Data Processing Method for the Measurement of Unburned Carbon in

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Data processing method for the measurement of unburned carbon in fly ash by PF-SIBS Shunchun Yao, Lifeng Zhang, Jialong Xu, Ziyu Yu, and Zhimin Lu Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b02692 • Publication Date (Web): 16 Oct 2017 Downloaded from http://pubs.acs.org on October 16, 2017

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Data processing method for the measurement of unburned carbon in fly ash by PF-SIBS Shunchun Yaoa,b,c*, Lifeng Zhang a,b,c, Jialong Xu a,b,c, Ziyu Yu a , Zhimin Lu a,b,c a

School of Electric Power, South China University of Technology, Guangzhou, Guangdong

510640, China b

Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, Guangzhou,

Guangdong 510640, China c

Guangdong Province Engineering Research Center of High Efficient and Low Pollution

Energy Conversion, Guangzhou, Guangdong, 510640, China *[email protected]

Abstract In thermal power plants, the unburned carbon in fly ash is an important indicator of the boiler combustion efficiency. On-line detection of unburned carbon content could benefit the optimization of the boiler operation. In this paper, a new scheme named as the particle flow-spark induced breakdown spectra was employed for the rapid measurement of unburned carbon content in fly ash. In order to improve the measurement accuracy, a series of data processing methods which mainly include spectral interference correction and quantitative analysis methods were specially carried out in this paper. The W 248.88 nm and Fe 254.60 nm lines were used as the correction lines to obtain the true C 247.86 nm integral intensity from the overlapping peak, then the corrected intensity was substituted into the multivariate linear model to improve the measurement accuracy. The result shows that the regression coefficient 1

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(R2) of the multivariate calibration model and the averaged absolute error are 0.987 and 0.15 wt %, respectively. The limit of detection of unburned carbon content determination is 0.28 wt %. These results are enough to meet the requirement of power industry standard and the commercial instruments for online analysis of unburned carbon in fly ash. Keywords: unburned carbon, on-line detection, particle flow-spark induced breakdown spectra, data processing, line interference correction, quantitative analysis

1. Introduction Coal is the most widely used fuel in coal-fired power plants and contributes the most of the energy for industrial activities.1,2 The unburned carbon (UC) in fly ash, which is a significant economic indicator for coal-fired boiler operation, reflects the boiler combustion efficiency. So it is of great significance for UC measurement. At present, most thermal power plants use the loss-on-ignition (LOI) method to measure UC in fly ash. As a typical off-line measurement method, it has high measurement accuracy. But the sample pretreatment is time consuming and the whole process is laborious, leading to that the analysis results can not accurately reflect the real-time combustion in the boiler, which makes it unsuitable for on-line measurement of UC in fly ash. Additionally, microwave absorption, which is the most widely used on-line measurement method in thermal power plants, is very simple and sensitive.3,4 However, the fly ash samples need to be sent to the measurement chamber for testing and it is very likely to block the measuring chamber. This critical flaw makes it 2


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inappropriate for long-term online monitoring. Recently, Laser Induced Breakdown Spectroscopy (LIBS) has been widely applied as a potential on-line measurement technology for its simple or no sample preparation, multi-element analysis and fast response. There have been many significant studies on the capability of LIBS in real time measurement.5-7 However, the high-power lasers required in the LIBS experiment are expensive and unstable in extreme environments, limiting its further deployment in power plants. Due to these factors, there is a need for an analytical technique which is robust, yet simple and portable. Spark Induced Breakdown Spectroscopy (SIBS) is such a technique which is considered to overcome the limitations of established analytical techniques. It produces plasma by a high voltage pulse power instead of pulse laser, which makes it more advantageous in terms of cost, stability and portability. In addition, the generated plasma of SIBS is more stable and larger than LIBS: the diameter of SIBS plasma reaches several millimeters while that of LIBS is on the order of 0.1mm.8 So developing SIBS as a low-cost alternative of LIBS has gradually attracted attentions. The work of Taefi et al. demonstrated that quantitative determination of major and minor elements of cement powder samples is possible with SIBS, which means that it can be applied in industry for on line measurement of powder samples.9 Khalaji et al. studied the potential of SIBS for continuous dust monitoring and the result shows that SIBS is sensitive and fast enough for dust level monitoring in industrial environments like mines.10 Hunter et al. studied the effect of SIBS on the rapid detection of heavy metal elements in the soil by using the standard addition method and the detection limits for lead, chromium, 3


