The Hardgrove grindability index (HGI) is a crucial indicator for assessing the grindability of coal, and accurate prediction of HGI is essential for improving the production efficiency and economic benefits of the coal industry. This study employed six decision tree-based machine learning models to predict the HGI values of 129 coal samples, with hyperparameter optimization performed using Optuna, and model interpretability analyzed using SHapley Additive exPlanations (SHAP). The results showed that the optimized natural gradient boosting (NGBoost) model outperformed all other models, which achieved the highest performance on the test set with a coefficient of determination (R2) of 0.9715, a mean absolute error (MAE) of 1.1507, and a root mean squared error (RMSE) of 1.4735. SHAP analysis further revealed that volatile matter (VM) contributed the most to the model’s predictions, while pyrite (FeS2) had the least contribution. This study provides an efficient machine learning approach for accurate HGI prediction, offering excellent predictive performance, interpretability, and application value.

Hardgrove grindability index (HGI) is a significant index used to determine a mill's capability and overall efficiency in the grinding of coal. There are several factors that affect HGI due to the complex nature of coal. The influence of proximate analysis, calorific value, total sulfur, and pyrite content on the HGI values of 292 coal samples from South African coalfields were examined. Predictive models of HGI are developed by using soft computing techniques such as Long Short-Term Memory (LSTM), support vector regression (SVR), and Artificial Neural Network (ANN). This study shows that ANN is the most effective predictive model for all the three coalfields, with SVR models being second and LSTM models being the least effective. The correlation between the predicted value and the input data is established by using the cosine amplitude method (CAM). It was found that HGI is most influenced by the fixed carbon content, calorific value, ash content and volatile matter content while pyrite has the least influence.

This study explores the extraction of rare earth elements (REEs) from high-ash run-of-mine and discard coal sourced from the Waterberg Coalfield. Three distinct methods were employed: (1) ultrasonic-assisted caustic digestion; (2) direct acid leaching; and (3) ultrasonic-assisted caustic-acid leaching. Inductively coupled plasma mass spectrometry was utilized to quantify REEs in both the coals and resultant leachates. Leaching the coals with 40% NaOH at 80 °C, along with 40 kHz sonication, yielded a total rare earth element (TREE) recovery of less than 2%. Notable enrichment of REEs was observed in the run-of-mine and discard coal by 17% and 19%, respectively. Upon employing 7.5% HCl, a recovery of less than 11.0% for TREE was achieved in both coal samples. However, leaching the caustic digested coal samples with 7.5% HCl significantly enhanced the TREE recovery to 88.8% and 80.0% for run-of-mine and discard coal, respectively. X-ray diffraction analysis identified kaolinite and quartz as the predominant minerals. Scanning electron microscopy-energy dispersive microanalysis revealed monazite and xenotime as the REE-bearing minerals within the coal samples. These minerals were found either liberated, attached to, or encapsulated by the clay-quartz matrices. Further mineralogical assessments highlighted the increased REE concentrations in coals post-caustic digestion and subsequent recovery during acid leaching. This increase was attributed to the partial dissolution of kaolinite encapsulating the RE-phosphates and the digestion of REE-bearing minerals. Notably, undissolved REE-bearing elements in the caustic-acid-leached coal indicated the necessity of harsh leaching conditions to augment REE recovery from these coal samples.

The use of coal in applications such as power generation is influenced by its combustibility, sulphur content, Hardgrove grindability index (HGI), and abrasivity index (AI), including its volatile matter and ash content, etc. Collectively, these properties affect the quality and performance of coal for its intended use. In particular, the abrasive characteristics of coal from both the same and different coal fields pose challenges during milling operations, leading to accelerated wear and tear of machinery within the plant. In this study, the abrasive index (AI) of coal samples from three South African coalfields using the Yancey, Geer, and Price (YGP) method was conducted. A predictive model using artificial intelligence was developed using the AI obtained from individual samples and other characteristics of the coals. A reliable model utilizing Shallow Neural Network (SNN) and Deep Neural Network (DNN) was developed to relate the coal properties to AI across the different coalfields tested. It is noteworthy that the DNN demonstrated superior performance in modeling AI for all three coalfields, with ash content being the most influential factor. The research findings underscore the importance of other coal properties, including volatile matter, fixed carbon, calorific value, and HGI, in forecasting coal AI. Furthermore, it contributed to the knowledge of the use of artificial intelligence, specifically for a diverse range of coal samples collected from various South African coalfields.

