煤发热量的近红外光谱定量检测研究  

Quantitative detection of coal calorific value using near-infrared spectroscopy

作　　者：张海军 ZHANG Hai-jun(Tunlan Coal Preparation Plant,Xishan Coal Electricity Group Co.,LTD,Gujiao,Shanxi 030206,China)

机构地区：[1]西山煤电(集团)有限责任公司屯兰矿选煤厂,山西古交030206

出　　处：《煤炭加工与综合利用》2025年第2期88-91,共4页Coal Processing & Comprehensive Utilization

摘　　要：为了快速、准确检测出煤炭发热量指标,研究采集了150个烟煤样品的近红外光谱,并分别对800~1000 nm、1400~1600 nm、2000~2200 nm及2200~2400 nm下的光谱进行分析,采用学生式残差方法剔除了异常样品,建立了偏最小二乘回归(PLSR)定量检测模型。通过采用一阶微分、二阶微分、5点平滑、9点平滑、15点平滑、多元散射校正(MSC)和衰减全反射校正(ATR)进行光谱预处理,并结合不同建模波段进行建模结果分析。研究结果表明:采用5点平滑预处理结合1400~1600 nm建模波段的模型预测能力最佳,校正集和交叉验证集相关系数分别达到0.952和0.946,均方根误差分别为0.029和0.037,二者相差仅0.008,模型更具有一定代表性,模型预测精度较强。In order to quickly and accurately detect the content of coal calorific value indicators,collected near-infrared spectra of 150 bituminous coal samples,and analyze the spectra at 800~1000 nm、1400~1600 nm、2000~2200 nm,and 2200~2400 nm,respectively,a student style residual method was used to remove abnormal samples and a partial least squares regression(PLSR)quantitative detection model was established.By using first-order differentiation,second-order differentiation,5-point smoothing,9-point smoothing,15-point smoothing,multivariate scattering correction(MSC),and attenuated total reflection correction(ATR)for spectral preprocessing,and combining different modeling bands for modeling result analysis.The research results show that the model with 5-point smoothing preprocessing combined with 1400~1600 nm modeling band has the best predictive ability.The correlation coefficients of the correction set and cross validation set reach 0.952 and 0.946,respectively,with root mean square errors of 0.029 and 0.037,and the difference between the two is only 0.008.The model is more representative,with strong prediction accuracy.

关 键 词：近红外光谱 偏最小二乘回归 发热量 光谱预处理 建模波段 

分 类 号：TQ533.6[化学工程—煤化学工程]