key Laboratory of Building Materials Preparation and Testing Technology, University of Jinan, Jinan 250022, China
2. School of Automation and Electrical Engineering, University of Jinan, Jinan 250022, China
|
|
|
|
|
Abstract 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.
|
|
Received: 2021-02-02
Accepted: 2021-04-15
|
|
|
|
Corresponding Authors:
JIANG Ping
E-mail: cse_jiangp@ujn.edu.cn
|
|
[1] Xiao H, Yang Z, Zhang L, et al. Analytical Letters, 2019, 52(2): 1.
[2] Shan Qing, Zhang XinLei, Zhang Yan, et al. Spectroscopy Letters, 2016, 49(3): 188.
[3] Jia Junwei, Fu Hongbo, Hou Zongyu, et al. Plasma Science and Technology, 2019, 21(3): 034001.
[4] ZHANG Wei-dong，LI Ling-qiao，HU Jin-quan, et al（张卫东，李灵巧，胡锦泉，等）. Chinese Journal of Analytical Chemistry（分析化学），2018，46(9): 1446.
[5] Krepper G，Romeo F，de Sousa Fernandes D D, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2018，189: 300.
[6] Costa M C A，Morgano M A，Ferreira M M C, et al. LWT—Food Science and Technology，2017，80: 76.
[7] Mir-Marqués A，Martínez-García M，Garrigues S, et al. Food Chem.，2016，196: 1023.
[8] XIAO Hang, YANG Zhen-fa, ZHANG Lei, et al（肖 航, 杨振发, 张 雷, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2020, 40(3): 867.
[9] XIAO Hang, YANG Zhen-fa, ZHANG Le, et al（肖 航, 杨振发, 张 雷, 等）. Chinese Journal of Analytical Chemistry（分析化学），2020, 48(2): 275.
[10] Yang Zhenfa, Xiao Hang, Zhang Lei, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 223.
[11] Yang Zhenfa, Xiao Hang, Zhang Lei, et al. Analytical Letters, 2020, 53(17): 1.
[12] Wang Yifei, Wang Kai, Zhou Zhao, et al. Frontiers of Chemical Science and Engineering, 2019, 13(3): 599.
[13] Slobodan Sasic, Andrew Gilkison, Mark Henson. Journal of Pharmaceutical and Biomedical Analysis, 2018, 148: 256.
[14] QIU Yan-nan, SUN Li-ning, LIU Tao, et al(仇衍楠, 孙丽宁, 刘 涛, 等). Journal of the Chinese Society of Rare Earths（中国稀土学报）, 2012, 30(2): 129.
[15] Alexey N Romanov, Alexander A Veber, Daria N Vtyurina, et al. Journal of Luminescence, 2015, 167: 371.
|
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|