This study introduces modified coal tar pitch (CTP) and dimethylpolysiloxane (DMPS) polymer as resins for the fixation of carbon-containing wastes into value-added composites. Secondary heat treatment of coal tar at 400 degrees C with a residence time of 9 h using the air-blowing pretreatment method yielded CTP with a hydrogen-to-carbon ratio of 0.4 and total carbon content (77 %-95 - 95 %). The effects of blending coal tar pitch to coal waste regarding the carbon structure, thermal stability, flame ablation rates, corrosion resistance, and leaching potential of coal composites were systematically investigated. The CTP-based composites have more ordered carbon structures compared to the DMPS-based composites. The composites are thermally stable up to 600 degrees C, beyond which they degrade rapidly. Composites containing 10 % CTP recorded the lowest linear and mass ablation rates. Interestingly, DMPS-based composites recorded higher corrosion rates compared to CTP-based composites. Finally, metal ion contaminants in the composites were below the low-risk threshold, suggesting these wastes can be bound in the composites in that they pose minimal environmental risks. Overall, the CTP binder is better than DMPS for producing composite materials that may serve as carbon sinks for coal waste. The combination of high thermal stability, low resistance to corrosion, low ablation rates, and low susceptibility to leaching suggests that the composites could be shaped into building materials such as tiles or pavers.

This study aimed to assess the optimal conditions for the extraction of chlorophyll and the stability of chlorophyll with postharvest storage period in Moringa Oleifera leaves harvested in 3 different years for its preservation and lifespan. For this purpose, chlorophylls a (Chla) and b (Chlb) were extracted from the leaves using acetone, methanol, N, N-dimethylformamide (DMF), and the ‘green’ solvent (ethanol). In addition, the chlorophylls were extracted under various conditions, including temperatures (4, 25, and 45 °C), and times (10, 30, and 60 min) from dry leaves that were harvested in different years (2020, 2021, and 2022). The results showed that the Chla content extracted exceeded that of Chlb in the four solvents in all temperatures and extraction times, except for acetone and ethanol extracts under 45 °C at 30 and 60-min extraction times in samples harvested in 2022. An increase in extraction time and temperature resulted in higher chlorophyll content. Overall chlorophyll content decreased with the increasing postharvest storage period, particularly in methanol and ethanol extracts. The Chlorophyll Stability Index showed that chlorophyll is stable in moringa, such that the chlorophyll content obtained in a 2-year postharvest storage period samples was also found to be high. In general, the chlorophyll obtained from this study was found to be compatible with what is used in the industrial market. This suggests that the chlorophyll from moringa is stable and can be considered a major source of chlorophyll.

Amidst global resource depletion and population growth, repurposing carbon-containing waste as raw materials into products offers promise for a more resource-efficient, circular economy. This research examines the efficacy of using coal tar modified through air-blowing as a binder for producing coal composite, alongside exploring dimethylpolysiloxane as an alternative binder. The effects of the binder type, mixing ratios, and coal fine composition on the properties of the composites were systematically studied. Microscopic analysis revealed anisotropic spherules in pitch formed at 400 degrees C/9 h and 450 degrees C/6 h possess favorable chemical properties like low H/C ratios (0.24 -0.25) and high carbon content (96 -97 %). Pyrolyzed coal fines and modified pitch-based composites exhibited moderate weight loss (11 -17 %), notable compressive strength (106.58 -344.71 MPa), and flexural strength (49 -160 MPa). However, composites produced from coal tar pitch (400 degrees C/9 h) blended with 50 % GG1 and further pyrolyzed at 600 degrees C for 5 h exhibited a high water absorption (19 %). Also, composites with inferior flexural strength were produced using dimethylpolysiloxane as a binder, rendering them unsuitable for structural applications. This straightforward approach offers a viable means to repurpose significant quantities of coal fines and coal tar destined for landfills. This research is intended as a reference for researchers seeking to tranform coal waste into structural composites, thereby promoting circularity, and mitigating associated environmental risks.

