Coal Preparation Research Articles

Effect of coal quality and preparation on the stamping performance and quality of coke.

This article presents the results of the research on real coal charges of different compositions intended for coking with loading into the chamber by the stamping method. The results of the study established that with an increase in the content of coal at a low stage of metamorphism and a decrease in the content of coal at a high stage of metamorphism in the charges, a decrease in the quality indicators of the coke obtained from them leads to a decrease in the yield of coke. At the same time, there is a gradual decrease in the compaction of the charges from 22.5 to 21.1kPa; their expansion pressure decreases from 6.8 to 5.9kPa; and the work of stamping decreases from 8966 to 6822J. It was also discovered that with the increase in the degree of grinding of the charge, and, accordingly, with the decrease in the average diameter of its particles, the work of stamping coal charges decreases from 7407 to 6238J. The results can be used in the manufacturing of blast furnace coke by stamping coal charges.

Production process of 800 t/d sintered lime rotary kiln

This paper presents a detailed analysis of the production process and technical characteristics of the 800t/d sintered lime rotary kiln in Beitai Iron Mine of Bensteel Group Corporation Limited. The production process encompasses a number of key stages, including the charging of raw materials, firing, the crushing and screening of finished products, storage and transportation, exhaust gas treatment, and pulverized coal preparation. The study will prove a valuable point of reference for the enhancement of the overall performance of the domestic sintered lime industry.

Research on the Detection Principle of Coal Ash by X-Ray Transmission Based on FLUKA

This study addresses the timely and accurate measurement of coal ash content by proposing a detection model based on nuclear science technology, which is validated using FLUKA 4-4.0 simulation software. The background provided highlights the fact that coal ash content is a critical sales indicator, and its precise measurement is essential for adjusting production parameters in coal preparation plants. In terms of methodology, this study employs the widely used FLUKA4-4.0 software in the field of nuclear physics to simulate X-ray transmission through coal, investigating the impact of changes in coal type on the accuracy of ash measurements. The results indicate that, when the proportions of high-atomic-number elements in coal remain constant, the ash measurement results are accurate and reliable. However, significant fluctuations occur when these proportions change. The conclusion emphasizes the fact that variations in coal type are the primary cause of inaccuracies in ash measurement, particularly when the ratios of high-atomic-number elements are altered. This research provides a new perspective on the online measurement of coal ash content and offers theoretical support for improving measurement accuracy.

Optimization Study of Coal Gangue Detection in Intelligent Coal Selection Systems Based on the Improved Yolov8n Model

To address the low recognition accuracy of models for coal gangue images in intelligent coal preparation systems—especially in identifying small target coal gangue due to factors such as camera angle changes, low illumination, and motion blur—we propose an improved coal gangue separation model, Yolov8n-improvedGD(GD—Gangue Detection), based on Yolov8n. The optimization strategy includes integrating the GCBlock(Global Context Block) from GCNet(Global Context Network) into the backbone network to enhance the model’s ability to capture long-range dependencies in images and improve recognition performance. The CGFPN (Contextual Guidance Feature Pyramid Network) module is designed to optimize the feature fusion strategy and enhance the model’s feature expression capabilities. The GSConv-SlimNeck architecture is employed to optimize computational efficiency and enhance feature map fusion capabilities, thereby improving the model’s robustness. A 160 × 160 scale detection head is incorporated to enhance the sensitivity and accuracy of small coal and gangue detection, mitigate the effects of low-quality data, and improve target localization accuracy.

