Coal Quality Analysis Research Articles

Analisis Cara Penanganan Penurunan Kualitas Batubara di Stock ROM Ulak Pandan Sampai Jetty SDJ di PT Bumi Merapi Energy

Coal quality analysis is increasingly important considering the increasing global energy demand. Coal, as the main source of energy, has varying qualities, affecting combustion efficiency and environmental impact. Two commonly used analysis methods are proximate analysis and ultimate analysis, which provide information about the composition of coal. Previous research has shown that chemical analysis affects energy efficiency and environmental impact. This study aims to analyze the quality of coal using both methods and evaluate its characteristics for industrial applications. The benefits of this research include guidance for coal producers and users, as well as contributions to reducing the environmental impact and sustainability of the energy sector. This study uses a descriptive approach with qualitative and quantitative methods, focusing on Seam E coal at PT Bukit Asam Tbk. Primary data includes coal handling, temperature, and quality, while secondary data includes location maps and rainfall data as a reference. This study resulted in the conclusion that 1) Factors that affect the decline in coal quality at mining sites and stockpiles include the mining process, working front conditions, loading, processing in the stockpile, sampling, and handling in the ROM to Jetty SDJ. 2) Efforts made include contamination avoidance, fine coal handling, stacking management, and blending process.

Study on the Thermal Effect of Spontaneous Combustion of Coal-Based Activated Carbon By-Products Oxidized and Carbonized Powders

ABSTRACT To investigate the spontaneous combustion thermal effect of the by-products oxidized powder (OP) and carbonized powder (CP) produced during the preparation of coal-based activated carbon (CBAC). First, the changes in composition and pore structure of OP and CP compared with raw coal (RC) were measured by coal quality analysis and nitrogen adsorption experiments. The ability of OP and CP to physically adsorb oxygen was determined. Then, differential scanning calorimetry (DSC) was used to study the whole process thermal effect of the spontaneous combustion of OP and CP. Finally, the thermal effect of OP and CP during low-temperature oxidation process was further explored by the C80 micro-calorimeter, and the thermodynamic characteristics were also probed. The results show that the content of fixed carbon and carbon elements in OP and CP are significantly greater than that in RC, which makes the pore structure of both complicated. In addition, the total pore volume, average pore size, and specific surface area of OP and CP are greater than that of RC, which is more conducive to the physical adsorption of oxygen by both. No transition platform is caused by the decomposition of intermediate products on the DSC curve of CP. The characteristic temperatures of CP (except T D10) are lower than that of OP, indicating that the spontaneous combustion reaction of CP started earlier than that of OP. However, part of the combustible substance of CP was consumed in the dry distillation process, leading to the thermal release and maximum thermal release rate being smaller than that of OP. The apparent activation energy (E a) of stages H2 and H3 in the low-temperature oxidation process of OP is 75.04 and 11.42 kJ mol−1, while that of CP is 83.11 and 30.91 kJ mol−1. The threshold of CP entering stages H2 and H3 is higher than that of OP. Nevertheless, from the pre-exponential factor (A), the low-temperature oxidation process of CP is more rapid.

Construction of the Online and Offline Hybrid Teaching Courses of “Coal Chemistry and Coal Quality Analysis” under the Background of “New Engineering”

Construction of the Online and Offline Hybrid Teaching Courses of “Coal Chemistry and Coal Quality Analysis” under the Background of “New Engineering”

Developing a compact coal quality rapid detection integrated machine based on laser‐induced breakdown spectroscopy

AbstractCoal's calorific value and carbon content are crucial in calculating carbon emissions. Accurately detecting these two indicators is of great significance for carbon accounting. In this study, we developed a compact coal quality rapid detection integrated machine based on laser‐induced breakdown spectroscopy (LIBS), which can directly measure coal particle flow. A partial least squares model, based on data set selection according to cluster analysis results, was applied to establish the relationship between coal quality and plasma spectra. The R2 of the calorific value is .93, the root mean square error of prediction (RMSEP) is 0.41 MJ/kg, and the mean absolute error (MAE) is 0.33 MJ/kg. The R2 of carbon content is .94, the RMSEP is 0.97%, and the MAE is 0.91%. These results indicated that the developed compact coal quality rapid detection integrated machine could conduct precise coal quality analysis in real time.

