Rapidly Measuring Unburned Carbon in Fly Ash Using Molecular CN

Jan 18, 2015 - In coal-fired plants, the balance between unburned carbon and NOx emissions stresses the need for rapid and accurate methods for the ...
1 downloads 0 Views 6MB Size
Download PDF
Article pubs.acs.org/EF

Rapidly Measuring Unburned Carbon in Fly Ash Using Molecular CN by Laser-Induced Breakdown Spectroscopy Shunchun Yao,*,†,§,⊥ Yueliang Shen,‡ Kejing Yin,† Gang Pan,†,§ and Jidong Lu*,†,§,⊥ †

School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong 510080, China § Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, Guangzhou, Guangdong 510640, China ‡

ABSTRACT: In coal-fired plants, the balance between unburned carbon and NOx emissions stresses the need for rapid and accurate methods for the measurement of unburned carbon. In this paper, molecular CN was adopted to rapidly measure unburned carbon in fly ashes by laser-induced breakdown spectroscopy technique for the first time. The use of molecular CN overcame the interference between the Fe 247.98 and C 247.86 nm lines and the strong diminishing of the C 193.09 nm line intensity in air. Especially, the multivariate regression method combined with the correction of plasma temperature and selfabsorption was used to construct calibration model. The performance of the calibration model was evaluated by the quantitative analysis of unkown fly ashes from different types of coal. The results show that the averaged relative error of prediction and the limit of detection are 0.26% and 0.16 wt %, respectively, while the averaged relative standard deviation is less than 5%. The performance of the quantitative analysis of unburned carbon meets the requirement of PRC power industry standard and the most of commercial instruments for online or rapidly analysis of unburned carbon. contents of fly ash.12 They found that the plasma conditions such as plasma temperature are dependent on the size of particles, and these effects could be corrected by the emission line intensity ratio from the same atom. Ctvrtnickova et al.13 focused on the impact of the binder on pelletizing fly ash powder, as well as the requirement of experimental parameters for constructing calibration models of UC and other elements in fly ash. They also employed the single pulse and doublepulse LIBS mode to analyze the elements in fly ash and bottom ash, respectively.14 It is worthy to mention that the C 247.86 nm line is most appropriate for the carbon analysis by LIBS. Unfortunately, the potential interference between the Fe 247.98 and the C 247.86 nm lines present in the analysis of fly ash15,16 and soil;17,18 consequently, C could not be quantified independently from Fe. In the above references,10−14 a high resolution spectrograph technique was employed specifically to detect the C emission line, but this will increase the size and cost of the apparatus. Certainly, deconvolution is one of the useful methods in enhancing the spectral resolution and minimizing spectral interferences.19 However, high-frequency noise will be amplified along with the deconvolution spectra.20,21 The interferences between the high-frequency noises and emission lines could result in poor measurement accuracy. In most cases, these emission lines are irregular due to serious peak overlap of the C and Fe lines at about 248 nm and is unable to be deconvolved correctly.15 Thus, the 193.09 nm line of C where there are no interferences from Fe or other elements is chosen to analyze the carbon in fly ash and soil.16−18 However, the problem is that the emission lines in the vacuumultraviolet are absorbed by atmospheric oxygen, which

