Ash Content Of Coal Research Articles

Research on the Detection Principle of Coal Ash by X-Ray Transmission Based on FLUKA

This study addresses the timely and accurate measurement of coal ash content by proposing a detection model based on nuclear science technology, which is validated using FLUKA 4-4.0 simulation software. The background provided highlights the fact that coal ash content is a critical sales indicator, and its precise measurement is essential for adjusting production parameters in coal preparation plants. In terms of methodology, this study employs the widely used FLUKA4-4.0 software in the field of nuclear physics to simulate X-ray transmission through coal, investigating the impact of changes in coal type on the accuracy of ash measurements. The results indicate that, when the proportions of high-atomic-number elements in coal remain constant, the ash measurement results are accurate and reliable. However, significant fluctuations occur when these proportions change. The conclusion emphasizes the fact that variations in coal type are the primary cause of inaccuracies in ash measurement, particularly when the ratios of high-atomic-number elements are altered. This research provides a new perspective on the online measurement of coal ash content and offers theoretical support for improving measurement accuracy.

Geochemical features of coals of the Mezhegey deposit

An analysis of the correlations between the ash content of coal (Adср) and the content of ash-forming elements was carried out. Significant positive pairwise correlations were revealed in the accumulations of SiO2, Al2O3, TiO2 and each of them with ash content Adср (rcr0.05=0.23). In the group Fe2O3, MgO, CaO, a significant positive correlation is observed between the accumulations of only Fe2O3 and MgO, but each of them correlates negatively with Adср. A qualitative analysis of the distributions of petrochemical modules (GM, AM, ZhM, TM) of coal ash from the 2.2-Ulug seam of the Mezhegey deposit was carried out. Hydrolyzate destruction of the mineral matter of coal in 94% of the studied sample (n = 70) is represented by superhydrolysates (GMmin 2.07, GMmax 7.45), normohydrolysates (GMmin 0.86, GMmax 1.95), hypohydrolysates (GMmin 0.56, GMmax 0.84). A combined analysis of correlations between the ash content of coal (Adср) and the contents of ash-forming elements and petrochemical modules of ash made it possible to establish the origin of ash-forming elements - ash-carrier Fe is aquagenic, ash-carriers Al, Ti are allotigenic.

Particle Properties and Flotation Characteristics of Difficult-to-Float Lean Coal

The flotation effect of lean coal is crucial for its clean utilization. Therefore, the flotation characteristics of difficult-to-float lean coal were studied. The analysis results of the feed properties showed that the ash content of the feed was high and the particle size was very fine. The minerals in the gangue mainly included sericite, kaolinite, quartz, white mica, and other substances. After flotation, the functional groups of the coal particles in the tailings decreased, and the absorption peak intensity weakened. Furthermore, the results of multi-factor flotation experiments showed that the dosages of the collector and the frother were significant factors affecting the yield of clean coal. The clean coal yield gradually increased with an increase in the two factors. The ash content of the clean coal increased with an increase in the frother dosage. Within the range of feed concentrations used in this work, the feed concentration was not a significant factor affecting the clean coal’s yield and ash content. Prediction models for the clean coal yield and ash content were proposed. Under optimized experimental conditions, the clean coal yield and the flotation perfection index were 72.15% and 46.63%, respectively, indicating a good flotation effect.

A novel algorithm combined X-ray fluorescence and Neural Network (XRF-NN) for coal ash content prediction: Algorithm design and performance evaluation