GFNet: A pioneering approach for precisely estimating ash content in coal through the fusion of graph convolution and feedforward network

Accurate determination of ash content in coal is paramount for quality control in the coal industry. However, conventional methods are time-consuming and ill-suited for real-time process control. To address this challenge, our study proposed a novel approach, GFNet, which integrated graph convolution and a feedforward network for ash content estimation. The image was divided into patches and transformed into a graph structure using a k-nearest Neighbor (k-NN) function. We introduced relative position encoding to each node feature to enhance feature representation. The GFNet architecture consisted of a stack of GF (graph convolution and FFN), facilitating feature extraction from the graph representation of image patches. Graph convolution captured intricate spatial relationships and interdependencies among patches, enabling effective information aggregation and updating within the graph. The FFN module, featuring two linear layers, facilitated node feature transformation. We developed pyramid (PGFNet) and isotropic (IGFNet) architectures by stacking GF blocks. Extensive experiments substantiated the superior performance of the proposed approach compared to traditional methods, emphasizing accuracy and efficiency. The GFNet achieved remarkable results, with PGFNet-S attaining a 96.36 accuracy and a mean absolute error (MAE) of 0.145%. IGFNet-Ti demonstrated a higher accuracy of 98.30% with an MAE of 0.093%. Visualization of the graph structure elucidated the model’s proficiency in processing graph structure and node features, illustrating its capability to capture content-relevant nodes and gradually connect nodes with similar semantics. This pioneering work presented a transformative approach to ash content estimation in coal quality analysis, offering an invaluable tool for the coal industry’s quality control needs.

Fuzzy Structured Element Integrated Decision of Metamorphism on Coal Dust Ignition Hazard Rating

To discuss the coal dust ignition hazard rating of different metamorphism, the fuzzy structure element integrated decision model about “no danger”, “weak danger”, “general danger”, and “strong danger” of coal dust ignition is established. 40 kinds of coal samples are selected to carry out coal quality analysis test, overpressure test, temperature test, flame propagation properties test and particle size distribution test. Based on this, coal dust ignition hazard rating decision-making index system is built. The triangular structure element is added to fuzzy comprehensive decision model, by sequencing weighted Hamming distance between fuzzy index value function and fuzzy maximal function, the coal dust ignition hazard rating of different coal quality can be decided. The result shows that: with increasing metamorphic grade, coal dust ignition hazard rating is decreasing, which provides study basis for relation between metamorphism and ignition hazard rating.

LKDPNet: Large-Kernel Depthwise-Pointwise convolution neural network in estimating coal ash content via data augmentation

Product quality monitoring is one of the most critical demands in the coal industry. Conventional coal quality analysis is offline, laborious, and lagging behind coal production. Using machine vision for determining ash content in coal has been recently developed. However, there are some challenges in the model design due to its task complexity. A data augmentation method for a specific task was proposed to deal with the peculiarities of a small, unbalanced industrial dataset. For estimating ash content in coal from an image without background, we provided a trajectory going from a ResNet to an LKDPNet under the guidance of receptive field size. First, the model depth was determined by changing the stage ratio. Second, patch embedding was employed to substitute the stem cell of ResNet for downsampling. Third, a residual connection block of depthwise convolution followed by pointwise convolution was designed to replace the ResNet identify and conv blocks. The receptive field of the model was adjusted by increasing the kernel size of the depthwise convolution layer and removing the downsampling block. The results and visualization revealed that the proposed LKDPNet could significantly improve performance with an accuracy of 98.91% and an MAE of 0.082. It could pave the way to an online analysis of coal, thus accelerating intelligent coal production.

