Experimental Study of Laser-Induced Breakdown Spectroscopy (LIBS) for Direct Analysis of Coal Particle Flow Jianping Zheng, Jidong Lu,* Bo Zhang, Meirong Dong, Shunchun Yao, Weiye Lu, Xuan Dong South China University of Technology, School of Electric Power, Guangzhou 510640, Guangdong, China
Laser-induced breakdown spectroscopy (LIBS) was employed to directly analyze coal particles in the form of descending flow. Coalparticle ablation was performed using a 1064 nm neodymiumdoped yttrium aluminum garnet (Nd : YAG) laser at atmospheric conditions. Spectral identification schemes were used to acquire spectra containing all the emission lines of the important elements in coal. These acquired spectra were classified as representative spectra. The background of the line emission plus three times the standard deviation of the background of the representative spectra was chosen as the threshold value. A method using a single line and a method using combined multiple lines (C, 247.8 nm; N, 746.8 nm; Si, 288.2 nm; and Ca, 396.8 nm) were compared to obtain the best results for the spectral identification of coal particle flow. The feasibility of rejecting the partial breakdown spectra was verified using quantitative analysis of fixed carbon in coal. Index Headings: Coal particles; LIBS; Spectral identification; Representative spectra; Partial breakdown spectra.
INTRODUCTION Laser-induced breakdown spectroscopy (LIBS) is a powerful analytical technique that offers the in situ, rapid, highly selective, and sensitive detection and analysis of both natural and manufactured materials. In LIBS, a short-pulsed, high-power density laser pulse is focused on a target material. A small portion of the sample is vaporized to generate a high-temperature plasma. As the plasma plume expands and cools, the excited atomic, ionic, and molecular fragments emit radiation characteristic of the elemental composition of the material. There has been significant progress in the commercial application of this technology in identifying elements, material identification, process monitoring, material sorting, and on-site screening.1–5 The determination of the chemical composition of coal prior to combustion is vitally important for a coal-fired power plant to obtain optimal boiler performance. Laserinduced breakdown spectroscopy was tested for determining coal combustion diagnostics by Ottesen and colleagues6–8 as early as the late 1980s at the Combustion Research Facility at Sandia National Laboratories. The method was also developed as an in situ, real-time monitor of the elemental composition of ash deposits formed during coal combustion processes.9 Wallis et Received 4 September 2013; accepted 28 January 2014. * Author to whom correspondence should be sent. E-mail: [email protected]. DOI: 10.1366/13-07278
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al.10 and Brody and Chadwick,11,12 at the Cooperative Research Centre for Clean Power from Lignite, developed and applied the LIBS method to the coal power industry for the analysis of Australian lignite. A few metallic elements and silicon were detected in lignite samples using calibration curves from standard samples. The detection limits were found to be between 60 and 200 parts per million (ppm).10–12 Noda et al.13 and Deguchi et al.14 used LIBS to detect unburned carbon in fly ash in a combustion field. The application of LIBS to the elemental analysis of coal was also investigated by Gaft et al.15 and Mateo et al.16 Zhang et al.17 measured the organic oxygen content of pulverized anthracite coal under atmospheric conditions using LIBS and found an average relative error in the quantitative measurement of 19.4%. Ctvrtnickova et al.18 used LIBS and thermomechanical analysis to determine the elemental composition of coal (C, H, Si, Al, Fe, Ti, Ca, Mg, Na, K, Mn, Sr, and Ba) and predict the slag propensity for five coal blends. Feng et al.19 and Wang et al.20 used LIBS combined with partial least squares (PLS) regression to analyze the elemental content of coal. Our group also performed a series of studies analyzing coal quality, evaluating element detection21,22 and doing a proximate analysis (volatile matter and ash) using multivariate analysis.23,24 The samples used in the reports mentioned so far were in the form of coal pellets, prepared from pulverized coal. Yin et al.25 designed a LIBS system for the on-line quality analysis of pulverized coal in power plants using a complicated sampling module. Samples from the entrance pipe were transferred to the sample holder and the loading was determined using a signal receiver. Redundant coal particles were scraped away to smooth the sample surface in the hold for LIBS analysis. The sampling-preparation module contained a cyclone collector, vibrator, transmitter, receiver, and scraper. The processes of operation and control of the sample collection and loopback were complicated. Adequate coal samples in the form of particle flows from the power plant were sampled from the transmission pipes before they entered the furnace. However, this system would be greatly simplified if LIBS were used to analyze the coal particle flow directly. An on-line LIBS system would be more appropriate for longtime on-site monitoring of coal properties and power plant control. We evaluated the unique application of LIBS as a coal particle flow monitoring technology. In this study, we performed a direct analysis of coal properties in the form
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FIG. 1. Schematic diagram of the LIBS experimental setup for coal particle flow analysis.
of a particle flow. A gas–solid system was used to produce coal particles for laser ablation. A suitable spectral identification scheme was developed to identify representative spectra and characterize coal content. In addition, the quantitative analysis of fixed carbon in coal in the form of particle flow was used to verify the necessity of rejecting partial breakdown spectra.
EXPERIMENTAL To simulate coal particle flow in power plants, a gas– solid system was developed to produce coal particles for direct LIBS analysis (Fig. 1). The system for producing the particle flow contained an ejector, cyclone separator, measuring chamber, pipes, and the other accessories. The pulverized coal (particle diameter, dp , 200 lm; particle density, qc,ac 1.4 g/cm3) in the feeder was carried to a coal blast pipe by compressed air at the entrance of the ejector. The particle flow rate was controlled by adjusting the valve between the feeder and the ejector. The coal particle flow was transferred to the cyclone separator to separate the gas from the particles. The exhaust gas was drawn out through the upper exit of the cyclone, and the coal particles fell downward into the measurement chamber through the lower exit pipe. The coal particles were collected in a bottle after measurement. The flow rate of particles per unit of chamber inlet area, with a pipe diameter of 4 mm, was kept at 0.73 6
0.02 g min1 mm2). This corresponded to a volume concentration of particles of 0.9 %. Volume concentration was defined as the particle volume per unit volume of gas volume and was reported as a percentage. A Q-switch neodymium-doped yttrium aluminum garnet (Nd : YAG) laser operating at 1064 nm with a 4 ns pulse duration was used as the ablation source. Pulse energy was attenuated to 42 mJ using a Glan polarizer with a repetition rate of 1 Hz. The energy was focused using a 50 mm diameter, 100 mm focal-length lens to create the plasma. The plasma emission was collected at a 458 angle to the incident laser beam and fibercoupled (core diameter, 400 lm) to a dual-channel spectrometer (AvaSpec-2048; Avantes, Holland). Each channel contained a separate grating and chargecoupled device (CCD) array. All the spectra were measured simultaneously using a spectral resolution of 0.2–0.3 nm in the spectral coverage of 235–345 nm (channel 1) and 580–790 nm (channel 2). These spectral ranges covered the emission lines of the elements of interest for the coal-quality analysis. Samples that are commonly used in coal-fired power plants in China were selected for this experiment. The results of the proximate analysis of coal samples (airdried basis) are listed in Table I. The analysis was performed using a thermogravimetric analyzer (TGA). We used 22 samples in this experiment; 17 (C1–C17) were used to construct the calibration model, and the
TABLE I. Proximate analysis of coal samples (air-dried basis) using TGA. Sample number C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
Moisture (%)
Volatiles (%)
Ash (%)
Fixed carbon (%)
Sample number
Moisture (%)
Volatiles (%)
Ash (%)
Fixed carbon (%)
12.16 4.59 10.14 5.14 2.55 3.98 6.41 3.36 2.24 3.98 3.88
13.63 10.39 3.24 6 20.44 38.13 16.52 17.84 19.21 12.21 12.21
33.04 43.58 43.32 40.88 28.79 8.52 26.98 27.5 26.21 30.32 28.78
41.17 41.44 43.3 47.98 48.22 49.37 50.09 51.3 52.34 53.49 55.13
C12 C13 C14 C15 C16 C17 VC1 VC2 VC3 VC4 VC5
3.48 4.32 1.54 3.02 4.29 1.98 5.62 6.88 4.34 4.04 2.66
12.31 9.6 25.98 27.36 24.69 19.88 3.12 6.26 4.7 11.27 13.49
27.85 29.65 12.62 8.39 6.28 10.71 45.28 40.24 40.96 29.51 24.4
56.36 56.43 59.86 61.23 64.74 67.43 45.98 46.62 50 55.18 59.45
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FIG. 2. Representative spectra of laser-induced coal particle flow.
remaining five (VC1–VC5) were used to validate the measurement accuracy and reproducibility of LIBS. The optimum delay time between the output-triggered signal for the laser pulse and data acquisition by the spectrometer was set at 1.3 ls. The integration time gate was 1.1 ms, the minimum value for the spectrometer. The LIBS spectrum was recorded for each laser pulse using direct analysis mode. A total of 1500 individual spectra were stored for each analyzed sample.