Hydrophobic regulation enhances the filtration and dehydration for coal preparation products, an experimental exploration

To enhance the filtration and dewatering performance while reducing the moisture content of coal preparation products, we conducted an investigation into the dehydration efficacy of various coal preparation products using different filter aids. The study involved examining the dehydration effects of both conventional hydrophobic filter aids and novel alternatives on flotation cleaned coal and coarse fine slime at the Huaibei Linhuan coal preparation plant. Our findings revealed a significantly superior dehydration performance of the new filter aids compared to conventional hydrophobic ones. Subsequently, we explored the dehydration efficacy of a specific new filter aid, designated as 1239, on four types of coal samples sourced from the Huainan mining area. The results demonstrated that filter aid 1239 exhibited remarkable effectiveness on both flotation cleaned coal and coarse fine slime, with a discernible impact on coal slime as well. By modulating the hydrophobicity of particle surfaces, hydrophobic filter aids effectively facilitated the dehydration of coal preparation products. These findings bear substantial significance for improving the dewatering and transportation processes of coal preparation products.

Comparison and selection of flotation processes for the separation of fine and difficult-to-float coal particles

ABSTRACT This work compares the performance of two coal preparation plants (CPPs) with similar flotation feeds to study the compatibility between flotation process and coal slime properties and to guide the design of flotation process and equipment selection. The two plants process coal slimes using “flotation column + flotation machine” and “flotation machine + flotation machine” flowsheet, respectively. The migration rules of different size and density fractions in the flotation were experimentally studied. The operation performance between the two flotation processes was comprehensively compared. The results show that, for feedstocks with high concentration of high-ash fine slimes and intergrown particles, the dual flotation processes can effectively reject intergrown particles and high-ash fine slimes, thereby reducing the ash of flotation concentrate to meet quality requirements. In the rougher flotation column, the flotation perfection index of < 0.045 mm size fraction is 55.67%, which is 4.74% higher than that of the rougher flotation machine. In the sorting of cleaner flotation machine, the particles with low density of < 1.3 g/cm3 are collected preferentially. With the extension of the flotation process, the intergrown particles and high-ash fine slimes gradually report to the clean coal products. The flotation recovery of > 0.25 mm size fraction is relatively poorer compared to other fractions. When the feed has high content of high-ash fine slimes, the process of “flotation column + flotation machine” is preferred. When the feed has high content of middle density fraction, the process of “flotation machine + flotation machine” is preferred for dual flotation. The upper limit of feed size should be strictly controlled in flotation.

Synergistic enhancement of dewatering of montmorillonite-containing coal slime by novel combined filter aid: an experimental and simulation study

ABSTRACT The efficient dewatering of montmorillonite (Mt)-containing coal slime is still an urgent problem to be solved in coal preparation plant. In this study, the dewatering effect of poly(o-phenylenediamine)/Poly dimethyl diallyl ammonium chloride (POPD/PDADMAC) combined filter aid on Mt-containing coal slime was studied. The dewatering performance of combined filter aid was investigated by vacuum filtration test. The action mechanism of combined filter aid was revealed by contact angle, zeta potential, cake pore and atomic force microscope (AFM). The co-adsorption principle of the combined filter aid was elucidated based on density functional theory (DFT) and molecular dynamics simulation (MD). Compared to PDADMAC, the combined filter aid has better performance for dewatering of Mt-containing coal slime, and has the advantage of low reagent dosage. The combined filter aid can greatly reduce the Mt potential, increase the Mt contact angle, and increase the porosity of the filter cake. There was a co-adsorption effect between the combined filter aid. Specifically, there were ion–dipole interaction, cation–π interaction and Cl ions bridged electrostatic interaction between POPD and PDADMAC. This study has developed a novel combined filter aid, which provides theoretical and technical support for the high-efficiency pressure filtration dewatering of Mt-containing coal slime in coal preparation plants.

