Article(id=1222488514604819122, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222488501866713569, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202305072, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1684684800000, receivedDateStr=2023-05-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769393574619, onlineDateStr=2026-01-26, pubDate=1690214400000, pubDateStr=2023-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769393574619, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769393574619, creator=13701087609, updateTime=1769393574619, updator=13701087609, issue=Issue{id=1222488501866713569, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='7', pageStart='1', pageEnd='199', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769393571583, creator=13701087609, updateTime=1769393973240, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222490186634743828, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222488501866713569, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222490186634743829, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222488501866713569, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=92, endPage=98, ext={EN=ArticleExt(id=1222488514873254588, articleId=1222488514604819122, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Study on the measurement of coal as fired calorific value based on synchronous collection and fusion of LIBS and NIRS signals, columnId=1222488504991474098, journalTitle=Thermal Power Generation, columnName=Intelligent management technologies for coal-fired power plants, runingTitle=null, highlight=null, articleAbstract=

Rapid and accurate measurement of the calorific value of incoming coal is the essential to provide guidance for the economic and safe operation of power plants. However, coal has complex components, and the calorific value is correlated with elemental composition and molecular structure, it is difficult to measure coal calorific value quickly and accurately by a single analytical technique. Based on laser-induced breakdown spectroscopy (LIBS) and near-infrared reflectance spectroscopy (NIRS), a method is proposed to detect the calorific value of incoming coal by combining two techniques. The LIBS and NIRS spectral signals of the coal on the conveyor belt are collected simultaneously. Fusion of two spectral information after data pre-processing, coupled with partial least squares (PLS) modeling method to quantify coal calorific value. This method is used in a coal sample measurement system built by lab, it is reached that the coefficient of determination of the calibration set was 0.98, and the root mean square error of the prediction set was 0.37 MJ/kg, with an average absolute error of 0.26 MJ/kg and an average relative error of 1.09%. The results show that the proposed method of simultaneous acquisition of LIBS and NIRS signals can measure coal calorific value rapidly and accurately.

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快速准确地对入炉煤发热量进行检测是指导电厂经济安全运行的关键,然而煤炭成分复杂,其发热量与元素组成和分子结构都有一定的相关性,单一分析技术快速准确测量入炉煤发热量比较困难。基于激光诱导击穿光谱(LIBS)技术和近红外光谱(NIRS)技术,提出联用2种技术检测入炉煤发热量的方法。同步采集输送带上煤炭的LIBS和NIRS光谱信号,进行光谱数据预处理后融合2种光谱信息,并结合偏最小二乘(PLS)建模方法定量分析煤炭发热量。将该方法用于搭建煤样测量系统,得出煤样发热量定标集的决定系数为0.98,预测集均方根误差为0.37 MJ/kg,平均绝对误差为0.26 MJ/kg,平均相对误差为1.09%。表明所提出的在输送带上LIBS和NIRS信号同步采集方法可快速准确测量入炉煤发热量。

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姚顺春(1983),男,博士,教授,博士生导师,主要研究方向为能源低碳清洁转化过程的智能感知和调控,
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茌方(1989),女,硕士,主要研究方向为煤质检测,

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茌方(1989),女,硕士,主要研究方向为煤质检测,

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基于LIBS和NIRS信号同步采集和融合的入炉煤发热量测量研究
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茌方 1 , 王庆松 1 , 李承峻 2 , 杨爱勇 1 , 卢伟业 2 , 王森 1 , 马维喆 2 , 窦有权 1 , 陈伟泽 2 , 张冬练 1 , 莫爵徽 3 , 卢志民 2 , 姚顺春 2, 3, 4
热力发电 | 燃煤电站智能化管理技术 2023,52(7): 92-98
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热力发电 | 燃煤电站智能化管理技术 2023, 52(7): 92-98
基于LIBS和NIRS信号同步采集和融合的入炉煤发热量测量研究
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茌方1 , 王庆松1, 李承峻2, 杨爱勇1, 卢伟业2, 王森1, 马维喆2, 窦有权1, 陈伟泽2, 张冬练1, 莫爵徽3, 卢志民2, 姚顺春2, 3, 4
作者信息
  • 1.国能南京煤炭质量监督检验有限公司,江苏 南京 210018
  • 2.华南理工大学电力学院,广东 广州 510641
  • 3.佛山华谱测智能科技有限公司,广东 佛山 528313
  • 4.广东省能源高效低污染转化与工程技术研究中心,广东 广州 510641
  • 茌方(1989),女,硕士,主要研究方向为煤质检测,

通讯作者:

姚顺春(1983),男,博士,教授,博士生导师,主要研究方向为能源低碳清洁转化过程的智能感知和调控,
Study on the measurement of coal as fired calorific value based on synchronous collection and fusion of LIBS and NIRS signals
Fang CHI1 , Qingsong WANG1, Chengjun LI2, Aiyong YANG1, Weiye LU2, Sen WANG1, Weizhe MA2, Youquan DOU1, Weize CHEN2, Donglian ZHANG1, Juehui MO3, Zhimin LU2, Shunchun YAO2, 3, 4
Affiliations
  • 1.Nanjing State Energy Coal Quality Supervision and Inspection Co., Ltd., Nanjing 210018, China
  • 2.School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
  • 3.Cntest Intelligent Scien-Tech Co., Ltd., Foshan 528313, China
  • 4.Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, Guangzhou 510641, China
出版时间: 2023-07-25 doi: 10.19666/j.rlfd.202305072
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快速准确地对入炉煤发热量进行检测是指导电厂经济安全运行的关键,然而煤炭成分复杂,其发热量与元素组成和分子结构都有一定的相关性,单一分析技术快速准确测量入炉煤发热量比较困难。基于激光诱导击穿光谱(LIBS)技术和近红外光谱(NIRS)技术,提出联用2种技术检测入炉煤发热量的方法。同步采集输送带上煤炭的LIBS和NIRS光谱信号,进行光谱数据预处理后融合2种光谱信息,并结合偏最小二乘(PLS)建模方法定量分析煤炭发热量。将该方法用于搭建煤样测量系统,得出煤样发热量定标集的决定系数为0.98,预测集均方根误差为0.37 MJ/kg,平均绝对误差为0.26 MJ/kg,平均相对误差为1.09%。表明所提出的在输送带上LIBS和NIRS信号同步采集方法可快速准确测量入炉煤发热量。