Hydrothermal carbonization (HTC) technologies for producing value-added carbonaceous material (hydrochar) from coal waste and sewage sludge (SS) waste might be a long-term recycling strategy for hydrogen storage applications, cutting disposal costs and solving waste disposal difficulties. In this study, hydrochars (HC) with high carbon content were produced using a combination of optimal HTC (HTC and Co-HTC) and chemical activation of coal tailings (CT), coal slurry (CS), and a mixture of coal discard and sewage sludge (CB). At 850 °C and 800 °C, respectively, with a KOH/HC ratio of 4:1 and a residence time of 135 min, activated carbons (ACs) with the highest Brunauer–Emmett–Teller specific surface ( S BET ) of 2299.25 m 2 g − 1 and 2243.57 m 2 g − 1 were obtained. The hydrogen adsorption capability of the produced ACs was further studied using gas adsorption isotherms at 77 K. At 35 bars, the values of hydrogen adsorbed onto AC-HCT (AC obtained from HTC of CT), AC-HCS (AC obtained from HTC of CS), and AC-HCB (AC obtained from HTC of the blending of coal discard (CD) and SS) were approximately 6.12%, 6.8%, and 6.57% in weight, respectively. Furthermore, the cost of producing synthetic ACs for hydrogen storage is equivalent to the cost of commercial carbons. Furthermore, the high proportion of carbon retained (>70%) in ACs synthesized by HTC from CD and SS precursors should restrict their potential carbon emissions.

A simple mixing-pressing followed by thermal curing and pyrolysis process was used to upcycle coal waste into high-value composites. Three coal wastes of different physicochemical properties were investigated. The hypothetical mechanisms of bonding between the coal particles and the preceramic polymer are presented. The textural properties of the coals indicated that the lowest volatile coal waste (PCD) had a dense structure. This limited the diffusion and reaction of the preceramic polymer with the coal waste during pyrolysis, thereby leading to low-quality composites. The water contact angles of the composites up to 104° imply hydrophobic surfaces, hence, no external coating might be required. Analysis of the carbon phase confirmed that the amorphous carbon structure is prevalent in the composites compared to the coal wastes. The dc volume resistivity of the composites in the range of 22 to 82 Ω-cm infers that the composites are unlikely to suffer electrostatic discharge, which makes them useful in creating self-heating building parts. The leached concentrations of heavy metal elements from the composites based on the end-of-life scenario were below the Toxicity Characteristic Leaching Procedure regulatory limits. Additionally, the release potential or mobility of the metals from the composites was not influenced by the pH of the eluants used. On the basis of the reported results, these carbon/ceramic composites show tremendous prospects as building materials due to these properties. Graphical Abstract

Abstract The authors have requested that this preprint be removed from Research Square.

Dry particle classification is a viable alternative to wet classification, both financially and environmentally, and has been used for decades with several approaches and techniques. One of these techniques, the wind-sifting principle, has been observed to be very effective for particle separation. Its separation mode is based on the use of the physical properties of these particles such as size, shape, and density to carry out separation. The principle of wind-sifting has been used to design multiple separators with various configurations for diverse kinds of applications, including recycling, agriculture, furniture, food and beverages, municipal and electronic waste sorting, and even mineral-processing industries. Although the wind-sifting principle has been implemented for various applications, research of this principle is ongoing owing to minimal literature. This Review seeks to provide some literature on wind-sifters as it delves into the three main types, their generic design features, and operational principles.

The co-firing of biomass is now widely acceptable as a clean coal technology option, and the storage and transportation of this fuel are essential as fuels ignite on their own. Spontaneous combustion (SPONCOM) is a well-known phenomenon in the coal mining sector, but little is known of the inherent properties of biomass toward SPONCOM. This study assessed the influence of three imidazolium-base ionic liquids (ILs), , , and [Bmim+OAc-] on the SPONCOM liability of Sersia lancea biomass sample harvested from the Vaal River Mine, South Africa. The results of the derivative thermogravimetric (DTG) analysis of this indigenous biomass showed that it is highly liable to SPONCOM prior to treatment with ILs. Following treatment with all three ILs, the DTG results showed that ILs can potentially inhibit the SPONCOM liability of biomass, with [Bmim+OAc-] showing the best inhibitory effects. With [Bmim+OAc-] (IL-C), the TGspc index of S. lancea biomass was reduced to 0.0207 from 0.1457%/°C min-1, and the sample was classified as low reactive after treatment. This indicates that less oxygen was consumed by the treated samples with [Bmim+OAc-] than by the untreated ones. From the textural properties of the IL-treated biomass, the mechanism responsible for the lower liability of the treated biomass was determined. This study establishes that biomass is very reactive, and it is important to understand its liability to SPONCOM, before being shipped as a fired or co-fired fuel across the Atlantic.