This paper investigated a precise algorithm combining X-ray Fluorescence and Neural Network (XRF-NN) for predicting ash content. The 261 sets of XRF tests show that the 34 elements in the chosen Guqiao coal can be categorized as major, secondary and tiny elements, whose cumulative ratio were ~95%, 4-5% and <1%, respectively. Referring to the machine learning theory, the construction strategy of Element-Ash dataset was determined viz. value determination → standardization → division → optimization of generalization ability. Then, the hyperparameters optimization displays that XRF-NN model with 34 inputs and 1 output was suitable to predict coal ash content, where the activation function, loss function, and optimizer were ReLU, MAE, and Momentum, respectively. After iterative training, the new XRF-NN model provides the precise prediction of coal ash content with the absolute errors between -2.0% and 2%. Moreover, the prediction accuracy rose from 57.69% to 100%, as the expected relative error increased from 1% to 5%. Furthermore, the comparisons between different prediction methods reveal that the minimum MRE of 1.22% can be obtained by XRF-NN with total elements, which was only half of those given by the conventional Multiple Linear Regression and Partial Least Squares Regression. Besides, XRF-NN model presents the root mean squared error 0.797%, the mean absolute error 0.625% and the coefficient of determination 0.999, which were significantly superior to those calculated by Dual-Energy Gamma-ray Transmission, Ploy, RFR, XGBoost and DNN model. The results of this study suggest the excellent performance of the new XRF-NN model in predicting ash content.

Research on Prediction of Ash Content in Flotation-Recovered Clean Coal Based on NRBO-CNN-LSTM

Research on Prediction of Ash Content in Flotation-Recovered Clean Coal Based on NRBO-CNN-LSTM

Comparative experimental study of ash formation behaviors during the fixed-bed gasification of coal water slurry and dry pulverized coal prepared from Shenmu coal

The mechanism of ash formation during coal gasification is very important for the safety and stability of gasifier operation. This study comparatively investigated the ash formation behaviors during the gasification of coal water slurry and dry pulverized coal. Shenmu coal was gasified in a CO2 atmosphere at 900 °C using a fixed-bed reactor to prepare chars and ashes. Computer-controlled scanning electron microscopy was employed to characterize the compositions and particle sizes of minerals in the raw coal, coal water slurry gasification ash, and dry pulverized coal gasification ash. Morphological characteristics reveal that, during gasification, coal water slurry gasification char exhibits obvious agglomeration compared to the dry pulverized coal gasification char. The changes of mineral compositions indicate that, during both coal water slurry and dry pulverized coal gasification, minerals follow nearly identical transformation pathways, but the degrees of transformation are different. The influence of char agglomeration on heat transfer may be the reason for the less transformation of K Al-silicate and kaolinite during the coal water slurry gasification. Furthermore, in comparison to dry pulverized coal gasification, more Ca in calcite and S in pyrite transfer into gypsum during the coal water slurry gasification. The results from thermodynamic equilibrium calculations indirectly suggest that this may be attributed to the limited internal diffusion of CO2 within coal water slurry gasification char resulting from char agglomeration during gasification. Overall particle size distributions of minerals in raw coal, coal water slurry gasification ash, and dry pulverized coal gasification ash demonstrate that the degrees of mineral fragmentation are almost consistent during both coal water slurry and dry pulverized coal gasification. However, differences in the degrees of mineral transformation led to different particle size distributions of various minerals between coal water slurry gasification ash and dry pulverized coal gasification ash. This study mainly focuses on the influence of water in coal water slurry on ash formation, and it has not considered the influence of other factors, such as ash content, hydrophilicity, caking property, and gasification reactivity of coal, which need further research to explore.

Effectiveness of silicomanganese smelting utilizing high-ash coal

This study investigates the utilization of high-ash coal as an alternative reductant in the smelting of silicomanganese, aiming to reduce the carbon footprint of traditional coke-based processes. Experiments were conducted using an ore-thermal furnace with a transformer power of 200 kVA to simulate industrial conditions. The charge consisted of manganese ore (Mn - 36%), slag from refined ferromanganese production (MnO - 25%), and high-ash coal (ash content 40%–50%). Chemical analysis of the produced alloy showed a composition of Mn–70%, Si–20%, C–1.28%, P–0.06%, and S–0.05%, meeting the standards for silicomanganese. The results demonstrate that high-ash coal can replace coke without compromising the quality of the alloy. This approach not only indirect CO2 emissions but also leverages the abundant availability of high-ash coal, which is often discarded as waste. The study highlights the potential for significant environmental benefits and cost savings, making this method a viable alternative for sustainable industrial practices. The findings support the industrial application of high-ash coal in silicomanganese production, contributing to more eco-friendly and economically feasible metallurgical processes.