RESULTS AND DISCUSSION Emission Spectra of Coal Particle Flow. Coal is a heterogeneous material with a complex chemical and physical structure. It contains almost all the elements in the periodic table. The particle flow of vaporized coal is obviously different from that of coal pellets. In a vaporized sample, the particles in the flow are discrete and their spatial distribution is related to the state of the particle flow. Coal quality analysis includes many studies, including ultimate analysis (of C, H, O, N, and S), proximate analysis (of volatile matter, ash, moisture, fixed carbon, and calorific value), ash composition (aluminum oxide (Al2O3), silicon dioxide (SiO2), sodium oxide (Na2O), potassium oxide (K2O), calcium oxide (CaO), magnesium oxide (MgO), titanium dioxide (TiO2), and iron(III) oxide (Fe2O3)), and slagging factor.26 The most important elements to be identified in coal are C, H, O, N, S, Si, Al, Fe, Ca, Mg, Ti, Na, and K. Figure 2 shows representative spectra containing the emission lines of these elements in the ranges 235–345 and 580–790 nm. The spectra were identified using National Institute for Standards and Technology (NIST) databases.27 Some of the acquired spectra did not contain all the emission lines of the most important elements (Figs. 3 and 4). We classified these as partial breakdown spectra. Two types of the partial breakdown spectra were identified: those with different amounts of coal particles for laser ablation (Figs. 3a–3d) and those consisting of the air breakdown signals (N, H, and O) and the emissions of the elements K, Ca, and Na due to a lack of coal particle ablation (Fig. 4). There are several possible explanations for partial breakdown spectra. First, the distribution of particle size
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was not uniform. We measured the particle size distribution of one sample by the sieving method in ASTM C136-06.28 In this sample, 28% of the particles were 100–200 lm in diameter, 31% were 70–100 lm, 36% were 54–70 lm, and 5% were , 54 lm. Second, fluctuation in the coal particle flow is inevitable and induced different concentrations in the finite laserinduced plasma volume. Third, the coal particle flow was analyzed in a column, resulting in some coal particles being located in front of the focal point, absorbing the laser energy and blocking the laser ablation of coal particles in the focal region. The most important reason for the existence of partial breakdown spectra is non-uniform coal particle flow. These limitations required that we design a system that would reject partial breakdown spectra and identify representative spectra and analyze them. Spectral Identification. The evaluation of particulates smaller than 10 lm that can be completely dissociated into plasma has been reported.29,30 Laser-induced breakdown spectroscopy has been used to analyze aerosols31,32, vapors,33 and combustion particulates7,34,35 containing Be, Pb, and Hg, where most of the particles were in a suspension. For processing aerosol LIBS spectra, a scheme called conditional data analysis was developed by Hahn et al.,34 Carranza et al.,36 and A´lvarez-Trujillo et al.37 A threshold value corresponding to the intensity of an adjacent and featureless spectral region was needed to standardize the spectra being analyzed. The intensity of the emission lines of the targeted species in each LIBS spectrum was then compared to the threshold value. If the ratio of the emission peak intensity to background exceeded the threshold value, the spectrum was classified as a representative spectrum. If not, the spectrum was classified as a partial breakdown spectrum.34,36 Background intensity follows a normal distribution. Emissions can be identified according to their confidence intervals of normal distribution. When the emission intensity was larger than its average background plus three times the standard deviation of the chosen background, it was classified as a detected signal rather than as background.38 The confidence coefficient of this method was higher than 99%, and the possibility of considering the background to be the detected signal due to background fluctuations was less than 1%. However, the standard deviation of the background and the emission lines were similar in our measurements. Therefore, we chose the averaged value of the background plus three times the standard deviation of the representative spectra as the threshold value. Two spectral identification schemes were evaluated: one using a single element emission line and one using combined multiple emission lines. Single elemental emission lines such as C (247.8 nm), H (656.28 nm), and N (746.83 nm) were chosen to evaluate the singleline method (Table II). Coal contains non-metal and metal elements with very different physical properties. The ionization potential (IP) of these elements ranges from 418 to 1402 kJ/mol, which affects the plasma temperature and electron density.39 The relevant elements were divided into five groups
FIG. 3. Partial breakdown of spectra. (a) Containing Na and K emissions. (b) Containing K, Na, Ca, and Al emissions. (c) Containing K, Na, Ca, Al, Mg, and Si emissions. (d) Containing K, Na, Ca, Al, Mg, Fe, Ti, Si, and C emissions.
based on their IP:40 (1) K and Na (418–496 kJ/mol); (2) Ca and Al (577–590 kJ/mol); (3) Ti, Mg, Fe, and Si (658–787 kJ/mol); (4) C (1000 kJ/mol); and (5) H, O, and N (1300– 1400 kJ/mol). The emissions of the group 1 elements (Na and K) are present in every spectrum. One element was chosen from each of the other four groups, and the four elements were used together to characterize the test spectra. The representative emissions selected in the ablation coal particle spectra were C (247.8 nm), N (746.8 nm), Si (288.2 nm), and Ca (396.8 nm) (Fig. 5). All the chosen elements are typically found in coal. The background regions were the horizontal section of spectra adjacent to the C, N, Si, and Ca peak lines (Fig. 5). This identification scheme was applied to all the coal samples. A comparison of the single-line method and the method using multiple emission lines is presented in Table II. For brevity, only one calibration sample (C9) and one validation sample (VC3) are presented in the table. The rejection rate was defined as the ratio of the number of the removed spectra to the 1500 total spectra. Two parameters were used to evaluate the spectral identification methods: the rejection rates of both the
partial breakdown spectra and representative spectra. The rejection rate of partial breakdown spectra was defined as the ratio of the partial breakdown spectra in the removed spectra to the total partial breakdown
FIG. 4. Partial breakdown spectra containing K, Ca, Na, N, H, and O but not C.
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TABLE II. Comparison of the different spectral identification schemes for two coal samples. Sample number C9
VC3
Spectral identification schemes Single emission line H N C Multiple emission lines Single emission line H N C Multiple emission lines
Rejection rate (%)
Rejection rate of partial breakdown spectra (%)
Rejection rate of representative spectra (%)
11.2 13.5 12.6 17.8
62.2 75.2 70 97.8
0 0 0 2.4
9.3 10.3 9.6 13.7
62.2 68.4 64 100
0 0 0 2.1
spectra; a higher value indicated a better result. Similarly, the rejection rate of representative spectra was defined as the ratio of the representative spectra in removed spectra to the total representative spectra; here a lower value indicated a better result. The method using multiple element emission lines had a higher rejection rate for partial breakdown spectra and a lower rejection rate for the representative spectra. Although the rejection rate of representative spectra using the single-line method was low, there were still some partial breakdown spectra that were not removed. Moreover, multiple element monitoring was needed for the measurement of the coal particle composition, and the physical properties of the elements of interest are significantly different. Therefore, it is better to use the combined multiple lines method for spectral identification. The results in Table II were calculated using a program and verified manually to make sure the method we chose is available for spectral identification. Evaluation of the Quantitative Analysis of Fixed Carbon in Coal. The physical properties and composition of the samples affected the emission signal of the laser-induced plasma. Chemometrics, such as linear multivariate regression41,42 and linear regression using a PLS algorithm,19,43,44 has been used to improve the accuracy of elemental quantitative analyses. Multivari-
ate calibration has been shown to be an alternative method for correcting the matrix effect. We have also used the multivariate analysis method to extract spectral information related to coal properties (volatile matter and ash).23,24 Fixed carbon content, expressed as a percentage, is the residue remaining after the removal of moisture, ash, and volatile materials from coal. The concentration of fixed carbon is one of the most important indices used in the classification and quality assessment of coal. In traditional off-line measurements, the fixed carbon content is determined as: FCad ¼ 100 ðMad þ Vad þ Aad Þ
where FCad, Mad, Vad, and Aad represent the fixed carbon, moisture, volatile matter, and ash content, respectively, of air-dried basis coal. Eq. 1 shows that fixed carbon is indirectly measured by calculating the other components and subtracting them from the whole to find the difference. The direct quantitative analysis of fixed carbon content will greatly facilitate the on-line monitoring of coal quality. Twenty-two samples were evaluated in this study. Seventeen of these (C1–C17) were used to construct a calibration model, and the rest (VC1–VC5) were used to validate the model. A multiple linear regression method was used to construct a model for
FIG. 5. Background region. (a) For C and Si. (b) For Ca and N.
