Coal Preparation Research Articles

Research on 1mm desliming before separation and high-efficiency separation to improve quality for steam coal

ABSTRACT For the established steam coal preparation plant that uses dense-medium shallow groove separation process, reducing the separation lower limit is conducive to improving its economic benefits. Reducing the lower limit to be consistent with the mesh size of the demedium screen, which is 1 mm, so that more coal can be separated without affecting the normal operation of the entire system. A process of 1 mm desliming before separation and dense-medium shallow groove separation was proposed in this paper. The kinematic characteristics and desliming performance of rigid screen surface and elastic screen surface were compared and analyzed. The separation performance of each size coal in the dense-medium shallow groove separator was investigated and the ratio of upward medium flow and horizontal medium flow was optimized. The industrial experiment results showed that the screening efficiency and desliming efficiency of elastic screen surface were increased by 4.31% and 4.37% respectively compared with rigid screen surface. The separation performance of each particle size coal in the dense-medium shallow groove separator became worse with the decrease of particle size. When the ratio of upward medium flow and horizontal medium flow was 8:2, the separation performance of 200–1 mm size coal was the best, and its probable error was 0.04 g/cm3. The optimized process could significantly decrease the slime content in separation system and improve clean coal yield.

Multi‐rate event‐triggered control with imperfect data for dense medium separation

AbstractThe stability control of suspension density is an important means to ensure the quality of fine coal in dense medium separation (DMS) process. This paper focuses on the tracking control issue of the dense medium suspension density in a non‐uniform sampling and networked control environment, and proposes a multi‐rate event trigger control design method. Firstly, lifting technique and multi‐rate identification method are adopted to establish the multi‐rate model. Secondly, a multi‐rate event‐triggered predictive control (MEPC) algorithm is proposed to reduce the waste of communication resources and ensure tracking performance. Furthermore, by using a Kalman filter to eliminate the effects of noise and developing a compensation mechanism to predict the lost data, a robust MEPC (RMEPC) algorithm is further proposed for imperfect data in the actual production process. Finally, the hardware‐in‐the‐loop simulation experiments with actual parameters of coal preparation plant have been carried out, showing the effectiveness of the proposed method.

Dense Medium Cyclone Separation of Fine Coal: A Discussion on the Separation Lower Limit

The separation of fine coal has been widely discussed in the coal preparation industry due to its high economic potential. Dense medium cyclone (DMC) is the most efficient equipment available for fine coal separation. However, the industrial application of DMC is far from satisfactory due to operational difficulties and maintenance. In this research, particle settling behavior in a dense medium cyclone was analyzed for improved separation. The calculation result about feed pressure and separation lower limit, which fits the experimental data well, might be a guidance for industrial DMC design and operation. According to the calculation result, it is highly recommended that the separation lower limit be set at 0.2 mm rather than 0.1 mm, because the feed pressure head required for the latter (50 D) is three times higher than the former (15 D).

Low-Temperature Solvothermal Method for Coal Tar-Based Carbonaceous Mesophase Preparation and Its Excellent Performance

Low-Temperature Solvothermal Method for Coal Tar-Based Carbonaceous Mesophase Preparation and Its Excellent Performance

Optimization of asphalt cold recycling containing ordinary and waste additives based on life cycle assessment considering the road traffic level- case study: Coal preparation plant

In recent years, extensive research has been conducted on the use of second-hand or waste materials in the road pavement construction layers. Most of these studies evaluated the mechanical properties of materials and emphasized the environmental benefits of using waste materials for use in road construction projects, regardless of the environmental consequences. The present study intends to compare the life cycle of waste materials with standard materials and determine the optimal conditions for the selection of waste materials based on environmental criteria by considering life cycle assessment (LCA) and several environmental indicators. The analysis considered emulsified cold recycled asphalt with different types of activator fillers including cement, coal waste+lime and coal waste ash+lime. Also, the recycled pavement was compared with that of conventional asphalt pavement in this environmental assessment. The LCA analysis was performed for pavements with design traffic of 0.1–100 million equivalent single axle loads (ESALs). In order to design the pavements structure, 96 resilient modulus tests were done for 32 recycled mixes. Environmental indicators under study included energy consumption, abiotic depletion potential (ADP), global warming potential (GWP), acidification potential (AP), and eutrophication potential (EP). The collected data for the studied functional unit was undergone an environmental analysis in SimaPro software. Apart from a standard case, parametric study was accomplished to analyze the effect of activator additive content. Accordingly, it was concluded that for the standard case, the traffic level or in other words the highway grade is of the main importance criterion in choosing the rehabilitation method based on environmental indicators. Emulsified cold recycled pavement with waste additives can be environmentally justified only in projects with low to medium traffic levels of less than 5–10 ESALs. For projects with medium to high traffic levels, only the ordinary recycling method with cement was comparable to conventional reconstruction.