1. INTRODUCTION Coal is the primary energy source of the electric power industry. Especially in China, about 69% of the total electricity was generated by coal-fired units in 2013.1 Burning coal produces byproducts such as fly ash and air pollutants such as NOx and SOx. Unburned carbon (UC) in fly ash is the major determinant of combustion efficiency in coal-fired boiler. The combustion efficiency decreases with the increase of UC levels in fly ash that means a loss of energy. Unfortunately, the implementation of low-NOx combustion technologies which are commonly used to decrease the emissions level of NOx may result in an increase of UC.2,3 Furthermore, the excess air and coal particle size also have a major impact on the UC levels. Hence, operating conditions should be optimized for the balance among the above indicates. UC content could be fall below 5 wt % under optimum conditions; on the contrary, it may be up to 20 wt % under nonoptimum conditions.4 Online measurement of UC would benefit the optimization of the combustion process and maintain the UC level. Therefore, several online monitoring devices have been developed to determine UC.5−8 Among them, the microwave-based method is the most widely used, but its measurement accuracy and precision are significantly affected by the fluctuations of power load and the types of coal. Furthermore, the loss-on-ignition (LOI) technique, which is the standard method used in the industry, has also been developed for on-site monitoring of UC.9 Recently, laser-induced breakdown spectroscopy (LIBS) technology has shown promise as a potential online or rapid measurement technology to determine UC in fly ash.10 Kurihara and co-workers developed an automatic LIBS prototype device11 and applied it for real-time UC monitoring in a coal-fired power plant. They also proved that the LIBS technique was applicable to measure size-segregated particle © XXXX American Chemical Society

Received: September 25, 2014 Revised: January 15, 2015

A

DOI: 10.1021/ef502174q Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels leads to a strong diminishing of line intensity in air. Undoubtedly, using a N2/He-filled spectrometer and reducing the sample-detector distances can enhance the intensity of these emission lines in the vacuumultraviolet,22,23 yet that would limit the application of LIBS in certain fields. For detecting UC with low resolution spectrograph in air ambient, the CN band head at 388.3 nm is proposed to quantitative analyze UC in this work, because there are no interferences from other elements. In our previous work, it is proved that the intensity of CN is related to the reaction C + N2 ↔ CN + N due to the existence of carbon evaporated directly from the sample in the air ambient.24 To the best of our knowledge, the analysis of UC in fly ash using molecular CN has not been reported. Thus, the purpose of this paper is to assess the feasibility of quantitative analysis of UC in fly ash using molecular CN from laser-induced plasma spectroscopy. A series of fly ash samples were prepared and compressed into pellets for LIBS measurements. Calibration curve was also plotted using the molecular CN band head at 388.3 nm. Finally, the performance of quantitative analysis of UC was validated using a set of unknown samples.

Figure 1. LIBS setup used for the analysis of UC in fly ash. experiment, the spot size diameter was about 800 μm, which yields a laser fluence of about 100 mJ/mm2. The plasma emission from the surface of the sample was collected in the direction of 45° angle to the incident laser beam and coupled to fibers (400 μm in core diameter). The fibers transmit the plasma emission into a multichannel spectrometer (AvaSpec-2048FT, Holland) with a spectral resolution of 0.05−0.13 nm, which integrated a synchronized multiple linear 2048 pixel CCD (Sony, Japan) detectors. The spectrometer has eight channels covering a spectral range of 175−1075 nm. To get the highest signal-to-noise ratio (SNR), the optimum delay time was set as 2.0 μs with an integration time gate of 2 ms (which is the minimum value of the spectrometer). During the experiment, the sample pellet was mounted on a rotary table that was rotated constantly with a motor. It is ensured that fresh region of the sample is ablated by each laser pulse to minimize crater effects. Furthermore, one hundred spectra were averaged from each pellet to reduce statistical error due to laser shot-to-shot fluctuation and also to partially compensate the sample heterogeneity. For each sample, three measurements were repeated, i.e., 300 laser pulses were fired.

2. EXPERIMENTAL DETAILS 2.1. Preparation of Fly Ash Samples. Two types of coal samples were taken from a coal-fired power plant and grinded to power with grain size of 100 μm. The proximate analysis results of these coal samples are listed in Table 1. For obtaining fly ashes with different UC

