Ash Content Of Coal Research Articles

Research into the coal quality with a new selective mining technology of the waste rock accumulation in the mined-out area

Purpose. The research purpose is to study the formation of quantitative-qualitative indicators of mined coal under conditions of dynamic changes in space and time with a new stope mining technology with waste rock accumulation in the underground mined-out area. Methods. The contours are formed for mining low-thickness coal reserves and extracting thicknesses, undercut rock volumes in the stoping and preparatory faces in the conditions of the Heroiiv Kosmosu mine. The average density values of coal, rock layers and wall rocks in the seam within the boundaries of mining contours are determined based on the geological data of wells and mining operations. The graphic basis is executed in the AutoCAD program. A digital spatial model of the С10t seam contours is used, according to the schedule for organizing stope and preparatory operations. The volumes of waste rocks and minerals involved in the formation of quantitative-qualitative rock mass indicators in a given time period are calculated. Findings. It has been determined that during mining of coal reserves from the studied mining area (equal to extraction site), the volume of production and the operational coal ash content in the mining technology with waste rock accumulation averages 376.5 thousand tons and 15.2%, while with traditional technology – 621.3 thousand tons and 46.7%. Nevertheless, it has been proven that in terms of energy equivalent, the value of mined coal using the mining technology with waste rock accumulation is higher by 7.4% than the traditional technology (9.6 TJ versus 8.9 TJ). Originality. For the first time, a mechanism for the formation of operational ash content and energy value of coal has been revealed when combining the processes of drifting operations to prepare reserves from new extraction pillars with associated stope operations into a new selective mining technology with waste rock accumulation in the mined-out area. Practical implications. An algorithm for predicting the operational ash content and quality of coal when using selective mining technology with waste rock accumulation in the mined-out area has been developed, which is important for the technical and economic indicators of coal mines.

Production of Ultra-Clean Coal by the Combined Method of Grinding and Collector Gasification Flotation

The preparation and application of ultra-clean coal is one of the important aspects of clean energy technology. However, the preparation of ultra-clean coal is mainly chemical methods, which are low in efficiency, high in energy consumption and expensive. It is urgent to find an effective method to prepare ultra-clean coal. In this paper, the combined method of grinding and the collector gasification flotation method was used to obtain ultra-clean coal. The effects of grinding time on the particle size composition, mineral dissociation, surface properties and flotation results of coal samples were studied. The grinding test results show that with the increase in grinding time, the particle size and the pore diameter of coal samples decreased gradually, while the specific surface area and pore volume of coal samples gradually increased. When the grinding time was 20 min, the D90 and D[4,3] of grinding products were 5.20 um and 4.23 um, respectively. The ash content of −1.3 g/cm3 was less than 1% when the grinding time was 20 min. Compared with the traditional flotation method, the collector gasification flotation method can obtain a higher concentrate yield and lower concentrate ash content. When the amount of collector was 2.0 kg/t, the yield of clean coal obtained by the collector gasification flotation method was 4.1% higher than that by the traditional flotation method, while the ash content of clean coal was 0.3% lower.

The spatial distribution characteristics of autogenous medium and its effect on low rank lignite beneficiation in differential-motion dry cascade beneficiation bed