A Comparative Investigation of the Adsorption Characteristics of CO2, O2 and N2 in Different Ranks of Coal

The adsorption mechanism of carbon dioxide, oxygen and nitrogen in coal is important for preventing and controlling coal spontaneous combustion and for understanding the technology of CO2 storage in goaf. Adsorption amount and adsorption heat are key adsorption parameters that are required to understand the material and energy conversions during adsorption in coal. In this study, we studied the factors that influence the adsorption amounts and adsorption heat values of carbon dioxide, oxygen and nitrogen in coal by testing four different coal samples using conventional coal quality analysis, low-pressure nitrogen and carbon dioxide adsorption, Fourier transform infrared spectroscopy and three gas adsorption experiments at different temperatures. Then, we analyzed the relationships between the structural parameters of the coal samples and the adsorption amounts and the adsorption heat values of carbon dioxide, oxygen and nitrogen. The results show that the adsorption isotherms of carbon dioxide conform to the Langmuir equation, and the adsorption isotherms of oxygen and nitrogen conform to Henry’s law between 0 and 110 kPa. The adsorption amounts of carbon dioxide, oxygen and nitrogen decreased with an increase in temperature, and the change in the rate of the adsorption amount with temperature was determined by the adsorption heat. The results of the pore structure show that the pores of the coal samples are composed of mesopores and micropores; the micropores contribute to the main specific surface area. The micropore and mesopore structures are the main determinants of the adsorption amounts of carbon dioxide, oxygen and nitrogen in coal. The gas adsorption heat is affected by the pore structure and the chemical composition of coal. The adsorption heat of nitrogen correlates positively with the pore structure of the coal. The adsorption heat of oxygen correlates positively with the ash, elemental nitrogen, elemental sulfur and mineral contents of the coal. The adsorption heat of carbon dioxide correlates positively with the elemental sulfur content of the coal.

Mineral Matter Analysis on Coal and Flanking Rocks in the Bulupoddo District, Sinjai Regency, South Sulawesi Province, Indonesia

South and West Sulawesi has coal resources of about 117 million tons or about 0.35% of the total resources nationally. Bulupoddo coal located in Sinjai Regency is a type of coal that is categorized as low rank coal. This study was conducted to identify the minerals contained in coal and its flanking rock (roof and floor) and determine the rank and quality of coal. The methods used are mineralogy and coal quality analysis methods. Field observations shows a type of coal that is about 50 cm thin and flanked by clay and sandstone. The results of microscope and XRD analysis (X-Ray Diffraction) showed minerals contained in Bulupoddo coal, namely in BP-01B (coal) and BP-01E (coal) samples containing kaolinite, illite, pyrite and quartz minerals. In the flanking rock samples, the samples of BP-01A (clay) and BP-01D (clay) contain the minerals kaolinite, illite, pyrite, quartz and chlorite. In the BP-01C (sand) sample indicated as sandstone contains feldspar, quartz and kaolinite minerals. The results of the coal quality analysis that have been carried out on the two coal samples, namely BP-01B and BP-01E, show that the BP-01B sample has a moisture content of 7.74%, an ash content of 54.23%, volatile matter is 23.35%, fixed carbon 14.68%, and total sulfur content by 0.37%. While the sample BP-01E has a moisture content of 9.2%, ash content of 67.76%, volatile matter 18.23%, fixed carbon 4.81% and sulfur content is 0.49%. The calorific value of the test results shows that the coal sample BP-01B has a calorific value of 1953 kcal/gr and BP-01E has a calorific value of 853 kcal/gr. The calorific value of Bulupoddo coal is categorized as lignite B type coal according to coal classification based on ASTM (American Society of Testing Materials).

