Highly stable analysis of coal calorific value using combined NIRS-XRF

SONG Jianchao; ZHANG Lei; MA Weiguang; YIN Wangbao; JIA Suotang

doi:10.37188/OPE.20233113.1880

您当前的位置：

首页 >

文章列表页 >

Highly stable analysis of coal calorific value using combined NIRS-XRF

Modern Applied Optics | 更新时间：2023-07-17

- Highly stable analysis of coal calorific value using combined NIRS-XRF
- Optics and Precision Engineering Vol. 31, Issue 13, Pages: 1880-1889(2023)
- 作者机构：
  
  1.山西大学量子光学与光量子器件国家重点实验室山西大学激光光谱研究所，山西太原 030006
  2.山西大学极端光学协同创新中心，山西太原 030006
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233113.1880
  CLC： O433;O657.34
- Received：29 November 2022，
  
  Revised：30 December 2022，
  
  Published：10 July 2023
- 稿件说明：
移动端阅览
宋健超,张雷,马维光等.NIRS-XRF联用的煤炭发热量高稳定检测[J].光学精密工程,2023,31(13):1880-1889.

SONG Jianchao,ZHANG Lei,MA Weiguang,et al.Highly stable analysis of coal calorific value using combined NIRS-XRF[J].Optics and Precision Engineering,2023,31(13):1880-1889.
宋健超,张雷,马维光等.NIRS-XRF联用的煤炭发热量高稳定检测[J].光学精密工程,2023,31(13):1880-1889. DOI： 10.37188/OPE.20233113.1880.

SONG Jianchao,ZHANG Lei,MA Weiguang,et al.Highly stable analysis of coal calorific value using combined NIRS-XRF[J].Optics and Precision Engineering,2023,31(13):1880-1889. DOI： 10.37188/OPE.20233113.1880.

摘要

实时获知煤炭发热量对于及时调整电站锅炉风粉配比和提高煤炭燃烧效率具有重要意义，为了实现电力生产中发热量的稳定快速检测，提出了一种近红外光谱（Near Infrared Spectroscopy， NIRS）与X射线荧光光谱（X-ray Fluorescence， XRF）联用的煤炭发热量高稳定检测方法，它结合了NIRS能高稳定检测煤中与发热量正相关的有机基团的优势与XRF能高稳定检测与发热量负相关的成灰元素的特点，大大提高了对煤炭发热量的测量重复性。在光谱预处理中，先将两套光谱融合作为偏最小二乘回归的输入变量进行全谱初步建模，依据回归系数选择NIRS光谱中的有效波段，再将它与XRF光谱中的成灰元素谱线一并融合进行归一化处理。建模时将预处理后的融合光谱数据作为输入变量，利用偏最小二乘回归对煤炭发热量进行建模。实验结果表明，NIRS-XRF联用方法对定标集煤样发热量预测的线性相关度系数（

）为0.995，对验证集煤样发热量预测的最小均方根误差、平均相对误差和标准偏差分别为0.24 MJ/kg，0.61%和0.05 MJ/kg，测量重复性满足小于0.12 MJ/kg的国家标准。NIRS-XRF联用的煤炭发热量高稳定检测方法有望推广应用于火力发电、煤化工、冶金、水泥和焦化等“高碳”行业，助力我国按期实现碳中和目标。

Abstract

It is important to know the calorific value of coal in real time for adjusting the ratio of air to powder of power plant boilers and increasing the coal combustion efficiency. Currently， power production is in urgent need of a rapid， highly stable calorific-value detection method. Therefore， this paper innovatively proposes a highly stable detection method for the coal calorific value using the combination of near-infrared spectroscopy （NIRS） and X-ray fluorescence （XRF） spectroscopy， which significantly improves the measurement repeatability of the coal calorific value by combining the advantages of NIRS for highly stable detection of organic groups positively related to the calorific value in coal and XRF spectroscopy for ash-forming elements negatively related to the calorific value. The spectral preprocessing method used in this study involves fusing the two sets of spectra as input variables for partial least squares regression （PLSR） for preliminary modeling of the full spectrum， selecting the effective bands in the NIRS spectrum according to the regression coefficients， and then fusing them with the ash-forming element XRF spectra for normalization. All the data of the preprocessed fused spectra are used as input variables to model the coal calorific value using PLSR. The experimental results indicated that the linear correlation coefficient （

