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The beneficiation of indigenous high ash bituminous coal is crucial for optimizing its suitability for Pulverized Coal Injection (PCI) in the steel industry. This study investigates the impact of size and density-based beneficiation on key coal properties, including proximate and ultimate analysis, calorific value, organic petrography, swelling index and thermal analysis. The raw coal, with an initial ash content of 28.73%, volatile matter of 19.43%, and fixed carbon of 51%, goes through density separation into fractions ranging from 1.3 to 1.8 kg/m 3 . The < 1.44 kg/m 3 fraction shows a significant reduction in ash content average 16.23% in all sizes with total recovery of 32.58%, which attracted investigations to assess its utilisation potential as PCI coal after blending it with very low ash imported non-coking coal. Furthermore, beneficiated coal with a density of < 1.44 kg/m 3 reveals an optimal volatile matter content of 21.62% and fixed carbon above 61.46%, aligning with PCI specifications. Our study also magnifies the importance of maceral composition and vitrinite reflectance (Ro%), with low-density fractions (< 1.44 kg/m 3 ) containing 62.4–77.8% vitrinite, enhancing combustion efficiency. Ultimate analysis endorses that the < 1.44 kg/m 3 density fraction, with an average carbon content of 73.46% and minimal sulphur < 0.42%, ensures efficient energy transfer. Gross Calorific Value (GCV) unveils that this fraction consistently provides energy outputs between 6642 and 8355 kcal/kg, making it the most suitable for PCI applications. Thermal analysis (DSC-TGA-DTG) of the beneficiated coal samples revealed combustion profiles closely aligned with imported PCI coal, confirming their compatibility with significant recovery yield (32.58%), and the successful blending strategy that brings the final ash content to within PCI specifications (9.59%). The potential for synergistic effects in blended combustion further reinforces their suitability for efficient PCI application. This study accentuates the strategic advantage of beneficiation in reducing dependency on imported PCI coal, enhancing domestic resource utilization, and promoting cost-effective steel production.

Hard coal run-of-mine contains a significant amount of waste rock, which must be removed to improve the quality of the extracted raw material. Coal processing is an integral and extremely important part of the production of commercial assortments. Water pulsating jig is one of the basic devices in which the material is separated. The article presents some possibilities of improving the qualitative and quantitative parameters of the beneficiation products due to design changes and control of the jig beneficiation node.

This study proposes considering the effective re–benefication of coal middlings and other such considered waste materials as a way to ensure that clean coal in coal by–products can be extracted and effectively utilized, saving costs and reducing coal waste. To quantify the clean–coal yield and ash reduction that can be achieved by re–beneficiating four typical by–product streams from the Guobei Coal Preparation Plant (6 Mt a−1) were used for the study. Coking–coal middlings, flotation tailings, and pressure–filter cakes from preparation plants still contain 30–60% combustible matter. Re–beneficiation techniques have been considered to recover this often-wasted coal, reduce waste rock disposal, and cut greenhouse–gas emissions per ton of clean coal produced. Representative samples (n = 4) were collected, sample size–classified as (fine coal particles ≤0.5 mm and coarse particles ≥) and subjected to (i) magnetite removal, (ii) laboratory froth flotation (diesel 507 g t−1, sec–octanol 103 g t−1), and (iii) fine and large particle density separation at 1.3–1.8 g cm−3 ZnCO3 media. Clean–coal yield and ash were measured for each stream and the coal’s particle liberation was examined by SEM. Crushing, grinding and liberation equipment and techniques that aid in the treatment of coal and the re–beneficiation of coal middlings and tailings. The key findings recorded during the experiment are as follows: Flotation of <0.5 mm fractions delivered 46.9–58.3% clean–coal yield at 10.3–17.0% ash. Density separation of 0.5–1.0 mm middlings peaked at 1.4–1.5 g cm−3, yielding 34.2% clean coal at 15–18.4% ash. Scanning Electron Microscope analysis confirmed partial liberation as results from re–grinding + second flotation which increased yield by a further 8–12%. A calculated theoretical examination of the preliminary cost–benefit analysis indicates ≈36 CNY t−1≈9 million CNY a−1 in saved disposal costs alone. savings in disposal and 0.25 Mt a−1 additional clean coal for the Guobei plant. The research presented in this paper highlights the current work by Anhui University of Science and technology in collaboration with Guobei coal preparation plant and the results therein achieved.