Enhancement of interaction between Shenhua coal and GON-based dispersant with “surface-to-surface” adsorption by regulating the oxidation degree of GON: Experiments and simulations

The present study focuses on the synthesis of several graphene oxide nanosheet (GON)-based dispersants with an adsorption mode of “surface-to-surface” for the preparation of Shenhua coal (SC) water slurry (SCWS), achieved by grafting allyl polyoxyethylene ether (APEG) onto GON edges with varying degrees of oxidation using emulsion polymerization and esterification techniques. The impacts of the oxidation degree of GON on its structural characteristics and the performance of GON-based dispersants in SCWS were comprehensively assessed through a range of experimental techniques, alongside molecular dynamics (MD) simulations. Results demonstrated that with the increase in oxidation degree, there was an elevation of oxygen-containing groups, in which the content of COH decreased while the content of COC increased. Among all dispersants, GA-2, which was synthesized using GON-2 as the raw material with a graphite-to-KMnO4 mass ratio of 1:2.5, exhibited superior viscosity-reducing and stabilizing abilities compared to other dispersants. All SCWSs demonstrated pseudoplastic fluid behavior and showed good agreement with the Herschel-Bulkley model. After the adsorption of dispersants in SC, both the Zeta potential and surface wettability of SC significantly improved, especially with GA-2. The interaction mechanism between the dispersant and SC was analyzed by establishing and employing three adsorption models (M-1, M-2, and M-3). The interfacial adsorption structure confirmed that the dispersant exhibited “surface-to-surface” mode on coal through hydrophobic interaction, hydrogen bonding, and π–π interaction. Energy studies revealed that M-2 with the highest Eads (−105.31 kcal/mol) demonstrated superior adsorption strength compared to M-1 and M-3. Investigations into water molecule mobility revealed that the interaction between the GON-based dispersant and SC resulted in a reduction of water molecule mobility, thereby promoting water molecule aggregation to facilitate the formation of hydration films and enhance SCWS stability. In summary, an optimal oxidation degree of GON was found to be crucial for enhancing the performance of GON-based dispersants; higher oxidation levels may hinder dispersant adsorption on coal surfaces by converting hydroxyl and carboxyl groups into epoxide groups, thus impairing the conjugated structure.

Preparation and growth mechanism of high-quality multilayer graphene from simulated coal pyrolysis gas via chemical vapor deposition

Chemical vapor deposition (CVD) of graphene from coal pyrolysis gas provides new ways for both large-scale graphene preparation and high value-added utilization of coal. However, green, efficient and continuous preparation of high-quality multilayer graphene with both good uniformity and ideal structure characteristic remains a great challenge. Herein, we first investigated the influence of main carbonaceous species (CH4, C2H6, C3H8, CO2, and CO) in coal pyrolysis gas on the quality of CVD graphene products. The experimental results indicated that CO2 and CO have little effect on the growth of graphene, while methane as carbon source is conducive to obtain ideal graphene materials, propane and ethane could also influence structural defects and layer number of graphene products remarkably. On this basis, we designed a mixed gas of methane, ethane, and propane with optimized ratio as simulated coal pyrolysis gas, and successfully prepared high-quality multilayer graphene products on nickel foam from the as-designed simulated coal pyrolysis gas by CVD method. Notably, with a CH4:C2H6:C3H8 ratio of 2:1:1, the graphene products prepared from simulated coal pyrolysis gas outperformed that of raw coal pyrolysis gas in terms of physical structure, layer number, and quality uniformity. The corresponding I2D/IG value reached 0.97. The graphene growth process and mechanism were investigated and discussed macroscopically and microscopically. This work is of great significance for the intrinsic understanding of CVD growth of graphene from coal pyrolysis gas, and also inspires an efficient and environmentally friendly avenue for high-throughput, uniform production of high-quality graphene materials.

