Search Results (447)

Efficient and clean utilization remains a pivotal development focus within the coal industry. Nevertheless, the application of weakly caking coal results in energy loss due to the caking property, thereby leading to a waste of resources. This paper, therefore, concentrates on the caking property, offering insights into the relevant caking mechanism, evaluation indexes, and regulation technologies associated with it. The caking mechanism delineates the transformation process of coal into coke. During pyrolysis, the active component generates the plastic mass in which gas, liquid, and solid phases coexist. With an increase in temperature, the liquid phase is diminished gradually, causing the inert components to bond. Based on the caking mechanism, evaluation indexes such as that characteristic of char residue, the caking index, and the maximal thickness of the plastic layer are proposed. These indexes are used to distinguish the strength of the caking property. However, they frequently exhibit a poor differentiation ability and high subjectivity. Additionally, some technologies have been demonstrated to regulate the caking property. Technologies such as rapid heating treatment and hydrogenation modification increase the amount of plastic mass generated, thereby improving the caking property. Meanwhile, technologies such as mechanical breaking and pre-oxidation reduce the caking property by destroying agglomerates or consuming plastic mass. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R²: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R² = 0.92), outperforming RF (R² = 0.85) and GBT (R² = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article

The Junggar Basin contains a large amount of coal resources and is an important coal production base in China. The coal seam in Zhundong coalfield has a large single-layer thickness and high content of inertinite, but large particle Fe-sulphide minerals are associated with coal seams in some mining areas. A series of economic and environmental problems caused by the combustion of large-grained Fe-sulphide minerals in coal have seriously affected the economic, clean and efficient utilization of coal. In this paper, the ultra-thick coal seam of the Xishanyao formation in the Yihua open-pit mine of the Zhundong coalfield is taken as the research object. Through the analysis of coal quality, X-ray fluorescence spectrometer test of major elements in coal, inductively coupled plasma mass spectrometry test of trace elements, SEM-Raman identification of Fe-sulphide minerals in coal and LA-MC-ICP-MS test of sulfur isotope of marcasite, the coal quality characteristics, main and trace element characteristics, macro and micro occurrence characteristics of Fe-sulphide minerals and sulfur isotope characteristics of marcasite in the ultra-thick coal seam of the Xishanyao formation are tested. On this basis, the occurrence state and genesis of large particle Fe-sulphide minerals in the ultra-thick coal seam of the Xishanyao formation are clarified. The main results and understandings are as follows: (1) the occurrence state of Fe-sulphide minerals in extremely thick coal seams is clarified. The Fe-sulphide minerals in the extremely thick coal seam are mainly marcasite, and concentrated in the YH-2, YH-3, YH-8, YH-9, YH-14, YH-15 and YH-16 horizons. Macroscopically, Fe-sulphide minerals mainly occur in three forms: thin film Fe-sulphide minerals, nodular Fe-sulphide minerals, and disseminated Fe-sulphide minerals. Microscopically, they mainly occur in four forms: flake, block, spearhead, and crack filling. (2) The difference in sulfur isotope of marcasite was discussed, and the formation period of marcasite was preliminarily divided. The overall variation range of the δ³⁴S value of marcasite is wide, and the extreme values are quite different. The polyflake marcasite was formed in the early stage of diagenesis and the δ³⁴S value was negative, while the fissure filling marcasite was formed in the late stage of diagenesis and the δ³⁴S value was positive. (3) The coal quality characteristics of the thick coal seam were analyzed. The organic components in the thick coal seam are mainly inertinite, and the inorganic components are mainly clay minerals and marcasite. (4) The difference between the element content in the thick coal seam of the Zhundong coalfield and the average element content of Chinese coal was compared. The major element oxides in the thick coal seam are mainly CaO and MgO, followed by SiO₂, Al₂O₃, Fe₂O₃ and Na₂O. Li, Ga, Ba, U and Th are enriched in trace elements. (5) The coal-accumulating environment characteristics of the extremely thick coal seam are revealed. The whole thick coal seam is formed in an acidic oxidation environment, and the horizon with Fe-sulphide minerals is in an acidic reduction environment. The acidic reduction environment is conducive to the formation of marcasite and is not conducive to the formation of pyrite. (6) There are many matrix vitrinite, inertinite content, clay content, and terrigenous debris in the extremely thick coal seam. The good supply of peat swamp, suitable reduction environment and pH value, as well as groundwater leaching and infiltration, together cause the occurrence of large-grained Fe-sulphide minerals in the extremely thick coal seam of the Xishanyao formation in the Zhundong coalfield. Full article