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ð1Þ
FIG. 6. Comparison between LIBS predicted value and standard analysis procedures for the fixed carbon content of 17 calibration coal samples (C1–C17). (a) Without rejection of partial breakdown spectra. (b) With rejection of partial breakdown spectra.
regression was used to determine the effective input variables and establish the calibration model. The total measured spectra and spectra with partialbreakdown spectra removed were used to establish the calibration model. The LIBS predicted values (with and without rejection) and those from a standard analysis procedure, thermogravimetric analysis (TGA), for the fixed carbon were compared. Seventeen calibration samples (C1–C17) and five validation samples (VC1– VC5) were used. The correlation coefficients without and with rejection for the global prediction curve (Figs. 6a and 6b) were 0.921 and 0.930, respectively. The root mean square error of calibration (RMSEC) was improved from 2.86 to 2.92% and the root mean square error of prediction (RMSEP) was reduced from 7.30 to 2.40% after removal of the partial breakdown spectra. The five samples used to validate this method (VC1– VC5) were then tested (Table III). The same averaged number of the laser shots (250) was used to evaluate the repeatability of the measurement. The uncertainty for the 1500 total measured spectra were calculated using six replicates and three to five replicates for the spectra after rejection, depending on the rejection rate of different samples with the same number of total spectra. Rejecting the partial breakdown spectra improved the calibration model and prediction. However, note that the type of partial breakdown spectra removed affected the improvement of the model. For example, samples VC2 and VC4 had a high rejection rate. Most of the partial
evaluating the fixed carbon content of coal. The multiple linear regression method characterized the outcome and fixed carbon content, using n independent variables xi. This was expressed as: ym ¼ am þ
n X
si x i þ e m
ð2Þ
i¼1
where ym is the measured fixed carbon content, am is the intercept, em is the residual error, and si is the multivariate regression coefficient determined by PLS estimation. The predicted residual sum of the squares, P S ¼ ni¼1 e2i , was minimized. The independent variables xi represent the selected spectral features. In a multiple linear regression analysis, it is very important to determine the correct independent variables. Statistical methods (F test and t test) are used to evaluate the significance of each variable to establish an effective calibration equation. There are three common independent variable screening methods: forward selection, backward selection, and stepwise selection. In the model for the quantitative analysis of fixed carbon in coal, the emission line intensities of the main elements—C (247.8 nm), Ca (393.4 nm), Al (308.2 nm), Fe (344.1 nm), Mg (285.1 nm), Si (288.1 nm), H (656.3 nm), O (777.2 nm), N (746.8 nm), and K (766.5 nm)—were chosen as the independent variables. Backward selection linear
TABLE III. Analytical results using the LIBS method for validation samples. LIBS value without rejection Validated sample VC1 VC2 VC3 VC4 VC5
Reference value (wt %) 45.98 46.62 50.00 55.18 59.45
LIBS value with rejection
Predicted value (wt %)
Absolute error (wt %)
Relative error (wt.%)
6 6 6 6 6
4.2 2.1 4.2 15.1 0.2
9.1 4.6 8.3 27.3 0.4
41.8 44.5 45.8 40.1 59.2
1.5 1.0 0.9 0.9 1.2
Predicted value (wt %)
Absolute error (wt %)
Relative error (wt %)
Rejection rate (%)
6 6 6 6 6
3.3 1.5 3.8 0.6 1.0
7.3 3.3 7.5 1.1 1.6
8.13 35.93 13.73 35.07 23.6
42.7 45.1 46.3 55.8 60.4
0.6 1.0 0.7 0.5 0.5
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breakdown spectra in sample VC4 belonged to the second type of the partial breakdown spectra (weak carbon signal and strong air breakdown signal). The fixed carbon in coal has strong relationship with the carbon signal, and the weight of the carbon signal in the quantitative analysis model (Eq. 2) is high. In contrast, most of the rejected spectra were of the first type (containing only some of the elements of interest). The averaged value was used for the quantitative analysis. The averaged intensities of the emission lines as the independent variables were increased had a similar ratio after rejection, and there was no extremely small value to affect this tendency of the independent variables; this may explain why the sample VC2 had little improvement associated with a high rejection rate.
CONCLUSION We studied the spectral characteristics of laserinduced coal particle flow in the air environment at atmospheric pressure. The partial breakdown spectra were mainly due to non-uniform coal particle flow. The background value of the emission plus three times the standard deviation of the background from a representative spectrum was chosen as the threshold value. We compared two methods for spectral identification: a single-line method and the method using combined multiple emission lines: C (247.8 nm), N (746.8 nm), Si (288.2 nm) and Ca (396.8 nm). The single-line method identified all the representative spectra but did not reject all the partial breakdown spectra. The method using combined multiple emission lines rejected most (. 98%) of the partial breakdown spectra and provided robust spectral identification of coal particle flow. We used the quantitative analysis of coal properties to verify the feasibility of rejecting the partial breakdown spectra in laser-induced coal particle flow plasma. Rejecting the partial breakdown spectra not only reduced the uncertainty but also improved the calibration model and prediction. The RMSE of prediction improved from 7.30 to 2.40% after the removal of the partial breakdown spectra. This information is a step forward in the development of detection using pulverized coal and the on-line detection of coal qualities using LIBS. ACKNOWLEDGMENTS The authors acknowledge the Electric Power Research Institute of Guangdong Power Grid Co. Ltd. (Guangzhou, China) for the coal analysis. Special thanks to the National Natural Science Foundation of China (51206055), the Foundation of State Key Laboratory of Coal Combustion (FSKLCC1106), the Fundamental Research Funds for the Central Universities (2012ZM0014), the Research Foundation of Education Bureau of Guangdong Province (2012LYM_0018), and the Natural Science Foundation of Guangdong Province (S2012040007220) for financial support of this work. We thank LetPub for its linguistic assistance during the preparation of this manuscript. 1. D.A. Cremers, L.J. Radziemski. Handbook of Laser-Induced Breakdown Spectroscopy. Chichester, UK: John Wiley and Sons, 2006. 2. A.W. Miziolek, V. Palleschi, I. Schechter. Laser-Induced Breakdown Spectroscopy (LIBS): Fundamentals and Applications. Cambridge, UK: Cambridge University Press, 2006. 3. D.A. Rusak, B.C. Castle, B.W. Smith, J.D. Winefordner. ‘‘Recent Trends and the Future of Laser-Induced Plasma Spectroscopy’’. TrAC-Trend. Anal. Chem. 1998. 17(8-9): 453-461.
