SPATIAL CONFINEMENT; BEAM GEOMETRY; SPECTROSCOPY; IMPROVEMENT; EXPOSURE

The inevitable problems in laser induced breakdown spectroscopy are matrix effect and statistical fluctuation of the spectral signal, which can be partly avoided by utilizing a proper confined unit. The dependences of spectral signal enhancement on relative permittivity were studied by varying materials to confine the plasma, which include polytetrafluoroethylene( PTFE), nylon/dacron, silicagel, and nitrile-butadiene rubber (NBR) with the relative permittivity 2.2, similar to 3.3, 3.6, 8 similar to 13, 15 similar to 22. We found that higher relative permittivity rings induce stronger enhancement ability, which restricts the energy dissipation of plasma better and due to the reflected electromagnetic wave from the wall of different materials, the electromagnetic field of plasma can be well confined and makes the distribution of plasma more orderly. The spectral intensities of the characteristic lines Si I 243.5 nm and Si I 263.1 nm increased approximately 2 times with relative permittivity values from 2.2 to similar to 20. The size dependent enhancement of PTFE was further checked and the maximum gain was realized by using a confinement ring with a diameter size of 5 mm and a height of 3 mm (D5mmH3mm), and the rings with D2mmH1mm and D3mmH2mm also show higher enhancement factor. In view of peak shift, peak lost and accidental peaks in the obtained spectra were properly treated in data progressing; the spectral fluctuation decreased drastically for various materials with different relative permittivities as confined units, which means the core of plasma is stabilized, attributing to the confinement effect. Furthermore, the quantitative analysis in coal shows wonderful results-the prediction fitting coefficient R-2 reaches 0.98 for ash and 0.99 for both volatile and carbon. (C) 2017 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

BUILDING EXPERT-SYSTEMS; FUEL PROPERTIES; SPECTROSCOPY; MODEL

A quiet but interesting trend has been occurring in material analysis, coincident with the rise of artificial intelligence (Al) and so-called ""deep"" machine learning methods. Astute spectroscopists have always known that there is more information in the spectra that they obtain than simply the molecular or atomic peaks that are directly measured. Particularly with methods such as infrared, Raman, and laser-induced breakdown spectroscopy (LIBS), the spectral background contains a wealth of information about the sample, and analytical combinations of the peaks can provide material properties. Traditionally, such analytical combinations of peaks were performed explicitly by analysts, but now information about material properties embedded in the spectra can be derived implicitly by Al and machine learning algorithms. This column introduces these ideas and touches on recent results indicative of what more may be coming in this direction.

Laser-induced breakdown spectroscopy; Proximate analysis of coal; Spectral fitting; Support vector machine;LIBS; SYSTEM

Online accurate proximate analysis of coal is vitally important to the optimization of industrial production and reduction in coal consumption. However, due to the ""matrix effect"" caused by the complex and diverse coal species in China, the measurement accuracy needs to be improved by using laser-inducedbreakdown spectroscopy (LIBS). In our experiment, both the spectral pretreatment method and the calibration model for the conversion of laser induced coal plasma spectra to the coal proximate analysis results were optimized. Experimental results showed that, compared with the traditional method, the proposed single or multiple-peak Lorentzian spectral fitting for spectral line intensity calculation reduced the mean RSD from 12. 1% to 9. 7%. For kernel function parameters optimization, the mean absolute error (MAE) of the particle swarm optimization (PSO) was smaller than that of the grid parameter (Grid) and the genetic algorithm (GA). The root mean square error (RMSEP) of support vector machine (SVM) regression model based on PSO parameter optimization was less than that of partial least squares regression (PLS). By combining the single- or multiple-peak Lorentzian spectral fitting method with the PSO based SVM for regression modeling, the average absolute errors (AAE) of predicted proximate analysis results were certified to be: 1. 37% for coal ash content of 16%similar to 30%, 1. 77% for coal ash content of 30% or more, 0. 65 MJ . kg(-1) for calorific value of 9 similar to 24 MJ . kg(-1), 1. 09% for volatile matter of 20% or less, and 1. 02% for volatile matter of 20% or more.

Laser induced breakdown spectroscopy (LIBS); Laser wavelength; Plasma time evolution; Plasma shielding effect; Coal;INDUCED BREAKDOWN SPECTROSCOPY; ELEMENTAL ANALYSIS; DEPENDENCE; PARAMETERS

In face of the complicated and changeable coal in Chinese power stations, the on-line measurement of coal elemental composition is extremely significant in terms of enhancing the safety and efficiency of boilers as well as lowering the waste emission during operation. In this work, laser induced breakdownspectroscopy (LIBS) was applied to coal quality measurement. Laser wavelength was changed as 355, 532 and 1 064 nm to investigate the influence on coal LIBS features, including plasma time evolution and the spectral lines intensity-time characteristics of different elements. Energy threshold for shielding effect was also tested to verify how it varies with laser wavelengths. Additionally, coal LIBS spectrum was analyzed under different laser wavelengths. It has been proven by experiments that higher intensity of coal LIBS spectrum and energy threshold can be easily achieved when using laser of 532 nm, making it a fantastic energy source for coal LIBS tests. Results of these experiments serve to guide the industrial application of LIBS technology in the field of coal quality measurement.

