The Experts below are selected from a list of 4626 Experts worldwide ranked by ideXlab platform
Zhe Wang - One of the best experts on this subject based on the ideXlab platform.
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validated ensemble variable selection of laser induced breakdown spectroscopy data for Coal Property analysis
Journal of Analytical Atomic Spectrometry, 2021Co-Authors: Weiran Song, Zhe Wang, Zongyu Hou, Muhammad Sher Afgan, Hui Wang, Jiacheng Cui, Yun WangAbstract:Laser-induced breakdown spectroscopy (LIBS), an emerging elemental analysis technique, provides a fast and low-cost solution for Coal characterization without complex sample preparation. However, LIBS spectra contain a large number of uninformative variables, resulting in reduction in the predictive ability and learning speed of a multivariate model. Variable selection based on a single criterion usually leads to a lack of diversity in the selected variables. Coupled with spectral uncertainty in LIBS measurements, this can degrade the reliability and robustness of the multivariate model when analysing spectra obtained at different times and conditions. This work proposes a validated ensemble method for variable selection which uses six base algorithms and combines the returned variable subsets based on the cross-validation results. The proposed method is tested on two sets of LIBS spectra obtained within one month under variable experimental conditions to quantify the properties of Coal, including fixed carbon, volatile matter, ash, calorific value and sulphur. The results show that the multivariate model based on the proposed method outperforms those using benchmark variable selection algorithms in six out of the seven tasks by 0.3%–2% in the coefficient of determination for prediction. This study suggests that variable selection based on ensemble learning improves the predictive ability and computational efficiency of the multivariate model in Coal Property analysis. Moreover, it can be used as a reliable method when the user is not sure which variables to choose in LIBS application.
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a model combining spectrum standardization and dominant factor based partial least square method for carbon analysis in Coal using laser induced breakdown spectroscopy
Spectrochimica Acta Part B: Atomic Spectroscopy, 2014Co-Authors: Zhe WangAbstract:Abstract Quantitative measurement of carbon content in Coal is essentially important for Coal Property analysis. However, quantitative measurement of carbon content in Coal using laser-induced breakdown spectroscopy (LIBS) suffered from low measurement accuracy due to measurement uncertainty as well as the matrix effects. In this study, our previously proposed spectrum standardization method and dominant factor based partial least square (PLS) method were combined to improve the measurement accuracy of carbon content in Coal using LIBS. The combination model utilized the spectrum standardization method to accurately calculate dominant carbon concentration as the dominant factor, and then applied PLS with full spectrum information to correct residual errors. The combination model was applied to measure the carbon content in 24 bituminous Coal samples. Results demonstrated that the combination model can further improve measurement accuracy compared with the spectrum standardization model and the dominant factor based PLS model, in which the dominant factor was calculated using traditional univariate method. The coefficient of determination, root-mean-square error of prediction, and average relative error for the combination model were 0.99, 1.63%, and 1.82%, respectively. The values for the spectrum standardization model were 0.90, 2.24%, and 2.75%, respectively, whereas those for the dominant factor based PLS model were 0.99, 2.66%, and 3.64%, respectively. The results indicate that LIBS has great potential to be applied for the Coal analysis.
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Coal Property analysis using laser induced breakdown spectroscopy
Journal of Analytical Atomic Spectrometry, 2013Co-Authors: Tingbi Yuan, Zhe Wang, Siulung Lui, Jianming LiuAbstract:Fast or online Coal Property analysis would greatly improve Coal pricing reliability and combustion optimization for power generation, especially in China. A non-linearized multivariate dominant factor based partial least square (PLS) model was applied to analyze Coal properties through laser-induced breakdown spectroscopy (LIBS). The dominant factor explicitly modeled the direct correlation between Coal Property and spectral line intensities, whereas residual errors were corrected by PLS method with full spectral information. The results demonstrated an overall improvement over conventional PLS method for ash content, volatile content, and calorific value measurement. For example, the root mean square error of prediction of calorific value was decreased from 1.63 MJ kg−1 to 1.33 MJ kg−1; and the average relative error of all samples was reduced from 3.55% to 2.71%. Although current LIBS application results for Coal proximate analysis have not met the national standard, LIBS is shown to be fully capable of providing useful information for Coal combustion optimization and reliable references for Coal pricing, showing LIBS has great potential for Coal Property analysis for the power generation industry.
Jianming Liu - One of the best experts on this subject based on the ideXlab platform.
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Coal Property analysis using laser induced breakdown spectroscopy
Journal of Analytical Atomic Spectrometry, 2013Co-Authors: Tingbi Yuan, Zhe Wang, Siulung Lui, Jianming LiuAbstract:Fast or online Coal Property analysis would greatly improve Coal pricing reliability and combustion optimization for power generation, especially in China. A non-linearized multivariate dominant factor based partial least square (PLS) model was applied to analyze Coal properties through laser-induced breakdown spectroscopy (LIBS). The dominant factor explicitly modeled the direct correlation between Coal Property and spectral line intensities, whereas residual errors were corrected by PLS method with full spectral information. The results demonstrated an overall improvement over conventional PLS method for ash content, volatile content, and calorific value measurement. For example, the root mean square error of prediction of calorific value was decreased from 1.63 MJ kg−1 to 1.33 MJ kg−1; and the average relative error of all samples was reduced from 3.55% to 2.71%. Although current LIBS application results for Coal proximate analysis have not met the national standard, LIBS is shown to be fully capable of providing useful information for Coal combustion optimization and reliable references for Coal pricing, showing LIBS has great potential for Coal Property analysis for the power generation industry.