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barium, mercury and cadmium were observed at ~25 mg / kg for all studied metals, which is sufficient to meet the requirements of field analysis screening.11 They also verified that the SIBS technology is capable for the use of continuous industrial hygiene monitoring and built an instrument to measure lead and chromium in real time and in situ.12,13 Kawahara et al. determined the local equivalence ratio of a CH4/air mixture by using the SIBS technology and the result indicates that SIBS is useful as a diagnostic tool for spark-ignition engines.14 Pavan et al. compared the effects of SIBS and LIBS on the measurement of mercury in the soil and found that LIBS analysis is better at higher concentrations while SIBS analysis is better at lower concentrations.15 Rahman et al. investigated the effects of the ambient pressure on fuel concentration measurements of an injected jet of hydrogen by SIBS.16 Schmidt et al. built a multivariate statistical model with the line intensity of C, Fe, Si to quantify total carbon levels in soils and obtained a good result.17 The main problem with the SIBS technology is that, on the one hand, the ablation of solid samples requires the spark energy to be high and therefore requires high power voltage, and on the other hand, the high spark energy can cause the enhancement of the line interference from the electrod material, which will greatly affect the measurement accuracy of SIBS. To solve this problem, the laser beam was applied in some previous studies to ablate the sample and produce the original plasma and then the spark discharge was used to enhance the plasma.18-20 By doing this, the voltage can be selected relatively low, thus reducing the spectral line interference from the electrode material. However, this approach, due to the addition of the laser, 4


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will inevitably lead to the increased investment and complexity of measurement system. Other studies have chosen to analyze samples in powder rather than pressed pellet form.9,15 And the electrodes were placed into or above the surface of the powder sample to produce plasma. This has ensured that the sample remains smaller in size and the required voltage for the ablation and excitation is lower, but the issue is that the powder samples might be easily ejected by the shock wave generated by spark discharge, resulting in a change in concentration that affects the measurement performance. Because of the above issues, it was proposed in this work to combine SIBS with particle flow (named as particle flow spark induced breakdown spectroscopy, PF-SIBS), it forms plasma by directly ablating the falling granular particles with spark, which can reduce the required voltage and the cost of equipment. In addition, considering that the fly ash of coal-fired power plants is also granular, this technology appears to be suitable for rapid measurement of UC in fly ash of power plants. In order to improve the measurement accuracy of PF-SIBS, a data processing method which mainly includes the spectral interference correction and the quantitative analysis methods is specially carried out in this paper. On the one hand, as the electrode material in this experiment, the characteristic line of the W element would interfere with the C 247.86 nm line during the experiment. What’s more, some of the previous researches pointed out that there is C-Fe line interference in the vicinity of C 247.86 nm.21,22 These line interferences will definitely have an effect on the result of the analysis and need to be corrected. On the other hand, to eliminate the influence of the plasma state fluctuation and the matrix effect of fly ash, the quantitative analysis 5


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is also needed. In this study, a series of fly ash samples with different carbon contents were prepared. The integrated intensity of the spectral lines was chosen for line interference correction, then a multivariate calibration model was established for PF-SIBS. The main elements in fly ashes such as Si, Al, Mg, Ca, etc. were all taken into account. And the influence factors including matrix effect, self-absorption and the plasma temperature were also considered in the model. Finally, the measurement accuracy of the calibration model was verified by a set of unknown samples. 2. Experimental 2.1 Sample Preparation The fly ash samples used in this experiment were obtained from different coal-fired power plants, and the UC of each fly ash sample was measured by the LOI method.23 The fly ash samples were placed in a muffle furnace and heated at 815±10 ℃ until the carbon in the fly ash samples burned out. The UC content can be calculated by the weight change before and after the burning. In order to obtain samples with different UC contents, the selected fly ash samples were mixed with each other in different proportions and were fully stirred. As shown in Table 1, a total of 11 samples named from C1 ~ C11 were used as calibration samples to establish the calibration curve of UC in fly ash. Considering the uniform distribution of sample concentration, V1, V2, V3 were chosen as unknown samples to estimate the measurement accuracy of the model. 2.2 PF-SIBS experimental setup 6