The temperature at which coal ash melts has a significant impact on the operation of a coal-fired boiler. The coal ash fusion temperature (AFT) is determined by its chemical composition, although the relationship between the two varies. Therefore, it is important to have mathematical models that can reliably predict the coal AFTs when designing coal-based processes based on their coal ash chemistry and proximate analysis. A computational intelligence model based on the interrelationships between coal properties and AFTs was used to predict the AFTs of the coal investigated. A model that integrates the ash, volatile matter, fixed carbon contents, and ash chemistry as input and the AFT [softening temperature, deformation temperature, hemispherical temperature, and flow temperature] as an output provided the best indicators to predict AFTs. The findings from the models indicate (a) a method for determining the AFTs from the coal properties; (b) a reliable technique to calculate the AFTs by varying the proximate analysis; and (c) a better understanding of the impact, significance, and interactions of coal properties regarding the thermal properties of coal ash. This study creates a predictive model that is easy to use, computer-efficient, and highly accurate in predicting coal AFTs based on their ash chemistry and proximate analysis data.

Biomass resources are gaining attention to address environmental issues, ensure energy efficiency, and ensure long-term fuel sustainability. The use of biomass in its raw form is known to present a number of issues, including high shipping, storage, and handling costs. Hydrothermal carbonization (HTC), for example, can increase the physiochemical properties of biomass by converting it into a more carbonaceous solid hydrochar with enhanced physicochemical properties. This study investigated the optimum process conditions for the HTC of woody biomass (Searsia lancea). HTC was carried out at varying reaction temperatures (200-280 °C) and hold times (30-90 min). The response surface methodology (RSM) and genetic algorithm (GA) were used to optimize the process conditions. RSM proposed an optimum mass yield (MY) and calorific value (CV) of 56.5% and 25.8 MJ/kg at a 220 °C reaction temperature and 90 min of hold time. The GA proposed an MY and a CV of 47% and 26.7 MJ/kg, respectively, at 238 °C and 80 min. This study revealed a decrease in the hydrogen/carbon (28.6 and 35.1%) and oxygen/carbon (20 and 21.7%) ratios, indicating the coalification of the RSM- and GA-optimized hydrochars, respectively. By blending the optimized hydrochars with coal discard, the CV of the coal was increased by about 15.42 and 23.12% for RSM- and GA-optimized hydrochar blends, respectively, making them viable as an energy alternative.

Recycling coal wastes into composites suitable for the built environment may be a necessary response to their environmental crises. Therefore, the objective of this study is to investigate the microstructure and performance of coal matrix composites produced from the blend of different coal wastes and polysiloxane polymer (SPR–212). Four types of coal wastes that differ in physicochemical properties were used. The microstructure of the composites was determined using Fourier transform infrared spectroscopy (FTIR), X–ray diffraction (XRD), scanning electron microscopic (SEM), and Raman spectroscopy. The technological properties of the composites were investigated based on their bulk density, apparent porosity, pyrolysis shrinkage, water absorption, and ultimate compressive stress. Thermal stability and contact angle measurements were used to probe the continuous operating temperature and the surface property of the composites. Pyrolysis mass loss in the range of 5.28–29.62% was obtained for all the samples tested. The density range of the composites is between 1.5 and 1.9 g/cm3 and the continuous operating temperature of the composites is up to 600 °C. The water absorption of the composites is within the range of building materials (0–25%). Composites of relatively high total carbon displayed the worst structural property and had contact angle < 90°. The outcomes of this study lay the foundation for further development of high–quality structural coal composites from coal waste and SPR–212 through optimization of the processing conditions.