Table 1. Proximate Analysis Results of Coal Samples (wt %) coal sample

moisture

ash

volatile matter

fix carbon

1# 2#

4.32 2.55

9.6 20.44

29.65 28.79

56.43 48.22

3. RESULTS AND DISCUSSION A representative averaged LIBS spectrum of F1 sample in the regions of 190−300 and 300−400 nm is shown in Figure 2. This spectral region between 190 and 300 nm contains strong atomic lines of C, Si, Fe and Mg, whereas the spectral region of 300−400 nm contains strong atomic lines of Al, Ca and the CN molecular band. The main atomic lines and the molecular band of CN presented in the spectrum have been identified based on NIST (National Institute of Standards and Technology) atomic database27 and ref 28, respectively. These emission lines are coordinated with the main components of fly ash, i.e., SiO2, Al2O3, Fe2O3, CaO, MgO and UC. The characteristic parameters of these spectral lines used for identifying and analyzing these element components are listed in Table 3. These lines, except for C 247.86 nm, are chosen suitably in order to avoid spectral interferences and selfabsorption. C appears in the spectrum in two different forms: two atomic peaks at 193.09 and 247.86 nm, and a CN molecular emission with band head at 388.34 nm. Obviously, the C 247.86 nm line is interfered with the Fe line at approximately 248 nm in our experiment, as shown in Figure 3a. For the F1 sample that contains a high level of UC, the C line dominates the peak at 248 nm. However, the intensity of the C line decreases with the reduction of UC content from F1 to F9, resulting in the serious overlap between the Fe and C lines at 248 nm. Furthermore, the C 193.09 nm line where

content, coal samples were placed into a muffle furnace and heated at 815 °C ± 10 °C for different durations according to the fast ashing method.25 Then, the specific UC content of fly ashes was determined by loss-on-ignition (LOI) method.26 The 12 fly ash samples (F1−F12) from 1# coal and the three fly ash samples (F13−F15) from 2# coal are listed in Table 2. The UC content ranged from 1.17 to 13.18 wt % that coverd the range of UC content in fly ash produced in coal-fired bolier. Of these samples, three (F9, F11 and F15) were prepared as unknown samples to estimate the measurement accuracy and reproducibility of LIBS, and the rest samples were used to construct calibration curves. In the experiment, all samples were prepared for LIBS analysis in form of pellets. For the pelletizing procedure of each sample, 1 g of powderd fly ash was mixed with 2 g of Na2SiO3 binder. The mixture was grounded and homogenized in a centrifugal ball mill (ND7-1L, China). Then, it was poured into a 30 mm die and compressed by a manual press (Sichuang, China) with 20 MPa of pressure for 3 min. All pelleted samples were analyzed immediately after the preparation to avoid the influence of humidity from the air ambient. 2.2. Experimental Procedure. The LIBS setup used for the analysis of UC in fly ash is shown schematically in Figure 1. A 50 mJ pulse laser from a Nd:YAG laser (Brilliant EaZy, Quantel) was focused on the surface of sample to create plasma by a 50 mm-diameter, 100 mm-focal length lens. The pulse laser operated at 1064 nm with a pulse duration of 4 ns. To reduce the effect of aerosol produced at a high repetition rate, the repetition rate was set as 1 Hz. In the

Table 2. Unburned Carbon Content of Fly Ash Samples (wt %) fly ash

F1

F2

F3

F4

F5

F6

F7

F8

F9

F10

F11

F12

F13

F14

F15

unburned carbon

13.18

10.93

7.75

10.26

6.26

3.81

3.36

2.55

2.42

5.17

5.69

1.17

10.60

2.94

1.70

B

DOI: 10.1021/ef502174q Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 2. Representative spectrum of fly ash: (a) 190−300 nm and (b) 300−400 nm.

Table 3. Characteristic Parameters of Spectral Lines Used for the Analysis element

lines (nm)

Ak (×108 s−1)

Ek (ev)

gk

C C CN Si Al Fe Mg Mg Mg Ca Ca Ca Ca

193.09 247.86 388.34 288.16 256.80 274.95 279.55 280.27 285.21 315.89 317.93 393.37 396.85

3.51 × 1000 3.40 × 10−01

7.7 7.7 3.2 5.0 4.8 5.6 4.43 4.42 4.3 7.0 7.0 3.2 3.1

3 3

1.89 2.30 1.10 2.60 2.57 4.91 3.10 3.60 1.47 1.40

× × × × × × × × × ×

1000 10−01 1000 1000 1000 1000 1000 1000 1000 1000

3 4 2 4 2 3 4 6 4 2

Figure 3. Spectra of atomic and molecular of carbon: (a) C 247.86 nm, (b) C 193.09 nm and (c) CN band head at 388.34 nm.