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A self-designed PF-SIBS system was used in this work, as shown in Figure 1. The sample to be analyzed was placed in a piezoelectric type vibrational feeder (PEF-90A, Sanki, Japan). A tapered tube with an outlet diameter slightly less than the electrode spacing (2 mm) was connected to the outlet of the feeder to ensure that the falling particles could fall between the electrodes easily rather than deposited on them. The two 99.9999% tungsten electrodes (diameter = 2 mm) were mounted on two specially designed plastic holders which allowed flexible adjustment of the electrode separation and position of the electrode tip. In this experiment, the electrodes were settled 3 mm apart and 1.5 cm below the tapered tube. In the spark gap, pulsed discharge (voltage = 10 kV) was produced by the capacitance charge and discharge in the high voltage power supply. The energy of the spark formed by pulsed discharge was absorbed by air and sample particles so that the plasma was formed. The signal from the spark induced plasma was collected by an optic fiber and was sent into a spectrometer for analysis. The dual channel spectrometer used in this experiment covers wavelength ranges from 235 nm to 400 nm and 575 nm to 790 nm and the spectral resolution is 0.1 nm. A digital delay generator (DG535, Stanford Research Systems, America) was used to synchronize the high voltage power supply and the spectrometer to provide the desired pulse frequency and delay time. In this experiment, the pulse frequency was set to be 5 Hz, the delay time was 10 µs and the integration time gate was 1.05 ms for optimal signal-to-noise ratio. The spectra from 250 shots of each sample were collected and averaged for analysis.

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Figure 1. Schematic diagram of the PF-SIBS system 3. Results and discussion 3.1 Line interference correction As mentioned before, the overlapping interference of C and W characteristic lines near 248 nm is an important factor influencing the PF-SIBS measurement accuracy of UC in fly ash. There is no interference near the C 193 nm line, but noticing the fact that the 193 nm band is in the vacuum ultraviolet region, it is difficult to directly measure the line intensity in the atmospheric environment if the measuring instrument is not specially treated, thus affecting the application value of PF-SIBS. The overlapping degree of two spectral lines is related to UC in fly ash. It can be seen from Figure 2 that the W 247.78 nm line intensity in C1 sample with UC content of 0.93 wt % is even higher than that of C 247.86 nm. For the C9 sample with a high UC

8


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content of 8.84 wt %, the line intensity of W 247.78 nm is also very close to that of C 247.86 nm. Obviously, there is a strong interference between C 247.86 nm and W 247.78 nm both in low and high UC content conditions. This is because that the content of W in the electrode used in this experiment reaches 99.9999%, resulting in a strong W 247.78 nm line intensity all the time. However, the line intensity of C 247.86 nm is related to the C content in fly ash samples. When the C content in the sample is low, the line intensity of C 247.86 nm is also weak while that of W 247.78 nm is relatively strong, thus occupying a large area in the overlapping peak; and when the fly ash sample contains high UC content, the line intensity of C 247.86 nm has improved significantly, but the W 247.78 nm still occupies a large proportion in the overlapping peak due to its high line intensity. Therefore, the W 247.78 nm spectral line interference in the overlapping peak need to be corrected. The true integrated intensity of C 247.86 nm is obtained by subtracting the integrated intensity of W 247.78 nm from the C-W overlapping peak in this paper. The multi-peak fitting is not used here because there is always a large part of overlap between C 247.86 nm and W 247.78 nm peaks whether in low or high carbon content. That is to say, there will always be a part of the C 247.86 nm integrated intensity affected by the C-W interference if the multi-peak fitting is used, which will inevitably affect the final analysis results.

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Figure 2. Overlapping peak at C 247.86 nm in different samples： (a) C1； (b) C9 According to the principle of atomic emission spectroscopy, when the same atoms transmit from two upper levels whose level energy are equal or close to different lower levels under the same experimental condition.24 The emission intensity ratio of 10


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the two given spectral lines can be calculated as follows.