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO₂/NO_x will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results. Full article

Promoting clean energy transition in rural areas is a key path to achieving global sustainable development, protecting public health, and promoting ecological livability. Based on data from the China Family Panel Studies (CFPS), this paper employs a multi-dimensional fixed effects model to evaluate the impact of income inequality on rural households’ clean energy transition (CET) and examines its underlying mechanisms. Research findings indicate that income inequality significantly suppresses rural households’ CET, primarily by reducing basic energy consumption and hindering the upgrading of basic energy consumption structures. Government governance quality exerts a significant negative moderating effect on the relationship between income inequality and rural households’ CET. Further analysis shows that the inhibitory effect of income inequality on CET is more significant in the regions with a low economic development level and low coal resource endowment, and in the western and northeastern regions of China. Therefore, while continuously promoting rural income growth, the government should prioritize equitable distribution, strengthen institutional capacity-building, improve the social service and security system, and facilitate rural households’ CET. Full article

Under the global context of addressing climate change and actively promoting energy transition, green power has become increasingly vital in the energy structure due to its clean and sustainable advantages. However, the development of green power’s environmental value faces multiple challenges that hinder its marketization. This study first systematically analyzes the current status of developing the environmental value of green power and identifies existing issues. Second, it designs a green power environmental value mechanism and constructs a quantitative model from the perspective of coal-fired power carbon abatement costs, analyzing the emission reduction value of green power in replacing different types of coal-fired power generation. The results show the following: (1) When power generation types are not differentiated, the environmental value exhibits significant seasonal variations. (2) The environmental value for coal-fired units above 300 MW is lower than the overall average, while that of gas-fired units falls between coal-fired units and the average; the environmental value of generating units with a capacity of 300 MW or less is the lowest, followed by that of unconventional coal-fired units. (3) The environmental value calculated based on the marginal carbon abatement cost of coal-fired units, is slightly higher than the tradable green certificate (TGC) price. This study provides policy support for promoting the low-carbon transition of the power sector and facilitating the development of a green power trading market. Full article

Pyrolysis is an important methodology for achieving efficient and clean utilization of coal. Lump coal pyrolysis demonstrates distinct advantages over pulverized coal processing, particularly in enhanced gas yield and superior coke quality. As a critical parameter in lump coal pyrolysis, particle size significantly influences heat transfer and mass transfer during pyrolysis, yet its governing mechanisms remain insufficiently explored. This research systematically investigates pyrolysis characteristics of the low-rank coal from Ordos, Inner Mongolia, across graded particle sizes (2–5 mm, 5–10 mm, 10–20 mm, and 20–30 mm) through pyrolysis experiments. Real-time central temperature monitoring of coal bed coupled with advanced characterization techniques—including X-ray diffraction (XRD), Raman spectroscopy, Brunauer–Emmett–Teller (BET) analysis, scanning electron microscopy (SEM), gas chromatography (GC), and GC–mass spectrometry (GC-MS)—reveals particle-size-dependent pyrolysis mechanisms. Key findings demonstrate that the larger particles enhance bed-scale convective heat transfer, accelerating temperature propagation from reactor walls to the coal center. However, excessive sizes cause significant intra-particle thermal gradients, impeding core pyrolysis. The 10–20 mm group emerges as optimal—balancing these effects to achieve uniform thermal attainment, evidenced by 20.99 vol% peak hydrogen yield and maximum char graphitization. Tar yield first demonstrates a tendency to rise and then decline, peaking at 14.66 wt.% for 5–10 mm particles. This behavior reflects competing mechanisms: enlarging particle size can improve bed permeability (reducing tar residence time and secondary reactions), but it can also inhibit volatile release and intensify thermal cracking of tar in oversized coal blocks. The BET analysis result reveals elevated specific surface area and pore volume with increasing particle size, except for the 10–20 mm group, showing abrupt porosity reduction—attributed to pore collapse caused by intense polycondensation reactions. Contrasting previous studies predominantly focused on less than 2 mm pulverized coal, this research selects large-size (from 2 mm to 30 mm) lump coal to clarify the effect of particle size on coal pyrolysis, providing critical guidance for industrial-scale lump coal pyrolysis optimization. Full article