Exploring the impact of poly(sodium styrene sulfonate) dispersants’ architecture on their performance in coal water slurry preparation

In this work, the architecture of poly(sodium styrene sulfonate) (PSSNa) dispersants were flexibly tailored by various strategies. Three categories of PSSNa dispersants with different topologies were obtained, namely, linear structured PSSNa (1-A-PSSNa), three-arm star PSSNa (3-AS-PSSNa) and six-arm star PSSNa (6-AS-PSSNa). The molecular weights (Mn) of these dispersants were adjusted in the range of 3200–26,200 g/mol by varying the feeding ratio of monomer to initiator. In addition, the polydispersity index (PDI) of resultant dispersants were also varied by using different polymerization technologies, such as atom transfer radical polymerization (ATRP) and conventional free radical polymerization technique (RP). Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy and gel permeation chromatography (GPC) verified these architecture parameters of resultant PSSNa dispersants. Afterwards, the impact of these architectural factors on the performance of PSSNa dispersants in coal water slurry (CWS) preparation were evaluated. The results revealed that with the Mn of dispersants increased from 9800 to 26,200 g/mol, the apparent viscosity of CWS increased by around 11 %. The comparison between dispersants with approximate Mn (26,200 and 26,900 g/mol) and very different PDI (1.27 and 2.25) indicated that wide PDI are favourable for the performance. This work also confirmed that the topology of dispersants has a significant impact on their performance. The maximum coal loaded can increase from 63.9 % to 64.3 % when 1-A-PSSNa dispersant was replaced by 6-AS-PSSNa. Meanwhile, these architectural factors have a limited impact on the fluid type of CWSs, as all CWSs obtained in this work were pseudoplastic fluids regardless of the used dispersants. The potential mechanism was investigated using various technologies, such as the measurements of zeta potentials, contact angles, and low-field nuclear magnetic resonance spectra. The results revealed that topologizing did not alter the interaction between dispersant and coal surface. However, due to the special features of topological polymers, they can grant adsorbed coal particles with enhanced electrostatic repulsion and steric hindrance, which facilitated the dispersion of coal particles.

Source Apportionment and Human Health Risks of Potentially Toxic Elements in the Surface Water of Coal Mining Areas.

Contamination with potentially toxic elements (PTEs) frequently occurs in surface water in coal mining areas. This study analyzed 34 surface water samples collected from the Yunnan-Guizhou Plateau for their hydrochemical characteristics, spatial distribution, source apportionment, and human health risks. Our statistical analysis showed that the average concentrations of PTEs in the surface water ranked as follows: Fe > Al > Zn > Mn > Ba > B> Ni > Li > Cd > Mo > Cu > Co > Hg > Se > As > Pb > Sb. The spatial analysis revealed that samples with high concentrations of Fe, Al, and Mn were predominantly distributed in the main stream, Xichong River, and Yangchang River. Positive matrix factorization (PMF) identified four sources of PTEs in the surface water. Hg, As, and Se originated from wastewater discharged by coal preparation plants and coal mines. Mo, Li, and B originated from the dissolution of clay minerals in coal seams. Elevated concentrations of Cu, Fe, Al, Mn, Co, and Ni were attributed to the dissolution of kaolinite, illite, chalcopyrite, pyrite, and minerals associated with Co and Ni in coal seams. Cd, Zn, and Pb were derived from coal melting and traffic release. The deterministic health risks assessment showed that 94.12% of the surface water samples presented non-carcinogenic risks below the health limit of 1. Meanwhile, 73.56% of the surface water samples with elevated As posed level III carcinogenic risk to the local populations. Special attention to drinking water safety for children is warranted due to their lower metabolic capacity for detoxifying PTEs. This study provides insight for PTE management in sustainable water environments.

Research on alarm of coal content in gangue based on binocular vision and YOLACT segmentation network

ABSTRACT The coal content in the gangue of coal preparation plants cannot be detected online, especially in the jigging sorting process. There is a large time lag in manual testing. An approach based on the YOLACT segmentation network and binocular vision was proposed. First, the YOLACT segmentation network was used to identify coal and gangue. At the same time, the coal and gangue particle regions are segmented from the background of the bucket elevator. A binocular matching method based on deep learning was proposed to obtain better disparity maps and establish better volume models of coal and gangue particles. A stereo-matching global fusion network model was also presented by improving the Pyramid Stereo Matching Network. Finally, the coal and gangue volume parameters were combined with their respective empirical densities to predict the coal content in gangue of the bucket. When the load in the bucket elevator is small, the average prediction error of coal content in gangue is 9.19%. When the load is large i.e. the coal and gangue are stacked in multiple layers, it is not appropriate to calculate the coal content in gangue due to serious particle coverage. Therefore, a fuzzy alarm strategy was proposed based on actual needs. The situation of coal content in gangue was divided into three levels: normal, warning, and alarm. It meets the alarm requirements for coal content in gangue in most industrial plants.