I1 g A = k 1 ki 1 I2 g k 2 Aki 2

(1)

Where I is the intensity of the two emission lines of the same atom, g k is the upper level statistical weight, and Aki is the energy level transition probability, the two parameters of the spectrum can be found by looking up the database.25 According to the above principle, by searching the database and analyzing spectra, W 248.88 nm is found to be the most suitable correction line. The upper level energy of W 248.88 nm and W 247.78 nm lines are very close. In addition, the peak of the W 248.88 nm line is relatively independent, without obvious interference from other lines, and the line intensity of W 248.88 nm is also strong enough for correction. The characteristic parameters of W 248.88 nm and W 247.78 nm are shown in Table 2. By substituting the characteristic parameters and the intensity of the spectral lines into eq 1, we can get the corrected line intensity of W 247.78 nm, and then the corrected intensity of C 247.86 nm line can be calculated according to eq 2:

I 'C = I all − I 'W

247.78 nm

(2)

Where I 'C is the corrected integral intensity of the C 247.86 nm spectral line,

I all is the integral intensity of the C-W overlapping peak, and I 'W 247.78 nm is the corrected W 247.78 nm spectral line integral intensity. The calibration curves before and after the correction of C-W line interference are presented in Figure 3. It can be seen that the regression coefficient R2 of the modified calibration curve has risen from 0.885 to 0.931, which indicates the necessity of C-W interference correction to improve the PF-SIBS quantitative analysis accuracy. 11


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Figure 3. Calibration curves of carbon content (a) Uncorrected intensity;

(b) Corrected intensity by W 248.88 nm

Previous studies have pointed out that there is interference between the C 247.86 nm and Fe 247.98 nm lines.21,22 It can be seen from Figure 2 that the C-Fe line interference is not that serious compared with the C-W line interference, but it does exist. In this paper, the Fe 254.60 nm line was selected following the same criteria for selecting the W correction line. The characteristic parameters of Fe lines are shown in Table 3. The lower level energy of two Fe lines are equal and the upper level energy are close, so the intensity ratio is equal to the statistical weight ratio of upper level. That is to say, eq 1 can be simplified to eq 3.

I1 g = k1 I2 gk2

(3)

Similar to the C-W interference correction method, the characteristic parameters of the Fe spectrum in Table 3 and the intensity data of the Fe 254.60 nm line obtained from the experiment are substituted into eq 4 to obtain the quadratic corrected integral intensity of C 247.86 nm line. 12


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I ''C = I 'C − I 'Fe 247.98 nm

(4)

Where I ''C is the integral intensity of C 247.86 nm spectral line after two interference correction, I 'C is the integral intensity of C 247.86 nm spectral line after C-W correction, and I 'Fe 247.98 nm is the calculated integral intensity of Fe 247.98 nm spectral line. The calibration curve after C-W, C-Fe spectral interference correction is shown in Figure 4. After the C-Fe line interference correction, the R2 value has slightly risen from 0.930 to 0.934, indicating that the C-W interference dominates in this experiment. It is consistent with what we saw in Figure 2. The reason for this phenomenon is that, as the electrode material, the content of W element is much higher than that of Fe in fly ash. Therefore, the peak intensity of W 247.78 nm is higher, and its interference to C 247.86 nm is more obvious.

Figure 4. Calibration curve of PF-SIBS with C-W, C-Fe interference correction

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3.2 Quantitative analysis methods Obviously, the regression coefficient R2 of the calibration curve after the line interference correction has been greatly improved, but still far from enough to meet the requirement of power plants for real-time online measurement of UC in fly ash. This is because besides spectral interference, there are some other factors that affect the final results, such as signal strength fluctuations and matrix effect of fly ash. So the quantitative analysis methods need to be taken to further improve its measurement accuracy. In the experiment, the fluctuation of spark energy, plasma state, excitation degree and light receiving efficiency will obviously have an effect on the spectral signal, this leads to the deviation of analysis results. Therefore, the internal standard method is considered to compensate for the influence of these factors on the experimental results. In this paper, the Si element was chosen as the internal standard element because it is the most abundant element in fly ash and the content is relatively stable. The calibration curve after the internal standard method correction is shown in Figure 5.

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Figure 5. Calibration curve of PF-SIBS with internal standard method correction It can be seen from Figure 5 that the regression coefficient R2 has improved greatly, from 0.933 to 0.969 after the correction of the internal standard method, it proves that this method can greatly improve the quantitative analysis accuracy of PF-SIBS for UC in fly ash. But at the same time, it can be found that there are still several points that deviate from the calibration curve, which means that further correction is necessary. The components of fly ash are very complex, mainly including C, Si, Al, Fe, Ca, Mg, etc. Each of them has an influence on the spectral line strength. Considering the serious matrix effect of fly ash, just using the information of one single line (C 247.86 nm) for analysis will cause the loss of much useful spectral information, while the multivariate analysis can make better use of the information in the spectrum, thus improving the repeatability and accuracy of the PF-SIBS quantitative analysis. So the multiple linear regression method is considered for quantitative analysis of UC, specific parts of elements that affect the C line intensity are all included in the 15