In the mining process, a large amount of inorganic mineral impurities are produced, leading to the grade reduction of lignite. Promoting the efficient separation and utilization of low rank lignite is the key measure to stabilize energy structure. In this paper, differential-motion dry cascade separation bed was used for separation, ash reduction and upgrading for low rank lignite. The spatial distribution characteristics of 1.5–0 mm pulverized coal particles in the differential-motion dry cascade separation bed was emphatically investigated. The influence of selecting 1.5–0 mm pulverized coal particles as autogenous medium on the uniformity and stability of bed density was systematically analyzed. The interlayer distribution law of low rank lignite in differential-motion dry cascade separation bed was dissected. The results shows that the leading particle size fraction and the leading density fraction of autogenous medium particles are d = 0.25–0.125 mm, ρ = 1.8–2.0 g/cm3, respectively and the autogenous medium particles could be diffused uniformly and stably along XOY and XOZ cross section and could form autogenous medium fluidized bed with uniform and stable density. When the pulverized coal particle size d = 0.25–0.125 mm, the average bed density reaches the maximum value of 1.815 g/cm3 and the standard deviation of bed density reaches the minimum value of 0.029, at this time, the stability of bed space density reaches the optimum. When the leading density fraction of pulverized coal is 1.8–2.0 g/cm3, the distribution range of bed density is 1.81–1.82 g/cm3 and the fluctuation amplitude is 0.01 g/cm3, at this time, the density of autogenous medium bed is uniform and stable. The interlayer distribution law of low rank lignite in the separation bed was determined. The ash content of upper layer materials is distributed relatively uniform and the distribution range is 9.44–9.76 % with small fluctuation amplitude. The ash content distribution fluctuation of middle layer materials is large and the distribution range is 18.69–31.02 %. The ash content of lower layer materials is distributed relatively uniformly and the distribution range is 41.22–42.06 %. The low rank lignite was separated and upgraded. The ash content of clean coal is 9.58 % and the yield is 63.14 %. The ash content of gangue is 42.09 % and the yield is 36.86 %. The probable error is 0.11 g/cm3. The separated products are relatively pure and the efficient ash reduction and upgrading for low rank lignite are realized.

Rapid detection of coal ash based on machine learning and X-ray fluorescence

Real-time testing of coal ash plays a vital role in the chemical, power generation, metallurgical, and coal separation sectors. The rapid online testing of coal ash using radiation measurement as the mainstream technology has problems such as strict coal sample requirements, poor radiation safety, low accuracy, and complicated equipment replacement. In this study, an intelligent detection technique based on feed-forward neural networks and improved particle swarm optimization (IPSO-FNN) is proposed to predict coal quality ash content in a fast, accurate, safe，and convenient manner. The data set was obtained by testing the elemental content of 198 coal samples with X-ray fluorescence (XRF). The types of input elements for machine learning (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) were determined by combining the X-ray photoelectron spectroscopy (XPS) data with the change in the physical phase of each element in the coal samples during combustion. The mean squared error and coefficient of determination were chosen as the performance measures for the model. The results show that the IPSO algorithm is useful in adjusting the optimal number of nodes in the hidden layer. The IPSO-FNN model has strong prediction ability and good accuracy in coal ash prediction. The effect of the input element content of the IPSO-FNN model on the ash content was investigated, and it was found that the potassium content was the most significant factor affecting the ash content. This study is essential for real-time online, accurate, and fast prediction of coal ash.

Research on Mechanisms of Improving Flotation Selectivity of Coal Slime by Adding Sodium Polyphosphate

A high percentage of high-ash fine slime materials can deteriorate flotation selectivity through surface covering. This covering of the surface is one of the issues that need to be addressed for efficient flotation processing of difficult-to-separate and high-ash coals. In this study, we investigated the depression effect of SPP on high-ash fine mud by flotation kinetic tests. We also revealed the mechanism of SPP depression of fine slime flotation and enhanced flotation selectivity of difficult-to-separate and high-ash coals by means of Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and Atomic Force Microscopy (AFM) analyses. The results showed that under the best condition of SPP dosage, clean coal with 9.75% ash content and 76.76% yield was obtained. Compared to the blank group, the ash content of the clean coal decreased by 2.39%, while the yield was only reduced by 2.18% in the presence of SPP. The reason for this was that the addition of SPP enhanced the stripping and dispersion of the gangue from the coal particle surface. The result was a reduction in the cover of the coal grain surface and an increase in the hydrophobic sites on the coal surface, thereby depressing the non-selective flotation of the gangue and enhancing the adsorption of the collector on the coal surface. The ash content of the flotation concentrate decreased, but the yield remained almost unchanged, which was the main reason for the better performance of SPP as a depressant compared to conventional depressants.

Design and performance optimization of alkali-activated waste coal bottom ash/slag porous concrete

When alkali-activated slag material is used in porous concrete, it has the problems of short setting time and poor operability. In this paper, the effect of coal bottom ash content on the rheological properties, the mechanical properties, porosity, permeability coefficient, and PFT of AACS-PC of the paste was investigated. The results showed that the fluidity and porosity of the paste with a water–solid ratio of 0.35 were positively correlated with the coal bottom ash content, but negatively correlated with the PFT. When the content of coal bottom ash was 20 wt% and the waterglass modulus was 1.4, the compressive strength of AACS-PC can reach 22.1Mpa and the water permeability coefficient of AACS-PC was 2.9 mm/s. Both the compressive strength and water permeability coefficient could meet the requirements of the standard of ASTM and JIS A. The addition of CBA can effectively improve the operability of alkali -activated materials in the preparation of porous concrete.

