Abstract
The coal mine pump station liquid supply system adapts to changes in the main pressure for supply, but under conditions of long-distance liquid supply beyond 2000 m, there is a problem with the difficult system response to the hydraulic support liquid demand, resulting in significant pressure fluctuations in the hydraulic system and serious performance degradation of the supply system. To solve this problem, a long-distance intelligent liquid supply method based on liquid demand prediction for coal mining faces is proposed to ensure stable liquid supply. The method integrates parameters such as the mining machine’s position, the hydraulic support’s column pressure before and after the mining machine, the forward travel distance, and action codes to construct a feature vector. Based on the Transformer Model, a Liquid Demand Prediction Model for hydraulic support is established. Attention mechanisms are combined to solve the problem of time prediction for long sequences. The model is validated using real measured data from a hydraulic system in a coal mine, and the experimental results show that the model can accurately predict liquid demand and achieve long-distance intelligent liquid supply for coal mining faced by combining pump station control strategies.
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