In order to investigate the situation of heavy metal pollution in the heavy metal industry in Gansu Province, a large copper mining province, two large and typical copper mining and beneficiation enterprises with differences in topographic features, climatic conditions, and soil types were selected as the target of this study based on similar ore types and beneficiation processes. Around these two enterprises, geochemical baselines of the six heavy metals were established, while the degree of local soil heavy metal pollution and potential hazards to humans were assessed based on statistical analysis, single-factor and multi-factor index analysis, and health risk evaluation models. In addition, Spearman’s correlation analysis and hierarchical cluster analysis were used to explore the intrinsic association between each heavy metal in the two mining industries to reveal the pattern of soil heavy metal pollution in the copper mining and beneficiation industry and to propose targeted measures to improve and prevent soil heavy metal pollution. The results showed that the heavy metal pollution in the soil around Shengxi Mining Co., Ltd. of Subei County (SX enterprise) was higher than that around Yangba Copper Co., Ltd. of Gansu Province (YB enterprise), but the two enterprises had similar patterns of pollution, with an overall medium level of pollution. The carcinogenic and non-carcinogenic risks for children and adults were within acceptable limits for both enterprises. Besides, the correlation between the different heavy metals to similarity in their sources of contamination and the different degrees of association between the soil heavy metals of the two enterprises due to their environmental characteristics. Graphical abstract

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD eff in wastewater was proposed. ● The COD eff prediction performances of the three models in the paper were compared. ● The COD eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination ( R 2) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R 2, and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.

The beneficiation of low-grade iron ores is a key research and development topic in the mineral processing industry. The gradual exhaustion of high-grade iron ore reserves, and rising consumer iron and steel demand globally necessitate efficient low-quality iron ore beneficiation to meet steelmaking quality requirements. This comprehensive review explores various beneficiation techniques for low-quality iron ore, focusing on conventional methods including comminution, froth flotation and gravity separation. This article discusses the principles, processes, and equipment used in these techniques and highlights recent advancements and research efforts in the field. This review also emphasizes the importance of effective beneficiation processes in enhancing economic viability, sustainable resource management, and environmental conservation. Furthermore, it presents a case study of iron ore deposits in Botswana, highlighting the potential economic growth and sustainable development that can be achieved by maximizing resource utilization through reductive roasting, followed by magnetic separation of iron ore using semi-bituminous coal as a reductant. Overall, this review provides valuable insights into low-grade iron ore beneficiation techniques and their significance in meeting the growing demand for high-quality iron and steel products.

The Mafube processing plant encountered significant discrepancies between the actual yield of middling product compared to the yield predicted by the geological model. This triggered a review of the performance of the coarse-fines circuit given its significant contribution to the middlings product. This paper presents the results of performance measurements conducted on the coarse-fines circuit performance to understand existing performance and improvements required. The Mafube coarse-fines circuit beneficiates coarse-fines within the particle size range from -0.50mm to 0.150mm and consist of the low-cut density three product (LC3) spirals and the teetered bed separator (TBS). The LC3 spirals operate as the primary beneficiation circuit of the coarse-fines whereas the TBS serves as a secondary upgrade stage, to achieve a target overall coarse-fines calorific value (CV) upgrade of 5 MJ/kg. The LC3 spirals results indicated an efficient performance in the primary beneficiation circuit with a CV upgrade of 3.2 MJ/kg with an ash yield of 51%. The secondary beneficiation stage in TBS indicated a further coarse-fines upgrade CV of 1.55 MJ/kg, thus yielding an overall coarse-fines CV upgrade of 4.82 MJ/kg from the Mafube coarse-fines circuit. Notably, the TBS discard stream showed a CV quality of 21.90 MJ/kg which still meets the Mafube middlings product specifications, thus indicating additional potential to capture the Mafube coarse-fines circuit's discard stream as middlings product. A further feasibility study must be conducted to evaluate the potential gains of capturing the Mafube coarse-fines product for the RB2 export stream and the coarse-fines discard for the RB4 middlings stream.