Preparation of Bio-Based microemulsion collector and its flotation performance for coal slime

Cardanol polyoxyethylene ether (CPE) is a green bio-degradable surfactant. Herein, we used CPE as an emulsifier to prepare a new environmentally friendly and high-efficiency bio-based microemulsion collector (HE-BMC) for coal slime flotation. The phase behavior studies showed that the single-phase microemulsion (SPM) area proportion was the largest at the mass ratio of CPE-12 to n-pentanol of 1.2. The dilution integrity study results demonstrated that the microemulsion collector average particle size was stabilized at nearly 30 nm when the mass ratio of SACS to n-dodecane was greater than 6: 4, with water content higher than 20 %. Based on the above research, the relationship model between the microemulsion collector size and the component contents was constructed using the mixture design method. Consequently, the optimal formulation for HE-BMC was obtained and consisted of 24.75 % CPE-12, 20.62 % n-pentanol, 27.69 % n-dodecane, and 26.94 % water, with an average size of 25.83 nm. The physical properties study results elucidated that HE-BMC has good daily storage and transportation as well as better dispersibility in water. The particle size in water was 2.28 μm when the cumulative particle size distribution reached 90 %. The flotation test results revealed that the HE-BMC yielded 3.91 % more clean coal than n-dodecane while maintaining a similar clean coal ash content. However, the dosage of HE-BMC was only 41.67 % of that required for n-dodecane. Furthermore, long flame coal slime flotation utilizing HE-BMC could be achieved without the need for adding frother.

Features of Distribution of Rare-Earth Elements in Coals of the Far East

For the first time, the distribution of rare earth elements (REE) has been studied in detail for a number of coal facilities (30 deposits, 650 samples of coal and 210 samples of carbonaceous rocks). The ubiquitous presence of elevated concentrations of REE in coals has been noted. The REE mineral cluster in coals includes the association: ash content of coals – SiO2 – K2O – Al2o3 – TiO2 – Sc – Y – Dy – Ho – Er – Tm – Yb – Lu, and the association La – Ce – Pr – Nd – Sm – Eu – Gd – Tb. The presence of these elements of the mineral part of the coals is preferably in the composition of phosphate minerals – monazite and apatite (according to electron microscopy with microanalysis, the correlation of REE with P2o5). The content of individual REE in humic acids isolated from coals and fractions of coals of different densities has been studied. The specific role of organic matter(s) in the concentration of REE, their presence in the humus component of S and in low-ash coals is shown. Selective accumulation (fractionation) of heavy REE by organic matter has been experimentally established for the first time. Two genetic types of REE mineralization have been identified in coals: mainly terrigenous (hydrogenic) and tufogenic. The increased concentrations of REE in coals are due to the influence of the petrofund. The deposits were ranked according to the degree of prospects for REE based on an assessment of the resource potential of associated REE in the coals of the studied brown coal deposits. REE raw materials (lanthanides in coal ash) differ significantly from traditional types of rare earths ores by an incomparably large relative amount of heavy REE (on average 3–4 times), sometimes reaching 46% of the total REE content. Thus, coal ash is a unique non–traditional source of heavy lanthanides – more rare, valuable and expensive. The coals of the studied deposits should be considered as associated raw materials for rare earths.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has become a widely used atomic spectroscopic technique for rapid coal analysis. However, the vast amount of spectral information in LIBS contains signal uncertainty, which can affect its quantification performance. In this work, we propose a hybrid variable selection method to improve the performance of LIBS quantification. Important variables are first identified using Pearson’s correlation coefficient, mutual information, least absolute shrinkage and selection operator (LASSO) and random forest, and then filtered and combined with empirical variables related to fingerprint elements of coal ash content. Subsequently, these variables are fed into a partial least squares regression (PLSR). Additionally, in some models, certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance. The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method. It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method. The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction (RMSEP) values of 1.605, 3.478 and 1.647, respectively, which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables, which are 1.959, 3.718 and 2.181, respectively. The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection, with RMSEP values dropping from 1.635, 3.962 and 1.647 to 1.483, 3.086 and 1.567, respectively. Such results demonstrate that using empirical knowledge as a support for data-driven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Real-time in situ monitoring of ash content in coal via dual-energy X-rays and an MLP neural network