Determining the flotation upgrading effect of shale oil on lignite via molecular dynamics simulation and experiment comparisons

ABSTRACT In this study, we combined experiments with molecular dynamics simulation to investigate the improvement of lignite flotation performance by shale oil and microscopic promotion mechanism. Through FTIR and GC/MS tests, our preliminary findings showed that shale oil contains polar groups and long-chain alkanes. Moreover, XPS tests confirmed that shale oil could render the C–C/C–H and cover the C–O, C=O and O=C–O functional groups. The induction time between lignite particles and shale oil bubbles was 15ms lower than that between kerosene bubbles. Besides, the flotation results indicated that shale oil could improve the yield and reduce the ash content of clean coal significantly. The molecular dynamics simulation results showed that shale oil molecules could preferentially adsorb on lignite pore surface and weak the direct adsorption and seepage of water molecules on lignite surface. This was consistent with the results of an analysis of the whole system’s relative concentration distribution and mean square displacement of water molecules. These data provide further evidence that shale oil can clearly improve the flotation recovery of low-rank coal, which is of great importance for the clean utilisation of low-rank coal.

Study on fluidization characteristics and drying characteristics of fine coal in thermal vibration separation fluidized bed

By studying the fluidization characteristics of the granular bed in the separation process of fine coal, this paper explores the effect of coal particles on the distribution and evolution of the fluidization behavior of the bed, and analyzes the separation and drying characteristics of fine coal. The results indicated that the stable value of pressure drop curve of bed with medium particles is larger than that of mixed particle bed of medium and coal. Meanwhile, with the increase of the coal particle size, the stable value of pressure drop gradually decreases, as well as the drying rate of lignite increases first and then decreases with the increase of gas velocity, vibration frequency and amplitude. Contrarily, the drying time of fine-grained coal decreases and then increase with the increase of vibration amplitude. Under the conditions of N = 1.4, f = 20 Hz, A = 2 mm, the contact efficiency between the gas–solid phases is good, and the bed pressure fluctuation is stable. After the separation of −6 + 3 mm and −3 + 1 mm fine coal, the ash content of clean coal is 13.4 % and 9.1 % respectively, and the gangue ash reached 43.7 % and 63.5 % respectively. With the increase of gas velocity, vibration frequency and amplitude, the activation energy Ea of fine coal drying process decreases first and then increases, and then the activation energy Ea in the separation process of fine coal is the lowest under the conditions of N = 1.4, f = 20 Hz, A = 2 mm.

Engineering and Microstructural Properties of Alluvium Clay Stabilized with Portland Cement and Coal Bottom Ash for Sustainable Future

A massive amount of industrial waste is readily available for civil engineering works. However, even a small amount can cause serious ecological problems. Global warming, on which cement production has a significant impact, has led to greater awareness of cement replacement materials. In this study, composites of alluvium clay with various percentages of cement (7%, 10%, and 13%) and two different proportions of coal bottom ash (20% and 40%) in two different densities (1,600 and 1,800 kg/m3) at 7, 28, and 60 days of curing period were prepared to investigate engineering properties, microstructure, and sustainability performance. Porosity, strength, and initial shear modulus values of composites were determined and strength-porosity relationships were proposed for these materials at all curing ages. The microstructure of the composites was investigated to evaluate the mechanism of the chemical reactions. The 20% coal bottom ash replacement level showed the highest strength (≈30% compared with control samples) and the highest improvement in the initial shear modulus. Samples with 13% cement and 40% coal bottom ash showed an 8% reduction in porosity at 60 days. The proposed relationships could be valuable to estimate the engineering properties of coal bottom ash clay blends and promotes higher waste utilization in civil engineering applications based on the coal bottom ash content and density. Additionally, the sustainability performance of laboratory-produced composites was analyzed based on equivalent carbon dioxide (eCO2) emission, embodied energy, and cost calculation. This study may help to evaluate the performance of composites composed of waste and promote sustainable construction works.