Coal Quality Analysis Based on Proximate and Ultimate Test Results in Massenreng Pulu Village, Lamuru District, Bone Regency

Coal is one of the minerals that has an important role in the mining industry in Indonesia. Massenreng Pulu Village, Lamuru District, Bone Regency, South Sulawesi Province is an area that has coal resources, the quality of coal in that area needs to be known. The quality of coal has a very important role in the utilization of coal. Coal quality parameters will affect the intended use of coal. The purpose of this study was to analyze the quality of coal based on the proximate and ultimate analysis tests. The sampling method was channel sampling, samples from the field were then brought to the laboratory for preparation and analysis proximately and ultimately. The results of the proximate analysis of coal samples contained an average moisture content of 15.75% ar, ash content of 3.48% ar, volatile matter of 42.35% ar and fixed carbon of 37.91% ar while the results of the ultimate analysis of coal samples contained an average of 66.52% carbon. wt, 4.6% wt hydrogen, 0.93% wt nitrogen and 23.05 wt oxygen. Based on the ash content, if classified according to The UNECE 1998 classification of in seam coals, it can be seen that the rank of coal in the study area is classified as high grade coal.

Analysis of Jet Structure and Physical Properties in the Coalfields of Northern China

Archeological discoveries have identified China as one of the first countries in the world to use jet. However, many differences are evident between the jet currently found in existing mines and the archaeological discoveries of cultural relics in terms of texture and quality according to the definition of organic gem jet in gemology. This paper reports the results of microscopic analysis and coal quality analysis of the coal and jet samples from coal seams in Fushun Open-pit Mine and Datong Coalfield. The findings reveal that the physical and chemical composition of coal in different mining areas differs markedly. However, the differences between jet and coal in both mining areas are similar; that is, jet has lighter density and greater hardness (2–4) compared to coal, as well as elasticity (engravability), and both jet and coal occur in the (rock slurry) hydrothermal environment. Lastly, the analysis shows that the formation of jet depends on rubber-like hydrocarbon coal with a high degree of corruption in a sedimentary environment under the vulcanization of a hydrothermal, high-sulfur environment.

Accuracy Enhancement of LIBS-XRF Coal Quality Analysis Through Spectral Intensity Correction and Piecewise Modeling

The combination of laser-induced breakdown spectroscopy and energy dispersive X-ray fluorescence spectroscopy in the coal quality analysis was reported formerly. But in the practical test of the prototype instrument in the real power plant, the X-ray fluorescence signals suffered from intensity fluctuations over long-time measurements. The long-term signal fluctuations cause lower efficiency on the establishment of the calibration model and relatively larger root-mean-squared error of prediction (RMSEP) for unknown samples. Therefore, the spectral intensity correction was performed in the measurements; a randomly selected sample was measured several times in the whole measurements, including the modeling samples and unknown samples, recording the signal fluctuations and searching for a set of factors suitable for the intensity correction of a full-spectrum–based partial least square calibration model. In addition, as the signals of the coal samples of the power plant showed the potential of classification, the piecewise models were also established in case of further enhancement of the model or prediction accuracy. The RMSEPs of the calorific value, ash, volatile, and sulfur were lowered from 0.68 MJ/kg, 1.62%, 0.32%, and 0.24% to 0.51 MJ/kg, 1.34%, 0.16%, and 0.14% after spectral intensity correction, respectively. The piecewise modeling with spectral intensity correction achieved similar RMSEP for volatile and sulfur prediction but with more accurate models. The spectral intensity correction showed the ability to reduce the long-term signal fluctuation, and piecewise modeling also showed more efficiency in the model establishments for volatile and ash determination.

