An optimized LSTM model for clean coal ash content prediction in dense medium separation scenarios on the basis of the dual decomposition method DOI
Yongqi Liu,

Yuping Fan,

Xiaomin Ma

и другие.

International Journal of Coal Preparation and Utilization, Год журнала: 2024, Номер unknown, С. 1 - 26

Опубликована: Дек. 10, 2024

To increase the accuracy of clean coal ash content prediction during dense medium separation process and address time lag issue encountered when measuring content, a model based on WaOA-VMD-SGMD-WaOA-LSTM was proposed. The adopts dual decomposition techniques optimized Variational Mode Decomposition (VMD) Symplectic Geometric (SGMD), which can completely decompose original data, uses Walrus optimization algorithm (WaOA) to optimize hyperparameters Long Short-Term Memory (LSTM) model. In construction, characteristic data ore (𝑍2), raw (𝑍3), heavy mesoporous cyclone pressure (𝑍4), suspension density (𝑍5), magnetic (𝑍6) were combined with decomposed cleaned grouping S-IMF0~S-IMFn, CO-IMF1, CO-IMF2 as input variables construct multiple LSTM models. Finally, value is superimposed realize content. Based industrial preparation plant in Shanxi, China, results show that coefficient determination (R2) 0.9974. After adding secondary technology, average absolute error reduced by 60.99% compared single strategy.

Язык: Английский

An optimized LSTM model for clean coal ash content prediction in dense medium separation scenarios on the basis of the dual decomposition method DOI
Yongqi Liu,

Yuping Fan,

Xiaomin Ma

и другие.

International Journal of Coal Preparation and Utilization, Год журнала: 2024, Номер unknown, С. 1 - 26

Опубликована: Дек. 10, 2024

To increase the accuracy of clean coal ash content prediction during dense medium separation process and address time lag issue encountered when measuring content, a model based on WaOA-VMD-SGMD-WaOA-LSTM was proposed. The adopts dual decomposition techniques optimized Variational Mode Decomposition (VMD) Symplectic Geometric (SGMD), which can completely decompose original data, uses Walrus optimization algorithm (WaOA) to optimize hyperparameters Long Short-Term Memory (LSTM) model. In construction, characteristic data ore (𝑍2), raw (𝑍3), heavy mesoporous cyclone pressure (𝑍4), suspension density (𝑍5), magnetic (𝑍6) were combined with decomposed cleaned grouping S-IMF0~S-IMFn, CO-IMF1, CO-IMF2 as input variables construct multiple LSTM models. Finally, value is superimposed realize content. Based industrial preparation plant in Shanxi, China, results show that coefficient determination (R2) 0.9974. After adding secondary technology, average absolute error reduced by 60.99% compared single strategy.

Язык: Английский

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