Feasibility study of gross calorific value, carbon content, volatile matter content and ash content of solid biomass fuel using laser-induced breakdown spectroscopy

被引:42
作者
Lu, Zhimin [1 ,2 ,3 ]
Chen, Xiaoxuan [1 ,2 ,3 ]
Yao, Shunchun [1 ,2 ,3 ]
Qin, Huaiqing [1 ,2 ,3 ]
Zhang, Lifeng [1 ,2 ,3 ]
Yao, Xiayang [1 ,2 ,3 ]
Yu, Ziyu [1 ,2 ,3 ]
Lu, Jidong [1 ,2 ,3 ]
机构
[1] South China Univ Technol, Sch Elect Power, Guangzhou 510640, Guangdong, Peoples R China
[2] Guangdong Prov Key Lab Efficient & Clean Energy U, Guangzhou 510640, Guangdong, Peoples R China
[3] Guangdong Prov Engn Res Ctr High Efficient & Low, Guangzhou 510640, Guangdong, Peoples R China
关键词
Laser-induced breakdown spectroscopy (LIBS); Solid biomass fuel; Gross calorific value; Carbon content; Volatile matter; Ash content; COAL; LIBS; OPTIMIZATION; CALIBRATION; PARAMETERS; STABILITY; ELEMENTS; RELEASE; SODIUM; MODELS;
D O I
10.1016/j.fuel.2019.116150
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Rapid determination of the solid biomass fuel properties is essential for optimizing the combustion process of biomass. In this work, a feasibility study on using laser-induced breakdown spectroscopy (LIBS) in conjunction with partial least squares (PLS) for simultaneous measurement of gross calorific value, carbon content, volatile matter content and ash content was carried out for 66 wood pellet samples. The best quantitative analysis results were obtained with the PLS model based on spectra that combined baseline correction with Z-score standardization. The root mean square error of prediction (RMSEP) of the gross calorific value, carbon content, volatile matter content and ash content were 0.33 MJ/kg, 0.65%, 1.11% and 0.38% respectively, while the average standard deviation (ASD) were 0.08 MJ/kg, 0.15%, 0.43% and 0.16% respectively.
引用
收藏
页数:8
相关论文
共 51 条
[21]   Enhanced discrimination and calibration of biomass NIR spectral data using non-linear kernel methods [J].
Labbe, Nicole ;
Lee, Seung-Hwan ;
Cho, Hyun-Woo ;
Jeong, Myong K. ;
Andre, Nicolas .
BIORESOURCE TECHNOLOGY, 2008, 99 (17) :8445-8452
[22]   Chemical elemental characteristics of biomass fuels in China [J].
Liao, CP ;
Wu, CZ ;
Yanyongjie ;
Huang, HT .
BIOMASS & BIOENERGY, 2004, 27 (02) :119-130
[23]   Rapid Determination of the Gross Calorific Value of Coal Using Laser-Induced Breakdown Spectroscopy Coupled with Artificial Neural Networks and Genetic Algorithm [J].
Lu, Zhimin ;
Mo, Juehui ;
Yao, Shunchun ;
Zhao, Jingbo ;
Lu, Jidong .
ENERGY & FUELS, 2017, 31 (04) :3849-3855
[24]  
Mao j., 2017, Distributed. Energy, V2, P47
[25]   Optimization of NIR spectroscopy based PLSR models for critical properties of vegetable oils used in biodiesel production [J].
Moreira, Silvana A. ;
Sarraguca, Jorge ;
Saraiva, Daniel F. ;
Carvalho, Renato ;
Lopes, Joao A. .
FUEL, 2015, 150 :697-704
[26]  
National Energy Administration, 2019, INTR OP REN EN GRID
[27]  
National Energy Administration, 2013, 5682013 DLT NAT EN A
[28]   Chromatogram baseline estimation and denoising using sparsity (BEADS) [J].
Ning, Xiaoran ;
Selesnick, Ivan W. ;
Duval, Laurent .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2014, 139 :156-167
[29]   Comparative Assessment of Wet Torrefaction [J].
Quang-Vu Bach ;
Khanh-Quang Tran ;
Khalil, Roger Antoine ;
Skreiberg, Oyvind ;
Seisenbaeva, Gulaim .
ENERGY & FUELS, 2013, 27 (11) :6743-6753
[30]   Coal analysis by laser-induced breakdown spectroscopy: a tutorial review [J].
Sheta, Sahar ;
Afgan, Muhammad Sher ;
Hou, Zongyu ;
Yao, Shun-Chun ;
Zhang, Lei ;
Li, Zheng ;
Wang, Zhe .
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY, 2019, 34 (06) :1047-1082