摘要
燃煤中硫含量和热值是影响发电厂锅炉工作效率的重要指标之一.将自主研制的激光诱导击穿光谱在线检测系统用于燃煤的实时快速定量分析.以实际生产过程中的57个煤样品为分析对象,43个样品用于建模,14个样品用于验证.根据在线检测系统获取的特征光谱,选取硫、碳、镁、硅、铁、钙、氢、氧、氮等元素的多条特征谱线,采用多元线性回归建立分析模型,再用偏最小二乘法对多元线性回归进行修正,最终建立基于两种分析方法的定量分析模型.结果表明,采用多元线性回归与偏最小二乘法结合的建模方法具有比较理想的分析结果,有助于提高燃煤的硫元素和热值检测的精确度和准确度,模型的决定系数R2分别为0.925、0.951、0.951,相对偏差的平均值(ARE)分别为3.16％、0.67％、0.52％.
Abstract
This paper presents the application of a self-developed laser-induced breakdown spectroscopy(LIBS)online detec-tion system for the rapid and quantitative analysis of coal combustion parameters,specifically sulfur content and calorific val-ue,which are critical indicators affecting the efficiency of power plant boilers.A total of 57 coal samples from actual produc-tion were analyzed,with 43 dedicated to model development and 14 for validation.The system captured characteristic spectra,from which multiple spectral lines corresponding to sulfur,carbon,magnesium,silicon,iron,calcium,hydrogen,oxygen,and nitrogen were selected.Analytical models were initially developed using multiple linear regression(MLR)and subsequent-ly refined with partial least squares regression(PLSR).The combined MLR and PLSR approach yielded superior analytical outcomes,enhancing the precision and accuracy of sulfur and calorific value determination in coal.The models achieved coeffi-cients of determination(R2)of 0.925,0.951,and 0.951,with average relative errors of 3.16％,0.67％,and 0.52％,respectively.
基金项目
国家自然科学基金(61505001)
安徽省科技重大专项(202103a07020009)
NETL
NSTL
万方数据