Impacts of Nano SiO2 Addition on the Formation of Ultrafine Particulate Matter during Coal Combustion

Clay minerals composed of Si and Al could help reduce ultrafine particulate matter (PM) formation as an additive during coal combustion while currently unacceptable high adding dosages (normally 3–5 wt.%) are required due to their inadequate capture efficiency. To find additives that could effectively reduce the formation of ultrafine PM, coal combustion with a novel nano SiO2 additive (<100 nm) was performed to evaluate its effects on reducing ultrafine PM. The generated PM10 was sampled to characterize their particle size distribution, mass yield, size-resolved composition and micromorphology. The results showed that adding a small dosage (0.6%) of nano SiO2 reduced the mass yield of ultrafine PM by 30.70%, showing a much higher ultrafine PM capture efficiency than an existing micron-sized natural clay mineral. However, its performance on different coals varied due to disparities in ash content and composition in coal. A composition analysis revealed that the Na content in the ultrafine PM was decreased after adding nano SiO2, indicating that nano SiO2 inhibited the migration of volatile alkali metals such as Na into ultrafine PM because the Na-containing mineral vapor reacted with the nano SiO2 additive particles with a large specific surface area at a high temperature and inhibited their transformation into ultrafine PM via homogenous nucleation. Changes in the element size distributions and micromorphology showed that the majority of the added nano SiO2 particles reacted or coalesced with each other and/or the minerals embedded in coal, finally growing into a larger PM.

Explainable machine learning rapid approach to evaluate coal ash content based on X-ray fluorescence

As one of the most important indexes of coal quality, accurate and rapid prediction of ash content is urgent and important significance for the coal processing industry. In this work, combined with Shapley Additive exPlanations (SHAP), a machine learning model has been developed to predict the ash content of coal samples based on composition data of XRF analysis. Among the many supervised regression learning algorithms, Poly, RFR, XGBoost, and DNN are used in this predictive model to overcome the ash content prediction research gap. The input parameters were the elements content and ash contents of the coal samples. To evaluate the proposed method, a dataset of XRF data was constructed, containing 217 sets of element content with different ash content labels. Specifically, the dataset is divided into a training set and test set in the proportion 8:2, and RandomizedSearchCV is used to optimize hyperparameters during model training. Experimental results show that the RFR model produced a superior prediction performance over other models (the RMSE, MAE and R2 were 1.3278, 0.9339 and 0.9916, respectively). The contribution and role of each element to the ash prediction model are explained and investigated. Moreover, as a result of SHAP interpretation, the nine most important elements (Al, S, Si, Fe, Ca, Ti, K, Sr and Zr) has the greatest contribution to model performance. The case of this paper suggests that interpreted machine learning models and XRF data are good alternatives to ash content prediction.

Implication of nuclear analytical techniques for the assessment of coal quality in terms of ash content

ABSTRACT The manuscript deals with the utilization of Neutron Activation Analysis (NAA), external (in air) Particle Induced Gamma-ray Emission (PIGE) and X-Ray Fluorescence (XRF) for comprehensive characterization of Indian coal in order to evaluate their quality in terms of ash contents. NAA was used involving short irradiation at Pneumatic Carrier Facility (PCF) at Dhruva reactor, India, for determination of Na, Mg, Al and K; whereas long irradiation at Apsara-U reactor was used for estimation of Fe and Ca. External PIGE was applied for quantification of low Z elements (Na, Mg, Al and Si). Method was validated using coal-based standard reference materials: NIST 1635a and 1633c. Comparative evaluation was also carried out using Energy Dispersive X-Ray Fluorescence (EDXRF) spectroscopy. Total concentration of Na, Mg, Al, Si, K, Ca, Ti and Fe oxides has been used as markers for identifying the quality of coal in terms of coal ash contents. Elements such as Sc, Cr, Co, Zn, Se, Cs, Ce, Sm, Hf, U and Th were determined at trace level using conventional NAA techniques after long irradiation at Apsara-U reactor. The coal with higher ash content was found to contain high concentration of trace elements and the better quality coal contains lower concentration of trace elements.