） of the present NIRS-XRF coupled method for the prediction of the calorific value of the calibration set coal samples was 0.995， and the minimum root-mean-square error， average relative error， and standard deviation for the prediction of the calorific values of the validation-set coal samples were 0.24 MJ/kg， 0.61%， and 0.05 MJ/kg， respectively. The measurement repeatability fully satisfied the requirement of

0.12 MJ/kg based on the national standard. The proposed highly stable detection method combining NIRS and XRF spectroscopy for the coal calorific value is expected to be popularized and applied in high carbon industries such as thermal power generation， the coal chemical industry， metallurgy， cement， coking， etc.， to help China achieve carbon neutrality on schedule.

关键词

Keywords

references

YAN R ， ZHU H J ， ZHENG C G ， XU M H . Emissions of organic hazardous air pollutants during Chinese coal combustion ［J］. Energy ， 2002 ， 27 （ 5 ）： 485 - 503 . doi: 10.1016/s0360-5442(02)00003-8 http://dx.doi.org/10.1016/s0360-5442(02)00003-8

REVEL D . BP Statistical review of world energy 2021 . ［J］. The Catalyst Review Newsletter ， 2021 （ 7 ）： 34 .

LEE S H ， YOON S J ， RA H W ， et al . Gasification characteristics of coke and mixture with coal in an entrained-flow gasifier ［J］. Energy ， 2010 ， 35 （ 8 ）： 3239 - 3244 . doi: 10.1016/j.energy.2010.04.007 http://dx.doi.org/10.1016/j.energy.2010.04.007

ROSS A B ， JONES J M ， CHAIKLANGMUANG S ， et al . Measurement and prediction of the emission of pollutants from the combustion of coal and biomass in a fixed bed furnace ［J］. Fuel ， 2002 ， 81 （ 5 ）： 571 - 582 . doi: 10.1016/s0016-2361(01)00157-0 http://dx.doi.org/10.1016/s0016-2361(01)00157-0

NAZARI S ， SHAHHOSEINI O ， SOHRABI-KASHANI A ， et al . Experimental determination and analysis of CO 2 ， SO 2 and NO x emission factors in Iran’s thermal power plants ［J］. Energy ， 2010 ， 35 （ 7 ）： 2992 - 2998 . doi: 10.1016/j.energy.2010.03.035 http://dx.doi.org/10.1016/j.energy.2010.03.035

潘越，曾哲，张恩瑜 . 基于MATLAB和图像灰度值对X射线探测煤矸识别的研究［J］. 煤炭技术， 2017 ， 36 （ 11 ）： 307 - 309 . doi: 10.13301/j.cnki.ct.2017.11.117 http://dx.doi.org/10.13301/j.cnki.ct.2017.11.117

PAN Y ， ZENG ZH ， ZHANG E Y . Research on X ray detection of coal gangue recognition based on MATLAB and image gray value ［J］. Coal Technology ， 2017 ， 36 （ 11 ）： 307 - 309 . （in Chinese） . doi: 10.13301/j.cnki.ct.2017.11.117 http://dx.doi.org/10.13301/j.cnki.ct.2017.11.117

LI Y S ， LU W Y ， ZHAO J B ， et al . Detection of caloric value of coal using laser-induced breakdown spectroscopy combined with BP neural networks ［J］， Spectroscopy and Spectral Analysis ， 2017 ， 37 （ 8 ）： 2575 - 2579 .

YUAN T B ， WANG Z ， LUI S L ， et al . Coal property analysis using laser-induced breakdown spectroscopy ［J］. Journal of Analytical Atomic Spectrometry ， 2013 ， 28 （ 7 ）： 1045 - 1053 . doi: 10.1039/c3ja50097g http://dx.doi.org/10.1039/c3ja50097g

HOU Z Y ， WANG Z ， YUAN T B ， et al . A hybrid quantification model and its application for coal analysis using laser induced breakdown spectroscopy ［J］. Journal of Analytical Atomic Spectrometry ， 2016 ， 31 （ 3 ）： 722 - 736 . doi: 10.1039/c5ja00475f http://dx.doi.org/10.1039/c5ja00475f