T F Wall - One of the best experts on this subject based on the ideXlab platform.
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fine ash formation during combustion of pulverised Coal Coal Property impacts
Fuel, 2006Co-Authors: Bart J P Buhre, Rajender Gupta, Jim Hinkley, Peter F Nelson, T F WallAbstract:Abstract In many countries, legislation has been enacted to set guidelines for ambient concentrations and to limit the emission of fine particulates with an aerodynamic diameter less than 10 μm (PM 10 ) and less than 2.5 μm (PM 2.5 ). Ash particles are formed during the combustion of Coal in pf boilers and fine ash particulates may potentially pass collection devices. The ash size fractions of legislative interest formed during Coal combustion are the result of several ash formation mechanisms; however, the contribution of each of the mechanisms to the fine ash remains unclear. This study provides insight into the mechanisms and Coal characteristics responsible for the formation of fine ash. Five well characterized Australian bituminous Coals have been burned in a laminar flow drop tube furnace in two oxygen environments to determine the amount and composition of the fine ash (PM 10 , PM 2.5 and PM 1 ) formed. Coal characteristics have been identified that correlate with the formation of fine ash during Coal combustion. The results indicate that Coal selection based on (1) char characterization and (2) ash fusion temperature could play an important role in the minimization of the fine ash formed. The implications of these findings for Coal selection for use in pf-fired boilers are discussed.
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mineral matter organic matter association characterisation by qemscan and applications in Coal utilisation
Fuel, 2005Co-Authors: Yinhhui Liu, Rajender Gupta, Atul Sharma, T F Wall, Alan R Butcher, Gavin Miller, P Gottlieb, David FrenchAbstract:Abstract The association of mineral matter with organic matter is extremely important for Coal utilization process such as pf Coal combustion. With the development of advanced analytical instruments such as QEMSCAN, it is now possible to measure directly the mineral matter–organic matter association on a particle-by-particle basis. The mineral matter and mineral–organic associations of a suite of fourteen CCSD Coal bank Coals (as pf) have been determined by QEMSCAN. An interface program was developed to make QEMSCAN data compatible with the CCSEM-based ash formation model developed previously in CCSD. Size and chemistry of flyash was predicted by a partial Coalescence sub-model for included mineral grains, and a fragmentation sub-model for excluded mineral grains, respectively. The size and chemistry of predicted flyash was estimated on a particle-by-particle basis, and was used to rank the ash effect on heat transfer reduction for all the CCSD Coals using the CCSEM-based model, in which Coal Property, furnace geometry and operational conditions have been taken into account. Other applications and further developments of the technique are also outlined.
Junfu Lu - One of the best experts on this subject based on the ideXlab platform.
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research on carbon content in fly ash from circulating fluidized bed boilers
Energy & Fuels, 2005Co-Authors: Xianbin Xiao, Hairui Yang, Hai Zhang, Junfu LuAbstract:The carbon content in the fly ash from most Chinese circulating fluidized bed (CFB) boilers is much higher than expected, which directly influences the combustion efficiency. In the present paper, carbon burnout was investigated in both field tests and laboratory experiments. The effect of Coal Property, operation condition, gas-solid mixing, char deactivation, residence time, and cyclone performance are analyzed seriatim based on a large amount of experimental results. A Coal index is proposed to describe the Coal rank, having a strong effect on the char burnout. Bad gas-solid mixing in the furnace is another important reason of the higher carbon content in the fly ash. Some chars in the fly ash are deactivated during combustion of large Coal particles and have very low carbon reactivity. Several suggestions are made about design, operation, and modification to reduce the carbon content in the fly ash.
Luke D Connell - One of the best experts on this subject based on the ideXlab platform.
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modelling of anisotropic Coal swelling and its impact on permeability behaviour for primary and enhanced Coalbed methane recovery
International Journal of Coal Geology, 2011Co-Authors: Luke D ConnellAbstract:Coal swelling/shrinkage during gas adsorption/desorption is a well-known phenomenon. For some Coals the swelling/shrinkage shows strong anisotropy, with more swelling in the direction perpendicular to the bedding than that parallel to the bedding. Experimental measurements performed in this work on an Australian Coal found strong anisotropic swelling behaviour in gases including nitrogen, methane and carbon dioxide, with swelling in the direction perpendicular to the bedding almost double that parallel to the bedding. It is proposed here that this anisotropy is caused by anisotropy in the Coal's mechanical properties and matrix structure. The Pan and Connell Coal swelling model, which applies an energy balance approach where the surface energy change caused by adsorption is equal to the elastic energy change of the Coal solid, is further developed to describe the anisotropic swelling behaviour incorporating Coal Property and structure anisotropy. The developed anisotropic swelling model is able to accurately describe the experimental data mentioned above, with one set of parameters to describe the Coal's properties and matrix structure and three gas adsorption isotherms. This developed model is also applied to describe anisotropic swelling measurements from the literature where the model was found to provide excellent agreement with the measurement. The anisotropic Coal swelling model is also applied to an anisotropic permeability model to describe permeability behaviour for primary and enhanced Coalbed methane recovery. It was found that the permeability calculation applying anisotropic Coal swelling differs significantly to the permeability calculated using isotropic volumetric Coal swelling strain. This demonstrates that for Coals with strong anisotropic swelling, anisotropic swelling and permeability models should be applied to more accurately describe Coal permeability behaviour for both primary and enhanced Coalbed methane recovery processes.