Characterization of Fly Ash Laser-Induced Plasma for Improving the

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Characterization of Fly Ash Laser-Induced Plasma for Improving the On-line Measurement of Unburned Carbon in Gas−Solid Flow Shunchun Yao,*,†,‡ Jialong Xu,†,‡ Jingbo Zhao,†,‡ Kaijie Bai,†,‡ Jidong Lu,†,‡ and Zhiming Lu†,‡ †

School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, Guangzhou, Guangdong 510640, China

‡

ABSTRACT: A new route for directly measuring the fly ash unburned carbon in a gas−solid flow by laser-induced breakdown spectroscopy (LIBS) was proposed. A homemade gas−solid flow generation system was developed to simulate the gas−solid flow in the duct of a coal-fired plant. For improving the measurement performance, the emission characteristics of the laserinduced plasma, the influence of the fly ash mass flow rate, and the correlation between the unburned carbon content and the carbon emission intensity were studied in detail. The SNR of the Si spectral line at 288.15 nm was selected as the index for false spectra identification because the distribution of Si is not related to the particle size. The results highlight the change of the plasma shape and volume with fluctuations of the gas−solid flow because of the uneven distribution of the fly ash particle size and number in the laser focal spot. The mass flow rate of the fly ash affected the false hit rate, while it did not affect the intensity of the analyte lines. The regression coefficients (R2) between the normalized intensity of C 247.86 nm and the unburned carbon content improved from 0.93 to 0.98 when the false spectra were rejected. The good agreement between the normalized intensity of C 247.86 nm and the unburned carbon content indicates that LIBS can be developed as a promising tool for directly measuring the fly ash unburned carbon in a gas−solid flow. Then, fly ash particles dropped from the outlet of the cyclone and were ablated by the laser. Deguchi’s group demonstrated a series of experiments for optimizing the performance of the LIBS analysis of UC in fly ash.11,17 A stainless steel pipe with an inner diameter of 1.8 mm was used to introduce the fly ash particles into a LIBS measurement chamber by a vacuum pump. The breakdown of the particles occurred along the laser path around the focusing point. In summary, in the abovementioned publications, fly ash particles were ablated in the form of pellets,12,13 stacked particles,14 and gas−solid flow.15−17 Undoubtedly, measuring fly ash UC in the gas−solid flow is beneficial to a significant simplification of the mechanical system, as no pellet preparation devices or belt conveyors are used. However, a sampling system is still required to extract the gas−solid flow from the flue gas duct in the studies above. The sampling system not only complicates the mechanical structure of the measurement apparatus but also reduces the reliability of the measurement apparatus. This is due to the clogging and attrition of the sampling pipeline during lengthy runs. Therefore, it is essential to develop an on-line monitor of UC without the sampling procedure. For the LIBS application, a pulse laser is focused into the flue gas duct and then used to create plasma in the gas−solid flow. There are substantial differences between the monitoring system with sampling and that without sampling: (1) the number density and distribution of fly ash particles; (2) the flow velocity of fly ash particles; and (3) the composition and temperature of the ambient atmosphere. In particular, fly ash particles are randomly distributed in the flue gas duct, while the particles can be

1. INTRODUCTION Reducing greenhouse gas emissions is critical for meeting global climate objectives. It has been estimated that CO2 from fossil fuel and industrial processes accounts for 65% of the global greenhouse gas emissions.1 Improving the coal combustion efficiency is an effective method for reducing the CO2 emission from coal-fired boilers. It is well-known that the unburned carbon (UC) in fly ash is indicative of the combustion efficiency of a coal-fired boiler.2,3 Thus, the real-time measurement of UC is a useful tool for monitoring the combustion process and optimizing the combustion efficiency. Several technologies have been developed as on-line monitors of UC, such as the nondispersive infrared (NDIR) technique, the microwave-based method, and the loss-on-ignition (LOI) technique.4−6 Recently, laser-induced breakdown spectroscopy (LIBS) technology, which is potentially one of the most promising technologies for quick elemental analysis,7,8 was also employed to determine UC in fly ash.9−11 Ctvrtnickova et al.12,13 focused on the experimental conditions and optimization of the sample preparation for improving the construction of the calibration curve of carbon and other elements in fly ash. They compressed a mixture of fly ash particles and binders into pellets for LIBS analysis in the laboratory. Zhang et al.14 designed an automated prototype LIBS apparatus for the on-line analysis of UC in fly ash; it comprised an isokinetic sampler, a sample preparation module, and a LIBS module. In the sample preparation module, fly ash samples were deposited on a conveyor belt and measured by LIBS after the sample surface was smoothened. Kurihara and co-workers15,16 developed an automatic LIBS prototype device for real-time UC monitoring in a coal-fired power plant. A cyclone embedded in the fly ash sampling system was used to collect fly ash particles from exhaust gas. © 2017 American Chemical Society