ABSTRACT The accurate estimation and measurement of coal ash are crucial for fuel selection, combustion efficiency assessment and quality control. However, the most widely used ash measurement method is combustion; this method is highly accurate but has a certain lag; additionally, an older radiation measurement method has a certain error. In this regard, in this study, a detection model and measurement method are proposed based on the combination of dual-energy X-ray measurement results and artificial intelligence algorithms, i.e. a coal ash detection model based on a multilayer perceptron (MLP) neural network. A model training database was created, and 6468 raw data points were measured with an experimental apparatus and organized. The results showed that the root-mean-square error between the predicted value and the true value of the trained model was 0.0857. By comparing several indices with the traditional backpropagation (BP) neural network, the root-mean-square error was reduced by 1.27%, and the model’s errors in different predicted output values were uniformly distributed without evident systematic deviation; these results demonstrated and confirmed that our proposed ash prediction model achieved high estimation accuracy and had strong robustness.

Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest algorithm.

Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with Random Forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-Random Forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450; coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models’ oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks.

Investigating the Influence of Froth Image Attributes on Clean Coal Ash Content: A Novel Hybrid Model Employing Deep Learning and Computer Vision Techniques for Prediction Exploration

Investigating the Influence of Froth Image Attributes on Clean Coal Ash Content: A Novel Hybrid Model Employing Deep Learning and Computer Vision Techniques for Prediction Exploration

Fluidization characteristics of loaded vibrating fluidized bed and dewatering and deashing effect of moist fine-grained coal

ABSTRACT The influence of a thermal vibrating fluidized bed on the dewatering and deashing effect of moist fine-grained coal was investigated by digital image processing and signal analysis methods. Comparative analysis results of the pressure signals of a loaded vibrating fluidized bed indicated that high or low gas velocity and strong or weak vibration conditions easily caused the formation of numerous irregular bubbles and contributed to violent fluctuations in the bed. An interaction between gas velocity and vibration energy was found, with vibration energy being the dominant factor influencing the bubble behavior at low gas velocities. Moreover, at high gas velocities, the advantages arising from vibrations were weakened. Under the condition of f = 20 Hz, A = 2 mm, v = 18 cm⋅s−1, the contact efficiency between the gas – solid two phases was higher, and the pressure fluctuation of the bed became more stable. Under this condition, the ash content of clean coal and the moisture removal rate were 11.76% and 83.54%, respectively. Finally, comparison and analysis of the influence of different parameters on the calorific value of clean coal revealed that the influence of each parameter on heat generation of the selected product was found to be in the following order: f > A > V > T.

Deep learning with TabNet: rapid coal ash content estimation via X-ray fluorescence

ABSTRACT As a paramount index of coal quality, the quick and accurate prediction of ash content bears substantial significance for the coal preparation plant. In this study, an innovative approach that integrates SHapley Additive exPlanations with a deep learning model Tabular Networks has been introduced, which efficiently predict the ash content by employing the compositional data obtained from rapid elemental analysis. Pictures of the relationship between elemental and ash content reveal a significant correlation. To further elucidate this relationship, Polynomial regression, Random Forest regression, and eXtreme Gradient Boosting are used as comparison prediction models to verify the accuracy of the deep learning model. The R2 for TabNet, Poly, XGBoost, and RFR are 0.9867, 0.9755, 0.9797, and 0.9714, respectively. The optimization is continued based on SHAP averages, and the best combination of elemental feature contributions obtained is used to continue the prediction of the experiments. The experimental results show that the TabNet model has better predictive performance than other models (RMSE, MAE, and R2 are 1.5126, 1.0953, and 0.9903). An examination and exploration of the contributions and roles of elements to the ash prediction model has been conducted. The case study presented in this paper illustrates that the interpreted deep learning model holds promising prospects for industrial ash fraction prediction.