Mode of Occurrence and Distribution of Critical Metal Lithium and Other Trace Elements during Coal Preparation from Jiashun High-Sulfur Coal in Guizhou Province, China

During the Late Permian period, the coal from the Jiashun Mine in Xingren City, Guizhou Province, China, is characterized by a high sulfur (5.84%) and lithium (Li) (94.5 μg/g) content. Lithium is a critical metal in the context of global energy transition. Considering the importance of environmental protection and resource utilization, the mode of occurrence and distribution of trace elements in different coal preparation products were investigated. The obtained results indicated the following: (1) The minerals in Jiashun coal were mainly composed of veined and epigenetic pyrite, quartz, and kaolinite. Most of the minerals could be effectively removed from the cleaned coal through gravity separation. (2) The mode of occurrence of rare earth elements and yttrium (REY) in the coal was relatively complex, and they were mainly found in middlings from gravity separation and in flotation tailings. (3) The mode of occurrence of the trace elements in the coal significantly influenced their removal rate. The removal rate was significantly higher for trace elements removed through gravity separation compared to those removed using flotation. The trace elements mainly found in fine-grained minerals wrapped by organic matter or combined with organic portions had better removal results through flotation. The trace elements that occurred both in the minerals and organic matters had low removal rates (<25%) using two coal preparation methods. (4) Li was more enriched in the middlings from gravity separation (98 μg/g) and in flotation tailings (102 μg/g), reaching the marginal grade (80 μg/g) of Li in coal. (5) Li in Jiashun coal may be derived from intermediate-felsic rocks at the top of the Kangdian Upland and late hydrothermal solutions.

Study on the wet dust collection mechanism of metal-based filter screens and the effect of its inclination angle on dust removal performance

The wet filter dust collector (WFDC) is widely used in dust control in various industries, but its dust collection mechanism is unclear, and the influence of the metal-based filter screen (MBFS) inclination angle on the dust removal performance is still uncertain. In this paper, the liquid film crushing and collecting dust mechanism of MBFS and the influence of its inclination angle on the dust removal performance of WFDC are studied. The results show that with the increase of inclination angle (30° to 150°), the average pressure drop of WFDC first decreases and then increases, the average dust concentration first increases and then decreases, and the MBFS dust removal performance is optimal at 90°. The optimized WFDC was applied on-site in a coal preparation plant, which exceeded the dust suppression efficiencies of 91 % and 88 % for total dust and respirable dust, respectively. The research results are expected to provide theoretical guidance for WFDC design and improvement.

Corrosion and coating defect assessment of coal handling and preparation plants (CHPP) using an ensemble of deep convolutional neural networks and decision-level data fusion

Corrosion and coating defect assessment of coal handling and preparation plants (CHPP) using an ensemble of deep convolutional neural networks and decision-level data fusion

Structure design and separation test of a conical AC magnetic separator for recovering magnetite powder

ABSTRACT In the dense-medium coal preparation process magnetite powder is used, however, due to poor recovery of the magnetite, the consumption of magnetite increases which ultimately reflects in the economics. In order to continuously and efficiently recover the magnetite, a novel three-phase conical magnetic separator based on the principle of an alternating magnetic field generated by a three-phase linear motor, was developed. The distribution law of the magnetic field in the separation chamber was studied, and the magnetic separator’s basic structure and parameters were determined. By varying the feed rate, coil current, coil turns, and particle size, magnetic separation experiments were conducted and compared with traditional magnetic separators. The results confirmed the excellent sorting performance of the conical AC magnetic separator.