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4. L.C. Nunes, G.A. da Silva, L.C. Trevizan, D.S. Ju´nior, R.J. Poppi, F.J. Krug. ‘‘Simultaneous Optimization by Neuro-Genetic Approach for Analysis of Plant Materials by Laser Induced Breakdown Spectroscopy’’. Spectrochim. Acta B. 2009. 64(6): 565-572. 5. F.C. De Lucia, J.L. Gottfried, A.W. Miziolek. ‘‘Evaluation of Femtosecond Laser-Induced Breakdown Spectroscopy for Explosive Residue Detection’’. Opt. Express. 2009. 17(2): 419-425. 6. D.K. Ottesen, L.J. Radziemski. ‘‘In Situ Elemental Analysis of Particulate Fuels Busing Laser Spark Spectroscopy’’. In: Optical Society of America (Ed.), Topical Meeting on Laser Applications to Chemical Analysis: Summaries of Papers Presented at the Laser Applications to Chemical Analysis Topical Meeting, January 26-29, 1987, Incline Village, Nevada. Washington, DC: Optical Society of America, 1987. Pp. 67-70. 7. D.K. Ottesen, L.J. Radziemski, J.C.F. Wang. ‘‘Real-Time Laser Spark Spectroscopy of Particulates in Combustion Environments’’. Appl. Spectrosc. 1989. 43(6): 967-976. 8. D.K. Ottesen, L.L. Baxter, L.J. Radziemski, J.F. Burrows. ‘‘Laser Spark Emission Spectroscopy for In Situ, Real-Time Monitoring of Pulverized Coal Particle Composition’’. Energ. Fuel. 1991. 5(2): 304312. 9. D.K. Ottesen. ‘‘The Development of Laser Spark Emission Spectroscopy for the Characterization of Ash Deposits’’. Symposium (International) on Combustion. 1992. 24(1): 1579-1585. 10. F.J. Wallis, B.L. Chadwick, R.J.S. Morrison. ‘‘Analysis of Lignite Using Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 2000. 54(8): 1231-1235. 11. D. Body, B.L. Chadwick. ‘‘Optimization of the Spectral Data Processing in a LIBS Simultaneous Elemental Analysis System’’. Spectrochim. Acta B. 2001. 56(6): 725-736. 12. B.L. Chadwick, D. Body. ‘‘Development and Commercial Evaluation of Laser-Induced Breakdown Spectroscopy Chemical Analysis Technology in the Coal Power Generation Industry’’. Appl. Spectrosc. 2002. 56(1): 70-74. 13. M. Noda, Y. Deguchi, S. Iwasaki, N. Yoshikawa. ‘‘Detection of Carbon Content in a High-Temperature and High-Pressure Environment Using Laser-Induced Breakdown Spectroscopy’’. Spectrochim. Acta B. 2002. 57(4): 701-709. 14. Y. Deguchi, M. Noda, Y. Fukuda, Y. Ichinose, Y. Endo, M. Inada, Y. Abe, S. Iwasaki. ‘‘Industrial Applications of Temperature And Species Concentration Monitoring Using Laser Diagnostics’’. Meas. Sci. Technol. 2002. 13(10): 103-105. 15. M. Gaft, I. Sapir-Sofer, H. Modiano, R. Stana. ‘‘Laser Induced Breakdown Spectroscopy for Bulk Minerals Online Analyses’’. Spectrochim. Acta B. 2007. 62(12): 1496-1503. ˜ 16. M.P. Mateo, G. Nicolas, A. Yanez. ‘‘Characterization of Inorganic Species in Coal by Laser-Induced Breakdown Spectroscopy Using UV and IR Radiations’’. Appl. Surf. Sci. 2007. 254(4): 868-872. 17. L. Zhang, L. Dong, H.P. Dou, W.B. Yin, S.T. Jia. ‘‘Laser-Induced Breakdown Spectroscopy for Determination of the Organic Oxygen Content in Anthracite Coal Under Atmospheric Conditions’’. Appl. Spectrosc. 2008. 62(4): 458-463. ˜ 18. T. Ctvrtnickova, M.P. Mateo, A. Yanez, G. Nicolas. ‘‘Application of LIBS and TMA for the Determination of Combustion Predictive Indices of Coals and Coal Blends’’. Appl. Surf. Sci. 2011. 257(12): 5447-5451. 19. J. Feng, Z. Wang, L. West, Z. Li, W. Ni. ‘‘A PLS Model Based on Dominant Factor for Coal Analysis Using Laser-Induced Breakdown Spectroscopy’’. Anal Bioanal Chem. 2011. 400(10): 3261-3271. 20. Z. Wang, T.B. Yuan, S.L. Lui, Z.Y. Hou, X.W. Li, Z. Li, W.D. Ni. ‘‘Major Elements Analysis in Bituminous Coals Under Different Ambient Gases by Laser-Induced Breakdown Spectroscopy with PLS Modeling’’. Front. Phys. 2012. 7(6): 708-713. 21. L. Yu, J. Lu, C. Xie, W. Chen, G. Wu, K. Shen, W. Feng. ‘‘Analysis of Pulverized Coal by Laser-Induced Breakdown Spectroscopy’’. Plasma Sci. Technol. 2005. 7(5): 3041-3044. 22. J. Li, J. Lu, Z. Lin, S. Gong, C. Xie, L. Chang, L. Yang, P. Li. ‘‘Effects of Experimental Parameters on Elemental Analysis of Coal by Laser-Induced Breakdown Spectroscopy’’. Opt. Laser Technol. 2009. 41(8): 907-913. 23. S. Yao, J. Lu, M. Dong, K. Chen, J. Li, J. Li. ‘‘Extracting Coal Ash Content from Laser-Induced Breakdown Spectroscopy (LIBS) Spectra by Multivariate Analysis’’. Appl. Spectrosc. 2011. 65(10): 1197-1201. 24. M. Dong, J. Lu, S. Yao, J. Li, J. Li, Z. Zhong, W. Lu. ‘‘Application of LIBS for Direct Determination of Volatile Matter Content In Coal’’. J. Anal. Atom. Spectrom. 2011. 26(11): 2183-2188.
25. W. Yin, L. Zhang, L. Dong, W. Ma, S. Jia. ‘‘Design of a LaserInduced Breakdown Spectroscopy System for On-Line Quality Analysis of Pulverized Coal in Power Plants’’. Appl. Spectrosc. 2009. 63(8): 865-872. 26. X.X. Sun. Technology and Method of Combustion Test for CoalFired Boiler. Beijing, China: China Electric Power Press, 2001. [in Chinese.] 27. NIST. ‘‘Atomic Spectra Database, Version 3.1.5’’. NIST Physical Measurement Laboratory. 2010. http://www.nist.gov/pml/data/asd. cfm [accessed Mar 25 2010]. 28. ASTM International. ‘‘ASTM C136-06: Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates’’. 2006. http://www. astm.org/Standards/C136.htm [accessed Feb 15 2006]. 29. L.J. Radziemski, T.R. Loree, D.A. Cremers, N.M. Hoffman. ‘‘TimeResolved Laser-Induced Breakdown Spectrometry of Aerosols’’. Anal. Chem. 1983. 55(8): 1246-1252. 30. S. Yalcin, D.R. Crosley, G.P. Smith, G.W. Faris. ‘‘Spectroscopic Characterization of Laser-Produced Plasmas for In Situ Toxic Metal Monitoring’’. Hazard. Waste Hazard. 1996. 13(1): 51-61. 31. U. Panne, R.E. Neuhauser, M. Theisen, H. Fink, R. Niessner. ‘‘Analysis of Heavy Metal Aerosols on Filters by Laser-Induced Plasma Spectroscopy’’. Spectrochim. Acta B. 2001. 56(6): 839-850. 32. G. Gallou, J.B. Sirven, C. Dutouquet, O. Le Bihan, E. Frejafon. ‘‘Aerosols Analysis by LIBS for Monitoring of Air Pollution by Industrial Sources’’. Aerosol Sci. Technol. 2011. 45(8): 918-926. 33. C. Lazzari, M. DeRosa, S. Rastelli, A. Ciucci, V. Palleschi, A. Salvetti. ‘‘Detection of Mercury in Air by Time-Resolved LaserInduced Breakdown Spectroscopy Technique’’. Laser Part. Beams. 1994. 12(3): 525. 34. D.W. Hahn, W.L. Flower, K.R. Hencken. ‘‘Discrete Particle Detection and Metal Emissions Monitoring Using Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 1997. 51(12): 1836-1844. 35. L.G. Blevins, C.R. Shaddix, S.M. Sickafoose, P.M. Walsh. ‘‘LaserInduced Breakdown Spectroscopy at High Temperatures in Industrial Boilers and Furnaces’’. Appl. Optics. 2003. 42(30): 61076118.
36. J.E. Carranza, B.T. Fisher, G.D. Yoder, D.W. Hahn. ‘‘On-Line Analysis of Ambient Air Aerosols Using Laser-Induced Breakdown Spectroscopy’’. Spectrochim. Acta B. 2001. 56(6): 851-864. 37. L.A. A´lvarez-Trujillo, A. Ferrero, J.J. Laserna, D.W. Hahn. ‘‘Alternative Statistical Methods for Spectral Data Processing: Applications to Laser-Induced Breakdown Spectroscopy of Gaseous and Aerosol Systems’’. Appl. Spectrosc. 2008. 62(10): 1144-1152. 38. R. Noll. Laser-Induced Breakdown Spectroscopy: Fundamentals and Applications. Berlin: Springer-Verlag, 2012. 39. M.A. Ismail, H. Imam, A. Elhassan, W.T. Youniss, M.A. Harith. ‘‘LIBS Limit of Detection and Plasma Parameters of Some Elements in Two Different Metallic Matrices’’. J. Anal. Atom. Spectrom. 2004. 19(4): 489-494. 40. R.A. Dragoset, A. Musgrove, C.W. Clark, W.C. Martin. ‘‘Periodic Table: Atomic Properties of the Elements’’. NIST Physical Measurement Laboratory. 1999. http://www.nist.gov/pml/data/periodic. cfm. [accessed Apr 1999]. 41. S. Palanco, J.J. Laserna. ‘‘Full Automation of a Laser-Induced Breakdown Spectrometer for Quality Assessment in the Steel Industry with Sample Handling, Surface Preparation and Quantitative Analysis Capabilities’’. J. Anal. Atom. Spectrom. 2000. 15(10): 1321-1327. 42. S. Yao, J. Lu, J. Zheng, M. Dong. ‘‘Analyzing Unburned Carbon in Fly Ash Using Laser-Induced Breakdown Spectroscopy with Multivariate Calibration Method’’. J. Anal. Atom. Spectrom. 2012. 27(3): 473-478. 43. M.M. Tripathi, K.E. Eseller, F.Y. Yueh, J.P. Singh. ‘‘Multivariate Calibration of Spectra Obtained by Laser Induced Breakdown Spectroscopy of Plutonium Oxide Surrogate Residues’’. Spectrochim. Acta B. 2009. 64(11-12): 1212-1218. 44. Z. Wang, J. Feng, L. Li, W. Ni, Z. Li. ‘‘A Non-Linearized PLS Model Based on Multivariate Dominant Factor for Laser-Induced Breakdown Spectroscopy Measurements’’. J. Anal. At. Spectrom. 2011. 26(11): 2175-2182.