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multiple linear regression model. In this study, we chose C, Si, Mg, Al, Ca as independent variables in the model because the excitation of C line was significantly influenced by these elements with lower ionization potentials in fly ashes.26 So the multivariate linear regression formula was constructed using the integrated intensity ratios of C 247.86 nm, Al 309.34 nm and Ca 393.63 nm to Si 288.16 nm. These characteristic lines were chosen to establish multiple regression model primarily because there are no other interfering lines near them, and these lines have a better correlation with the UC content. The equation for the multiple regression model is shown as below. m

C pv = a +

bi ∑ i =1

Ii I Si 288.16

+ c1 nm

I Mg 279.60 I Mg 280.38

nm nm

+ c2

I Mg 280.38 I Mg 285.32

nm

(5)

nm

Where C pv is the predictive value of UC content, a is a constant, I i is the integrated intensity of the chosen lines (C 247.86 nm, Al 309.34 nm, Ca 393.63 nm),

bi is the coefficient of the independent variable in the multivariate linear equation, I Mg

279.60 nm

I Mg

280.38 nm

and I Mg

280.38 nm

I Mg 285.32 nm are used to modify the effects of

spectral self-absorption and plasma temperature on the predicted results, respectively.

c1 and c 2 are the coefficients of the last two variables.27 The calibration curve after the internal standard method combined with multiple linear regression correction is shown in Figure 6.

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Figure 6. Multivariate linear Calibration curve As shown in the picture, the regression coefficient R2 of the calibration model using the multiple linear regression method is 0.987, which is better than 0.969. This proves that the multi-element calibration method can effectively reduce the matrix effect of multi-component samples, therefore greatly improving the accuracy of PF-SIBS quantitative analysis of UC in fly ash. In order to further evaluate the effect of the above correction on the accuracy of UC in fly ash measured by PF-SIBS, V1, V2 and V3 were all repeated measured for three times, and the spectral intensity information obtained from these repeated measurements were respectively substituted into the above calculation process to calculate the final predicted UC content. As shown in Table 4, compared with the predicted values of three samples without any corrections, the measurement accuracy and repeatability has significantly improved after a series of data processing correction and the averaged prediction absolute error is only 0.08 wt %, which is far 17


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below the required value of the power industry standard (≤0.4 wt %).23 Also, the limit of detection (LOD) of PF-SIBS for the measurement of UC in fly ash was calculated and the result is 0.28 wt %,28 which is enough to meet the demand of power plants for the analysis of UC content in fly ash. 4. Conclusions There exists the problem that C-W, C-Fe line interference, signal strength fluctuations and matrix effect of fly ash will all make a difference to the experimental results, thereby influencing the measurement accuracy of UC in fly ash by PF-SIBS. So a series of data processing methods were proposed to compensate for these influence factors in this paper. The W 248.88 nm and Fe 254.60 nm were selected as the modified spectral lines in order to correct the C-W, C-Fe line interference in the vicinity of 248 nm wavelength, respectively. Then the internal standard method and multiple linear regression method were used to build a calibration model to eliminate the influence of signal intensity fluctuation and fly ash matrix effect. Finally, the carbon content in fly ash was quantitatively analyzed by three unknown fly ash samples. The result shows that the regression coefficient R2 of the multivariate linear calibration model reached 0.987 after data processing. Compared with that without correction, there has been a great improvement. The LOD of the UC content in fly ash measured by PF-SIBS was also calculated and the value was 0.28 wt %, which is enough for the analysis of UC content in fly ash. By comparing the predicted value and the averaged prediction absolute error before and after the correction, it can be found that the data processing methods have improved the measurement accuracy and 18