Generally, based on the assumption of local thermodynamic equilibrium (LTE) and optically thin conditions, the measured line intensity of a given element s can be represented as29 Aij g ⎛ −E ⎞ Iij = FC s s i exp⎜ i ⎟ U (T ) ⎝ kT ⎠ (1)

there are no interferences from Fe or other elements has a poor SNR in our experiment, as shown in Figure 3b. As shown in Figure 3c, the CN molecular emission provides a clear band head at 388.34 nm without interferences. Thus, the CN molecular emission with band head at 388.34 nm is chosen to analyze UC. Its peak intensity enhances with the increase of UC content that means the CN molecular emission is also expected to be proportional to UC content.

where Iij is the measured line intensity of a given element s, and F is the experimental parameter that take into account the optical efficiency of the collection system as well as the plasma density and volume. Cs is the concentration of the given C

DOI: 10.1021/ef502174q Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels element s in the sample, Aji is the transition probability, Us(T) is the partition function, while k, gi, Ei and T are the Boltzmann’s constant, the statistical weight energy of upper level i and the plasma temperature, respectively. According to eq 1, a calibration curve can be established for the quantitative analysis of UC, as all factors in the equation are common except the concentration and the line intensity. Figure 4 shows the calibration curves of UC in fly ashes that were

Figure 4. Calibration curves of UC in fly ashes.

prepared from 1# and 2# coal, respectively. The data of each sample are averaged from the spectrum of three repeated measurements. It is necessary to mention that the averaged spectrum of each sample was normalized to the total integrated intensity of each spectral channel prior to establishing calibration curves. To obtain the intensity of molecular CN band head at 388.34 nm, the regions immediately on either side of the spectral peak were averaged and subtracted from the normalized peak intensity as background. For each type of coal, there is a consistent relationship between UC content and the intensity of CN. The regression coefficient of the fly ashes from 1# and 2# coal are 0.98 and 0.99, respectively. Unfortunately, it is obvious that the correlation between the CN intensity and the UC content of the all fly ashes (between the two coal samples, i.e., F1−F15) is poor. This phenomenon named as “matrix effect”, which is related to the types of coal, has been found in our previous work.16 This behavior of matrix effect owing to the influence of physical and chemical properties of samples on the complex processes involved in laser-induced plasma formation, ablation, atomization, excitation and ion recombination. Figure 5 shows the scanning electron microscopy (SEM) photographs of F8 and F14 fly ashes. These two fly ashes contain similar UC content, but belong to different types of coal. Obviously, the distribution of particles is different with each other. The variation in physical state or composition of such samples could lead to the fluctuation of plasma characteristics. If the calibration samples and unknown samples are not with similar matrix, there would be no good correlation between analyte line intensity and its concentration. In most case, the plasma temperature can be evaluated the difference among different samples. The plasma temperature of each sample is deduced from the Boltzmann plot method30 based on four Ca atomic lines (listed in Table 3) and shown in Figure 6. As it can

Figure 5. SEM photograph of fly ashes: (a) F8 and (b) F14.