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Laser-induced breakdown spectroscopy (LIBS) was employed to directly analyze coal particles in the form of descending flow. Coalparticle ablation was performed using a 1064 nm neodymiumdoped yttrium aluminum garnet (Nd : YAG) laser at atmospheric conditions. Spectral identification schemes were used to acquire spectra containing all the emission lines of the important elements in coal. These acquired spectra were classified as representative spectra. The background of the line emission plus three times the standard deviation of the background of the representative spectra was chosen as the threshold value. A method using a single line and a method using combined multiple lines (C, 247.8 nm; N, 746.8 nm; Si, 288.2 nm; and Ca, 396.8 nm) were compared to obtain the best results for the spectral identification of coal particle flow. The feasibility of rejecting the partial breakdown spectra was verified using quantitative analysis of fixed carbon in coal. Index Headings: Coal particles; LIBS; Spectral identification; Representative spectra; Partial breakdown spectra.
INTRODUCTION Laser-induced breakdown spectroscopy (LIBS) is a powerful analytical technique that offers the in situ, rapid, highly selective, and sensitive detection and analysis of both natural and manufactured materials. In LIBS, a short-pulsed, high-power density laser pulse is focused on a target material. A small portion of the sample is vaporized to generate a high-temperature plasma. As the plasma plume expands and cools, the excited atomic, ionic, and molecular fragments emit radiation characteristic of the elemental composition of the material. There has been significant progress in the commercial application of this technology in identifying elements, material identification, process monitoring, material sorting, and on-site screening.1–5 The determination of the chemical composition of coal prior to combustion is vitally important for a coal-fired power plant to obtain optimal boiler performance. Laserinduced breakdown spectroscopy was tested for determining coal combustion diagnostics by Ottesen and colleagues6–8 as early as the late 1980s at the Combustion Research Facility at Sandia National Laboratories. The method was also developed as an in situ, real-time monitor of the elemental composition of ash deposits formed during coal combustion processes.9 Wallis et Received 4 September 2013; accepted 28 January 2014. * Author to whom correspondence should be sent. E-mail: [email protected]. DOI: 10.1366/13-07278
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al.10 and Brody and Chadwick,11,12 at the Cooperative Research Centre for Clean Power from Lignite, developed and applied the LIBS method to the coal power industry for the analysis of Australian lignite. A few metallic elements and silicon were detected in lignite samples using calibration curves from standard samples. The detection limits were found to be between 60 and 200 parts per million (ppm).10–12 Noda et al.13 and Deguchi et al.14 used LIBS to detect unburned carbon in fly ash in a combustion field. The application of LIBS to the elemental analysis of coal was also investigated by Gaft et al.15 and Mateo et al.16 Zhang et al.17 measured the organic oxygen content of pulverized anthracite coal under atmospheric conditions using LIBS and found an average relative error in the quantitative measurement of 19.4%. Ctvrtnickova et al.18 used LIBS and thermomechanical analysis to determine the elemental composition of coal (C, H, Si, Al, Fe, Ti, Ca, Mg, Na, K, Mn, Sr, and Ba) and predict the slag propensity for five coal blends. Feng et al.19 and Wang et al.20 used LIBS combined with partial least squares (PLS) regression to analyze the elemental content of coal. Our group also performed a series of studies analyzing coal quality, evaluating element detection21,22 and doing a proximate analysis (volatile matter and ash) using multivariate analysis.23,24 The samples used in the reports mentioned so far were in the form of coal pellets, prepared from pulverized coal. Yin et al.25 designed a LIBS system for the on-line quality analysis of pulverized coal in power plants using a complicated sampling module. Samples from the entrance pipe were transferred to the sample holder and the loading was determined using a signal receiver. Redundant coal particles were scraped away to smooth the sample surface in the hold for LIBS analysis. The sampling-preparation module contained a cyclone collector, vibrator, transmitter, receiver, and scraper. The processes of operation and control of the sample collection and loopback were complicated. Adequate coal samples in the form of particle flows from the power plant were sampled from the transmission pipes before they entered the furnace. However, this system would be greatly simplified if LIBS were used to analyze the coal particle flow directly. An on-line LIBS system would be more appropriate for longtime on-site monitoring of coal properties and power plant control. We evaluated the unique application of LIBS as a coal particle flow monitoring technology. In this study, we performed a direct analysis of coal properties in the form
0003-7028/14/6806-0672/0 Q 2014 Society for Applied Spectroscopy
APPLIED SPECTROSCOPY
FIG. 1. Schematic diagram of the LIBS experimental setup for coal particle flow analysis.
of a particle flow. A gas–solid system was used to produce coal particles for laser ablation. A suitable spectral identification scheme was developed to identify representative spectra and characterize coal content. In addition, the quantitative analysis of fixed carbon in coal in the form of particle flow was used to verify the necessity of rejecting partial breakdown spectra.
EXPERIMENTAL To simulate coal particle flow in power plants, a gas– solid system was developed to produce coal particles for direct LIBS analysis (Fig. 1). The system for producing the particle flow contained an ejector, cyclone separator, measuring chamber, pipes, and the other accessories. The pulverized coal (particle diameter, dp , 200 lm; particle density, qc,ac 1.4 g/cm3) in the feeder was carried to a coal blast pipe by compressed air at the entrance of the ejector. The particle flow rate was controlled by adjusting the valve between the feeder and the ejector. The coal particle flow was transferred to the cyclone separator to separate the gas from the particles. The exhaust gas was drawn out through the upper exit of the cyclone, and the coal particles fell downward into the measurement chamber through the lower exit pipe. The coal particles were collected in a bottle after measurement. The flow rate of particles per unit of chamber inlet area, with a pipe diameter of 4 mm, was kept at 0.73 6
0.02 g min1 mm2). This corresponded to a volume concentration of particles of 0.9 %. Volume concentration was defined as the particle volume per unit volume of gas volume and was reported as a percentage. A Q-switch neodymium-doped yttrium aluminum garnet (Nd : YAG) laser operating at 1064 nm with a 4 ns pulse duration was used as the ablation source. Pulse energy was attenuated to 42 mJ using a Glan polarizer with a repetition rate of 1 Hz. The energy was focused using a 50 mm diameter, 100 mm focal-length lens to create the plasma. The plasma emission was collected at a 458 angle to the incident laser beam and fibercoupled (core diameter, 400 lm) to a dual-channel spectrometer (AvaSpec-2048; Avantes, Holland). Each channel contained a separate grating and chargecoupled device (CCD) array. All the spectra were measured simultaneously using a spectral resolution of 0.2–0.3 nm in the spectral coverage of 235–345 nm (channel 1) and 580–790 nm (channel 2). These spectral ranges covered the emission lines of the elements of interest for the coal-quality analysis. Samples that are commonly used in coal-fired power plants in China were selected for this experiment. The results of the proximate analysis of coal samples (airdried basis) are listed in Table I. The analysis was performed using a thermogravimetric analyzer (TGA). We used 22 samples in this experiment; 17 (C1–C17) were used to construct the calibration model, and the
TABLE I. Proximate analysis of coal samples (air-dried basis) using TGA. Sample number C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
Moisture (%)
Volatiles (%)
Ash (%)
Fixed carbon (%)
Sample number
Moisture (%)
Volatiles (%)
Ash (%)
Fixed carbon (%)
12.16 4.59 10.14 5.14 2.55 3.98 6.41 3.36 2.24 3.98 3.88
13.63 10.39 3.24 6 20.44 38.13 16.52 17.84 19.21 12.21 12.21
33.04 43.58 43.32 40.88 28.79 8.52 26.98 27.5 26.21 30.32 28.78
41.17 41.44 43.3 47.98 48.22 49.37 50.09 51.3 52.34 53.49 55.13
C12 C13 C14 C15 C16 C17 VC1 VC2 VC3 VC4 VC5
3.48 4.32 1.54 3.02 4.29 1.98 5.62 6.88 4.34 4.04 2.66
12.31 9.6 25.98 27.36 24.69 19.88 3.12 6.26 4.7 11.27 13.49
27.85 29.65 12.62 8.39 6.28 10.71 45.28 40.24 40.96 29.51 24.4
56.36 56.43 59.86 61.23 64.74 67.43 45.98 46.62 50 55.18 59.45
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FIG. 2. Representative spectra of laser-induced coal particle flow.
remaining five (VC1–VC5) were used to validate the measurement accuracy and reproducibility of LIBS. The optimum delay time between the output-triggered signal for the laser pulse and data acquisition by the spectrometer was set at 1.3 ls. The integration time gate was 1.1 ms, the minimum value for the spectrometer. The LIBS spectrum was recorded for each laser pulse using direct analysis mode. A total of 1500 individual spectra were stored for each analyzed sample.