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repeatability effectively. These results suggest that it is feasible to use the data processing methods to improve the measurement accuracy of PF-SIBS, indicating the great potential of PF-SIBS for on-line rapid detection of UC in fly ash. Acknowledgments The work was supported by National Natural Science Funds of China (51676073), Guangdong Province train high-level personnel special support program (2014TQ01N334) and Science and Technology Project of Guangdong Province (2015A02021500). Reference (1) Liu, G.; Vassilev, S. V.; Gao, L.; Zheng, L.; Peng, Z. Mineral and chemical composition and some trace element contents in coals and coal ashes from Huaibei coal field. Energy Convers. Manage. 2005, 46 (13-14), 2001-2009 (2) Kumar, V.; Labhsetwar, N.; Meshram, S.; Rayalu, S. Functionalized Fly Ash Based Alumino-Silicates for Capture of Carbon Dioxide. Energy Fuels 2011, 25 (10), 4854-4861 (3) Liu, H.; Tan, H.; Gao. Q.; Wang, X.; Xu, T. Microwave attenuation characteristics of unburned carbon in fly ash. Fuel 2010, 89 (11), 3352–3357. (4) Cheng, Q. M.; Hu, X. Q.; Wang, Y. F. Summary of Measuring Methods of Carbon Content in Fly Ash. J. Shanghai Univ. Elec. Power 2011, 27 (5), 519-524. (5) Zheng, P.; Liu, H.; Wang, J.; Shi, M.; Wang, X. M.; Zhang, B.; Yang, R. Online mercury determination by laser-induced breakdown spectroscopy with the assistance of solution cathode glow discharge. J. Anal. At. Spectrom. 2015, 30, 867-874. 19


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(6) He, Y.; Zhu J. J.; Li, B.; Wang, Z. H.; Li Z. S.; Marcus, A.; Kefa, C. In-situ Measurement of Sodium and Potassium Release during Oxy-Fuel Combustion of Lignite using Laser-Induced Breakdown Spectroscopy: Effects of O2 and CO2 Concentration. Energy Fuels 2013, 27 (2), 1123-1130. (7) Haider, A. F. M. Y.; Rony, M. A.; Abedin, K. M. Determination of the Ash Content of Coal without Ashing: A Simple Technique Using Laser-Induced Breakdown Spectroscopy. Energy Fuels 2013, 27 (7), 3725-3729. (8) Tsuruoka, R.; Ikeda, Y. Characteristics of microwave plasma induced by lasers and sparks. Appl. Opt. 2012, 51 (7), B183-B191. (9) Taefi, N.; Khalaji, M.; Tavassoli, S. H. Determination of elemental composition of cement powder by Spark Induced Breakdown Spectroscopy. Cem. Concr. Res. 2010, 40, 1114-1119. (10) Khalaji, M.; Roshanzadeh, B.; Mansoori, A.; Taefi, N. Tavassoli, S. H. Continuous dust monitoring and analysis by spark-induced breakdown spectroscopy. Opt. Laser Eng. 2012, 50, 110-113. (11) Hunter, A. J. R.; Wainner, R. T.; Piper, L. G.; Davis, S. J. Rapid field screening of soils for heavy metals with spark induced breakdown spectroscopy. Appl. Opt. 2003, 42, 2102-2109. (12) Hunter, A. J. R.; Davis, S. J.; Piper, L. G.; Holtzclaw, K. W.; Fraser, M. E. Spark-Induced Breakdown Spectroscopy: A New Technique for Monitoring Heavy Metals. Appl. Spectrosc. 2000, 54 (4), 575-582. (13) Hunter, A. J. R.; Morency, J. R.; Senior, C. L.; Davis, S. J.; Fraser, M. E. 20


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Continuous emissions monitoring using spark-induced breakdown spectroscopy. J. Air Waste Manage. Assoc. 2000, 50 (1), 111-7. (14) Kawahara, N.; Tomita, E.; Takemoto, S.; Ikeda, Y. Fuel concentration measurement of premixed mixture using spark-induced breakdown spectroscopy. Spectrochim. Acta, Part B 2009, 64 (10), 1085-1092. (15) Srungaram, P. K.; Ayyalasomayajula, K. K.; Fang, Y. Y.; Singh, J. P. Comparison of laser induced breakdown spectroscopy and spark induced breakdown spectroscopy for determination of mercury in soil. Spectrochim. Acta, Part B 2013, 87, 108-113. (16) Rahman, M. T. A.; Kawahara, N.; Tsuboi, K.; Tomita, E. Effect of ambient pressure on local concentration measurement of transient hydrogen jet in a constant-volume vessel using spark-induced breakdown spectroscopy. Int. J. Hydrogen Energy 2015, 40 (13), 4717-4725. (17) Schmidt, M. S.; Sorauf, K. J.; Miller, K. E.; Sonnenfroh, D.; Wainner, R.; Bauer, A. J. R. Spark-induced breakdown spectroscopy and multivariate analysis applied to the measurement of total carbon in soil. Appl. Opt. 2012, 51, 176-182. (18) Chen, Y.; Zhang, Q.; Li, G.; Li, R.; Zhou, J. Laser ignition assisted spark-induced breakdown spectroscopy for the ultra-sensitive detection of trace mercury ions in aqueous solutions. J. Anal. At. Spectrom. 2010, 25, 1969-1973. (19) Li, K.; Zhou, W.; Shen, Q.; Ren, Z.; Peng, B. Laser ablation assisted spark induced breakdown spectroscopy on soil samples. J. Anal. At. Spectrom. 2010, 25, 1475-1481. (20) Hou, Z. Y.; Wang, Z.; Liu, J. M.; Ni, W. D.; Li, Z. Combination of cylindrical 21