Figure 6. Plasma temperature of each fly ash sample.

be seen, the plasma temperature of samples from 1# coal is in the range from 12 651 ± 198 to 16 473 ± 86 K, whereas that of samples from 2# coal is in the range from 10 193 ± 142 to 11 605 ± 298 K. The difference of plasma temperature among these samples is obvious, especially for the samples from different types of coal. Indeed, the plasma temperature of fly D

DOI: 10.1021/ef502174q Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels ashes from 2# coal is higher than that of fly ashes from 1# coal. It may be related to the concentration of elements in fly ash. Figure 7 shows the analysis results of F8 and F14 fly ashes by scanning electron microscopy with energy dispersive X-ray

where FT and FS are the correlation factors of plasma temperature and self-absorption, respectively. In the case of FT, the Mg ionic to atomic line ratio (i.e., IMgII280.27/IMgI285.21), which is commonly used as an index of the robustness of operating conditions, is employed as the independent variable. It is attributed to the univocal relationship existing between the ionic to atomic line intensity ratio and plasma temperature.33 In addition, self-absorption can be indicated by the intensity ratio of spectral lines of the same element and the same ionization state. The measured intensity ratios are compared to the theoretically expected values that are calculated by the Boltzmann equation:34 Iqp Inm

=

⎛ Eq − En ⎞ exp⎜ − ⎟ gnA nmλqp ⎝ kT ⎠ gqAqpλnm

(4)

where Iqp and Inm are spectral line intensities of the same element and the same ionization state from the upper energy level q and n to the lower energy level p and m, respectively. λ is the wavelength and A, g, E, k, T are the same as defined in eq 1. If the measured intensity ratios are not equal to the calculated intensity ratios by eq 4, it means self-absorption appears in the experiment. In this work, the lines intensity ratio of Mg II 279 nm and Mg II 280 nm (i.e., IMgII279.55/IMgII280.27) is selected as the index to indicate the self-absorption, because they have very close upper excitation levels, as listed in Table 3. The intensity ratio IMgII279.55/IMgII280.27 is practically independent of temperature and number density of excited ions, but only relate to self-absorption.35 Hence, the intensity ratio IMgII279.55/ IMgII280.27 is employed as independent variable (FS) of multivariate calibration model to correct the self-absorption. Based upon the foregoing analysis, the regression plots generated by multivariate calibration model of UC are shown in Figure 8. The performance of the calibration coupled with the

Figure 7. Analysis results of SEM-EDX.

(SEM-EDX). It appears that the concentrations of Ca and Fe of F8 are higher than that of F14, whereas the concentrations of O and Si of F8 are lower than that of F14, respectively. Because the excitation energy of Ca and Fe are lower than that of Si and O,27 more laser energy is used to heat the plasma that result in higher temperature. As mentioned above, it means that the actual analysis of fly ashes departs from the assumption of all factors in eq 1 are common except the concentration and the line intensity. As noted above, the line intensity varies not only with the analyte concentration but also with the plasma temperature and lines intensities of other elements from sample-to-sample. Furthermore, optically thin of laser plasma is one of prerequisite conditions for the quantitative analysis of LIBS. The optical thinness means that there is no self-absorption of the emission lines. However, self-absorption is found in the fly ashes from 2# coal with high level UC content in our experiment, as shown in Figure 4. Hence, the influence of the matrix effects and selfabsorption need to be considered for the analysis of UC in fly ashes. Generally, the other elements that affect the line intensity of the analyte are considered in multivariate calibration model as independent variables.31,32 The linear multivariate regression method as one of chemometrics is widely used in LIBS for the quantitative analysis of UC and can be expressed as m

C UC = aUC + bUCICN +

∑ biIi + εUC i=1

(2)

where CUC is the UC content in fly ashes, αUC is the intercept, bUC and bi are the regression parameters of the CN and other elemental lines that affect the line intensity of CN, respectively. ICN and Ii are the line intensity of CN and other elemental lines (i) that affect the intensity of CN, respectively, m is the number of other elements and εUC is the error between the prediction UC content given and the reference UC content. In this work, plasma temperature and self-absorption correlation are considered. So, eq 2 is rewritten as

Figure 8. Regression plots generated by multivariate calibration models of UC.

