Clean Coal Ash Content Research Articles

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.

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

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.

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.

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.

Research on Dual GRU model driving by time series alignment for multi-step prediction of ash content of dense medium clean coal

ABSTRACT Dense medium cyclone separation is a widely used coal processing method. However, duetween the ash content measurement and the other sensors. However, existing studies either do not consider the impact of time lag or only conduct single-step ash prediction, making it difficult to consider the systematic changes and uncertainties of multiple time steps in the future entirely. To solve these problems, first, this study uses time lag correlation to quantify and eliminate the lag effect between each variable and the ash content. Secondly, this study designs a Dual GRU model combining time series and regression prediction. Finally, this approach is adapted to some of the time series forecasting models proposed in recent years to compare with the model proposed in this study. Experiments conducted on production data from a coal preparation plant showed that input and prediction lengths were determined to be 7 based on the time lag correlation. Then, the mean absolute error of the 7-step ash prediction was 0.1495. The MAE value was reduced by 4.66% compared to the model without time series alignment. Compared to the model without aligned process variables, the MAE value was reduced by 6.77%. Compared to Informer, the best performer among the Transformer-based models, Dual GRU accuracy is improved by about 7.94%. Time series alignment and Dual GRU provide technical support for realizing density control based on ash content multi-step prediction.

Predicting the ash content in coal flotation concentrate based on convolutional neural network

ABSTRACT Convolutional neural networks (CNNs) are currently one of the most popular image classification technologies. Their excellent image classification ability enables the prediction of the ash content in clean coal using coal flotation froth images. Herein, a scheme is proposed to predict the ash content in coal flotation concentrate using coal flotation froth images and CNN architecture. According to the ash content of the concentrate, the coal flotation froth image data set is divided into seven interval categories, and data augmentation is used to expand the data set. Then, several recently trained CNNs (ResNet, EfficientNet, EfficientNetV2, ConvNeXt) are used to classify the coal flotation froth images with different concentrate ash content interval (integer ±1%). The classification performance of each network model, relationship between model performance and hyperparameters, and the abstract pixel features of the best model are visualized. The EfficientNetV2-L network achieves the highest classification accuracy (99%) after fine-tuning. The results indicate that the CNNs are effective in predicting the ash content of clean coal by classifying coal flotation froth images. The optimized CNN model is applied to froth images in the industry, resulting in high classification performance, which demonstrates that the CNN has high potential for industrial applications.

Flotation of lean coal using trisodium citrate dihydrate as a dispersant in the presence of sodium oleate

Flotation is a highly efficient method for purifying low-ash coal particles from high-ash fine slimes. Herein, the dispersion effect of trisodium citrate dihydrate (TCD) on the reduction of slime coating on low-ash lean coal particle surface was studied using flotation tests, scanning electron microscope–energy Dispersive Spectroscopy (SEM–EDS) observation, turbidity measurements, and extended Derjaguin–Landau–Verwey–Overbeek (E-DLVO) calculations. Flotation tests revealed that after adding of 2000 g/t TCD, the yield of clean coal increased by 1.09 % and the ash content of clean coal decreased by 1.45 % with 1000 g/t NaOL at pH 7.0, thereby increasing the combustible recovery and flotation perfect index by 2.50 % and 6.42 %, respectively. The SEM–EDS observation and turbidity measurements indicated that the coating of the high-ash fine slimes on the low-ash coal particle surface was significantly weakened in the presence of 2000 g/t TCD. Furthermore, the E-DLVO calculations verified that the addition of 2000 g/t TCD increased the energy barrier between the low-ash coal particles and high-ash fine slimes, providing theoretical evidence for flotation tests and SEM–EDS observation. Therefore, TCD served as an effective dispersant for the high-ash fine slimes to improve the flotation performance of lean coal.

Prediction of Clean Coal Ash Content in Coal Flotation through a Convergent Model Unifying Deep Learning and Likelihood Function, Incorporating Froth Velocity and Reagent Dosage Parameters

This study successfully achieved high-precision detection of the clean coal ash content in the coal froth flotation domain by integrating deep learning with the likelihood function. Methodologically, a novel data processing and prediction framework was established by combining a deep learning Keras neural network with the likelihood function from probability statistics. The SIFT algorithm was utilized to extract key feature points and descriptors from the images, and keypoint matching and mean-shift clustering algorithms were employed to accurately obtain information on foam motion trajectories and velocities. For parameter optimization, the maximum likelihood estimation was applied to find the optimal parameter estimates of the likelihood function, ensuring enhanced model accuracy. By incorporating the optimized likelihood function parameters into the Keras deep neural network, an efficient prediction model was constructed for the dosage of flotation reagents, froth velocity, and clean coal ash content. The model’s evaluation involved six performance metrics. The experimental results were highly significant, with R2 at 0.99997%, RMSE at 0.04458%, MAE at 0.00170%, MAPE at 0.02329%, RRSE at 0.00994%, and MAAPE at 0.00067%.

Investigation on the preparation of coal slime flotation microemulsion collector: A combination of experimental and simulation method

Microemulsion collector is a new type of highly efficient collector for coal slime flotation, but the research on their formulation is complexity and time-consuming, which restricts the development and application of the collector. In this work, an efficient method was designed for preparing microemulsion collectors through combined experiments and simulation. The dissipative particle dynamics simulation studies showed that CPE-12 was the surfactant for the preparation of microemulsion collector, and n-pentanol was the cosurfactant. And the Km value range was between 1.0 and 1.5. The experimental studies based on simulation showed that the Km value was 1.2 and the content of water was greater than 20%. The composition of the microemulsion collector was 22.91% CPE-12, 19.09% n-pentanol, 28.00% n-dodecane, and 30.00% water. The centrifugation and short-term storage experiments showed HB-MC has good stability for daily storage and transportation. The flotation test studies showed that, under the condition of similar ash content of clean coal, the yield of clean coal floated by the HB-MC was 1.42% higher than that of n-dodecane, and its dosage was 18.18% of that of n-dodecane. Moreover, the flotation of long flame coal slime using HB-MC without adding frother.