Quantitative analysis of coal quality by mutual information-particle swarm optimization (MI-PSO) hybrid variable selection method coupled with spectral fusion strategy of laser-induced breakdown spectroscopy (LIBS) and fourier transform infrared spectroscopy (FTIR)

Coal quality analysis can provide basic work for the design and operation of related equipment and technological processes, which mainly including ash content and volatile matter. Variable selection plays an important role in the model and properly variable selection methods which can promote the predictive performance of the model and reduce the modeling time. In this article, a hybrid variable selection method mutual information-particle swarm optimization (MI-PSO) was used to realize precise screening of laser-induced breakdown spectroscopy (LIBS) and fourier transform infrared spectrometer (FTIR) spectral characteristic variables. Firstly, to obtain the fusion spectrum matrix, the pre-processed LIBS and FTIR spectrum matrix were directly connected end to end. Then the MI was used to eliminate redundant variables in the spectral data, and the PSO was used to further filter the retained variables to find a set of variables with higher prediction accuracy. Finally, a mutual information-particle swarm optimization-kernel extreme learning machine (MI-PSO-KELM) model is constructed based on the selected variables that the ash content and the volatile matter variables are 6499, 5093 respectively. The performance of MI-PSO-KELM is better than the KELM model based on LIBS and FTIR and the KELM model based on LIBS and FTIR primary spectral fusion, and can more accurately predict the ash content and volatile matter of coal quality analysis. The results for the determination of ash content, the optimal cross-validation (root mean square error of cross validation (RMSECV) =1.6170, determination coefficient of cross validation (RCV2) =0.9684) and prediction set the root mean square error of prediction (RMSEP) =0.9687, determination coefficient of prediction (RP2 = 0.9821) results are obtained by the MI-PSO based on primary spectral fusion model. For the determination of the volatile matter, the best results of cross-validation (RMSECV = 1.2886, RCV2=0.9868) and prediction set (RMSEP = 1.3218, RP2=0.9789) are obtained by the MI-PSO based on primary spectral fusion model. It shows that the spectral fusion technology can help improve the performance of LIBS spectral quantitative analysis, and a spectrum fusion method based on LIBS and FTIR is established to realize the rapid determination of ash content and volatile matter of coal.

Development and Performance Characterization of a Hybrid Dust Suppressant Based on Sodium Ligninsulfonate Modification

AbstractA naturally occurring macromolecular material (sodium ligninsulfonate) is mixed with acrylic acid to perform grafting copolymerization and generate a sodium ligninsulfonate–acrylic acid graft copolymer. The chemical structure of the product is characterized based on infrared spectroscopy. Using viscosity and surface tension as indicators, the optimal ratio of wetting agents and coagulants is selected for auxiliary materials to prepare a novel hybrid dust suppressant customized for coal mines. The results indicate that, compared with spraying with water alone, this dust suppressant enhances the average anti‐weathering performance of coal samples by 28.34%, 30.13%, and 43.40% at wind speeds of 5–10, 11–15, and 16–20 m s−1, respectively. The hybrid dust suppressant can keep the weathering rate of dust to less than 40% at a wind speed of 19.3 m s−1(i.e., Beaufort scale 8). High‐resolution scanning electron microscopy shows the dust suppressant has a good curing effect. Finally, coal quality analysis experiments show the dust suppressant does not reduce coal quality.

CHARACTERIZATION OF COAL SEAMS IN THE ARANTIGA AND SELUANG MINE BENGKULU USING PROXIMATE ANALYSIS DATA

Indonesia as a country that has ample large coal reserves spread across the Sumatera and Kalimantan islands. The huge potential in the region needs further research to be able to find out the quality and excellence of coal resources in order to know the characteristics in detail, then the research was carried out in the Bengkulu Province area by testing coal sample based on Proximate analysis to obtain accurate coal quality results and analysis of coal characteristics in the area can be carried out. The results show that the coal seams in the Arantiga mine have an average value Inherent Moisture is worth 7.49 %, ASH is worth 9.82 %, Volatile Matter is worth 40.99 %, Fixed Carbon is worth 41.70 %, Total Sulfur is worth 0.34 %, Gross Caloric Value is worth 6305 kcal/kg and including of High Volatile A Bituminous coal type, while the Seluang mine has an average value Inherent Moisture is worth 2.07 %, ASH is worth 22.92 %, Volatile Matter is worth 20.26 %, Fixed Carbon is worth 54.78 %, Total Sulfur is worth 0.55 %, Gross Caloric Value is worth 6365 kcal/kg dan and including of Medium Volatile Bituminous coal type.