SITE QUANTITATIVE-ANALYSIS; LIBS SPECTRA; POWER-PLANTS; ASH CONTENT; WELL LOGS; VALUE GCV; CALIBRATION; PREDICTION; MODEL; PERFORMANCE

Online measurement for the gross calorific Value (GCV) of coal is important in the coal Utilization industry. This paper proposed a rapid GCV determination method that combined a laser-induced breakdown spectroscopy (LIBS) technique with artificial neural networks (ANNs) and genetic algorithm (GA). Input variables were selected according to the physical mechanism and mathematical significance to improve the prediction of the ANN. GA was applied to determine an,optirnal architecture for the network instead of a trial and error method. As a result, the mean standard deviatiori (MSD) of the GCV for four prediction set samples is,0.38 MJ/kg in 50 trials (repetitions of training the ANN with the same; input data but different random initial weights and biases), proving that the ANN model is able to provide high modeling repeatability in the GCV analysis. The mean absolute error (MAE) of the GCV for the prediction set is 039 MJ/kg. The result meets the requirements (0.8 MI/kg) for coal online analyses using the neutron activation method in the Chinese national standard (GB/T 29161-2012).

INDUCED BREAKDOWN SPECTROSCOPY; ELEMENTAL ANALYSIS; SPECTRUM STANDARDIZATION; UNBURNED CARBON; FLY-ASH; SAMPLES; SPECTROMETRY; PELLETS; MODEL; PLANT

Quantitative analysis of elements by laser-induced breakdown spectroscopy (LIBS) is significantly affected by matrix effects in coal. Coal powder was mixed with the KBr binder and pressed into pellets to reduce matrix effects. Four groups of mixed-pressed pellet samples were prepared from nine different coal types and different percentages of the KBr binder (KBr accounts for 0 wt%, 30 wt%, 60 wt%, and 90 wt%, respectively). To optimize the percentages of the KBr binder in the mixed-pressed pellets, the influence of KBr binder on laser-induced plasma was investigated in detail. The results indicate that the plasma excitation temperature decreases with increase of the KBr binder concentration. The difference of the excitation temperature between the nine different coal samples was minimal when KBr accounts for 60 wt% in the mixed-pressed pellets. The relative standard deviation of the excitation temperature is 4.26%. The matrix changes from coal to KBr when the percentages of KBr are higher than 60 wt%, which was confirmed by scanning electron microscopy images of the ablated crater. Finally, Si and K were individually chosen as the internal calibration element to construct the calibration curves of carbon. Better results were obtained when Si I 288.16 nm was used as the internal standard. The correlation coefficients R-2 of the four groups of mixed-pressed pellet samples are 0.835, 0.893, 0.983, and 0.903, respectively. Hence, the appropriate percentage of binders needs to be carefully confirmed to reduce matrix effects in quantitative analysis of coal by LIBS.

VICTORIAN BROWN-COAL; FIXED-BED REACTOR; ASH DEPOSITION; ALKALI REMOVAL; ATOMIC SODIUM; SORBENTS; GASIFICATION; TEMPERATURE; MECHANISMS; CORROSION

Zhundong coal represents a large portion of China's future energy supply, because of the large reserve capacity. Although Zhungdong coal has low ash and good ignition characteristics, it also contains large amounts of sodium, which can foul and corrode heat-transfer surfaces. For economically viable use of Zhundong coal or other high-alkali coals, the alkali release must be mitigated prior to or within the burner. This can be done either by washing the coal,, using sorbents to trap the sodium, or a combination of these methods. Additive influence on the release of sodium over the entire coal combustion process was measured using a calibrated laser-induced breakdown spectroscopy (LIBS) technique. The additives used were alumina, silica, and five mineral sorbents comprising blends of silica, alumina, and various other inorganic compounds; different coal/sorbent ratios were assessed (1%, 3%, and 5% additive, by weight). During the three stages of sample coal combustion, it was found that the first stage, devolatilization, releases only similar to 1.2% of the total sodium; the char combustion stage releases similar to 28% of the total sodium; and the ash stage releases the vast majority of sodium (similar to 70%). The peak instantaneous release rate occurs during the char combustion stage. The sodium retention efficiency for all of the sorbents tested was lowest during the devolatilization stage and there was no appreciable change to the duration of the stage. Sodium retention efficiency was greatest in the ash stage, primarily due to the sheer amount of sodium released during this stage. The ash stage was effectively shortened, in proportion to the retention efficiency of the sorbent. The sodium release during the char combustion stage showed an obvious difference in profile for the various types of sorbent tested. Alumina was the least effective in reducing sodium release and retained the characteristic shape of sodium release from raw coal during this stage. Silica and sepiolite demonstrated similar sodium release profiles, which were markedly different than the sodium release profiles of either alumina or aluminosilicate mineral samples. Time-resolved sodium retention plots show an enhancement in the sodium retention by alumina during the char burnout. The addition of sodium sorbent to the coal extended the duration of the char burnout stage, which may indicate that the reduction of available sodium reduces the alkali catalysis of the char decomposition.