Deep correlation and precise prediction between static features of froth images and clean coal ash content in coal flotation: An investigation based on deep learning and maximum likelihood estimation

This research combines deep learning with sophisticated likelihood analysis to achieve precise detection of clean coal ash content in the domain of froth flotation through image acquisition and processing. The utilization of maximum likelihood estimation method establishes a robust correlation between static features of froth images and clean coal ash content, while the application of the BFGS algorithm efficiently searches for optimal parameter estimates. Furthermore, a deep neural network model is constructed using Keras to accommodate multi-feature and hybrid data inputs. The model's performance is rigorously evaluated using metrics such as mean squared error, mean absolute error, and root mean squared error. Experimental results demonstrate remarkable accuracy, with the mean squared error, mean absolute error, and root mean squared error on the test set being 0.003475 %, 0.044740 %, and 0.044585 %, respectively.

New insights for improving low-rank coal flotation performance via emulsified waste fried oil collector

A novel emulsified waste fried oil collector (EWFOC) of the oil-in-water (O/W) type was prepared via transesterification and ultrasonic emulsification, and to be used as an alternative collector to improve the flotation performance of low-rank coal while reducing the collector dosage. The preparation process of EWFOC was optimized, and its performance was compared to that of a common collector (diesel) and waste fried oil collector (WFOC) in the flotation of long-flame coal collected from two top coal-producing province (autonomous region) in China. The micrograph, particle size distribution and dispersion of EWFOC were observed under optical microscope. In addition, the functional groups and wettability were also analyzed. The optimized emulsification conditions for EWFOC were determined as HLB = 14.0, emulsifier dosage of 1 %, oil–water ratio of 3:7, ultrasonic time of 5 min, and a NaCl concentration of 0.02 mol/L. Compared to diesel, WFOC improved the recovery of clean coal and reduced the ash content of clean coal. EWFOC, on the other hand, demonstrated better flotation performance, resulting in up to a 70 % reduction in WFOC dosage. This significant improvement is attributed to the cavitation mechanism of phacoemulsification, which renders EWFOC stable in water with homogenous dispersion and enhances its attachment to coal particles. EWFOC showed better performance compared to diesel and WFOC in terms of improved contact angle as well as adherence to oxygen-containing functional groups (especially O=C–O and C–O) on the surface of long-flame coal, thus enhancing the hydrophobicity of the said coal.

Study on the mechanism of adsorption and hydrophobic modification of glycerol monooleate/kerosene mixed collector on low-rank coal surface

A new collector (GK) consisting of GMO (glycerol monooleate)/kerosene was developed to address the problem that low-rank coal is difficult to be recovered by flotation with conventional non-polar oil collector. The flotation test proved that GK collector has excellent flotation performance for low-rank coal (combustible recovery rate close to 90 % while the ash content of clean coal is about 6 %). The contact angle, infiltration time, wetting heat and XPS analysis demonstrated that GK collector can significantly improve the hydrophobicity of low-rank coal, resulting in a great improvement of flotation efficiency. Molecular dynamics simulation shows that the hydrophilic head of GMO molecules can adsorb with low-rank coal directly through hydrogen bonding or indirectly through water bridges. The presence of GMO molecules in GK collector can increase the overall interaction strength of collector oil droplet with low-rank coal surface by more than half. A schematic diagram of collector adsorption on the surface of low-rank coal is drawn, which gives a complete overview of the stacking and hydrogen bonding connection modes at the molecular level of low-rank coal, water, GMO and kerosene adsorbed at the interface. The adsorption mechanism of this mixed collector on the surface of low-rank coal was clarified.

Insights into the interaction of polycarboxylate with coal and kaolinite and their application in flotation

Kaolinite-rich coal slurries suffer from a low yield and high ash content owing to the easy mudification of kaolinite in water during flotation. This study investigates the efficient recycling of a kaolinite-rich coal slurry using sodium polycarboxylate flotation pretreatment. The differences in the mechanism of action of sodium polycarboxylate on coal and kaolinite were investigated using high-sensitivity microelectromechanical balancer (DCAT), Fourier infrared spectroscopy (FTIR), and scanning electron microscope (SEM). The results show that sodium polycarboxylate reduces the adhesion of the coal slurry and increases the adhesion of kaolinite. Sodium polycarboxylate can cover the hydrophilic groups of –OH by binding to the hydroxyl groups on the coal surface at multiple points via the polyhydroxy structure on the molecular chain. Covering the hydrophobic potential of kaolinite increases the percentages of hydrophilic groups such as Si-O and OH in FTIR. The above findings help to elucidate the separation mechanism of sodium polycarboxylate on carbon ash and improve the flotation separation effect. The optimum dosage of sodium polycarboxylate is 4 kg/t, kerosene, and 2-octanol are 10 kg/t and 12 kg/t, respectively. The yield of clean coal is 70.43 %, ash content of clean coal is 12.62 %, which increases the yield of clean coal by 40.89 % and decreases the ash content of clean coal by 8.56 % compared to the optimum dosage of froth flotation.

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.

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.