plasma temperature and self-absorption correction was compared with the original calibration without correction. It is necessary to mention that the lines intensity used for calibration is normalized by line intensity of Si I 288.16 nm, because Si is the main component of the mixture samples and its concentration variations appear to be relatively weak from sample-to-sample. It is useful to minimize the signal variations caused by fluctuations of laser energy and plasma position.

m

C UC = aUC + bUCIUC +

′ ∑ biIi + FT + FS + εUC i=1

(3) E

DOI: 10.1021/ef502174q Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Correcting calibration by plasma temperature and selfabsorption obtains the best REP and LOD values, which are 0.26% and 0.16 wt %, respectively. Regarding the RSD, it is similar in the two cases, less than 5%. The performance of molecular CN for quantitative analysis of UC is as good as that of C 247.98 nm15 and C 193.09 nm.16 The results also meet the requirement of PRC power industry standard.26 According to the standard, the SD of repeated measurements is required no greater than 0.3 wt % when the UC content less than 5 wt %, and no greater than 0.5 wt % when the UC content greater than 5 wt %. Meanwhile, the accuracy of the most of commercial instruments for online or rapidly analysis of UC is less than 0.5 wt % (e.g., The Thermo Scientific TEOM Series 4200 based on LOI and nondispersive infrared technique,36 The Greenbank G-CAM based on microwave technique,37 etc.). It is confirmed that results of rapidly measuring UC using molecular CN by LIBS has comparable accuracy and precision. Also, the effect of the type of coal on the multivariate regression is reduced by employing the correction of the plasma temperature and self-absorption.

As shown in Figure 8, the regression coefficient of the multivariate calibration model without correction is 0.977, whereas that of the multivariate calibration model coupled with the plasma temperature and self-absorption correction is 0.987. Furthermore, the multivariate calibration model coupled with the plasma temperature and self-absorption correction yields a near zero y-intercept, which represents a higher quantification sensitivity of UC. Obviously, combining plasma temperature correction with self-absorption correction provides a reasonable regression for the calibration of fly ashes. To evaluate the performances of the multivariate calibration models for predicting UC in the unknown samples, the averaged relative error of prediction (REP), the averaged relative standard deviation (RSD) and the limit of detection (LOD) are calculated and compared with each other. The formulas of REP, RSD and LOD can be found in our previous work.16 Three fly ashes (F9, F11 and F15) were used as unknown samples to evaluate the performance of the multivariate calibration models for predicting UC content. Figure 9 shows the comparison of the predicted and the reference UC content obtained with the standard method (i.e., LOI).

4. CONCLUSIONS The feasibility of LIBS for quantitative analysis UC in fly ash using molecular CN was examined. However, matrix effects were found in the case of the analysis of fly ashes from different types of coal. It is confirmed by the analysis results of SEMEDX. To reduce the influence of matrix effects on quantitative analysis, a linear multivariate regression method coupled with the correction of plasma temperature and self-absorption is employed to construct the calibration model of UC. The intensity ratios of IMgII280.27/IMgI285.21 and IMgII279.55/IMgII280.27 were considered as independent variables of multivariate regression for the correction of plasma temperature and selfabsorption, respectively. The results confirmed that the measurement of unburned carbon in fly ash using molecular CN by LIBS had comparable accuracy, precision and LOD, and met the requirement of PRC power industry standard as well.

■

Figure 9. Comparison of the predicted and the reference UC content.

Corresponding Authors

*Jidong Lu. E-mail address: [email protected]. Fax: +86 20 87110613. *Shunchun Yao. E-mail address: [email protected]. Fax: +86 20 87110613.

As shown in Figure 9, there is a good agreement between the reference value and the predicted value of the calibration model coupled with the plasma temperature and self-absorption correction. Moreover, the vertical error bars on each sample indicate the standard deviation (SD) of repeat measurements by LOI and LIBS, respectively. Evidently, the best result is provided by combining plasma temperature correction with self-absorption correction. The SD of reference UC determined by LOI and the predicted UC with plasma temperature and self-absorption correction are in the ranges of 0.06−0.13 and 0.07−0.39 wt %, respectively. As well as, the predicted UC content is close to the reference ones. On the other hand, the REP, RSD and LOD are calculated and listed in Table 4.