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confinement and spark discharge for signal improvement using laser induced breakdown spectroscopy. Opt. Express 2014, 22, 12909-12914. (21) Yao, S. C.; Chen, J. C.; Lu, J. D.; Shen, Y. L.; Pan, G. Influence of C–Fe Lines Interference Correction on Laser-Induced Breakdown Spectroscopy Measurement of Unburned Carbon in Fly Ash. Spectrosc. Spect. Anal. 2015, 35 (6),1719-1723. (22) Bai, K. J.; Yao, S. C.; Lu, J. D.; Zhao, J.; Xu, J. L.; Lu, Z. M. Correction of C– Fe line interference for the measurement of unburned carbon in fly ash by LIBS. J. Anal. At. Spectrom. 2016, 31, 2418-2426. (23) The Power Industry Standard of China, Test Method for Combustible Matter in Fly Ash and Cinder from Coal; DL/T 567.6; Department of International Cooperation: Beijing, 2016; http://www.csres.com/detail/287991.html (accessed Sept. 9, 2017). (24) Zheng, G. J.; Ji, Z. H.; Yu, X. Atomic Emission Spectrometry Technology and Application; Chemical Industry Press: Beijing, 2010; pp 40-41. (25) Kramida, A.; Ralchenko, Y.; Reader, J. NIST Atomic Spectra Database, NIST: Gaithersburg, MD, 2012; https://physics.nist.gov/asd (accessed Sept. 9, 2017). (26) Ismail, M. A.; Imam, H.; Elhassan, A.; Youniss, W. T.; Harith, M. A. LIBS limit of detection and plasma parameters of some elements in two diﬀerent metallic matrices. J. Anal. At. Spectrom. 2004, 19 (4), 489–494. (27) Yao, S. C.; Shen, Y. L.; Yin, K.; Pan, G.; Lu, J. D. Rapidly Measuring Unburned Carbon in Fly Ash Using Molecular CN by Laser-Induced Breakdown Spectroscopy. Energy Fuels 2015, 29, 1257–1263. (28) Cremers, D. A.; Radziemski, L. J. Handbook of laser-induced breakdown 22


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spectroscopy; John Wiley & Sons, Ltd: New York, 2013; pp 158-159.

Tables Table 1. The UC content in fly ashes (wt %) Sample

Carbon Content

Sample

Carbon Content

C1

0.93

C8

7.61

C2

1.47

C9

8.84

C3

3.18

C10

9.46

C4

3.91

C11

10.69

C5

4.75

V1

2.45

C6

5.40

V2

6.12

C7

6.88

V3

10.04

Table 2. Characteristic parameters of W lines Wavelength (nm)

gkAki (108 s-1)

Ei (eV)

Ek (eV)

W 247.78

2.84

0.7621414

5.764441

W 248.88

5.04

0.9199951

5.9002402

Table 3. Characteristic parameters of Fe lines Wavelength (nm)

gk

Ei (eV)

Ek (eV)

Fe 247.98

5

0.0872854

5.0855897

Fe 254.60

7

0.0872854

4.9556288

23


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Table 4. Comparison results of quantitative analysis (wt %) Reference

Predicted value

Averaged absolute error

Sample value

Uncorrected

Corrected

V1

2.45

3.55±1.06

2.39±0.14

V2

6.12

9.41±0.73

6.23±0.22

V3

10.04

8.19±1.29

10.11±0.21

Uncorrected

Corrected

2.08

0.08

24


Data Processing Method for the Measurement of Unburned Carbon in

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