Transfer learning using small-sized dataset for concentrate ash content prediction of coal flotation

ABSTRACT Real-time and accurate acquisition of ash content is crucial for coal flotation monitoring but existing modeling approaches have been hindered from a large dataset labeled by the target task. The method proposed in this paper relies on a transfer learning approach that is able to exploit the knowledge of a source domain (ImageNet and S1) to improve the result of a task performed on the target domain (S2). The ResNet 101 is selected as the main Convolutional neural network (CNN) framework of transfer learning among six state-of-the-art CNN modules in this study. Freeze and fine-tune are used to transfer the weights of the CNN layers to the industry ash content classification task. The tuning results of neurons and fully connected layers show that the classification results based on the weights knowledge of S1 obtained through the proposed transfer method outperform the training direct method by achieving an accuracy of 86.26% and F1-score of 85.19% over the small-sized industry dataset. The 3-D discriminant feature score and ROC curves demonstrate satisfactory prediction performance in coal flotation industrial ash content prediction. Generally, this work provides new options for flotation monitoring in tasks of training ash content labels shortage.

Optimization of performance parameters and investigation of the heavy-medium separation column for the separation of 3-13mm steam coal

ABSTRACT To effectively separate 3–13 mm steam coal, new separation equipment which was a heavy-medium separation column was designed and tested in this study. Particles were measured to analyze the changes in the axial medium force of particles in the medium by employing the force measuring system. The influence of the medium flow rate (q), medium density (ρ), separation column height (h), and feed volume (g) on the separation effect was investigated by using the control variable method to establish the optimum separation condition. Under the optimum separation condition, the yield and the ash content of the clean coal were 74.05% and 16.18%, respectively. Based on the single factor study, the Box-Behnken response surface methodology (BBRSM) was used to investigate the influence of each interaction factor on the separation effect. The significance of each factor on the quantitative efficiency was in the order of ρ > q > h. Through the regression analysis, the mathematical model for the relationship between the quantitative efficiency and the separation parameters was established. The predicted quantitative efficiency and experimental quantitative efficiency were 87.88% and 89.32%, respectively. The difference between them was small, verifying the high accuracy and reliability of the prediction results of the regression equation model.

Combustion effectivity of high ash coal in adiabatic calorimeter

A series of coal-mineral matter mixtures was investigated by bomb combustion calorimetry (IKA C4000, Germany) to elucidate boundary for ash content in coal at which combustion proceeds with lowered efficiency. For the experiments, a high rank bituminous coal was mixed with particles of mine stone, both being milled to size below 0.2 mm. Before the usage, the sample of stone was heated at 900°C for 2 hours in air to stabilize mineral matter composition. Mixtures 5, 10, 15, 20, 25, 30, 40, and 50% of coal were used, with sample weights both of 1 g and/or 2 grams being applied. The efficiency of the combustion process at the bomb test was assessed of the measured calorific value and that of “theoretically” calculated. The “theoretical” calorific value of the mixture was determined from the actual content of the coal proving known specific calorific value (36.6 MJ/kg). Based on the measurements, mineral matter content of about 50% was found as limiting for fully efficient combustion of coal in the bomb calorimeter. At content of 90% of mineral matter, the efficiency of combustion is about 70 % (sample weight 1 g) and/or 50 % (sample weight 2 g). Afterwards, the combustion efficiency steeply decreases to zero.

Exploiting Data Uncertainty for Improving the Performance of a Quantitative Analysis Model for Laser-Induced Breakdown Spectroscopy.

The accuracy and precision of laser-induced breakdown spectroscopy (LIBS) quantitative analysis are significantly limited by the spectral noise. Normalization and ensemble averaging of multiple spectra were often used to preprocess spectra. However, these methods cannot completely remove the spectral noise. Data uncertainty due to the irremovable spectral noise will affect LIBS quantitative analysis. Therefore, this paper proposes a method using data uncertainty to improve the performance of LIBS quantitative analysis. The proposed method uses several spectra to characterize each sample to preserve some data uncertainty in the calibration data matrix. Thus, the data uncertainty is used to optimize the calibration model for improving the toleration to the spectral signal variation. As a result, the optimized calibration model had better accuracy and robustness than the calibration model trained by conventional method. The best root mean square error of prediction (RMSEP) of the ash content of coal was 1.152% for the optimized calibration model, while that for the conventional calibration model was 1.718%. The optimized calibration model also showed a lower relative standard deviation (RSD) value of repeated predictions. Moreover, the calibration model for predicting the ash content in biomass was also optimized by the proposed method. The optimized calibration model outperformed the conventional calibration model again, which demonstrated the extensive applicability of the proposed method.

