Search

Coal fly ash (CFA) as a potential resource for rare earth elements and yttrium (REY) has attracted extensive attention over the past few years. This paper focuses on studying microwave systems for the leaching of REY from CFA. Leaching parameters including hydrochloric acid (HCl) concentration, microwave heating time, microwave heating rate, and temperature were assessed. The optimal recovery of 74.91% REY, 86.77% light rare earth elements (LREY), and 34.79% heavy rare earth elements (HREY) are obtained at the conditions of 3 M HCl, T = 220°C, heating rate 39°C/min, and leaching time 30 min. The closed microwave system produces hydrogen chloride fluid or hydrogen chloride gas that is easier to react with CFA than hydrogen ions. It was revealed by the analysis of XRD, particle size, porosity, and SEM that fracture development benefits the entrance of leaching reagents into the inner layer of CFA. A positive linear correlation was found between leaching efficiency and REY3+ ionic radius. The stability of the REY-O bond also remains consistent with the ionic radius, leading to behavior differences during the leaching. This work was targeted to provide a theoretical and technological foundation for microwave-assisted HCl leaching for the extraction of REY in CFA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Lithium is an important strategic metallic element, which plays a vital role in the production of batteries. Coal gangue has been regarded as a promising resource of lithium. In this study, the coal gangue collected from Jungar coalfield contains 437 ppm of lithium, which is significantly higher than the average content of coal (31.8 ppm) in China. Lithium extraction from the coal gangue was systematically investigated via the integrated process of roasting and acid leaching with the mechanism analysis. The majority (94%) of lithium was extracted when raw samples are roasted at 400°C for 20 min before 4 h leaching with 2 mol/L hydrochloric acid at 60°C, which represented the optimal conditions. X-Ray Diffraction patterns and surface microscopic analysis of the roasting samples showed that thermal decomposition of kaolinite, as the carrier minerals of lithium in the gangue, happened during the roasting, resulting in the easy release of lithium by acid leaching. It was found that diffusion is the dominant control factor of lithium leaching based on the kinetic analysis. In this paper, an integrated process of roasting and leaching with moderate conditions was developed and performed for lithium extraction from coal gangue. [ABSTRACT FROM AUTHOR]

Coal fly ash is regarded as a potential alternative source of rare-earth elements and has gained much attention over the past few years. However, the majority of rare-earth elements in coal fly ash are entrapped in the glassy phase, which makes the recovery of rare-earth elements from coal fly ash extremely challenging. In this paper, two pre-treatments (mechanical grinding and alkali solution treatment) were conducted to enhance the leaching recovery of rare-earth elements from coal fly ash. The best recovery of rare-earth elements by acid leaching after grinding is 79.19% at the optimal conditions. That is due to the increase of surface area, resulting in more rare-earth elements exposed from the glass phase and increase the rate of leaching. The highest rare-earth element recovery gained by acid leaching after alkali pre-treatment is 95.50% in the paper. Alkali solution destroyed the glassy aluminosilicate spheres (such as metal oxide, amorphous silicon in amorphous silica-aluminosilicate) of coal fly ash more efficiently, making it more solvable in hydrochloric acid. This research has focussed on grinding and alkali pre-treatment to provide a theoretical foundation and technology support to the extraction of rare-earth elements from coal fly ash. [ABSTRACT FROM AUTHOR]

Coal fly ash (CFA) in some areas contains higher levels of rare earth elements (REEs), which can be seen as a potential substitute for rare earth resources. As an effective method of hydrometallurgy, alkali fusion has been widely used in the recovery of precious metals from secondary resources. In this study, taking a CFA sample and five REEs named Y, La, Ce, Pr, and Nd in the sample as objects, the mechanism of alkali fusion processes of CFA with different alkali fluxes (Na2CO3, NaCl, Na2O2, NaOH, KOH, and Ca(OH)2) was studied. The results showed that Na2CO3, Na2O2, NaOH, and KOH can destroy the structure of CFA to effectively improve the leaching efficiency of REEs from CFA. The total leaching efficiency of the five REEs can reach to 57.45%, 49.25%, 74.23%, and 64.93%, respectively, when these four alkalis were used as fluxs. In addition, with DSC analysis, the non-model fitting dynamics Kissinger method was used to calculate the kinetic parameters of alkali fusion reaction between CFA and Na2CO3. The calculated alkali fusion activation energy E was 895.52 kJ·mol−1, the pre-exponential factor A was 3.47 × 1041 min−1, and the reaction rate constant K was 1.68 min−1. [ABSTRACT FROM AUTHOR]

The concentrate ash content is the most effective evaluation index in the flotation process. However, the ash content obtained by the current technology has a serious hysteresis. In this study, a real-time prediction system based on image processing and BP neural network modeling was established to monitor the flotation process. It will be beneficial to predict the concentrate ash content thereby adjust the flotation conditions opportunely to improve the flotation index. The results show that the predicted values obtained from the BP neural network model established in this study agree well with the actual value. [ABSTRACT FROM AUTHOR]

Flotation is a crucial separation technique in coal beneficiation. However, the inability to quickly and accurately detect ash content during the flotation process hinders the development of flotation automation. This paper introduced a novel hybrid model based on convolutional neural networks, establishing the ash content in tailings. In this method, convolutional neural network is utilized to automatically extract features from tailings images. In the model training and optimization phase after inputting the augmented data, the optimal ResNet18 model is identified by comparing five model optimization methods and replacing the SoftMax classifier with the traditional SVR in ResNet18. Additionally, a PCA-SVR model for predicting tailings ash content is constructed for comparative analysis, employing six different feature combinations to validate the effectiveness of the proposed features, with optimal support vector parameters identified through random search and cross-validation algorithms. The prediction results show that the PCA-SVR model has the highest accuracy when the feature dimension is reduced to 16, and the prediction of ResNet18-SVR is more accurate compared to PCA-SVR. This indicates that the proposed method can predict the ash content more accurately than the traditional feature engineering methods and has a broad application prospect in the field of coal beneficiation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Flotation is considered as an efficient method for removing hazardous trace elements from coal. However, the flotation kinetic characteristics of trace elements in coal are still missing. This research presents a detailed investigation into the flotation behavior of As, Se, Pb, Be, and Hg in coal, using coal samples from TX and FGZ coal preparation plants. Six kinetic models were employed to describe their flotation behavior and separation efficiency was introduced to assess the removability of trace elements. Most of the coal floats within the first 2 minutes of flotation, with the highest yield observed in product C1, followed by C4. The trace elements content (excluding Pb) exhibited an increasing trend from C1 to C6, and trace elements were primarily distributed in the tailing. There is a strong positive correlation between cumulative trace elements and ash content, and the inorganic-affinity of trace elements in FGZ coal is stronger compared to that of TX coal. The second-order model with a rectangular distribution of floatability provides the most appropriate description of trace elements’ flotation behavior. Kinetic analysis and separation efficiency assessments demonstrated that As and Hg in FGZ coal are relatively easier to discharge, while Pb and Be in TX coal exhibit higher removability. [ABSTRACT FROM AUTHOR]