Time Series Prediction Method of Clean Coal Ash Content in Dense Medium Separation Based on the Improved EMD-LSTM Model

Time Series Prediction Method of Clean Coal Ash Content in Dense Medium Separation Based on the Improved EMD-LSTM Model

Real-time ash content control in dense medium coal separation is challenged by time lags between detection and density adjustment, along with nonlinear/noisy signals. This study proposes a hybrid model for clean coal ash content in dense medium separation by integrating empirical mode decomposition,...

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Bibliographic Details
Main Authors: Kai Cheng, Xiaokang Zhang, Keping Zhou, Chenao Zhou, Jielin Li, Chun Yang, Yurong Guo, Ranfeng Wang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Big Data and Cognitive Computing
Subjects:
dense medium separation
empirical mode decomposition (EMD)
long short-term memory (LSTM) neural network
clean coal ash prediction
vector value of the radial difference (VVRD)
trend consistency evaluation
Online Access:https://www.mdpi.com/2504-2289/9/6/159
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