RESULTS AND DISCUSSION Emission Spectra of Coal Particle Flow. Coal is a heterogeneous material with a complex chemical and physical structure. It contains almost all the elements in the periodic table. The particle flow of vaporized coal is obviously different from that of coal pellets. In a vaporized sample, the particles in the flow are discrete and their spatial distribution is related to the state of the particle flow. Coal quality analysis includes many studies, including ultimate analysis (of C, H, O, N, and S), proximate analysis (of volatile matter, ash, moisture, fixed carbon, and calorific value), ash composition (aluminum oxide (Al2O3), silicon dioxide (SiO2), sodium oxide (Na2O), potassium oxide (K2O), calcium oxide (CaO), magnesium oxide (MgO), titanium dioxide (TiO2), and iron(III) oxide (Fe2O3)), and slagging factor.26 The most important elements to be identified in coal are C, H, O, N, S, Si, Al, Fe, Ca, Mg, Ti, Na, and K. Figure 2 shows representative spectra containing the emission lines of these elements in the ranges 235–345 and 580–790 nm. The spectra were identified using National Institute for Standards and Technology (NIST) databases.27 Some of the acquired spectra did not contain all the emission lines of the most important elements (Figs. 3 and 4). We classified these as partial breakdown spectra. Two types of the partial breakdown spectra were identified: those with different amounts of coal particles for laser ablation (Figs. 3a–3d) and those consisting of the air breakdown signals (N, H, and O) and the emissions of the elements K, Ca, and Na due to a lack of coal particle ablation (Fig. 4). There are several possible explanations for partial breakdown spectra. First, the distribution of particle size
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was not uniform. We measured the particle size distribution of one sample by the sieving method in ASTM C136-06.28 In this sample, 28% of the particles were 100–200 lm in diameter, 31% were 70–100 lm, 36% were 54–70 lm, and 5% were , 54 lm. Second, fluctuation in the coal particle flow is inevitable and induced different concentrations in the finite laserinduced plasma volume. Third, the coal particle flow was analyzed in a column, resulting in some coal particles being located in front of the focal point, absorbing the laser energy and blocking the laser ablation of coal particles in the focal region. The most important reason for the existence of partial breakdown spectra is non-uniform coal particle flow. These limitations required that we design a system that would reject partial breakdown spectra and identify representative spectra and analyze them. Spectral Identification. The evaluation of particulates smaller than 10 lm that can be completely dissociated into plasma has been reported.29,30 Laser-induced breakdown spectroscopy has been used to analyze aerosols31,32, vapors,33 and combustion particulates7,34,35 containing Be, Pb, and Hg, where most of the particles were in a suspension. For processing aerosol LIBS spectra, a scheme called conditional data analysis was developed by Hahn et al.,34 Carranza et al.,36 and A´lvarez-Trujillo et al.37 A threshold value corresponding to the intensity of an adjacent and featureless spectral region was needed to standardize the spectra being analyzed. The intensity of the emission lines of the targeted species in each LIBS spectrum was then compared to the threshold value. If the ratio of the emission peak intensity to background exceeded the threshold value, the spectrum was classified as a representative spectrum. If not, the spectrum was classified as a partial breakdown spectrum.34,36 Background intensity follows a normal distribution. Emissions can be identified according to their confidence intervals of normal distribution. When the emission intensity was larger than its average background plus three times the standard deviation of the chosen background, it was classified as a detected signal rather than as background.38 The confidence coefficient of this method was higher than 99%, and the possibility of considering the background to be the detected signal due to background fluctuations was less than 1%. However, the standard deviation of the background and the emission lines were similar in our measurements. Therefore, we chose the averaged value of the background plus three times the standard deviation of the representative spectra as the threshold value. Two spectral identification schemes were evaluated: one using a single element emission line and one using combined multiple emission lines. Single elemental emission lines such as C (247.8 nm), H (656.28 nm), and N (746.83 nm) were chosen to evaluate the singleline method (Table II). Coal contains non-metal and metal elements with very different physical properties. The ionization potential (IP) of these elements ranges from 418 to 1402 kJ/mol, which affects the plasma temperature and electron density.39 The relevant elements were divided into five groups
FIG. 3. Partial breakdown of spectra. (a) Containing Na and K emissions. (b) Containing K, Na, Ca, and Al emissions. (c) Containing K, Na, Ca, Al, Mg, and Si emissions. (d) Containing K, Na, Ca, Al, Mg, Fe, Ti, Si, and C emissions.
based on their IP:40 (1) K and Na (418–496 kJ/mol); (2) Ca and Al (577–590 kJ/mol); (3) Ti, Mg, Fe, and Si (658–787 kJ/mol); (4) C (1000 kJ/mol); and (5) H, O, and N (1300– 1400 kJ/mol). The emissions of the group 1 elements (Na and K) are present in every spectrum. One element was chosen from each of the other four groups, and the four elements were used together to characterize the test spectra. The representative emissions selected in the ablation coal particle spectra were C (247.8 nm), N (746.8 nm), Si (288.2 nm), and Ca (396.8 nm) (Fig. 5). All the chosen elements are typically found in coal. The background regions were the horizontal section of spectra adjacent to the C, N, Si, and Ca peak lines (Fig. 5). This identification scheme was applied to all the coal samples. A comparison of the single-line method and the method using multiple emission lines is presented in Table II. For brevity, only one calibration sample (C9) and one validation sample (VC3) are presented in the table. The rejection rate was defined as the ratio of the number of the removed spectra to the 1500 total spectra. Two parameters were used to evaluate the spectral identification methods: the rejection rates of both the
partial breakdown spectra and representative spectra. The rejection rate of partial breakdown spectra was defined as the ratio of the partial breakdown spectra in the removed spectra to the total partial breakdown
FIG. 4. Partial breakdown spectra containing K, Ca, Na, N, H, and O but not C.
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TABLE II. Comparison of the different spectral identification schemes for two coal samples. Sample number C9
VC3
Spectral identification schemes Single emission line H N C Multiple emission lines Single emission line H N C Multiple emission lines
Rejection rate (%)
Rejection rate of partial breakdown spectra (%)
Rejection rate of representative spectra (%)
11.2 13.5 12.6 17.8
62.2 75.2 70 97.8
0 0 0 2.4
9.3 10.3 9.6 13.7
62.2 68.4 64 100
0 0 0 2.1
spectra; a higher value indicated a better result. Similarly, the rejection rate of representative spectra was defined as the ratio of the representative spectra in removed spectra to the total representative spectra; here a lower value indicated a better result. The method using multiple element emission lines had a higher rejection rate for partial breakdown spectra and a lower rejection rate for the representative spectra. Although the rejection rate of representative spectra using the single-line method was low, there were still some partial breakdown spectra that were not removed. Moreover, multiple element monitoring was needed for the measurement of the coal particle composition, and the physical properties of the elements of interest are significantly different. Therefore, it is better to use the combined multiple lines method for spectral identification. The results in Table II were calculated using a program and verified manually to make sure the method we chose is available for spectral identification. Evaluation of the Quantitative Analysis of Fixed Carbon in Coal. The physical properties and composition of the samples affected the emission signal of the laser-induced plasma. Chemometrics, such as linear multivariate regression41,42 and linear regression using a PLS algorithm,19,43,44 has been used to improve the accuracy of elemental quantitative analyses. Multivari-
ate calibration has been shown to be an alternative method for correcting the matrix effect. We have also used the multivariate analysis method to extract spectral information related to coal properties (volatile matter and ash).23,24 Fixed carbon content, expressed as a percentage, is the residue remaining after the removal of moisture, ash, and volatile materials from coal. The concentration of fixed carbon is one of the most important indices used in the classification and quality assessment of coal. In traditional off-line measurements, the fixed carbon content is determined as: FCad ¼ 100 ðMad þ Vad þ Aad Þ
where FCad, Mad, Vad, and Aad represent the fixed carbon, moisture, volatile matter, and ash content, respectively, of air-dried basis coal. Eq. 1 shows that fixed carbon is indirectly measured by calculating the other components and subtracting them from the whole to find the difference. The direct quantitative analysis of fixed carbon content will greatly facilitate the on-line monitoring of coal quality. Twenty-two samples were evaluated in this study. Seventeen of these (C1–C17) were used to construct a calibration model, and the rest (VC1–VC5) were used to validate the model. A multiple linear regression method was used to construct a model for
FIG. 5. Background region. (a) For C and Si. (b) For Ca and N.