LABUTIN T A ， POPOV A M ， RAIKOV S N ， et al . Determination of chlorine in concrete by laser-induced breakdown spectroscopy in air ［J］. Journal of Applied Spectroscopy ， 2013 ， 80 （ 3 ）： 315 - 318 . doi: 10.1007/s10812-013-9766-8 http://dx.doi.org/10.1007/s10812-013-9766-8

BONA M T ， ANDRéS J M . Coal analysis by diffuse reflectance near-infrared spectroscopy： hierarchical cluster and linear discriminant analysis ［J］. Talanta ， 2007 ， 72 （ 4 ）： 1423 - 1431 . doi: 10.1016/j.talanta.2007.01.050 http://dx.doi.org/10.1016/j.talanta.2007.01.050

ANDRéS J M ， BONA M T . Analysis of coal by diffuse reflectance near-infrared spectroscopy ［J］. Analytica Chimica Acta ， 2005 ， 535 （ 1/2 ）： 123 - 132 . doi: 10.1016/j.aca.2004.12.007 http://dx.doi.org/10.1016/j.aca.2004.12.007

ANDRéS J M ， BONA M T . ASTM clustering for improving coal analysis by near-infrared spectroscopy ［J］. Talanta ， 2006 ， 70 （ 4 ）： 711 - 719 . doi: 10.1016/j.talanta.2006.05.034 http://dx.doi.org/10.1016/j.talanta.2006.05.034

WANG Y S ， YANG M ， WEI G ， et al . Improved PLS regression based on SVM classification for rapid analysis of coal properties by near-infrared reflectance spectroscopy ［J］. Sensors and Actuators B： Chemical ， 2014 ， 193 ： 723 - 729 . doi: 10.1016/j.snb.2013.12.028 http://dx.doi.org/10.1016/j.snb.2013.12.028

HICKS D G ， O’REILLY J E ， KOPENAAL D W . On the rapid estimation of % ash in coal from silicon content obtained via FNAA， XRF， or Slurry-injection AA ［C］. AIP Conference Proceedings. AIP ， 1981 . doi: 10.1063/1.32925 http://dx.doi.org/10.1063/1.32925

王清亚 . 基于XRF的土壤重金属定量分析方法研究及应用［D］. 抚州：东华理工大学 .

WANG Q Y . Research and Application of Quantitative Analysis Method of Soil Heavy Metals Based on XRF ［D］. Fuzhou ： East China Institute of Technology . （in Chinese）

MA K F， Experimental study on determination of major elements in coal ash by X-ray fluorescence spectrometry ［J］. Coal Quality Technology ， 2019 ， 2 ： 32 - 35 .

LI X L ， ZHANG L ， TIAN Z H ， et al . Ultra-repeatability measurement of the coal calorific value by XRF assisted LIBS ［J］. Journal of Analytical Atomic Spectrometry ， 2020 ， 35 （ 12 ）： 2928 - 2934 . doi: 10.1039/d0ja00362j http://dx.doi.org/10.1039/d0ja00362j

YAO S C ， QIN H Q ， WANG Q ， et al . Optimizing analysis of coal property using laser-induced breakdown and near-infrared reflectance spectroscopies ［J］. Spectrochimica Acta Part A： Molecular and Biomolecular Spectroscopy ， 2020 ， 239 ： 118492 . doi: 10.1016/j.saa.2020.118492 http://dx.doi.org/10.1016/j.saa.2020.118492

褚小立，袁洪福，陆婉珍 . 近红外分析中光谱预处理及波长选择方法进展与应用［J］. 化学进展， 2004 ， 16 （ 4 ）： 528 - 542 . doi: 10.3321/j.issn:1005-281X.2004.04.008 http://dx.doi.org/10.3321/j.issn:1005-281X.2004.04.008

CHU X L ， YUAN H F ， LU W ZH . Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique ［J］. Progress in Chemistry ， 2004 ， 16 （ 4 ）： 528 - 542 . （in Chinese） . doi: 10.3321/j.issn:1005-281X.2004.04.008 http://dx.doi.org/10.3321/j.issn:1005-281X.2004.04.008

BRERETON R G . Introduction to multivariate calibration in analytical chemistry ［J］. Analyst ， 2000 ， 125 （ 11 ）： 2125 - 2154 . doi: 10.1039/b003805i http://dx.doi.org/10.1039/b003805i

Views

474

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