Research of the coal surface wettability by filtering the liquid through a porous layer

Many technological processes occurred upon the extraction and primary processing of coal (dust suppression, grouting, hydraulic fracturing, wet enrichment, etc.) depend on the wettability of the coal surface, determined by the physicochemical properties of the interacting media. The filtration properties of a fractured-porous coal massif significantly depend on the wettability of the surface. We present the results of studying the wettability of the coal surface with water and its filtration through a layer of coal powder. It is shown that the increased humidity of coal contributes to an increase in the wetting and filtration properties of the coal layer in relation to water due to the creation of a hydrate shell at the contact surface. It is revealed that the method of coal sample preparation significantly affects the functional composition and hydrophilicity of the outer surface of coal particles. Grinding coal in the presence of oxygen in air contributes to the formation of polar oxygen groups (hydroxyl, carboxyl) on the surface of coal particles, which leads to an increase in the hydrophilicity and filtration properties of coal. The results obtained can be used to predict the wettability of coals with process fluids, to improve technologies for mining, enrichment and processing of coals.

Ecological Responses of Soil Microbial Communities to Heavy Metal Stress in a Coal-Based Industrial Region in China.

Soil microorganisms play vital roles in ecosystem functions, and soil microbial communities might be affected by heavy metal contamination caused by the anthropogenic activities associated with the coal-based industry. This study explored the effects of heavy metal contamination on soil bacterial and fungal communities surrounding different coal-based industrial fields (the coal mining industry, coal preparation industry, coal-based chemical industry, and coal-fired power industry) in Shanxi province, North China. Moreover, soil samples from farmland and parks away from all the industrial plants were collected as references. The results showed that the concentrations of most heavy metals were greater than the local background values, particularly for arsenic (As), lead (Pb), cadmium (Cd), and mercury (Hg). There were significant differences in soil cellulase and alkaline phosphatase activities among sampling fields. The composition, diversity, and abundance of soil microbial communities among all sampling fields were significantly different, particularly for the fungal community. Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria were the predominant bacterial phyla, while Ascomycota, Mortierellomycota, and Basidiomycota dominated the studied fungal community in this coal-based industrially intensive region. A redundancy analysis, variance partitioning analysis, and Spearman correlation analysis revealed that the soil microbial community structure was significantly affected by Cd, total carbon, total nitrogen, and alkaline phosphatase activity. This study profiles the basic features of the soil physicochemical properties, the multiple heavy metal concentrations, and the microbial communities in a coal-based industrial region in North China.

Operating performance assessment method for industrial process with slowness principle-based LSTM network

Timely and accurate performance assessment and non-optimal regulation of industrial processes can effectively guarantee product quality. Most industrial processes are highly nonlinear and dynamic, so long short-term memory (LSTM) network is suitable for industrial performance assessment. However, in the network learning, the typical LSTM network focuses on the representation learning of input variables, lacks the representation of comprehensive economic indexes (CEI), and cannot selectively learn essential features, which increases the computational burden and easily mixes redundant information. Thus, a supervised slow feature analysis (SFA)-based LSTM (SSFALSTM) network is proposed for industrial operating performance assessment. By utilizing CEI information and SFA constraints, the network is guided to simultaneously learn features related to CEI and slow-changing features that reflect the inherent dynamics of the process. Further, cascade performance recognition model to construct the complete performance assessment framework. For the non-optimal performance, a reconstruction-based contribution plot method is proposed to identify the main cause variables and guide adjustment operations. Finally, the effectiveness of the proposed method is validated on the dense medium coal preparation process.