Deep learning-based estimation of ash content in coal: Unveiling the contributions of color and texture features

Determining coal ash content is paramount when evaluating coal quality and optimizing industrial processes. Conventional methods reliant on manual analysis prove exceedingly time-consuming and labor-intensive. The advent of deep learning has galvanized researchers to explore various models aimed at precision and efficiency in the coal industry. However, the selection of appropriate features assumes a pivotal role in achieving accurate estimation. This study meticulously scrutinizes the importance of color and texture features in estimating coal ash content using neural networks. The proposed framework for elucidating feature contributions encompasses the following steps: (1) A feature disentangled pipeline was employed to generate a color and texture dataset from the original dataset; (2) Harnessing convolutional neural network (CNN), vision Transformers(ViT), a hybrid model combing CNN and ViT, and graph convolution network (GCN) to learning feature representation for texture and color. 3) An interpretable decision module was employed to aggregate these two feature representations, thereby achieving an interpretable estimation of ash content. Experimental results and visualizations demonstrated the substantial importance of color in CNN, accounting for an impressive 64.77%, whereas the texture feature modestly contributed at 35.23%. The analysis of feature contributions assumes a crucial role in guiding the design of accuracy-driven models and in comprehending the inherent contributions or biases within deep learning architectures.

Recycling of waste plastic combustion soot and diesel oil mixing to prepare a new collector to improve the performance of low-rank coal flotation

Clean utilization of soot pollution and low-rank coal has received extensive attention worldwide. In this study, soot from plastic combustion was mixed with diesel oil as a new collector (SPC-DO) to enhance the low-rank coal flotation. The flotation results showed that SPC-DO improved the flotation of low-rank coal more effectively than diesel oil, with a higher yield of fine coal and lower ash content. In addition, the results of the scanning electron microscope, contact angle, X-ray photoelectron spectrometer, and Fourier transform infrared spectroscopy measurements showed that the soot from plastic combustion has superhydrophobicity, and the oxygen-containing functional groups on its surface can interact with the hydroxyl groups on the low-rank coal surface to form hydrogen bonds, which in turn cover the hydrophilic sites. When applying SPC-DO as the collector, the soot from plastic combustion not only increased the hydrophobic sites on the LRC surface but also enhanced the adsorption effect of diesel oil on the low-rank coal surface.

Study on the effect of flow field optimization on flotation efficiency

ABSTRACT With the help of computational fluid dynamics (CFD) methodology, the Eulerian model was used for numerical simulation of XFD type flotation machine. By altering the flow field pattern, analyzing the internal flow characteristics of the flotation machine in terms of velocity field and turbulivity, and optimizing its structure, the relationship between turbulence intensity and turbulent energy dissipation rate in the flotation machine is investigated. The findings demonstrate that altering the flow field pattern enhances the turbulence in the flotation machine’s mixing zone, enhances the velocity distribution of the three-phase fluid at the bottom of the flotation tank, and fosters bubble-particle contact, collision, and adhesion. This can successfully reduce the clean coal ash content.

Polyaspartic acid, an eco-friendly regulator for the improvement of coking coal flotation: applications and mechanism

ABSTRACT In this study, a novel approach is to apply polyaspartic acid (PASP) as an eco-friendly regulator for the flotation of coking coal, which is different from the conventional use of oil collectors only. Flotation trails showed that adding PASP contributes to a two-way improvement in the yield and ash content of clean coal. SEM-EDS observations directly revealed that adding PASP significantly reduces the coating of high-ash fine slimes on the surface of low-ash coal particles. Contact angle detections and zeta potential analysis presented that the preferential adsorption of PASP hardly affects the adsorption of sodium oleate (NaOL) on the surface of low-ash coal particles but significantly weakens the adsorption of NaOL on the surface of high-ash fine slimes, leading to a remarkably hydrophobic difference. Particle interaction energy calculations verified that adding PASP enlarges the energy barrier between low-ash coal particle and high-ash fine slime. Hence, it can be arrived that the preferential adsorption of PASP affects the adsorption of NaOL on the surface of high-ash fine slime, widens the hydrophobic difference, enlarges the energy barrier between low-ash coal particle and high-ash fine slime, reduces the high-ash fine slime coating, therefore leading to a better flotation performance of lean coal.