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ð1Þ
FIG. 6. Comparison between LIBS predicted value and standard analysis procedures for the fixed carbon content of 17 calibration coal samples (C1–C17). (a) Without rejection of partial breakdown spectra. (b) With rejection of partial breakdown spectra.
regression was used to determine the effective input variables and establish the calibration model. The total measured spectra and spectra with partialbreakdown spectra removed were used to establish the calibration model. The LIBS predicted values (with and without rejection) and those from a standard analysis procedure, thermogravimetric analysis (TGA), for the fixed carbon were compared. Seventeen calibration samples (C1–C17) and five validation samples (VC1– VC5) were used. The correlation coefficients without and with rejection for the global prediction curve (Figs. 6a and 6b) were 0.921 and 0.930, respectively. The root mean square error of calibration (RMSEC) was improved from 2.86 to 2.92% and the root mean square error of prediction (RMSEP) was reduced from 7.30 to 2.40% after removal of the partial breakdown spectra. The five samples used to validate this method (VC1– VC5) were then tested (Table III). The same averaged number of the laser shots (250) was used to evaluate the repeatability of the measurement. The uncertainty for the 1500 total measured spectra were calculated using six replicates and three to five replicates for the spectra after rejection, depending on the rejection rate of different samples with the same number of total spectra. Rejecting the partial breakdown spectra improved the calibration model and prediction. However, note that the type of partial breakdown spectra removed affected the improvement of the model. For example, samples VC2 and VC4 had a high rejection rate. Most of the partial
evaluating the fixed carbon content of coal. The multiple linear regression method characterized the outcome and fixed carbon content, using n independent variables xi. This was expressed as: ym ¼ am þ
n X
si x i þ e m
ð2Þ
i¼1
where ym is the measured fixed carbon content, am is the intercept, em is the residual error, and si is the multivariate regression coefficient determined by PLS estimation. The predicted residual sum of the squares, P S ¼ ni¼1 e2i , was minimized. The independent variables xi represent the selected spectral features. In a multiple linear regression analysis, it is very important to determine the correct independent variables. Statistical methods (F test and t test) are used to evaluate the significance of each variable to establish an effective calibration equation. There are three common independent variable screening methods: forward selection, backward selection, and stepwise selection. In the model for the quantitative analysis of fixed carbon in coal, the emission line intensities of the main elements—C (247.8 nm), Ca (393.4 nm), Al (308.2 nm), Fe (344.1 nm), Mg (285.1 nm), Si (288.1 nm), H (656.3 nm), O (777.2 nm), N (746.8 nm), and K (766.5 nm)—were chosen as the independent variables. Backward selection linear
TABLE III. Analytical results using the LIBS method for validation samples. LIBS value without rejection Validated sample VC1 VC2 VC3 VC4 VC5
Reference value (wt %) 45.98 46.62 50.00 55.18 59.45
LIBS value with rejection
Predicted value (wt %)
Absolute error (wt %)
Relative error (wt.%)
6 6 6 6 6
4.2 2.1 4.2 15.1 0.2
9.1 4.6 8.3 27.3 0.4
41.8 44.5 45.8 40.1 59.2
1.5 1.0 0.9 0.9 1.2
Predicted value (wt %)
Absolute error (wt %)
Relative error (wt %)
Rejection rate (%)
6 6 6 6 6
3.3 1.5 3.8 0.6 1.0
7.3 3.3 7.5 1.1 1.6
8.13 35.93 13.73 35.07 23.6
42.7 45.1 46.3 55.8 60.4
0.6 1.0 0.7 0.5 0.5
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breakdown spectra in sample VC4 belonged to the second type of the partial breakdown spectra (weak carbon signal and strong air breakdown signal). The fixed carbon in coal has strong relationship with the carbon signal, and the weight of the carbon signal in the quantitative analysis model (Eq. 2) is high. In contrast, most of the rejected spectra were of the first type (containing only some of the elements of interest). The averaged value was used for the quantitative analysis. The averaged intensities of the emission lines as the independent variables were increased had a similar ratio after rejection, and there was no extremely small value to affect this tendency of the independent variables; this may explain why the sample VC2 had little improvement associated with a high rejection rate.
CONCLUSION We studied the spectral characteristics of laserinduced coal particle flow in the air environment at atmospheric pressure. The partial breakdown spectra were mainly due to non-uniform coal particle flow. The background value of the emission plus three times the standard deviation of the background from a representative spectrum was chosen as the threshold value. We compared two methods for spectral identification: a single-line method and the method using combined multiple emission lines: C (247.8 nm), N (746.8 nm), Si (288.2 nm) and Ca (396.8 nm). The single-line method identified all the representative spectra but did not reject all the partial breakdown spectra. The method using combined multiple emission lines rejected most (. 98%) of the partial breakdown spectra and provided robust spectral identification of coal particle flow. We used the quantitative analysis of coal properties to verify the feasibility of rejecting the partial breakdown spectra in laser-induced coal particle flow plasma. Rejecting the partial breakdown spectra not only reduced the uncertainty but also improved the calibration model and prediction. The RMSE of prediction improved from 7.30 to 2.40% after the removal of the partial breakdown spectra. This information is a step forward in the development of detection using pulverized coal and the on-line detection of coal qualities using LIBS. ACKNOWLEDGMENTS The authors acknowledge the Electric Power Research Institute of Guangdong Power Grid Co. Ltd. (Guangzhou, China) for the coal analysis. Special thanks to the National Natural Science Foundation of China (51206055), the Foundation of State Key Laboratory of Coal Combustion (FSKLCC1106), the Fundamental Research Funds for the Central Universities (2012ZM0014), the Research Foundation of Education Bureau of Guangdong Province (2012LYM_0018), and the Natural Science Foundation of Guangdong Province (S2012040007220) for financial support of this work. We thank LetPub for its linguistic assistance during the preparation of this manuscript. 1. D.A. Cremers, L.J. Radziemski. Handbook of Laser-Induced Breakdown Spectroscopy. Chichester, UK: John Wiley and Sons, 2006. 2. A.W. Miziolek, V. Palleschi, I. Schechter. Laser-Induced Breakdown Spectroscopy (LIBS): Fundamentals and Applications. Cambridge, UK: Cambridge University Press, 2006. 3. D.A. Rusak, B.C. Castle, B.W. Smith, J.D. Winefordner. ‘‘Recent Trends and the Future of Laser-Induced Plasma Spectroscopy’’. TrAC-Trend. Anal. Chem. 1998. 17(8-9): 453-461.