Effect of coal preparation on atmospheric emissions of trace elements and their cross-media environmental risk from Chinese coal-fired industrial boilers

Effect of coal preparation on atmospheric emissions of trace elements and their cross-media environmental risk from Chinese coal-fired industrial boilers

Product quality prediction in dense medium coal preparation process based on recurrent neural network

ABSTRACT Dense medium separation is one of the most widely used methods for coal beneficiation, and an accurate estimation of product coal ash is the prerequisites to maximize the resource value. In this work, data pre-cleaning and multi-layer feature learning process of deep learning are explored to predict the product quality more accurately. The research results show that, the deep learning model considering the time series characteristics of process variables can predict the change of product quality better, which could be attributed to the fact that the full considers time series relationship between process data and product quality. Specifically, the prediction value obtained by using a single-layer, multi-unit recurrent neural network model (GRU) is improved with Root Mean Square Error (RMSE) at 0.061, and the RMSE for multi-step prediction (10 min) is 0.262. What’s more, the prediction error gradually decreases and tends to be smooth with increasing time step. It is suggested that the time step used for data reconstruction should be the time of raw coal coming through to the clean coal belt. This work provides an innovative idea for improving online prediction of coal ash and is of significant importance for precise control of dense medium separation in application.

Dynamic characteristics analysis of a novel vibrating screen based on electromechanical coupling simulation

The vibrating screen with statically indeterminate mesh beam structure (VSSIMBS) is a novel type of large vibrating screen, which is widely used in coal preparation plants due to its high strength and processing capacity. In this study, the VSSIMBS motion differential equations were obtained via on Lagrange equation, and the dynamic vibration system model was established. The eccentric block force conditions during the operation were analyzed, and the stable vibrating screen operational conditions were obtained. The electromechanical coupling simulation model of VSSIMBS was built in Simulink, allowing frequency control and vibration synchronization experiments. Experimental frequency control results have shown that when the output characteristics of both motors are either the same or similar, synchronous movement can be achieved. Regarding vibration synchronization, experiment results have shown that when two motors are in synchronous motion, and the power supply of one motor is cut off, they still can operate synchronously. Thus, the experimental results obtained in this study provide a theoretical reference for improving the motion stability of large vibrating screens.

Assessment of process operating performance with supervised probabilistic slow feature analysis

Assessment of operating performance, which is adopted to assess optimal degree for complex industrial process, has been increasing concerned in the last few years. In this study, a supervised probabilistic slow feature analysis (SPSFA) is proposed to assess the optimal degree of process performance by deep mining process variables information on the basis of the probability distribution model. First, the slow features (SFs) are modeled in state–space form as a serial correlation feature to effectively represent the potential variation of performance, and the serial correlation hiding in the performance features reveals the potential changes of performance, which helps improve cognitive depth and assessment accuracy of performance. Then, multi-order dynamic information of SFs is further combined to design the multi-order dynamic index, which accurately and timely depict the dynamic changes of the process. Hence, the proposed method provides an explicit representation of underlying driving forces of performance variations and meaningful physical interpretation of each stage from optimal to non-optimal. In addition, a sparse learning-based non-optimal factor identification method is proposed, which identifies non-optimal factor variables by reconstructing the online estimation of the Kalman filter and using Group Lasso. The feasibility and effectiveness of the proposed method is validated by an actual dense medium coal preparation process and a typical Tennessee Eastman process.

Sources and succession of microorganisms in industrial coal flotation system

The water system in a coal preparation plant harbors a variety of microorganisms, which may pose a great effect on coal recovery, however, usually be neglected. In this study, high-throughput sequencing and the growth of indigenous microorganisms in coal slurry containing organic reagents were conducted to trace the sources and succession of the microorganisms in a running plant, and the reason for microbial community succession was deeply explained. The microbiological analysis results found that the microorganisms in the coal slurry are assigned to 34 phyla and 98 genera, all source from the coal seam. Proteobacteria, Acidobacteria, Actinobacteria, and Crenachaeota are the dominant microorganisms at the phylum level, and the abundance of Proteobacteria, Bacteroidetes, and Nitrospirae in the flotation feeding decrease, and the abundance of GAL15 increase compared to the raw coal and mine water samples. Environmental factors are important reasons for microbial community succession. Organic reagents such as collectors and frothers commonly used in coal flotation processes can change the growth environment of microorganisms. The strain QX4, isolated from raw coal and identified as Bacillus sp. can accelerate growth in coal slurry containing organic reagents. Compared with raw coal, the content of chemical oxygen demand (COD) and dissolved oxygen (DO) in the flotation feeding increase by 340 mg/l and 5 mg/l respectively, which inhibit the metabolism of Nitrospirae, Bacteroides, Proteobacteria, and Actinobacteria, while improving the metabolism of GAL15, and change the microbial community and promote the microbial succession in the coal flotation system. Therefore, indigenous microorganisms from coal seam can grow and succeed in the coal flotation system, which has a potential effect on coal flotation efficiency.

