Biomass, as a carbon-neutral fuel, can support the transition of coal utilization toward low-carbon or negative-carbon systems through direct co-firing. This paper reviews coal-biomass direct co-combustion systems, focusing on combustion kinetics, ash melting and slagging behavior, boiler thermal efficiency optimization, gaseous pollutant emissions, and particulate matter formation mechanisms. It also summarizes the synergistic effects of various biomass types in co-firing with coal. Findings show that biomass fuel properties-such as volatile matter content, ash composition, and alkali metal enrichment-significantly affect boiler thermal performance, deposit formation, combustion stability, and pollutant pathways. Using boiler safety limits and environmental regulations as criteria, a method for evaluating the maximum allowable biomass blending ratio in coal-fired boilers is proposed. Furthermore, a predictive modeling framework is outlined for estimating the co-firing ratio accommodation limit in pulverized coal boilers. Comparative analyses of BP neural network, GA-BP neural network, and random forest prediction models are conducted.
Accurate characterization of coal rank is crucial for the efficient utilization of coal resources, yet traditional methods struggle to simultaneously correlate with its nanoscale surface structure. This study aims to explore a novel approach for the quantitative characterization of coal rank by integrating Atomic Force Microscopy (AFM) with multivariate statistical models. We collected nine samples ranging from medium-volatile bituminous coal to anthracite and extracted multidimensional parameters via AFM experiments, including pore quantity, average pore size, areal porosity, shape factor, as well as surface roughness and nano-ring roughness (Rr). The results indicate that as coal rank increases, the pore quantity and areal porosity in coal significantly rise, while the average pore size, surface roughness, and Rr all exhibit a linear decline, revealing a trend toward densification and ordering of the organic matter structure during coalification. Based on these findings, we developed a multivariate linear regression model for predicting coal rank, particularly the "4 R model" that incorporates Ro, surface roughness, and Rr. This model demonstrates significantly improved prediction accuracy compared to traditional models that do not consider nano-ring factors. This study provides new technological tools and theoretical foundations for the scientific classification of coal resources and the investigation of their microscopic mechanisms.
Lighting conditions, as a critical measurement factor affecting image quality and feature clarity, play a decisive role in coal and gangue image recognition. However, in real industrial scenarios, complex and dynamic lighting variations pose challenges to reliable recognition. To reveal the underlying mechanism by which lighting conditions affect recognition, this paper develops an experimental image acquisition platform and constructed datasets under seven controlled lighting conditions. Grayscale and texture features are extracted and analyzed. Tree models and neural network models are trained to assess how lighting conditions influence image processing performance. Experimental results show that a lighting intensity of 500 Lux provides the highest feature separability and optimizes classification performance. The comparative evaluation between convolutional neural networks-based frameworks and tree models under varying lighting conditions shows that the former maintain consistently higher accuracy and robustness, indicating their stronger capacity to extract illumination-invariant features. This study provides a valuable reference for designing instrumentation and measurement that enhance the accuracy of intelligent sensing and monitoring systems.
Coal slime is a solid waste generated during coal washing and is rich in aluminosilicate minerals and carbonaceous components, making it a promising precursor for porous ceramics. In this study, coal slime was used as the main raw material to prepare porous thermal-insulating ceramics by a combined freeze-drying and sintering process. The effects of sintering temperature, water addition, and carbonaceous component content in coal slime on the microstructure, porosity, and thermal conductivity of the resulting materials were systematically investigated. The results showed that, compared with conventional oven drying, freeze-drying significantly suppressed green-body shrinkage and improved pore-structure retention, increasing the final ceramic porosity by approximately 14%. Under the optimum conditions of a sintering temperature of 1050 degrees C, a water addition of 15%, and a carbonaceous component content of 55%, the obtained sample exhibited a porosity of 63.81% and good thermal-insulation performance, with thermal conductivities of 0.13 W & centerdot;m-1 & centerdot;K-1 at 25 degrees C and 0.17 W & centerdot;m-1 & centerdot;K-1 at 700 degrees C. These results demonstrate that the freeze-drying - sintering route can effectively construct a highly porous coal-slime-based ceramic structure and provide a new technical pathway for the resource utilization of coal slime in the preparation of porous thermal-insulating materials.