Received: November 14, 2016 Revised: February 18, 2017 Published: April 3, 2017 4681

DOI: 10.1021/acs.energyfuels.6b02997 Energy Fuels 2017, 31, 4681−4686

Article

Energy & Fuels

Figure 1. Schematic diagram of the experimental setup. In this experiment, the flow velocity of the air stream was maintained at 20 m/s, which is on the same order as the flue gas velocity in a coal-fired power plant duct. The temperature of the gas− solid flow was kept at room temperature because no significant changes in the emission intensity of the laser-induced plasma were observed when the sample temperature fluctuated within a narrow range (200 °C).18 To simplify the influences on the plasma characteristics, other gases were not added into the air stream in this work. In addition, a pulse laser (Beamtech Optronics, E-lite200, China) with an 80 mJ output energy at 1064 nm and a 10 Hz repetition rate was directly focused with a lens (25.4 mm diameter, 100 mm focal length) into the gas−solid flow to create plasma. Plasma emission was collected in the backward direction by a pierced mirror and collection lens; then, it was transmitted into the spectrometer (Avantes, AvaSpec-2048FT, Holland) coupled with a CCD. To optimize the signal-to-noise ratio and prevent detector saturation, the delay time was set to 1.3 μs with a 1.1 ms integration time gate. Four fly ashes were collected from different coal-fired power plants using an automatic isokinetic sampler located at the outlet of the air preheater. The UC content in the fly ashes was determined by the loss-onignition (LOI) method,19 and it is listed in Table 1.

separated from the gas−solid flow and enriched by the cyclone of the sampling system. Therefore, with sampling, the number density and distribution of the fly ash particles are significantly higher than those without sampling. The flow velocity of the gas−solid flow in the flue gas duct of the coal-fired plant is approximately 20 m/s, which is higher than that of the gas− solid flow at the outlet of the cyclone (approximately 1 m/s). The composition of the ambient atmosphere in the case of sampling is air, while the ambient atmosphere in the case of no sampling includes nitrogen, water, and carbon dioxide, which could affect the carbon emission of UC in fly ash. The temperature of the gas−solid flow at the outlet of the air preheater in the coal-fired plant is approximately 180 °C, while the temperature of the gas−solid flow at the outlet of the cyclone is equal to room temperature because it is cooled by compressed air in the sampling system. Compared to the LIBS analysis of UC in the case of sampling, the LIBS analysis of UC in the case of no sampling exhibits specific characteristics. In addition, the laser-induced plasma characteristics of fly ash in the gas−solid flow are unclear and require further investigation. In this paper, we aim to characterize the laser-induced plasma of fly ash in gas−solid flow to improve the on-line measurement of UC without sampling. The influence of the mass flow rates of fly ash in the gas−solid flow on the laserinduced plasma characteristics is investigated. The correlation between the UC content and the carbon emission intensity is also discussed to demonstrate the feasibility of LIBS for directly measuring the fly ash UC in gas−solid flow.

Table 1. UC Content of Fly Ashes (wt %) sample UC content

#1

#2

#3

#4

1.40

3.61

4.51

11.37

3. RESULTS AND DISCUSSION 3.1. Emission Characteristics of the Laser-Induced Plasma. The spectrum of fly ash (#2) averaged from 1000 laser shots is shown in Figure 2. Emission lines from the major elements of fly ash, such as C, Si, Fe, Al, Ca, and Mg, were identified using the NIST database.20 No emission lines of air constituents were observed, while parts of the spectra from the 1000 laser shots for each sample were identified as false spectra. The false spectra were previously defined by Hahn and coworkers for the analysis of aerosol by LIBS combined with a conditional data analysis scheme.21 In the data processing for the conditional data analysis, the signal-to-noise ratio (SNR) of the analyte emission line was used as the index to identify the false spectra. In general, the carbon line should be used to identify the false spectra for the analysis of UC in fly ash. However, the distribution of the carbon content in fly ash

2. EXPERIMENTAL DETAILS The experimental setup used to directly measure the UC of fly ash in the gas−solid flow is schematically illustrated in Figure 1. It consists of two units: a homemade gas−solid flow generation system and the LIBS equipment. The gas−solid flow generation system was developed to simulate the gas−solid flow in the flue gas duct of a coal-fired plant. Fly ash was continuously fed into the steel pipe (with an inner diameter of 9.5 cm) by a power feeder. Then, it was mixed with an air stream and flowed into the steel pipe as gas−solid flow. The mass flow rate of fly ash in the gas−solid flow was controlled by adjusting the rotation rate of the power feeder. Meanwhile, the flow velocity of the air stream was controlled by adjusting the speed of the fan. The temperature of the gas−solid flow can be adjusted by preheating the air stream, and the components of the gas (e.g., carbon dioxide and water vapor) can be varied by adding other gases into the air stream. 4682