Coal Gangue Recognition during Coal Preparation Using an Adaptive Boosting Algorithm

The recognition of coal and gangue is the premise and foundation of coal gangue intelligent sorting. Adaptive boosting (AdaBoost) algorithm-based coal gangue identification has not been studied in depth. This paper proposed a coal gangue image recognition algorithm and a strong classifier based on the AdaBoost algorithm with a genetic algorithm (GA)-optimized support vector machine (SVM). One thousand coal gangue images were collected on-site and expanded to five thousand via rotation and exposure adjustment. The 12 gray-level gradient co-occurrence matrix texture features of the images were extracted to construct a feature vector, establishing the training dataset and test dataset. Selection of the SVM kernel function, the GA optimization parameter setting, and the base classifier number was discussed. The coal gangue image recognition effects of the AdaB-GA-SVM classifier and the other strong classifiers with different base SVM classifiers were investigated. The results indicated that the recognition accuracy of GA-SVM was the best when the kernel function of SVM was RBF and the population number, crossover probability, and mutation probability were 80, 0.9, and 0.005, respectively. The AdaB-GA-SVM classifier has excellent identification and effective classification performance with the highest accuracy of 95%, a precision rate of 92.8%, recall rate of 97.3%, and KS values of 0.79.

Contaminants from a former Croatian coal sludge dictate the structure of microbiota in the estuarine (Raša Bay) sediment and soil.

Croatian superhigh-organic-sulfur Raša coal had been mined for nearly 400 years. The release of hazardous trace elements (HTEs) and toxic organic pollutants (TOPs) into the local environment by coal mining, preparation, and combustion activities has resulted in pollution. In this study, the diversity and composition of microbial communities in estuarine sediment and soil samples as well as community function responses to the pollutants were investigated. The results showed that PAH degradation does occur following 60 years of natural attenuation, the location is still heavily polluted by polycyclic aromatic hydrocarbons (PAHs) and HTEs. Microbial analyses have shown that high concentrations of PAHs have reduced the diversity and abundance of microbial communities. The pollution exerted an adverse, long-term impact on the microbial community structure and function in the brackish aquatic ecosystem. Microorganisms associated with the degradation of PAHs and sulfur-containing compounds have been enriched although the diversity and abundance of the microbial community have reduced. Fungi which are believed to be the main PAH degrader may play an important role initially, but the activity remains lower thereafter. It is the high concentrations of coal-derived PAHs, rather than HTEs, that have reduced the diversity and abundance of microbial communities and shaped the structure of the local microbiota. This study could provide a basis for the monitoring and restoration of ecosystems impacted by coal mining activities considering the expected decommission of a large number of coal plants on a global scale in the coming years due to growing global climate change concerns.

The distribution law of coarse particles in coal flotation and its density-size partition surface model

ABSTRACT In the process of coal preparation, large particles are often misplaced into the flotation feedings due to poor classification, and thus bring about negative effects on flotation results. In this paper, batch flotation tests of 0.5–2.5 mm were carried out at different reagent dosage to quantitatively analyze the effect of particle size and density on flotation. Flotation partition curves in corresponding conditions were plotted based on sizing analysis and float-and-sink tests of flotation products. Generalized skew-normal distribution model based on quartile was used to fit the experimental data and the variation rules of separation density, probable error and skewness, etc. with particle size and reagents were obtained, and the density-size partition surface mathematical model was established. The results show that floating ratio decreases with density and particle size. The separation density and probable error both decreases with particle size. The values of skewness were negative and tended to 0 with the decreasing of particle size. As the dosage of reagents increased, the separation density increased, the probable error increased slightly, and the difference of absolute values of skewness decreased. The research results put forward the theoretical instruction of expanding the upper limit of floatable size of particles and improving flotation efficiency.