South Africa's coal mining operations frequently produce fine coal which is usually discarded due to the high costs of handling. Studies have shown that these fines possess acceptable market calorific values and low ash content. Processes have been widely developed to convert these fines into a marketable product to blend with coarse washed coal to improve overall yields. Phola Coal Processing Plant makes use of triple start spirals to produce three streams of coal from the feed namely: product, middlings and discard. The spiral product is added primarily to the main DMS secondary product and opportunistically to the primary product depending on the quality, while the spiral discard and middlings are rejected. The spiral product contributes 5.2 % of the overall secondary product yield as a percentage of the head feed. Currently, the spiral material does not satisfy the primary product specification, and the goal of this test work was to ascertain whether the spiral product can be upgraded using the Optima® Classifier 500 pilot unit (OPC) in a second stage beneficiation to meet the primary product specification. The test work was based on upgrading the spiral feed and a combination of spiral product & middlings to primary product. The results showed that when desliming the -0.15 mm material in the final product, the OPC was able to achieve the necessary upgrade to meet the export product specification at an overall yield of 2.8 %.

In the mineral sector, many processes use water for ore beneficiation processes. A lack of sensing or control of water content can lead to operational problems in various mineral processing operations, especially in ore transport. Current instrumentation systems are either slow or inaccurate. Therefore, a novel bench system was developed to address this gap by achieving a fast response time and improved accuracy. The developed instrument measures the ore moisture by using the real-dual-frequency method (RDFM) to assess the ore's electrical conductivity and relative permittivity. Additionally, it takes into account the bulk density, the bench chamber level, and the compress torque. All these variables are used to create a tiny machine-learning (TinyML) model that evaluates the ore's moisture with a low time response. This process is done while the ore sample is compressed to reduce air bubbles inside the samples and improve measurement. Experiments were performed using the bench system in a mining company's physical analysis laboratory. The instrument was utilized to measure the moisture content in the ore, leading to the development of a dataset used to train and validate various tree-based tinyML models. The results indicate that ore compression enhances accuracy and that decision trees are effective for estimating moisture with a quicker response time.

Carbon dioxide utilization for enhanced metal recovery (EMR) during mineralization has been recently developed as part of CCUS (carbon capture, utilization, and storage). This paper describes fundamental studies on integrating CO₂ mineralization and concurrent selective metal extraction from natural olivine. Nearly 90% of nickel and cobalt extraction and mineral carbonation efficiency are achieved in a highly selective, singlestep process. Direct aqueous mineral carbonation releases Ni2+ and Co2+ into aqueous solution for subsequent recovery, while Mg2+ and Fe2+ simultaneously convert to stable mineral carbonates for permanent CO₂ storage. This integrated process can be completed in neutral aqueous solution. Introduction of a metal-complexing ligand during mineral carbonation aids the highly selective extraction of Ni and Co over Fe and Mg. The ligand must have higher stability for Ni-/Co- complex ions compared with the Fe(II)-/Mg- complex ions and divalent metal carbonates. This single-step process with a suitable metal-complexing ligand is robust and utilizes carbonation processes under various kinetic regimes. This fundamental study provides a framework for further development and successful application of direct aqueous mineral carbonation with concurrent EMR. The enhanced metal extraction and CO₂ mineralization process may have implications for the clean energy transition, CO₂ storage and utilization, and development of new critical metal resources.