Research on the intelligent control of the dense medium separation process in coal preparation plant

Wang, Zhangguo; Kuang, Yali; Deng, Jianjun; Wang, Guanghui; Ji, Li

doi:10.1016/j.minpro.2015.05.001

Cited by 21 publications

(10 citation statements)

References 11 publications

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“…We then determined the coal preparation process by the plants’ main raw and auxiliary materials. Dense medium separation generally uses diesel oil or magnetic powder as a dense medium . If the raw material of coal preparation plants contains these dense media, the plant applies a heavy medium process.…”

Section: Methodssupporting

confidence: 87%

“…Dense medium separation generally uses diesel oil or magnetic powder as a dense medium. 17 If the raw material of coal preparation plants contains these dense media, the plant applies a heavy medium process. If water was used or coal slime was one of the products and no dense medium was used, the coal preparation plant was determined to follow the jig separation process, as it consumes water but no dense medium.…”

Section: Methodsmentioning

confidence: 99%

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Effect of the Coal Preparation Process on Mercury Flows and Emissions in Coal Combustion Systems

Ouyang

Liu

et al. 2021

Environ. Sci. Technol.

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Coal preparation is effective in controlling primary mercury emissions in coal combustion systems; however, the combustion of coal preparation byproducts may cause secondary emissions. The inconsistent coal preparation statistics, unclear mercury distribution characteristics during coal preparation, and limited information regarding the byproduct utilization pathways lead to great uncertainty in the evaluation of the effect of coal preparation in China. This study elucidated the mercury distribution in coal preparation based on the activity levels of 2886 coal preparation plants, coal mercury content database, tested mercury distribution factors of typical plants, and then traced the mercury flows and emissions in the downstream sectors using a cross-industry mercury flow model. We found that coal preparation altered the mercury flows by reducing 68 tonnes of mercury to sectors such as coking and increasing the flows to byproduct utilization sectors. Combusting cleaned coal rather than raw coal reduced the mercury emissions by 47 tonnes; however, this was offset by secondary mercury emissions. Coal gangue spontaneous combustion and the cement kiln coprocessing process were dominant secondary emitters. Our results highlight the necessity of whole-process emission control of atmospheric mercury based on flow maps. Future comprehensive utilization of wastes in China should fully evaluate the potential secondary mercury emissions.

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Section: Methodssupporting

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Effect of the Coal Preparation Process on Mercury Flows and Emissions in Coal Combustion Systems

Ouyang

Liu

et al. 2021

Environ. Sci. Technol.

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“…Heavy medium separation is one of the effective density separation methods in removing impurities and reducing ash from coal. − Considering the characteristics of the raw coals from four places and the development of coal preparation technology, dense medium separation was chosen as the main process for the four plants, though there are some differences among these workflows.…”

Section: Statistical Law Of Mercury Removal During Coal Preparation P...mentioning

confidence: 99%

Mercury in Chinese Coals: Modes of Occurrence and its Removal Statistical Laws during Coal Separation

et al. 2016

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As one of the dangerous trace elements in coal, mercury has become an advanced research hotspot in coal purification. The content of ash, sulfur, and mercury of raw coals from four high-Hg coal preparation plants located in the main production areas of China was tested. To distinguish the connection between mercury content and the content of ash or sulfur, person correlation analysis and regression correlation analysis were performed by SPSS. The modes of occurrence of mercury in coal samples of these four plants were studied by sequential chemical extraction procedure (SCEP). The laws of mercury removal during coal preparation were researched by analyzing the mercury contents of feedings and products from the four plants. Results showed that mercury in coal was significantly associated with inorganic constituents or sulfur or both of them. The sulfide associated form is the dominant mode of occurrence of mercury in coals. Removal statistical laws of mercury during the process of preparation can be summarized as follows: (1) mercury in coal tends to concentrate in the fine fraction; (2) dense medium separation has a more obvious effect on mercury removal than flotation; (3) qualified clean coal products with up to standard mercury content are difficult to obtain by a conventional gravity separation or flotation process. This work facilitates understanding of the modes of mercury occurrence in other Chinese coals, and provides a foundation for removal of mercury in clean coal technology.

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“…Processes 2020, 8, x FOR PEER REVIEW 2 of 12 simultaneously collected real-time raw coal information, product information, and suspension density data are not correlated with each other, and a large time-lag exists [4][5][6][7]. Recent studies about the dense medium separation have focused on the flow characteristics in the separator, especially on the influence of operating parameters, structural parameters, and flow field on the separation efficiency [8][9][10][11][12].…”

Section: Processmentioning

confidence: 99%

“…The use of product ash data obtained approximately one or two hours prior provides uncertainty and lag. Moreover, because the dense medium separation is a flow process, the simultaneously collected real-time raw coal information, product information, and suspension density data are not correlated with each other, and a large time-lag exists [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

et al. 2020

Self Cite

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In the dense medium separation system of coal preparation plant, the fluctuation of raw coal ash and lag of suspension density adjustment often causes the instability of product quality. To solve this problem, this study established a suspension density prediction model for the dense medium separation system based on Long Short-Term Memory (LSTM). First, the historical data in the dense medium separation system of a coal preparation plant were collected and preprocessed. Moving average and cubic exponential smoothing methods were used to replace abnormal data and to fill in the missing data, respectively. Second, a LSTM network was used to construct the density prediction model, and the optimal number of time steps, hidden layers, and nodes was determined. Finally, the model was employed on a testing set for prediction, and a Back-Propagation (BP) network without a time series was used for comparison. Root Mean Squared Error (RMSE) were the minimum when the number of the hidden layers, nodes, and time steps was 6, 12, and 5, respectively. In this case, the RMSE and Mean Absolute Percent Error (MAPE) of the LSTM method were 0.009 and 0.007, respectively, while those of the BP method were 0.019 and 0.015, respectively. Therefore, the model established using LSTM can be used to accurately predict the suspension density of the dense medium separation system.

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Research on the intelligent control of the dense medium separation process in coal preparation plant

Cited by 21 publications

References 11 publications

Effect of the Coal Preparation Process on Mercury Flows and Emissions in Coal Combustion Systems

Effect of the Coal Preparation Process on Mercury Flows and Emissions in Coal Combustion Systems

Mercury in Chinese Coals: Modes of Occurrence and its Removal Statistical Laws during Coal Separation

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

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