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4. L.C. Nunes, G.A. da Silva, L.C. Trevizan, D.S. Ju´nior, R.J. Poppi, F.J. Krug. ‘‘Simultaneous Optimization by Neuro-Genetic Approach for Analysis of Plant Materials by Laser Induced Breakdown Spectroscopy’’. Spectrochim. Acta B. 2009. 64(6): 565-572. 5. F.C. De Lucia, J.L. Gottfried, A.W. Miziolek. ‘‘Evaluation of Femtosecond Laser-Induced Breakdown Spectroscopy for Explosive Residue Detection’’. Opt. Express. 2009. 17(2): 419-425. 6. D.K. Ottesen, L.J. Radziemski. ‘‘In Situ Elemental Analysis of Particulate Fuels Busing Laser Spark Spectroscopy’’. In: Optical Society of America (Ed.), Topical Meeting on Laser Applications to Chemical Analysis: Summaries of Papers Presented at the Laser Applications to Chemical Analysis Topical Meeting, January 26-29, 1987, Incline Village, Nevada. Washington, DC: Optical Society of America, 1987. Pp. 67-70. 7. D.K. Ottesen, L.J. Radziemski, J.C.F. Wang. ‘‘Real-Time Laser Spark Spectroscopy of Particulates in Combustion Environments’’. Appl. Spectrosc. 1989. 43(6): 967-976. 8. D.K. Ottesen, L.L. Baxter, L.J. Radziemski, J.F. Burrows. ‘‘Laser Spark Emission Spectroscopy for In Situ, Real-Time Monitoring of Pulverized Coal Particle Composition’’. Energ. Fuel. 1991. 5(2): 304312. 9. D.K. Ottesen. ‘‘The Development of Laser Spark Emission Spectroscopy for the Characterization of Ash Deposits’’. Symposium (International) on Combustion. 1992. 24(1): 1579-1585. 10. F.J. Wallis, B.L. Chadwick, R.J.S. Morrison. ‘‘Analysis of Lignite Using Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 2000. 54(8): 1231-1235. 11. D. Body, B.L. Chadwick. ‘‘Optimization of the Spectral Data Processing in a LIBS Simultaneous Elemental Analysis System’’. Spectrochim. Acta B. 2001. 56(6): 725-736. 12. B.L. Chadwick, D. Body. ‘‘Development and Commercial Evaluation of Laser-Induced Breakdown Spectroscopy Chemical Analysis Technology in the Coal Power Generation Industry’’. Appl. Spectrosc. 2002. 56(1): 70-74. 13. M. Noda, Y. Deguchi, S. Iwasaki, N. Yoshikawa. ‘‘Detection of Carbon Content in a High-Temperature and High-Pressure Environment Using Laser-Induced Breakdown Spectroscopy’’. Spectrochim. Acta B. 2002. 57(4): 701-709. 14. Y. Deguchi, M. Noda, Y. Fukuda, Y. Ichinose, Y. Endo, M. Inada, Y. Abe, S. Iwasaki. ‘‘Industrial Applications of Temperature And Species Concentration Monitoring Using Laser Diagnostics’’. Meas. Sci. Technol. 2002. 13(10): 103-105. 15. M. Gaft, I. Sapir-Sofer, H. Modiano, R. Stana. ‘‘Laser Induced Breakdown Spectroscopy for Bulk Minerals Online Analyses’’. Spectrochim. Acta B. 2007. 62(12): 1496-1503. ˜ 16. M.P. Mateo, G. Nicolas, A. Yanez. ‘‘Characterization of Inorganic Species in Coal by Laser-Induced Breakdown Spectroscopy Using UV and IR Radiations’’. Appl. Surf. Sci. 2007. 254(4): 868-872. 17. L. Zhang, L. Dong, H.P. Dou, W.B. Yin, S.T. Jia. ‘‘Laser-Induced Breakdown Spectroscopy for Determination of the Organic Oxygen Content in Anthracite Coal Under Atmospheric Conditions’’. Appl. Spectrosc. 2008. 62(4): 458-463. ˜ 18. T. Ctvrtnickova, M.P. Mateo, A. Yanez, G. Nicolas. ‘‘Application of LIBS and TMA for the Determination of Combustion Predictive Indices of Coals and Coal Blends’’. Appl. Surf. Sci. 2011. 257(12): 5447-5451. 19. J. Feng, Z. Wang, L. West, Z. Li, W. Ni. ‘‘A PLS Model Based on Dominant Factor for Coal Analysis Using Laser-Induced Breakdown Spectroscopy’’. Anal Bioanal Chem. 2011. 400(10): 3261-3271. 20. Z. Wang, T.B. Yuan, S.L. Lui, Z.Y. Hou, X.W. Li, Z. Li, W.D. Ni. ‘‘Major Elements Analysis in Bituminous Coals Under Different Ambient Gases by Laser-Induced Breakdown Spectroscopy with PLS Modeling’’. Front. Phys. 2012. 7(6): 708-713. 21. L. Yu, J. Lu, C. Xie, W. Chen, G. Wu, K. Shen, W. Feng. ‘‘Analysis of Pulverized Coal by Laser-Induced Breakdown Spectroscopy’’. Plasma Sci. Technol. 2005. 7(5): 3041-3044. 22. J. Li, J. Lu, Z. Lin, S. Gong, C. Xie, L. Chang, L. Yang, P. Li. ‘‘Effects of Experimental Parameters on Elemental Analysis of Coal by Laser-Induced Breakdown Spectroscopy’’. Opt. Laser Technol. 2009. 41(8): 907-913. 23. S. Yao, J. Lu, M. Dong, K. Chen, J. Li, J. Li. ‘‘Extracting Coal Ash Content from Laser-Induced Breakdown Spectroscopy (LIBS) Spectra by Multivariate Analysis’’. Appl. Spectrosc. 2011. 65(10): 1197-1201. 24. M. Dong, J. Lu, S. Yao, J. Li, J. Li, Z. Zhong, W. Lu. ‘‘Application of LIBS for Direct Determination of Volatile Matter Content In Coal’’. J. Anal. Atom. Spectrom. 2011. 26(11): 2183-2188.
25. W. Yin, L. Zhang, L. Dong, W. Ma, S. Jia. ‘‘Design of a LaserInduced Breakdown Spectroscopy System for On-Line Quality Analysis of Pulverized Coal in Power Plants’’. Appl. Spectrosc. 2009. 63(8): 865-872. 26. X.X. Sun. Technology and Method of Combustion Test for CoalFired Boiler. Beijing, China: China Electric Power Press, 2001. [in Chinese.] 27. NIST. ‘‘Atomic Spectra Database, Version 3.1.5’’. NIST Physical Measurement Laboratory. 2010. http://www.nist.gov/pml/data/asd. cfm [accessed Mar 25 2010]. 28. ASTM International. ‘‘ASTM C136-06: Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates’’. 2006. http://www. astm.org/Standards/C136.htm [accessed Feb 15 2006]. 29. L.J. Radziemski, T.R. Loree, D.A. Cremers, N.M. Hoffman. ‘‘TimeResolved Laser-Induced Breakdown Spectrometry of Aerosols’’. Anal. Chem. 1983. 55(8): 1246-1252. 30. S. Yalcin, D.R. Crosley, G.P. Smith, G.W. Faris. ‘‘Spectroscopic Characterization of Laser-Produced Plasmas for In Situ Toxic Metal Monitoring’’. Hazard. Waste Hazard. 1996. 13(1): 51-61. 31. U. Panne, R.E. Neuhauser, M. Theisen, H. Fink, R. Niessner. ‘‘Analysis of Heavy Metal Aerosols on Filters by Laser-Induced Plasma Spectroscopy’’. Spectrochim. Acta B. 2001. 56(6): 839-850. 32. G. Gallou, J.B. Sirven, C. Dutouquet, O. Le Bihan, E. Frejafon. ‘‘Aerosols Analysis by LIBS for Monitoring of Air Pollution by Industrial Sources’’. Aerosol Sci. Technol. 2011. 45(8): 918-926. 33. C. Lazzari, M. DeRosa, S. Rastelli, A. Ciucci, V. Palleschi, A. Salvetti. ‘‘Detection of Mercury in Air by Time-Resolved LaserInduced Breakdown Spectroscopy Technique’’. Laser Part. Beams. 1994. 12(3): 525. 34. D.W. Hahn, W.L. Flower, K.R. Hencken. ‘‘Discrete Particle Detection and Metal Emissions Monitoring Using Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 1997. 51(12): 1836-1844. 35. L.G. Blevins, C.R. Shaddix, S.M. Sickafoose, P.M. Walsh. ‘‘LaserInduced Breakdown Spectroscopy at High Temperatures in Industrial Boilers and Furnaces’’. Appl. Optics. 2003. 42(30): 61076118.
36. J.E. Carranza, B.T. Fisher, G.D. Yoder, D.W. Hahn. ‘‘On-Line Analysis of Ambient Air Aerosols Using Laser-Induced Breakdown Spectroscopy’’. Spectrochim. Acta B. 2001. 56(6): 851-864. 37. L.A. A´lvarez-Trujillo, A. Ferrero, J.J. Laserna, D.W. Hahn. ‘‘Alternative Statistical Methods for Spectral Data Processing: Applications to Laser-Induced Breakdown Spectroscopy of Gaseous and Aerosol Systems’’. Appl. Spectrosc. 2008. 62(10): 1144-1152. 38. R. Noll. Laser-Induced Breakdown Spectroscopy: Fundamentals and Applications. Berlin: Springer-Verlag, 2012. 39. M.A. Ismail, H. Imam, A. Elhassan, W.T. Youniss, M.A. Harith. ‘‘LIBS Limit of Detection and Plasma Parameters of Some Elements in Two Different Metallic Matrices’’. J. Anal. Atom. Spectrom. 2004. 19(4): 489-494. 40. R.A. Dragoset, A. Musgrove, C.W. Clark, W.C. Martin. ‘‘Periodic Table: Atomic Properties of the Elements’’. NIST Physical Measurement Laboratory. 1999. http://www.nist.gov/pml/data/periodic. cfm. [accessed Apr 1999]. 41. S. Palanco, J.J. Laserna. ‘‘Full Automation of a Laser-Induced Breakdown Spectrometer for Quality Assessment in the Steel Industry with Sample Handling, Surface Preparation and Quantitative Analysis Capabilities’’. J. Anal. Atom. Spectrom. 2000. 15(10): 1321-1327. 42. S. Yao, J. Lu, J. Zheng, M. Dong. ‘‘Analyzing Unburned Carbon in Fly Ash Using Laser-Induced Breakdown Spectroscopy with Multivariate Calibration Method’’. J. Anal. Atom. Spectrom. 2012. 27(3): 473-478. 43. M.M. Tripathi, K.E. Eseller, F.Y. Yueh, J.P. Singh. ‘‘Multivariate Calibration of Spectra Obtained by Laser Induced Breakdown Spectroscopy of Plutonium Oxide Surrogate Residues’’. Spectrochim. Acta B. 2009. 64(11-12): 1212-1218. 44. Z. Wang, J. Feng, L. Li, W. Ni, Z. Li. ‘‘A Non-Linearized PLS Model Based on Multivariate Dominant Factor for Laser-Induced Breakdown Spectroscopy Measurements’’. J. Anal. At. Spectrom. 2011. 26(11): 2175-2182.
APPLIED SPECTROSCOPY
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