To investigate the occurrence modes of arsenic (As) and cadmium (Cd) in coal and their removal rules during flotation. Representative coal samples from the Shendong (C1) and Zhunneng (C2) coal preparation plants were selected. The occurrence modes of As and Cd in different density fractions were determined through sequential chemical extraction experiments and float - sink experiments. The removal rules of As and Cd were explored through flotation experiments and kinetic analysis. The results showed that As and Cd in C1 were mainly bound to organic matter and aluminosilicates, while those in C2 were mainly bound to sulfides and aluminosilicates. Both elements were enriched with the increase of coal samples density and ash content, showing an obvious inorganic affinity. The removal efficiency of As and Cd was significantly affected by their occurrence modes. As the collector dosage increased, the clean coal yield increased but the removal efficiency decreased, indicating that excessive reagent promoted the entrainment of organic matter or mineral particles. The flotation behaviors of As and Cd in two coals conformed to the first-order kinetic model (R-2 > 0.99). Cd exhibited a slower flotation rate than As.
Efficient dewatering of fly ash slurry is critical for minimizing storage requirements, improving material handling, and enhancing ash management performance. This study investigates the dewatering behavior of Seyit & ouml;mer thermal power plant Class F fly ash under repeated filling cycles using one-dimensional (1DFT) and two-dimensional filtration test (2DFT) systems. A novel 2DFT apparatus was developed to independently quantify radial and vertical filtration, enabling closer simulation of full-scale geotextile tube drainage. The influence of three anionic polymers with varying molecular mass and charge density, combined with 1% synthetic fiber addition, was evaluated. Results show that repeated filling cycles significantly modified filtration mechanisms. In the 2DFT system, successive fillings reduced radial filtration and increased vertical filtration. Approximately 85% of test cell capacity was reached after the fifth filling stage, beyond which efficiency gains diminished. Polymer characteristics significantly influenced filtration performance. Fiber addition enhanced vertical filtration rates in the 2DFT system but did not significantly affect total filtration duration. In contrast, fiber addition reduced dewatering time in the 1DFT system by up to 42%. These findings highlight the governing roles of cyclic filling, additives, and filter cake evolution. The proposed 2DFT system provides an improved experimental basis for predicting and optimizing geotextile tube dewatering.
Investigating the geochemical behavior of trace elements in coal under high-temperature conditions not only enables the reconstruction of their geochemical characteristics during thermal evolution but also holds great significance for the prevention and control of coal-derived pollution and the resource utilization of high-value-added elements. This study takes the No. 6 coal seam of the Guanbanwusu Mine in the Jungar Coalfield as the research object. Based on thermal simulation experiments at approximately 500 degrees C, combined with sequential chemical extraction and multiple analytical methods, the modes of occurrence, migration, and enrichment of trace elements during thermal simulation are revealed. The results show that among 48 trace elements, except for Sr, U, and As, the concentration coefficients of the remaining elements in partings are higher than those in raw coal. In raw coal, 23 elements, including Li, Ga, Sr, Zr, In, Pb, Th, U, most rare earth elements and yttrium (REY), have contents higher than the average values for Chinese coals, warranting focused investigation. As the thermal simulation temperature increases, moisture and volatile matter decrease significantly, while ash yield increases. The trace element contents in semicoke show an increasing trend, indicating that trace elements do not readily migrate with gaseous and liquid products but are more likely to remain in the semicoke. Sequential chemical extraction reveals that in raw coal, most trace elements are primarily present in the silicate fraction, followed by carbonate and organic-bonded fractions. With increasing temperature, the proportion of organic-bonded trace elements in semicoke increases, while the proportion of silicate fractions decreases, which is attributed to enhanced complexation and physical encapsulation within the organic matrix. At each thermal simulation temperature, elements with mobility above the average are classified as highly mobile, whereas those below the average are considered weakly mobile. A comparison of relative migration capacities shows that Pb, Y, Li, Zr, Pr, Th, Tm, Er, Gd, Ga, and La exhibit stronger migration ability than the average, while Lu, Dy, Ho, Yb, Nd, Tb, Ce, In, Sm, U, Sr, and Eu exhibit weaker migration ability than the average. The migration process of elements is jointly influenced by temperature, raw coal properties, thermal stability of minerals, and modes of occurrence.