DOI: 10.1021/acs.energyfuels.6b02997 Energy Fuels 2017, 31, 4681−4686

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Figure 2. LIBS spectra of #2 fly ash in the solid flow.

depends on the particle size, as shown in Figure 3. From the analysis of #2 fly ash, particles larger than 150 μm contribute to

Figure 4. Representative spectra with different SNR values of Si 288.15 nm.

the spectrum was classified as a false spectrum when the SNR of Si 288.15 nm is below 5. Specifically, the dramatic fluctuation of the optical breakdown induced in the gas−solid flow leads directly to a variation of the SNR of the spectrum from each single shot over a wide range. The optical breakdown of fly ash was recorded by a camera (Nikon, D90, Japan), as observed in Figure 5. The plasma images show that the shape and volume of the plasma change with the distribution of particles in the gas−solid flow. The optical breakdown can be classified into four types: (1) plasma was generated at the focal spot (Figure 5a); (2) plasma was formed before the focal spot (Figure 5b); (3) plasma was produced along the path of the laser beam (Figure 5c); and (4) plasma was generated behind the focal spot (Figure 5d). This clearly demonstrates the fluctuation of the plasma emission intensity over a wide range. It is observed that most of the false spectra were caused by optical breakdown due to the absence of fly ash particles at the focus volume, as observed in Figure 5d. Furthermore, the particle size inhomogeneity of fly ash is presumably an important reason for the uneven distribution of particles in gas−solid flow. To demonstrate this hypothesis, the particle size distribution of #2 fly ash (as a representative sample) was determined using a laser diffraction particle size analyzer (MAZ3000, Malvern Instruments, U.K.) to obtain a volume-weighted particle size distribution, as observed in Figure 6. The volume mean diameter D (4,3) and the median diameter Dv 50 are 69 and 50 μm, respectively. This means that 50% of the fly ash particles are larger than 50 μm. In reality, the fly ash particles cover a wide range of sizes from a few micrometers to hundreds of micrometers, and they are randomly distributed in the gas−solid flow. When fly ash particles are uniformly distributed in the gas−solid flow and few larger-sized particles are present at the focal spot, plasma can be generated at the focal spot of the laser (Figure 5a). If too many small-sized particles are distributed in front of the laser focal spot, most of laser will be absorbed by these small-sized

Figure 3. Variation of the UC content of different-sized particles.

58% of the total number of UC, although the mass fraction of these particles is approximately 8.5%. There are a few UC distributed in small-sized particles, while not in fine particles. This means that the carbon line does not correspond to the presence of fly ash particles in the plasma. Therefore, the carbon line is not suitable for identifying false spectra. On the contrary, SiO2 is the primary component of fly ash, and it accounts for more than 50 wt % of the fly ash mass in all-sized particles.22 The influence of the particle size inhomogeneity on the distribution of Si can be reduced or even eliminated. Therefore, the presence of the Si line can be used to determine the presence of fly ash particles in the plasma volume. According to the description above, the signal-to-noise ratio (SNR) of the Si spectral line at 288.15 nm was adopted as the index for identifying the false spectra. Figure 4 depicts the representative spectrum of #2 fly ash with different SNRs. The intensity of the emission lines changes as the SNR of Si 288.15 nm changes from 0 to 50. Clearly, no emission line was found in the spectrum with an SNR of 0. However, emission lines of Si, Mg, and Fe were present in the spectra with an SNR higher than 5. To effectively reject the false spectra with a low SNR, 4683

DOI: 10.1021/acs.energyfuels.6b02997 Energy Fuels 2017, 31, 4681−4686

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Figure 5. Optical breakdown induced in gas−solid flow.

Figure 6. Particle size distribution of #2 fly ash.