Efforts to examine coal and fly ash

Fossil fuels have met a large portion of the world's ever-increasing energy demands as technology has progressed. Coal-burning in thermal power plants releases gaseous and particulate pollution into the environment, as explained in previously. During the coal-burning fuel cycle, many constituents are released into the atmosphere. India's thermal power plants account for 65 percent of installed capacity and 70 percent of the country's total energy output. The current installed capacity in India, including power utilities, is roughly 90,000 MW. More than 150 million tonnes of coal are currently consumed by thermal power plants in the United States. By 2001, the ratio of thermal power stations is predicted to rise to 75 percent. As a result of the recent liberalization, several thermal power projects are being planned. It is expected that these capabilities will be completely realized during the next five to 10 years. The ash content of Indian coals has been reported to range from 35% to 55%. In general, the ash component makes up about half of the entire weight. 75 thermal power plants in the country generate more than 60 million tonnes of fly ash per year. [2-3].

Studies on Washability Characteristics of Low-Volatile Indian Coking Coal by Differential Crushing

The effect of differential crushing on liberation characteristics has been studied for a low volatile coking coal of Indian origin through washability studies. Two parameters, namely “Index of Washability” (IW) and “Near Gravity Material Index” (NGMI), are used to describe the ease of washability. The ROM Coal is crushed to four different top sizes namely, 75mm, 25mm, 13mm and 6mm.On the basis of calculated IW it is observed that relative ease of washability increases with decrease in top size. From calculated IW values it may be said that this coal can be economically beneficiated using gravity process after crushing to -6mm size. From the calculated NGMI values, the critical specific gravities have been estimated and the values for crushing to -75 mm, -25mm, -13mm and -6 mm are 1.65, 1.68, 1.53 and 1.58 respectively. These critical specific gravity values suggest the separation at this specific gravity range is most difficult task using gravity methods. From NGMI analysis, it may be said that the NGMI values for coals crushed to -25 mm & -6mm are identical (≈ 0.18) at 17% clean coal ash content. This suggests that with similar degree of difficulty, clean coal of 17% ash can be produced from these two different crushing sizes. In order to increase the yield for the clean coal of 17% ash, the decision on blending these two size coals may need to be taken.

Relationship of the parameters of physico-chemical and sorp-tion processes with the emission of coal beds. part I: on the influence of physical and chemical parameters of natural coals on their metane capacity

This part of the article shows shows that methane capacity is closely related to the physicochemical properties of coals and the physical structure of coal matter: porosity, moisture, ash content, petrographic composition and the release of volatile substances. It was found that the porosity of natural coals weakly depends on the stages of metamorphism and its effect on the methane content of coals can be ignored. Based on the catalogs of the physicochemical properties of coals from the developed deposits of Kuzbass, it is shown that differences in ash content and moisture content of coals have very little effect on their me-thane capacity, and the main strongest influencing factors on the methane capacity of coal were the depth of coal seams and the release of volatiles.

Thermogravimetric Analysis of the Combustion Characteristics and Combustion Kinetics of Coals Subjected to Different Chemical Demineralization Processes.

In order to explore the influence of different chemical demineralizations on coal combustion characteristics and combustion kinetics, five coals subjected to different chemical demineralization processes were investigated via thermogravimetric analysis. The ash contents of clean coal was reduced to 0.1–1.55% after different chemical demineralizations. The ignition temperature of coal decreased by 12–69 °C, and the peak temperature decreased by 7–62 °C. The burnout temperature of clean coal increased by 63 °C after demineralization by NaOH. The adsorption of noncombustible NaOH into the porous structure of TaiXi-3 caused an increase in burnout temperature. Alkali-soluble minerals were proven to have a negative effect on the combustion performance of coal, while acid-soluble minerals had the opposite effect. The combustion kinetics of five kinds of coals at a heating rate of 10 °C/min was investigated. The activation energy of coal obviously changes before and after demineralization (58.39–91.39 kJ mol–1). The activation energy of clean coal is obviously lower than that of raw coal.