Coal dense medium separation dynamic and steady-state modelling for process control

Meyer, Ewald Jonathan; Craig, I.K.

doi:10.1016/j.mineng.2014.05.018

Cited by 14 publications

(6 citation statements)

References 8 publications

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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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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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“…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

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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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“…The derivation is based on Meyer and Craig (2011) and Meyer and Craig (2014). A model fit was performed using the partition curve from equation 1.…”

Section: Partition Curvementioning

confidence: 99%

Dynamic model for a dense medium drum separator in coal beneficiation

Meyer

Craig

2015

Minerals Engineering

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Dense medium drum (DMD) separators are unit processes that are typically used to beneficiate coal, iron ore and other minerals by making use of density separation. Some coal dense medium separation plants typically include a DMD separator. The operational management of this unit process is often limited to localised control of medium density and feed mass flow rate. Dynamic models for coal dense medium separation have been developed by the authors with the intention of using them for dynamic control.A suitable dynamic model for a DMD separator could not been found in the available literature. This paper shows how the dynamic model for a dense medium cyclone has been applied to a DMD separator. The model parameters were determined and the performance of the model is evaluated using actual plant data from a Wemco drum. An additional aspect employing coal washability and drum partitioning behaviour is applied to estimate the grade of the product for model grade simulation and validation.

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Coal dense medium separation dynamic and steady-state modelling for process control

Cited by 14 publications

References 8 publications

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

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

Dynamic model for a dense medium drum separator in coal beneficiation

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