Real-time ash content control in dense medium coal separation is challenged by time lags between detection and density adjustment, along with nonlinear/noisy signals. This study proposes a hybrid model for clean coal ash content in dense medium separation by integrating empirical mode decomposition, long short-term memory networks, and sparrow search algorithm optimization. A key innovation lies in removing noise-containing intrinsic mode functions (IMFs) via EMD to ensure clean signal input to the LSTM model. Utilizing production data from a Shanxi coal plant, EMD decomposes ash content time series into intrinsic mode functions (IMFs) and residuals. High-frequency noise-containing IMFs are selectively removed, while LSTM predicts retained components. SSA optimizes LSTM parameters (learning rate, hidden layers, epochs) to minimize prediction errors. Results demonstrate the EMD-IMF1-LSTM-SSA model achieves superior accuracy (RMSE: 0.0099, MAE: 0.0052, MAPE: 0.047%) and trend consistency (NSD: 12), outperforming baseline models. The study also proposes the novel “Vector Value of the Radial Difference (VVRD)” metric, which effectively quantifies prediction trend accuracy. By resolving time-lag issues and mitigating noise interference, the model enables precise ash content prediction 16 min ahead, supporting automated density control, reduced energy waste, and eco-friendly coal processing. This research provides practical tools and new metrics for intelligent coal separation in the context of green mining. Full article

Ammonia is a carbon-free fuel with strong potential for emission reduction. However, its high auto-ignition temperature and low reactivity lead to poor ignitability and unstable combustion. In contrast, coal-to-liquid (CTL) fuel offers high cetane number, low sulfur content, and low aromaticity, making it a clean fuel with excellent ignition performance. Blending CTL with ammonia can effectively compensate for ammonia’s combustion limitations, offering a promising pathway toward low-carbon clean combustion. This study explores the effects of post-injection strategies on combustion and emission characteristics of a CTL–ammonia dual-fuel engine under different levels of ammonia energy fractions (AEFs). Results show that post-injection significantly improves combustion and emission performance by expanding ammonia’s the favorable reactivity range of ammonia and enhancing NH₃ oxidation, particularly under moderate AEF conditions (5–10%) where ammonia and CTL demonstrate strong synergy. For emissions, moderate post-injection notably reduces CO at low AEFs, while NO_X emissions consistently decrease with increasing post-injection quantity, with greater suppression observed at higher AEFs. Soot emissions are also effectively reduced under post-injection conditions. Although total hydrocarbon (THC) emissions increase due to ammonia’s low reactivity, post-injection mitigates this accumulation trend to some extent, demonstrating overall co-benefits for emission control. Comprehensive evaluation indicates that the combination of 5–10% AEF, 8–12 mg post-injection quantity, and post-injection timing of 10–15 °CA achieves the most favorable balance of combustion efficiency, emissions reduction, and reaction stability, confirming the potential of the CTL–ammonia dual-fuel system for clean and efficient combustion. Full article

Under China’s “dual carbon” goals (carbon peaking and carbon neutrality), the utilization of high-chlorine coal faces significant challenges due to its abundant reserves in regions such as Xinjiang and its notable environmental impacts. This study systematically investigates the combustion characteristics, environmental risks, and control strategies for high-chlorine coal. Key findings reveal that chlorine release occurs in three distinct stages, namely low-temperature desorption, medium-temperature organic bond cleavage, and high-temperature inorganic decomposition, with release kinetics governed by coal metamorphism and the reaction atmosphere. Chlorine synergistically enhances mercury oxidation through low-activation-energy pathways but exacerbates boiler corrosion via chloride–sulfate interactions. Advanced control technologies—such as water washing, calcium-based sorbents, and integrated pyrolysis–gasification systems—demonstrate substantial emission reductions. However, challenges remain in addressing high-temperature corrosion and optimizing multi-pollutant synergistic control. This study provides critical insights into the clean utilization of high-chlorine coal, supporting sustainable energy transitions. Full article

In practical coal preparation processes, influenced by mining methods and mechanization levels, the proportion of fine and even ultrafine pulverized coal continues to increase. However, due to the small particle size, significant inter-particle interactions, and the low efficiency of conventional physical separation techniques, the efficient deashing of fine coal remains a significant technical challenge. Consequently, in the face of growing demand for fine coal processing, efficient and mature dry separation technologies are still lacking. To address this issue, a pulsating circulating airflow separation device was designed and developed in this study to deash and desulfurize pulverized coal with a particle size of less than 1 mm. The effects of gas velocity and pulsating airflow frequency on the deashing performance were investigated. Using Design-Expert software (version 13), an optimized formula for deashing efficiency was established, and the optimal operating parameters were evaluated. The separation results demonstrated that under the optimal conditions of fluidization, the number N = 1.2 and pulsating airflow frequency f = 2.375 Hz, the standard deviation of ash segregation (σ_ash) reached 25%, and the ash content in the cleaned coal was reduced from 37.28% to 22.32% in the cleaned sample. Furthermore, the sulfur content decreased significantly from 0.971% in the raw coal to 0.617% in the cleaned coal, indicating effective desulfurization. In addition, the concentrations of other harmful elements in the raw coal were also reduced to varying degrees. These findings demonstrate that the application of pulsating airflow can effectively enhance ash and sulfur removal from pulverized coal particles smaller than 1 mm. This approach offers a novel and promising method for the dry beneficiation of fine coal particles. Full article