Figure 7. Variation of false hit rate with the mass flow rate of fly ash.

particles, resulting in the formation of plasma before the focal spot (Figure 5b). When a low number density of small-sized particles and no larger-sized particles are present, this results in the formation of plasma in the path of the laser beam (Figure 5c). Particularly, the absence of particles in the focus volume or the presence of too many large-sized particles distributed at the laser focal spot can lead to plasma formation behind the focal spot of the laser (Figure 5d) or even no plasma formation in some cases. 3.2. Influence of the Mass Flow Rate of Fly Ash. In a coal-fired power plant, the mass flow rate of fly ash in the flue gas duct varies with the operation load. To directly measure fly ash UC in the gas−solid flow, the variation of the mass flow rate leads to the fluctuation of the particle number density at the laser focal spot, which affects the plasma characteristics. Thus, it is necessary to investigate the influence of the fly ash mass flow rate on the plasma characteristics. The plasma characteristics at different mass flow rates were tested under the same experimental conditions described in section 2. Figure 7 shows the variation of the #2 fly ash false hit rate, which is defined as the false spectra number in all the spectra (1500 spectra were obtained for each case). It is clearly found that the false hit rates for mass flow rates of 2.99 and 9.02 kg/h are higher than those in other cases. A lower mass flow rate results in a lower particle number density in the gas−solid flow. Therefore, the laser energy was absorbed by the particles along the path of laser beam generating plasma, as shown in Figure 5c. Weak or no plasma emission was observed in Figure 5d since the lower mass flow rate led to a small amount of particles or an absence of particles in the plasma volume. Moreover, an extremely high mass flow rate, such as 9.02 kg/h, also resulted in a high false hit rate. This can be attributed to a large amount of particles that are randomly distributed in the gas−solid flow and the absorbed laser energy across the laser beam path, which results in only a part of the laser reaching the focal spot.

Furthermore, the mass flow rate may affect the emission intensity of analyte lines. Figure 8 illustrates the peak intensity of C 247.86 nm, Si 251.61 nm, Si 252.41 nm, and Si 288.16 nm as a function of the mass flow rate. The vertical error bar for each data point indicates the standard deviation of three repeated measurements averaged from 200 true spectra. This means that each data point is averaged from a set of 600 true spectra. As shown in Figure 8a, a slight fluctuation of the line intensities with the mass flow rate was found. The relative standard deviation (RSD) of these line intensities for the five mass flow rates ranged from 3.18% to 4.54%. This is consistent with the reproducibility results of LIBS measurements, which are often not better than 3−5%.23 To further reduce the RSD of the C 247.86 nm line intensity, the intensities of Si 251.61 nm, Si 252.41 nm, and Si 288.16 nm and the total integrated intensity were individually used to normalize the line intensity of C 247.86 nm, as presented in Figure 8b. The RSD of the C 247.86 nm line intensity improved from 3.18% to values in the range of 1.69−2.48% (the normalized line intensity), which matches the RSD of pelleted fly ash.7 It can be concluded that the mass flow rate of fly ash affects the false hit rate in the gas− solid flow. However, there is no significant influence on the emission intensity of the true spectra in the experiment. 3.3. Correlation between the UC Content and Carbon Emission Intensity. The correlation between the UC content and carbon emission intensity is the key metric for evaluating the performance of the LIBS analysis of UC in fly ash. Under the experimental conditions described in section 2, sets of 1500 spectra were recorded for each of the four fly ashes with a mass flow rate of 5.48 kg/h. The correlation between the normalized intensity of C 247.86 nm and the UC content is shown in Figure 9. It should be noted that the intensity of C 247.86 is normalized by the total integrated intensity. In addition, the signal variations can be effectively reduced, as observed in Figure 8. Each data point in Figure 9a is averaged from all the 4684

DOI: 10.1021/acs.energyfuels.6b02997 Energy Fuels 2017, 31, 4681−4686

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Figure 8. Variation of line intensity with mass flow rate: (a) peak intensity; (b) normalized intensity.

Figure 9. Correlation between the normalized intensity of C and the UC content: (a) average spectrum of all spectra; (b) average spectrum of the identified true spectra using the SNR of 5.

spectra obtained from 1500 shots, and those in Figure 9b are averaged from the true spectra identified with an SNR of 5. The regression coefficients (R2) improved from 0.93 to 0.98 when the false spectra were rejected. This clearly demonstrates that the identification of the true spectra is necessary to improve the LIBS-based analysis of UC in the gas−solid flow. From the results presented in Figure 9 b, it can be determined that the normalized intensity of C is in good agreement with the UC content. Simultaneously, a saturation behavior is observed for the normalized intensity as the UC content increases. The saturation does not result from the self-absorption of the carbon line at a high UC content; however, it may be caused by the nonstoichiometric ablation of large-sized particles. The number of large-sized particles present in the fly ash increases as the UC content increases, and the large-sized particles do not completely vaporize in the plasma volume, which results in a decrease in the normalized intensity.