Classification of coal gangue and identification of coal type based on first-derivative of mid-infrared spectrum

Efficiently sorting coal gangue and identifying coal types are vital operations in coal preparation, yet they are traditionally resource-consuming, labor-intensive, and potentially hazardous. This work puts forward an straightforward method employing mid-infrared spectroscopy with first derivative spectrum to address these issues. The proposed technique focuses on the delineation and enhancement of characteristic spectra to detect subtle differences among samples. The method utilizes just a few characteristic spectra of 3740–3700 cm−1, 1790–1750 cm−1, 1615–1583 cm−1, 1580–1540 cm−1, 1550–1440 cm−1, 1270–1210 cm−1 and 867–854 cm−1 to achieve 100 % high-accuracy classification of coal gangue and identification of coal types with total 250 spectra, such as bituminite, anthracite, lignite, roof sandstone and gangue, without the need for secondary sample processing or the assistance of machine learning algorithms, simplifying the process considerably. Such a strategy not only significantly improves the efficiency of coal sorting but also endorses real-time on-site detection. It offers a theoretical foundation for advanced coal separation technology and its implementation in real-world mining operations.

Research on carbon emission accounting and carbon emission reduction path of coal preparation plant

“Peak carbon dioxide emissions、Carbon neutrality” is the two stage goal of carbon emission reduction proposed by China. Carbon emission accounting is the basic premise for effectively carrying out various carbon emission reduction work and promoting the green transformation of the economy. The coal industry is one of the key objects of carbon emission reduction. Taking Zhenneng Coal Preparation Plant as the research object, this paper studies the carbon emission in the production process of the coal preparation plant and the selection of carbon emission factors, and constructs the carbon emission accounting model of the coal preparation plant. It is calculated that the carbon dioxide equivalent emission per 1 t of coal products produced by the coal preparation plant in 2018–2022 is 15.4391 kgCO2e. Based on the carbon emission reduction path at home and abroad, the development suggestions for carbon emission reduction in coal preparation plants are put forward. It is of great significance to establish a unified and standardized carbon emission accounting system for the coal industry, achieve the goal of “double carbon” and promote the comprehensive green and low-carbon transformation of economic and social development.

Updating strategy of safe operation control model for dense medium coal preparation process based on Bayesian network and incremental learning

AbstractThe effectiveness of control decisions provided by the safe operation control model for the dense medium coal preparation process may decline due to its inability to adapt to changing working conditions. To address this issue, this paper investigates a safe operation control model update strategy based on Bayesian network and incremental learning. This strategy can update the model structure and parameters according to different conditions, ensuring the effectiveness of the updated model. Considering that the old model has effective information to explain the new working conditions, the Bayesian network structure update learning method based on incremental learning is proposed. This method retains the components of the old model that can describe the joint probability distribution of the sampled data under the new working conditions while updating the remaining structure. This approach improves the efficiency of model updating. The simulation results show that the updated model obtained by the proposed method can effectively deal with new abnormal conditions.