Author Contributions ⊥

The paper was written through contributions of all authors. All authors have given approval to the final version of the paper. These authors contributed equally. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors thank Prof. Shizhi Qian from Old Dominion University for his helpful comments on the work. Special thanks to the National Natural Science Foundation of China (51206055), the Research Foundation of Education Bureau of Guangdong Province (2012LYM_0018), the Fundamental Research Funds for the Central Universities (2014ZZ0014), the Natural Science Foundation of Guangdong Province (S2012040007220) and the New Star of Pearl River on Science

Table 4. Comparison of REP, RSD and LOD performance without correction with temperature and self-absorption correction

REP (%)

RSD (%)

LOD (wt %)

0.49 0.26

4.91 4.77

0.43 0.16

AUTHOR INFORMATION

F

DOI: 10.1021/ef502174q Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels and Technology of Guangzhou (2014J2200054) for financial support of this work.

■

(30) Aydin, ü.; Roth, P.; Gehlen, C. D.; Noll, R. Spectrochim. Acta, Part B 2008, 63, 1060−1065. (31) Schröer, S.; Pavlov, S. G.; Rauschenbach, I.; Jessberger, E. K.; Hübers, H. W. Icarus 2013, 223, 61−73. (32) Tripathi, M. M.; Srinivasan, K. K.; Krishnan, S. R.; Yueh, F.Yu.; Singh, J. P. Fuel 2013, 106, 318−326. (33) Dennaud, J.; Howes, A.; Poussel, E.; Mermet, J. M. Spectrochim. Acta, Part B 2001, 56, 101−112. (34) Yun, J. I.; Klenze, R.; Kim, J. I. Appl. Spectrosc. 2002, 4, 437− 448. (35) Ciocan, A. C.; Mao, X. L.; Borisov, O. V.; Russo, R. E. Spectrochim. Acta, Part B 1998, 53, 463−470. (36) Thermo Scientific. Thermo Scientific: Waltham, MA. http:// www.bidonequipment.info/pdf%20files/ THERMO%20TEOM%204200%20Carbon-In-Ash%20Monitor.pdf. (37) Greenbank. G-CAM® Carbon-in-Ash Analyser Product Overview; Greenbank Group UK, Ltd.: Woodville, U. K. http://www. greenbankgroup.com/live/downloads/44a.pdf.