煤炭发热量  /  输送带  /  激光诱导击穿光谱  /  近红外光谱  /  定量分析

Rapid and accurate measurement of the calorific value of incoming coal is the essential to provide guidance for the economic and safe operation of power plants. However, coal has complex components, and the calorific value is correlated with elemental composition and molecular structure, it is difficult to measure coal calorific value quickly and accurately by a single analytical technique. Based on laser-induced breakdown spectroscopy (LIBS) and near-infrared reflectance spectroscopy (NIRS), a method is proposed to detect the calorific value of incoming coal by combining two techniques. The LIBS and NIRS spectral signals of the coal on the conveyor belt are collected simultaneously. Fusion of two spectral information after data pre-processing, coupled with partial least squares (PLS) modeling method to quantify coal calorific value. This method is used in a coal sample measurement system built by lab, it is reached that the coefficient of determination of the calibration set was 0.98, and the root mean square error of the prediction set was 0.37 MJ/kg, with an average absolute error of 0.26 MJ/kg and an average relative error of 1.09%. The results show that the proposed method of simultaneous acquisition of LIBS and NIRS signals can measure coal calorific value rapidly and accurately.

coal calorific value  /  conveyor belt  /  laser-induced breakdown spectroscopy  /  near-infrared reflectance spectroscopy  /  quantitative analysis
茌方, 王庆松, 李承峻, 杨爱勇, 卢伟业, 王森, 马维喆, 窦有权, 陈伟泽, 张冬练, 莫爵徽, 卢志民, 姚顺春. 基于LIBS和NIRS信号同步采集和融合的入炉煤发热量测量研究. 热力发电, 2023 , 52 (7) : 92 -98 . DOI: 10.19666/j.rlfd.202305072
Fang CHI, Qingsong WANG, Chengjun LI, Aiyong YANG, Weiye LU, Sen WANG, Weizhe MA, Youquan DOU, Weize CHEN, Donglian ZHANG, Juehui MO, Zhimin LU, Shunchun YAO. Study on the measurement of coal as fired calorific value based on synchronous collection and fusion of LIBS and NIRS signals[J]. Thermal Power Generation, 2023 , 52 (7) : 92 -98 . DOI: 10.19666/j.rlfd.202305072
  • 国家能源集团科技项目(GJNY-22-90)
  • 国家重点研发计划政府间国际科技创新合作项目(2019YFE0109700)
  • 国家自然科学基金(U22B20119))
  • 霍英东教育基金会高等学校青年教师基金(171047))
  • 广东省自然科学基金杰出青年项目(2021B1515020071)
  • 广东省能源高效清洁利用重点实验室(2013A061401005))
  • 佛山市科技创新项目(1920001000052)
2023年第52卷第7期
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doi: 10.19666/j.rlfd.202305072
  • 接收时间:2023-05-22
  • 首发时间:2026-01-26
  • 出版时间:2023-07-25
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  • 收稿日期:2023-05-22
基金
National Energy Group Science and Technology Project(GJNY-22-90)
国家能源集团科技项目(GJNY-22-90)
National Key Research and Development Program Intergovernmental International Science and Technology Innovation Cooperation Project(2019YFE0109700)
国家重点研发计划政府间国际科技创新合作项目(2019YFE0109700)
National Natural Science Foundation of China(U22B20119))
国家自然科学基金(U22B20119))
Fok Ying Tung Education Foundation Fund for Young Teachers in Higher Education(171047))
霍英东教育基金会高等学校青年教师基金(171047))
Guangdong Provincial Natural Science Foundation Outstanding Youth Project(2021B1515020071)
广东省自然科学基金杰出青年项目(2021B1515020071)
Guangdong Provincial Key Laboratory of Energy Efficient and Clean Utilization(2013A061401005))
广东省能源高效清洁利用重点实验室(2013A061401005))
Science and Technology Innovation Project of Foshan City(1920001000052)
佛山市科技创新项目(1920001000052)
作者信息
    1.国能南京煤炭质量监督检验有限公司,江苏 南京 210018
    2.华南理工大学电力学院,广东 广州 510641
    3.佛山华谱测智能科技有限公司,广东 佛山 528313
    4.广东省能源高效低污染转化与工程技术研究中心,广东 广州 510641

通讯作者:

姚顺春(1983),男,博士,教授,博士生导师,主要研究方向为能源低碳清洁转化过程的智能感知和调控,
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https://castjournals.cast.org.cn/joweb/rlfd/CN/10.19666/j.rlfd.202305072
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2种不同金属材料的力学参数

Family
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Number of
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种数
Number of
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Percentage of
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Genus
种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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