Flotation of ultra-low ash coal combined with ultrasonic pretreatment

ABSTRACT Ultra-low ash coal is an essential clean energy and material. The preparation of ultra-low ash coal by flotation is facing challenges due to the inherent drawbacks. This work aims to enhance the flotation efficiency of ultra-low ash coal by ultrasonic pretreatment. Timed-release flotation experiments were conducted with ultrasonic pretreatment before and after collector addition. The results showed that the recovery and combustible recovery and flotation perfect index of clean coal increased after ultrasonic pretreatment. Additionally, the flotation circuits can reduce from three to two times to obtain clean coal with ash content around 5%. The particle size distribution of coal varied from unimodal to bimodal after ultrasonic pretreatment no matter whether collector was added, indicating that ultrasonic had both dispersing and flocculation effects. Scanning electron microscope (SEM) analysis illustrated that the surface of coal was cleaned due to the ultra-fine gangue particles coated on coal surface was dispersed, and the flocculation of low-ash coal particles was promoted after ultrasonic pretreatment. Consequently, the ultra-low ash coal with higher recovery and quality could be obtained with simplified circuits.

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Study on the distribution of high ash slime in liquid-solid fluidized bed with different heights

ABSTRACT Liquid-solid fluidized bed (LSFB) sorting has been widely used in coarse coal slurry sorting in coal preparation plants in China. In this paper, the vertical distribution of high-ash fine sludge in the LSFB column was studied in depth, and the results showed that in the vertical direction of the column, the gradient of ash change of the + 0.25 mm particles was obviously larger than that of the −0.25 mm particles. For + 0.25 mm particles, the low-density fine coal particles accumulate in the middle and upper parts, while the high-density particles accumulate in the middle and lower parts, and the overflow −0.25 mm particles are basically uniformly distributed in each density class. In the overflow port and the upper part of the column, the ash content of −0.25 mm particle size is obviously higher than that of + 0.25 mm particle size, and the −0.125 mm high ash and fine sludge basically accumulate in the upper part of the LSFB, and eventually enter the overflow to seriously pollute the fine coal. This study is of great significance for the development and application of LSFB sorting machine and the optimization of coarse coal slurry sorting and recovery process.

Experimental investigation on the preparation of macroscopic 0–1 mm powdered coal by a vertical spindle mill

The vertical spindle mill (VSM) is the key equipment to realize the preparation of 0–1 mm fuel used in powdered-coal-fired circulating fluidized bed combustion technology (PC-CFB). In this paper, a pilot-scale VSM test system was set up to explore the feasibility and operating performance of coarse-grained pulverized coal preparation. The effects of loading force, table revolution rate, and ventilation rate on the product size distribution, as well as the energy consumption of the grinding process were emphatically discussed. The results show that the product size of VSM increases with the decrease of loading force and the increase of ventilation rate, but too small a loading force will affect the normal output of VSM. Additionally, the product size of VSM decreases firstly and then increases with the increase of table revolution rate. Further, the energy consumption of the powdered coal preparation process was quantitatively described, and a semi-empirical model of comminution energy based on the powdered coal particle size distribution was proposed and verified by the measured values with an average accuracy of ±30 %. This model can estimate the power consumption of the mill, which can be used as an input parameter of the heat balance calculation model for the PC-CFB pulverizing system.

Research on concentration dosing control based on the ISSA-LSTM model and dynamic concentration detection

ABSTRACT During the thickening process, the dosage of flocculant has a great influence on the overflow concentration and filter cake moisture. The dosage of flocculant cannot be automatically updated according to the feeding situation, especially when the properties of the concentrated feeding material change, and the overflow concentration detection feedback cannot be adjusted in time. To address these problems, a dynamic concentration detection device was designed. In addition, a dosage of flocculant correction model based on dynamic concentration detection was constructed, and a feed-forwards flocculant additioncontrol model based on the ISSA-LSTM neural network was proposed. The results showed that the relationship between the linear slope of the dynamic concentration and the dosage of flocculant was in line with that obtained using the ExpAssoc model. Then, a double-layer LSTMslime water feedforward flocculant addition control model was proposed based on the multistrategy jointly improved Sparrow SearchAlgorithm (SSA). This model can determine the optimal dosage according to the condition of the infeeding during the thickening process. This new method can improve the intelligence of coal preparation plants and reduce the cost of washing. The scientific application of these research results will help to realize intelligent solid – liquid separation.