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戴剑博,王忠宾,张 琰,等. 煤矿井下钻进速度影响因素及其智能预测方法研究[J]. 煤炭科学技术,2024,52(7):209−221. doi: 10.12438/cst.2023-1461
引用本文: 戴剑博,王忠宾,张 琰,等. 煤矿井下钻进速度影响因素及其智能预测方法研究[J]. 煤炭科学技术,2024,52(7):209−221. doi: 10.12438/cst.2023-1461
DAI Jianbo,WANG Zhongbin,ZHANG Yan,et al. Research on influencing factors for drilling rate in coal mines and its intelligent prediction methods[J]. Coal Science and Technology,2024,52(7):209−221. doi: 10.12438/cst.2023-1461
Citation: DAI Jianbo,WANG Zhongbin,ZHANG Yan,et al. Research on influencing factors for drilling rate in coal mines and its intelligent prediction methods[J]. Coal Science and Technology,2024,52(7):209−221. doi: 10.12438/cst.2023-1461

煤矿井下钻进速度影响因素及其智能预测方法研究

Research on influencing factors for drilling rate in coal mines and its intelligent prediction methods

  • 摘要: 在煤矿井下钻探领域,钻进速度(DR)是评估钻探作业最有效的指标之一,钻速预测是实现煤矿钻进智能化的前提条件,对于优化钻机钻进参数、降低作业成本、实现安全高效钻探具有重要意义。为此,提出煤矿井下钻进速度影响因素及其智能预测方法研究,探索基于钻压、转速、扭矩以及钻进深度等少量钻机参数采用机器学习算法实现钻进速度精准预测。首先通过实验室微钻试验,深入分析煤岩力学性能、钻压、转速和钻进深度对扭矩、钻进速度影响规律。研究结果显示,在煤矿井下钻进过程中,随着钻进压力增大,钻进速度呈逐渐升高趋势,在较高的转速条件下钻进压力对钻进速度影响更加明显,转速增加有利于提高钻进速度,但转速对硬度较低的煤层钻进速度影响更为显著;然后,根据煤矿井下防冲钻孔现场数据,采用K–近邻(KNN)、支持向量回归(SVR)和随机森林回归(RFR)3种不同的机器学习算法建立钻进速度预测模型,并结合粒子群算法(PSO)对3种模型超参数进行优化,最后对比分析PSO–KNN,PSO–SVR和PSO–RFR三种钻进速度预测模型预测结果。研究结果表明,PSO–RFR模型准确性最好,决定系数R2高达0.963,均方误差MSE仅有29.742,而PSO–SVR模型鲁棒性最好,在对抗攻击后评价指标变化率最小。本文研究有助于实现煤矿井下钻进速度的精准预测,为煤矿井下智能钻进参数优化提供理论支撑。

     

    Abstract: In the field of drilling in coal mine underground, drilling rate (DR) is one of the most effective indicators for assessing drilling operations. Accurate prediction of DR is a prerequisite for the realization of intelligent drilling in coal mines, which is of great significance for optimizing drilling parameters, reducing operational costs, and ensuring safe and efficient drilling. In the present research, the influence of drilling parameters on DR are investigated and the intelligent prediction methods are developed to achieve accurate DR predictions with different machine learning algorithms based on several drilling parameters, including weight on bit, rotation speed, torque, and drilling depth. Initially, micro-drilling experiments are conducted to analyze the impact of coal rock mechanical properties, weight on bit, rotation speed, and drilling depth on torque and drilling rate. The experimental results indicate that during the underground coal mining drilling process, drilling rate gradually rises with the increasing weight on bit. Under higher rotation speed conditions, the influence of weight on bit on drilling speed becomes more pronounced. Increasing the rotational speed is advantageous for improving drilling speed, but the impact of rotational speed on drilling speed is more significant in softer coal seams. Subsequently, three different machine learning algorithms, namely K–Nearest Neighbors (KNN), Support Vector Regression (SVR), and Random Forest Regression (RFR), are used to build drilling rate prediction models based on on-site drilling data from underground coal mining, and the hyperparameters of those models are optimized with Particle Swarm Optimization (PSO) method. Finally, the prediction results of three drilling rate models, PSO–KNN, PSO–SVR, and PSO–RFR, are analyzed comparatively. The results show that PSO–RFR model offers the highest accuracy, with R2 of 0.963, MSE of 29.742. On the other hand, the PSO–SVR model exhibits the best robustness, with minimal changes in evaluation metrics after withstanding adversarial attacks. This study is beneficial for the realization of precise DR prediction, providing theoretical support for the optimization of intelligent drilling parameters in underground coal mining operations.

     

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