Abstract:Alkali vapors present in the flue gas generated during coal-based combustion form fouling deposits as they condense. An additive added to coal can trap alkali elements in ash, therefore suppress the growth rate of fouling deposits, and increase thermal efficiency of a coalfired thermal power plant. Laser-induced breakdown spectroscopy (LIBS) technique is proposed and demonstrated to screen potential additives to trap alkali elements in ash. Five additives-namely, kaolinite, alumina, silica, magnesia, and pumic… Show more
“…Various studies have reported the characterization of coal using LIBS to determine C content, ash content, calorific value, volatile matter content, etc. 5–9 Most of the reported studies involve sample preparation where the coal samples were ball milled and compacted into pellets to achieve an even surface and homogeneous composition. 10,11 But in real-time, coal composition is not uniform throughout the sample, and coal pieces are irregularly shaped, which is the major challenge in LIBS analysis of coal.…”
A benchtop Laser-Induced Breakdown Spectroscopy (LIBS) is demonstrated to determine the elemental carbon content present in raw coal used for combustion in power plants. The spectral intensities of molecular CN and C2 emission are measured together with the atomic carbon (C) and other inorganic elements (Si, Fe, Mg, Al, Ca, Na, and K) in the LIBS spectrum of coal. The emission persistence time of C2 molecule emission is measured from the coal plasma generated by a nanosecond laser ablation with a wavelength of 266 nm in the Ar atmosphere. The emission persistence time of molecular C2 emission along with the spectral intensities of major ash elements (Fe, Si, Al, and Ca) and carbon emissions (atomic C, molecular CN, and C2) shows a better relationship with the carbon wt% of different coal samples. The calibration model to measure elemental carbon (wt%) is developed by combining the spectral characteristics (Spectral intensity) and the temporal characteristics (Emission persistence time of C2 molecule emission). The temporal characteristic studies combined with the spectroscopic data in the PLSR (Partial Least Square Regression) model has resulted in an improvement in the root mean square error of validation (RMSEV), and the relative standard deviation (RSD) is reduced from 10.86% to 4.12% and from 11.32% to 6.04%, respectively.
“…Various studies have reported the characterization of coal using LIBS to determine C content, ash content, calorific value, volatile matter content, etc. 5–9 Most of the reported studies involve sample preparation where the coal samples were ball milled and compacted into pellets to achieve an even surface and homogeneous composition. 10,11 But in real-time, coal composition is not uniform throughout the sample, and coal pieces are irregularly shaped, which is the major challenge in LIBS analysis of coal.…”
A benchtop Laser-Induced Breakdown Spectroscopy (LIBS) is demonstrated to determine the elemental carbon content present in raw coal used for combustion in power plants. The spectral intensities of molecular CN and C2 emission are measured together with the atomic carbon (C) and other inorganic elements (Si, Fe, Mg, Al, Ca, Na, and K) in the LIBS spectrum of coal. The emission persistence time of C2 molecule emission is measured from the coal plasma generated by a nanosecond laser ablation with a wavelength of 266 nm in the Ar atmosphere. The emission persistence time of molecular C2 emission along with the spectral intensities of major ash elements (Fe, Si, Al, and Ca) and carbon emissions (atomic C, molecular CN, and C2) shows a better relationship with the carbon wt% of different coal samples. The calibration model to measure elemental carbon (wt%) is developed by combining the spectral characteristics (Spectral intensity) and the temporal characteristics (Emission persistence time of C2 molecule emission). The temporal characteristic studies combined with the spectroscopic data in the PLSR (Partial Least Square Regression) model has resulted in an improvement in the root mean square error of validation (RMSEV), and the relative standard deviation (RSD) is reduced from 10.86% to 4.12% and from 11.32% to 6.04%, respectively.
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