Rate of Penetration Prediction in Steeply Dipping Coal Seams Using Data-Driven Modeling and Feature Engineering

To accurately predict the rate of penetration (ROP) for steeply inclined coal seam blocks, this paper proposes a data-driven ROP prediction method incorporating feature processing. First, Savitzky–Golay (SG) filtering is applied to key continuous monitoring parameters to mitigate the impact of noise on model training. Subsequently, features are comprehensively screened across linear, monotonic, and nonlinear dependency dimensions using the Pearson correlation coefficient, Spearman correlation coefficient, and mutual information evaluation, identifying structural parameters significantly contributing to ROP. Based on this, a Time Convolution Network (TCN)-Bidirectional Long Short-Term Memory (BiLSTM)-Attention prediction model is constructed: TCN extracts local temporal patterns, BiLSTM captures forward and backward dependencies, and the attention mechanism adapts weight distribution for information across different time steps. This architecture significantly enhances the model’s ability to capture complex operational variations and improves prediction accuracy. Experimental results demonstrate that compared to benchmark models such as BiLSTM, TCN-BiLSTM, and BiLSTM-Attention, our method achieves superior performance across all evaluation metrics and exhibits strong generalization capabilities on diverse operational datasets.