Impacts of intrinsic alkali and alkaline earth metals on chemical structure of low-rank coal char: Semi-quantitative results based on FT-IR structure parameters

The intrinsic alkali and alkaline earth metals (AAEMs) have obvious catalysis on pyrolysis of low-rank coal. Due to the differences in dispersion degree and chemical form, the catalytic effects of AAEMs with diverse occurrence states may be different. The chemical structures of coal and char are the most direct evidences to reveal the catalysis of AAEMs. However, quantitatively characterizing and evaluating these structures, especially the asymmetric functional groups, has always been an unsolved problem. In this study, the FT-IR spectra of Zhundong coal and char containing different forms of intrinsic AAEMs were fitted, the functional group structures of these samples were semi-quantitatively characterized by a series of original infrared structural parameters, and the volatile products during pyrolysis were detected using TG-FTIR. The comprehensive analysis of functional group structures, weight-loss rates and volatile product concentrations indicates that in the char, the water-soluble and ion-exchangeable AAEMs hinder the release/decomposition of –COO− and aliphatic C–H groups, and promote the thermal cracking of aromatic C–H groups. The inhibition of ion-exchangeable AAEMs on release/decomposition of Ar–O− groups and dehydrogenation/polycondensation of aromatic clusters in char is also confirmed. The infrared structural parameter method proposed in this paper is expected to be a reasonable, effective, comprehensive and universal method for quantitatively characterizing the functional group structures of coal and char, and improve the current method system of coal quality analysis, accordingly bring a new tool for the evaluation and communication of chemical structures of coal and char.

Modes of Occurrence and Origin of Minerals in Permian Coals from the Huainan Coalfield, Anhui, China

Minerals in coal provide useful information for not only paleo-environments of peat accumulation, but also for geological evolution during later diagenesis and/or epigenesis. This paper reports new data on coal quality and the mineralogical and geochemical compositions of 17 unaltered (by intrusion) coal samples collected from the Huainan coalfield, providing new insight into the origins and modes of occurrence of the minerals in coal and their geological evolution. The results showed that the studied coal samples were low rank bituminous coal, with low ash yield (11.92–38.31%, average 24.80%) and high volatile content (25.13–43.43%, average 37.29%). Minerals in the coal mainly included kaolinite and quartz; varying proportions of calcite, siderite, ankerite, and pyrite; and traces of chlorite, zircon, strontianite, apatite, and gorceixite. Typical modes of mineral occurrence could be used to determine the formation stage of minerals. The detrital mineral, occurring as sub-angular to rounded discrete fragments or thin layers intimately admixed with organic matter at particular horizons, was of terrigenous origin, deposited during peat accumulation. Cell infillings, as well as nodule siderites and polycrystalline aggregates of pyrite, precipitated during the syngenetic to early diagenetic stages. Cleat infillings, compressed cell infillings, and fracture infillings precipitated in the epigenetic stage. However, the stage of mineral formation of the pore infilling was difficult to determine. Combined with coal quality, mineralogy, and geochemical analysis, the sedimentary environment of Shanxi Formation was affected by seawater, and Fe-rich hydrothermal fluids filled into the No. 3 coal seam in the epigenetic stage. The sedimentary environment of the No. 8 coal seam had widespread reduction and acid conditions due to basin subsidence, and sulfate-rich hydrothermal fluids may have been formed during the peat deposition stage. In contrast, the peat accumulation environment of the Upper Shihezi Formation was oxidized with a low pH condition. Alkaline fluid then flowed into the No. 13-1 coal seam in the epigenetic stage.

