Abstract
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.
Highlights
Dense medium separation is one of the most crucial coal separation methods because of its high separation accuracy, strong adaptability to raw coal, and especially it is easy for automatic control [1–3]
This study proposed a suspension density prediction method based on Long Short-Term not considered
The data used were obtained from the historical production data of the dense medium separation system of a coal preparation plant in Inner Mongolia, China
Summary
Dense medium separation is one of the most crucial coal separation methods because of its high separation accuracy, strong adaptability to raw coal, and especially it is easy for automatic control [1–3]. 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 [4–7]. Of suspension density prediction, some separator, especially on the influence of operating structural parameters, and flowscholars field on have studied the relationship raw coal ash density and suspension to predict real-time the separation efficiency [8–12]. Crucial the relationship raw coal ash density to predict real-time influence on densesuch medium separation, too. Yield stress, and particle size,used havethe a crucial influence dense online to separation, control the dense medium this separation. Memory coal(product) ash, suspension density, a model on the established for (LSTM) to solve theand time-lag problem. Ash, and suspension density, a model based on the LSTM method was established for predicting the suspension density of the dense medium suspension system
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