基于近红外光谱的水泥生料氧化物含量快速测定方法研究
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  • 英文篇名:Rapid Determination of Oxides Content in Cement Raw Meal Based on Near Infrared Spectroscopy
  • 作者:杨振发 ; 肖航 ; 张雷 ; 张法业 ; 姜明顺 ; 隋青美 ; 贾磊
  • 英文作者:YANG Zhen-Fa;XIAO Hang;ZHANG Lei;ZHANG Fa-Ye;JIANG Ming-Shun;SUI Qing-Mei;JIA Lei;College of Control Science and Engineering, Shandong University;
  • 关键词:近红外光谱 ; 水泥生料 ; 氧化物 ; 成分分析 ; 快速测定
  • 英文关键词:Near infrared spectroscopy;;Cement raw meal;;Oxides;;Component analysis;;Rapid determination
  • 中文刊名:分析化学
  • 英文刊名:Chinese Journal of Analytical Chemistry
  • 机构:山东大学控制科学与工程学院;
  • 出版日期:2019-12-13 16:49
  • 年:2020
  • 期:02
  • 基金:国家自然科学基金项目(Nos.61873333,61803179);; 山东省重点研发项目(No.2017CXGC0610);; 山东大学青年学者项目(No.2016WLJH30)资助~~
  • 页:153-159
  • CN:22-1125/O6
  • ISSN:0253-3820
  • 分类号:O657.33;TQ172.4
摘要
采用近红外光谱分析技术快速测定了水泥生料中4种氧化物含量。以漫反射方式采集不同水泥生料样品的近红外光谱,采用X射线荧光光谱法测定氧化物含量作为参考值,根据马氏距离去除异常样品,然后利用SPXY(Sample set partitioning based on joint X-Y distance)法将样本集划分为校正集和验证集,应用向后间隔偏最小二乘和遗传算法选择最优波数变量,采用偏最小二乘算法建立了4种氧化物的定量校正模型,显示出了良好的预测效果,CaO、SiO_2、Al_2O_3和Fe_2O_3模型的验证集相关系数分别为0.9411、0.9337、0.8612和0.7351,预测均方根误差分别为0.0994、0.1044、0.0693和0.0387,平均绝对误差分别为0.075%、0.083%、0.051%和0.025%。与瞬发γ射线中子活化分析、激光诱导击穿光谱分析法对比,近红外光谱分析耗时短、精度最高:单次测量时间仅需3 min,测定CaO、SiO_2、Al_2O_3和Fe_2O_3的平均绝对误差均比瞬发γ射线中子活化分析法小一个数量级,分别比激光诱导击穿光谱分析减小了0.335%、0.137%、0.069%和0.025%。结果表明,近红外光谱分析技术可快速准确地测定水泥生料中4种氧化物的含量,为水泥生料的质量监测提供了新思路。
    The contents of four oxides in cement raw meal were determined by near infrared spectroscopy. In this method, the near infrared spectrum was collected by diffuse reflectance method, and X-ray fluorescence spectroscopy analysis was used to determine the reference values of oxides content. The outliers were eliminated according to the Mahalanobis distance, and then the samples were divided into calibration subset and validation subset by sample set partitioning based on joint X-Y distance(SPXY) method. The optimal wavelength variables were selected by backward interval partial least squares and genetic algorithm. Quantitative calibration models of four oxides were established by partial least squares algorithm, which showed good prediction performance. The correlation coefficients of validation subsets of CaO, SiO_2, Al_2O_3 and Fe_2O_3 models were 0.9411, 0.9337, 0.8612 and 0.7351, the root mean square error of prediction were 0.0994, 0.1044, 0.0693 and 0.0387, the average absolute errors were 0.075%, 0.083%, 0.051% and 0.025%, respectively. By comparing with prompt gamma-ray neutron activation analysis and laser-induced breakdown spectroscopy analysis, the near infrared spectroscopy analysis has short time-consuming, highest accuracy, and best effect. It is suitable for rapid and accurate determination of contents of four oxides in cement raw meal, and provides a new idea for quality monitoring of cement raw meal.
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