Coal Deformation, Metamorphism and Tectonic Environment in Xinhua, Hunan

This article discusses in detail chemical composition, molecular structure, microstructure phenomena, estimate of the palaeo-stress, paleo-temperature and the strain rate to deepen the knowledge for the correlation of coal deformation and metamorphism with structural environment in Xinhua Hunan by coal quality analysis, XRD and SEM methods, which provide dependable theoretical foundation for coal resource exploitation and utilization. The results show that 1) d002 value of six coal samples is from 3.36 to 3.39 nm, coal resolved itself into aphanitic graphite with the increase of coal rank during coalification, which is characterized by graphite flakes, and the crystallite size is from 50 nm to 250 nm; A certain degree of 3R-structure content is increases and the crystallite size is extend with the coalification process, but RH-structure content is decreased; 2) the tectonic environment of research area belongs to the ductile-brittle deformation, which was characterized by low temperature, low stress, high strain rate; 3) Tianlongshan magmatic intrusion provided heat source, its side-extrusion made the molecules structure of coal ordering, distance between layers decreased, finally it caused the formation of aphanitic graphite.

Review of methodological and experimental LIBS techniques for coal analysis and their application in power plants in China

Laser-induced breakdown spectroscopy (LIBS) is an emerging analytical spectroscopy technique. This review presents the main recent developments in China regarding the implementation of LIBS for coal analysis. The paper mainly focuses on the progress of the past few years in the fundamentals, data pretreatment, calibration model, and experimental issues of LIBS and its application to coal analysis. Many important domestic studies focusing on coal quality analysis have been conducted. For example, a proposed novel hybrid quantification model can provide more reproducible quantitative analytical results; the model obtained the average absolute errors (AREs) of 0.42%, 0.05%, 0.07%, and 0.17% for carbon, hydrogen, volatiles, and ash, respectively, and a heat value of 0.07 MJ/kg. Atomic/ionic emission lines and molecular bands, such as CN and C2, have been employed to generate more accurate analysis results, achieving an ARE of 0.26% and a 0.16% limit of detection (LOD) for the prediction of unburned carbon in fly ashes. Both laboratory and on-line LIBS apparatuses have been developed for field application in coal-fired power plants. We consider that both the accuracy and the repeatability of the elemental and proximate analysis of coal have increased significantly and further efforts will be devoted to realizing large-scale commercialization of coal quality analyzer in China.

Development of a coal quality analyzer for application to power plants based on laser-induced breakdown spectroscopy

It is vitally important for a power plant to determine the coal property rapidly to optimize the combustion process. In this work, a fully software-controlled laser-induced breakdown spectroscopy (LIBS) based coal quality analyzer comprising a LIBS apparatus, a sampling equipment, and a control module, has been designed for possible application to power plants for offering rapid and precise coal quality analysis results. A closed-loop feedback pulsed laser energy stabilization technology is proposed to stabilize the Nd: YAG laser output energy to a preset interval by using the detected laser energy signal so as to enhance the measurement stability and applied in a month-long monitoring experiment. The results show that the laser energy stability has been greatly reduced from ±5.2% to ±1.3%. In order to indicate the complex relationship between the concentrations of the analyte of interest and the corresponding plasma spectra, the support vector regression (SVR) is employed as a non-linear regression method. It is shown that this SVR method combined with principal component analysis (PCA) enables a significant improvement in cross-validation accuracy by using the calibration set of coal samples. The root mean square error for prediction of ash content, volatile matter content, and calorific value decreases from 2.74% to 1.82%, 1.69% to 1.22%, and 1.23MJ/kg to 0.85MJ/kg, respectively. Meanwhile, the corresponding average relative error of the predicted samples is reduced from 8.3% to 5.48%, 5.83% to 4.42%, and 5.4% to 3.68%, respectively. The enhanced levels of accuracy obtained with the SVR combined with PCA based calibration models open up avenues for prospective prediction in coal properties.