Climate change scenarios highlight the significance of modifying production patterns in order to transition to a low-carbon economy, as well as the value of recycling innovations in this regard. This study examines the co-hydrothermal treatment (Co-HTC) of coal discard (CD) and sewage sludge (SS) in order to enhance the carbon content of the produced hydrochars for a sustainable circular and low-carbon economy. Using a coal-sewage sludge blend ratio of 5:1, the optimal hydrothermal carbonization (HTC) and Co-HTC parameters for producing hydrochars with the highest fixed carbon (FC) and lowest ash content (A) were 150°C, 27bar, 92.13 minutes and 208°C, 22.5 bar, 331 minutes, respectively. The physicochemical parameters of the optimized hydrochar were evaluated and compared to those of the raw materials. According to the results, HTC and Co-HTC raised the calorific value of CT and CS to 19.33 MJ/kg, 25.79 MJ/kg, and 24.31 MJ/kg, respectively.

The mining industry contributes to the expansion of the global economy by generating vital commodities. For continuous production, the industry relies significantly on machinery and equipment, which, as a result of greater modernization, are becoming increasingly complex, with a variety of systems and subsystems. However, maintaining the machinery and equipment used in the mining industry can be complex and costly. To improve the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies for equipment maintenance and to determine the best maintenance strategies, a systematic literature review was conducted to summarise the current state of research on equipment-related predictive maintenance (RP) in the mining industry. The review provides an overview of maintenance practices in the mining sector and examines PdM methodologies and processes used in other industries that may be applicable to the mining industry. In addition, this study discusses the different PdM architectures, processes, phases, and models (statistical and ML-based) used in creating a PdM plan. Furthermore, the review explores potential implementation directions for the PdM in the mining industry and highlights the challenges.

ABSTRACT The preparation of porous carbons by KOH activation from three different coal wastes in South Africa was modeled and optimized using an artificial neural network and whale optimization algorithm (ANN-WOA). The potential for methane adsorption of the optimized porous carbons was investigated. The optimal conditions for preparing the porous carbons, as determined by maximum surface area and micropore volume, according to ANN-WOA results, were as follows: Temperature of reaction: 800°C; activation time: 120 minutes; impregnation ratio: 1:4. SEM/EDS and BET methods were used to examine the surface properties and structural morphology of the optimized activated carbons. The methane adsorption isotherm on the three optimized porous carbons was fitted with the Toth isotherm model, which had an average R2 value of 0.9999. The maximum adsorption capacity of the three optimized porous carbons were 158.4, 147.6, and 123.1 cm3/g at 25°C and an average pressure of 37 bar. In addition, because of their high surface area and methane adsorption capacity, these materials have the potential to be used in natural gas storage, demonstrating that coal wastes in South Africa have the potential to be used as a sustainable starting material for the synthesis of porous carbons for gas storage.

The coal ash fusion characteristics are a significant factor to consider while designing a boiler to match a coal or different coal range. Characterization of coal ash provides the fundamental mechanism that controls the heating efficiency of the coal in a pulverized coal-fired boiler, regarding the coal minerals association. Changes in coal properties could lead to changes in ash properties, i.e., ash fusion temperatures (AFT) and ash elemental composition, which could lead to slagging or fouling issues inside the boiler. The main cause could be attributed to the high temperature of the combustion process and the low melting point of ash, or vice versa. This study aimed to develop AFT prediction models using coal samples from different coalfields to predict the initial deformation temperature (DT), softening temperature (ST), hemispherical temperature (HT) and fluid temperature (FT) of coal ash. The artificial neural network (ANN), Gaussian process regression (GPR) and support vector regression (SVR) are the three machine learning tools used in this modeling. The resulting AFT predictive models indicated that the ANN model predicted DT, ST, HT and FT more adequately and reliably than the GPR and SVR. The Taylor’s diagram, which enabled easy identification of the closeness of the model predictions to the measured data, also indicated that the ANN outperformed all the other models.

Recycling coal-based waste (CBW) into composites suitable as a building material might be a necessary response to combat its risk to the environment. Therefore, the objective of this study was to investigate the microstructure and performance of coal composites produced from CBW and polysiloxane polymer (SPR-212). Four types of CBW that differ in physicochemical properties were examined. Fourier transform infrared spectroscopy results indicated that the higher the intensity of the C=C bonds in the CBW, the higher the pyrolysis mass loss and shrinkage experienced by the composites during pyrolysis. The continuous operating temperature of the composites is up to 600 °C. However, at temperatures above 600 °C, composites containing carbon content greater than 36