Underground coal mines face increasing challenges in the scheduling of Electric Rubber-Tired Vehicles (ERTVs) due to confined spaces, dynamic production demands, and the need to coordinate multiple constraints such as complex roadway topologies, strict time windows, and limited charging resources in the context of clean energy transitions. This study presents a Constraint Programming (CP)-based optimization framework that integrates Virtual Charging Station Mapping (VCSM) and sensor fusion positioning to decouple spatiotemporal charging conflicts and applies a dynamic topology adjustment algorithm to enhance computational efficiency. A novel RFID–vision fusion positioning system, leveraging multi-source data to mitigate signal interference in underground environments, provides real-time, reliable spatiotemporal coordinates for the scheduling model. The proposed multi-objective model systematically incorporates hard time windows, load limits, battery endurance, and roadway regulations. Case studies conducted using real-world data from a large-scale Chinese coal mine demonstrate that the method achieves a 17.6% reduction in total transportation mileage, decreases charging events by 60%, and reduces vehicle usage by approximately 33%, all while completely eliminating time window violations. Furthermore, the computational efficiency is improved by 54.4% compared to Mixed-Integer Linear Programming (MILP). By balancing economic and operational objectives, this approach provides a robust and scalable solution for sustainable ERTV scheduling in confined underground environments, with broader applicability to industrial logistics and clean mining practices. Full article

Ammonia is gaining increasing attention as a zero-carbon fuel and hydrogen carrier, offering high energy density, mature liquefaction infrastructure, and strong compatibility with existing energy systems. This review presents a comprehensive summary of the recent advances in ammonia-based clean energy systems. It covers the fuel’s physicochemical properties, green synthesis pathways, storage and transport technologies, combustion behavior, NO_X formation mechanisms, emission control strategies, and safety considerations. Co-firing approaches with hydrogen, methane, coal, and DME are evaluated to address ammonia’s low reactivity and narrow flammability limits. This paper further reviews engineering applications across power generation, maritime propulsion, and long-duration energy storage, drawing insights from current demonstration projects. Key technical barriers—including ignition delay, NO_X emissions, ammonia slip, and economic feasibility—are critically examined. Finally, future development trends are discussed, highlighting the importance of integrated system design, low-NO_X combustor development, solid-state storage materials, and supportive policy frameworks. Ammonia is expected to serve as a strategic energy vector bridging green hydrogen production with zero-carbon end-use, facilitating the transition to a sustainable, secure, and flexible energy future. Full article

To further quantify the role of coal-fired power units in a wind–solar–thermal storage system and improve the construction of clean energy bases, this study examined the temporal production characteristics of wind and solar power and established an operational model for coal-fired power units within a wind–solar–thermal storage system. This approach ensured a stable electricity supply on the basis of power balance. The findings indicate that the correlation between the installed capacity of coal-fired power and the daily power supply capability of energy storage that meets various scheduled power demands can be obtained via the model. As the proportion of wind and solar power in the output power decreases, the influence of the minimum operational load of the coal-fired power units on the curtailment rate intensifies. Notably, the operational cost savings from reducing this minimum operational load surpass those obtained by either downsizing the installed capacity of coal-fired power units or energy storage devices. Among the parameters of this study, the lowest operational cost for the system was observed when wind and solar power generation constituted 76% of the total. This scenario, which ensured stable power output for 95% of the days in a year, had a wind and solar power curtailment rate of 11.3%. Additionally, the energy supplied by storage devices amounted to 1000 MWh, with the ratio of the installed capacity of coal-fired power to the total installed capacities of wind and solar power remaining at 25%. When the ratio of wind and solar power generation to output power was 91%, 76%, and 58%, a 1% reduction in coal consumption by coal-fired units during low-load operation resulted in a decrease in total system operating costs of 0.012%, 0.093%, and 0.089%, respectively. These findings provide valuable data support for the development of clean energy infrastructures. Full article