spectra were present in the recorded spectra because of the fluctuation of the optical breakdown of fly ash particles in the gas−solid flow. The SNR of Si 288.15 nm was selected as the index for false spectra identification because the distribution of carbon in fly ash is related to the particle size. Moreover, Si accounted for the largest mass percentage in all-sized particles. The mass flow rate of the fly ash was found to affect the false hit rate, while there was no effect on the intensity of the analyte lines. When an appropriate threshold value (SNR = 5) was set to identify the false spectra, a good agreement between the normalized intensity of C 247.86 nm and the UC content was obtained with a regression coefficient (R2) of 0.98. Although the effects of the temperature and gas components on the UC measurement were not studied in detail, the results obtained prove that LIBS can be developed as a reliable quantitative analysis technique for directly measuring the fly ash UC in gas− solid flow.

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4. CONCLUSIONS A homemade gas−solid flow generation system was used to simulate the gas−solid flow in the flue gas duct of a coal-fired plant. To simplify the sampling process of fly ash in field applications, a new route for directly measuring the fly ash UC in the gas−solid flow by LIBS was proposed. No interferences from the emission lines of air constituents were observed in the wavelength range of the analyte lines in fly ash. However, false

AUTHOR INFORMATION

Corresponding Author

*Tel.: +86 13925150807. E-mail: [email protected]. ORCID

Shunchun Yao: 0000-0002-3287-9609 Notes

The authors declare no competing financial interest. 4685

DOI: 10.1021/acs.energyfuels.6b02997 Energy Fuels 2017, 31, 4681−4686

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quality and unburned carbon in fly ash. Front. Phys. 2012, 7 (6), 690− 700. (15) Noda, M.; Deguchi, Y.; Iwasaki, S.; Yoshikawa, N. Detection of carbon content in a high-temperature and high-pressure environment using laser-induced breakdown spectroscopy. Spectrochim. Acta, Part B 2002, 57 (4), 701−709. (16) Kurihara, M.; Ikeda, K.; Izawa, Y.; Deguchi, Y.; Tarui, H. Optimal boiler control through real-time monitoring of unburned carbon in fly ash by laser-induced breakdown spectroscopy. Appl. Opt. 2003, 42 (30), 6159−6165. (17) Wang, Z. Z.; Deguchi, Y.; Kuwahara, M.; Taira, T.; Zhang, X. B.; Yan, J. J.; Liu, J. P.; Watanabe, H.; Kurose, R. Quantitative elemental detection of size-segregated particles using laser-induced breakdown spectroscopy. Spectrochim. Acta, Part B 2013, 87 (9), 130−138. (18) Lópezmoreno, C.; Palanco, S.; Laserna, J. J. Quantitative analysis of samples at high temperature with remote laser-induced breakdown spectrometry using a room-temperature calibration plot. Spectrochim. Acta, Part B 2005, 60 (7), 1034−1039. (19) Pan, C. C.; Liang, Q. F.; Guo, X. L.; Dai, Z. H.; Liu, H. F.; Gong, X. Characteristics of Different Sized Slag Particles from EntrainedFlow Coal Gasification. Energy Fuels 2016, 30 (2), 1487−1495. (20) Kramida, A.; Ralchenko, Y.; Reader, J. NIST ASD Team 2012 NIST Atomic Spectra Database, ver. 5.0 [Online]; National Institute of Standards and Technology: Gaithersburg, MD, 2012. http://physics. nist.gov/asd (accessed August 12, 2015). (21) Hahn, D. W.; Flower, W. L.; Hencken, K. R. Discrete particle detection and metal emissions monitoring using laser-induced breakdown spectroscopy. Appl. Spectrosc. 1997, 51 (12), 1836−1844. (22) Qi, L.; Ji, Y. Experimental study on the distribution law of chemical composition in different size fly ash. Coal Convers. 2003, 26 (2), 87−90. (23) Miziolek, A. W.; Vincenzo, P.; Israel, S. Laser Induced Breakdown Spectroscopy; Cambridge University Press: Cambridge, U.K., 2006.

ACKNOWLEDGMENTS The authors would like to thank Dr. Kuo Zeng from CNRSPROMES (Processes, Materials and Solar Energy laboratory in France) for his helpful comments on this work. The work was supported by the National Natural Science Funds of China (51676073, 51206055, and 51476061), the Pearl River S&T Nova Program of Guangzhou (2014J2200054), the Open Research Fund of State Key Laboratory (SKLD15KZ05), the Guangdong Province Train High-level Personnel Special Support Program (2014TQ01N334), and the Science and Technology Project of Guangdong Province (2015A020215005).

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DOI: 10.1021/acs.energyfuels.6b02997 Energy Fuels 2017, 31, 4681−4686