基于BLE的多通道煤钻屑瓦斯解吸指标测定仪研制

Development of a BLE-Based Multi-Channel Measuring Instrument for Gas Desorption Indices in Coal Drill Cuttings

  • 摘要: 钻屑瓦斯解吸指标是预测井巷揭煤、煤巷掘进突出危险性的重要参数,为解决现有测定仪存在信息化程度低、操作复杂、测定速度慢,难以匹配钻孔施工节奏等问题,提出一种基于低功耗蓝牙(BLE)的多通道煤钻屑瓦斯解吸指标测定仪,通过分布式架构实现一主多从的协同工作模式。系统由本安型手持终端和多个测定罐体组成,测定罐体内置压力传感器、称重模块及BLE通信功能,可独立完成△h?和K?值的自动化测量,并通过唯一MAC地址与手持终端进行数据交互。硬件系统设计采用ESP32S3主控芯片,结合触摸屏、高精度称重、电源管理、传感器接口等功能模块,实现测定流程的自动控制、数据实时存储与无线传输。仪器测试结果表明,系统待机功耗仅为362.6mW,工作功耗为706.7mW,可连续工作8小时以上。与传统仪器对比测试数据表明,△h?最大相对误差为7.94%,K?值最大相对误差为6.89%,具有较高准确性。该系统通过优化时间控制与煤样重量校正,显著降低了人为误差,提高了测定效率与可靠性,为煤矿突出危险性预测提供了高效的技术手段。

     

    Abstract: The gas desorption indices of coal drill cuttings are critical parameters for predicting outburst risks during coal seam exposure in shafts and roadway excavation. To address the limitations of existing instruments, such as low informatization, operational complexity, slow measurement speed, and incompatibility with drilling construction rhythms, this study proposes a multi-channel coal drill cuttings gas desorption index measuring instrument based on Bluetooth Low Energy (BLE). The system adopts a distributed architecture to implement a master-slave collaborative mode, consisting of an intrinsically safe handheld terminal and multiple measurement chambers. Each chamber integrates pressure sensors, a weighing module, and BLE communication, enabling automated measurements of Δh? and K? values independently. Data interaction with the handheld terminal is facilitated through unique MAC addresses. The hardware system, designed around the ESP32S3 microcontroller, incorporates a touchscreen, high-precision weighing, power management, and sensor interfaces to automate measurement workflows, achieve real-time data storage, and support wireless transmission. Test results indicate a standby power consumption of 306.2 mW and an operational power consumption of 706.7 mW, with continuous operation exceeding 10 hours using a 1600 mAh intrinsically safe battery. Comparative tests with traditional instruments reveal maximum relative errors of 7.94% for Δh? and 6.89% for K?, demonstrating high accuracy. By optimizing time control and coal sample weight calibration, the system significantly reduces human error while enhancing measurement efficiency and reliability, providing an advanced technical solution for predicting coal mine outburst risks.

     

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