REFERENCES

(1) National Energy Administration of China. http://www.nea.gov. cn/2014-01/14/c_133043689.htm (in Chinese). (2) Wei, Z. B.; Li, X. L.; Xu, L. J.; Tan, C. Energy Fuels 2012, 26, 2821−2829. (3) Kuang, M.; Li, Z. Q. Energy 2014, 69, 144−178. (4) Styszko-Grochowiaka, K.; Gołaśa, J.; Jankowski, H.; Koziński, S. Fuel 2004, 83, 1847−1853. (5) Schneider, A.; Chabicovsky, R.; Aumuller, A. Sens. Actuators, A 1998, 67, 24−31. (6) Fan, M. H.; Brown, R. C. Fuel 2001, 80, 1545−1554. (7) Smith, K.; Dixon, T.; Taylor, J. World of Coal Ash Conference 2009, 1−10. (8) Ouazzanea, A. K.; Castagnerb, J. L.; Jones, A. R.; Ellahi, S. Fuel 2002, 81, 1907−1911. (9) Melick, T.; Sommer, T.; Conrads, H. World of Coal Ash/DOE UBC Conference, April 13, 2005; pp 1−10. (10) Noda, M.; Deguchi, Y.; Iwasaki, S.; Yoshikawa, N. Spectrochim. Acta, Part B 2002, 57, 701−709. (11) Kurihara, M.; Ikeda, K.; Izawa, Y.; Deguchi, Y.; Tarui, H. Appl. Opt. 2003, 42, 6159−6165. (12) Wang, Z. Z.; Deguchi, Y.; Kuwahara, M.; Taira, T.; Zhang, X. B.; Yan, J. J.; Liu, J. P.; Watanabe, H.; Kurose, R. Spectrochim. Acta, Part B 2013, 87, 130−138. (13) Ctvrtnickova, T.; Mateo, M. P.; Yañez, A.; Nicolas, G. Spectrochim. Acta, Part B 2009, 64, 1093−1097. (14) Ctvrtnickova, T.; Mateo, M. P.; Yañez, A.; Nicolas, G. Spectrochim. Acta, Part B 2010, 65, 734−737. (15) Zhang, L.; Ma, W. G.; Dong, L.; Yan, X. J.; Hu, Z. Y.; Li, Z. X.; Zhang, Y. Z.; Wang, L.; Yin, W. B.; Jia, S. T. Appl. Spectrosc. 2011, 65, 790−796. (16) Yao, S. C.; Lu, J. D.; Zheng, J. P.; Dong, M. R. J. Anal. At. Spectrom. 2012, 27, 473−478. (17) Ebinger, M. H.; Norfleet, M. L.; Breshears, D. D.; Cremers, D. A.; Ferris, M. J.; Unkefer, P. J.; Lamb, M. S.; Goddard, K. L.; Meyer, C. W. Soil Sci. Soc. Am. J. 2003, 67, 1616−1619. (18) da Silva, R. M.; Milori, D. M. B. P.; Ferreira, E. C.; Ferreira, E. J.; Krug, F. J.; Martin-Neto, L. Spectrochim. Acta, Part B 2008, 63, 1221− 1224. (19) Body, D.; Chadwick, B. L. Spectrochim. Acta, Part B 2001, 56, 725−736. (20) Zheng, J. B.; Liu, H. J. Northwest Univ. 2000, 30, 496−500 (in Chinese).. (21) Dong, Y. Ph.D. Dissertation, Chinese Academy of Sciences, Beijing, 2000; p 64 (in Chinese). (22) Salle, B.; Lacour, J. L.; Vors, E.; Fichet, P.; Maurice, S.; Cremers, D. A.; Wiens, R. C. Spectrochim. Acta, Part B 2004, 59, 1413−1422. (23) Gaft, M.; Nagli, L.; Fasaki, I.; Kompitsas, M.; Wilsch, G. Spectrochim. Acta, Part B 2009, 64, 1098−1104. (24) Dong, M. R.; Lu, J. D.; Yao, S. C.; Zhong, Z. M.; Li, J. Y.; Li, J.; Lu, W. Y. Opt. Express 2011, 19, 17021−17029. (25) Cao, C. W. The Test Methods and Applications of Thermal Power Plants; China Electric Press: Beijing, 2004; p 51 (in Chinese). (26) The Power Industry Standard of China. Test Method for Combustible Matter in Fly Ash and Cinder from Cola; DL/T 567.6; Department of International Cooperation: Beijing, 1995 (in Chinese). (27) Kramida, A.; Ralchenko, Yu.; Reader, J. NIST ASD Team 2012 NIST Atomic Spectra Database, ver. 5.0 [Online]. Available: http:// physics.nist.gov/asd [accessed July 17, 2013]; National Institute of Standards and Technology: Gaithersburg, MD. (28) Abdelli-Messaci, S.; Kerdja, T.; Bendib, A.; Malek, S. Spectrochim. Acta, Part B 2005, 60, 955−959. (29) Miziolek, A. W.; Palleschi, V.; Schechter, I. Laser-Induced Breakdown Spectroscopy (LIBS)-Fundamentals and Applications; Cambridge University Press: Cambridge, 2006; pp 5−13. G

DOI: 10.1021/ef502174q Energy Fuels XXXX, XXX, XXX−XXX