Method for Predicting Separation Density in Heavy-medium Coal Preparation Based on MHAGRU Model
The separation density in the heavy medium coal preparation process is one of the key factors affecting coal preparation efficiency.Accurate prediction of separation density can help optimize the coal preparation process and improve product quality.This paper proposes a separation density prediction model that combines a multi-head attention mechanism with a Gated Recurrent Unit.Traditional separation density prediction methods struggle to handle the nonlinearity,multidimensionality,and time-series correlations inherent in the data.By introducing the multi-head attention mechanism,the proposed model can effectively capture dependencies across different time steps and feature dimensions,thereby enhancing prediction accuracy.Meanwhile,the gated recurrent unit's strong memory capability for long-term time-series data helps avoid the vanishing gradient problem.Experimental results show that,compared with traditional models,the multi-head attention mechanism with a gated recurrent unit-based separation density prediction method significantly improves prediction accuracy and robustness,making it suitable for practical production scenarios in separation density prediction.
separation densitytime seriesmulti-head attentiongated recurrent unit
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万方数据
