Research on intelligent regulation and control system and key technology of mine local ventilatio
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摘要: 目前矿井局部通风自动控制方式采用手动调节通风机频率，且风筒参数依靠人工采集，缺少准确、可靠的监测方法来反映通风状态，无法为准确调节风量提供依据。针对矿井局部通风智能化建设需求，提出了矿井局部通风智能调控系统，从系统组成、原理、功能等方面详细介绍了系统总体设计方案。根据多传感器实时监测数据，提出了局部通风参数计算方法及通风系统功耗分析方法，通过分析风筒阻力动态分布对风筒阻力和功耗异常进行研判和快速定位，结合监测参数对风量进行超前模拟，以确定最佳供需匹配调控方案。根据工作面瓦斯涌出规律，提出了基于瓦斯涌出量监测和通风机变频调风稀释瓦斯的智能调风方案，制定了5种局部通风机变频调控规则，实现了局部通风智能化供需匹配。采用贝叶斯网络算法对局部通风机和传感器设备健康状况进行诊断，利用粗糙集和遗传算法提取局部通风正常供风和故障状态的特征样本和前兆信息，基于支持向量机建立局部通风故障决策规则，建立局部通风异常研判和预警模型，实现了对局部通风状态及发展态势的研判及预警。以某矿掘进工作面局部通风为例验证了该系统通风参数计算方法，为局部通风异常研判与预警提供了基础数据。Abstract: At present, the automatic control method of mine local ventilation adopts manual adjustment of ventilator frequency, and relies on manual collection of air duct parameters, which lacks accurate and reliable monitoring methods to reflect the ventilation status and cannot provide a basis for accurate adjustment of air volume. In order to meet the demand for intelligent construction of local ventilation in mines, an intelligent regulation and control system for local ventilation in mines is proposed, and the overall design of the system is introduced in detail in terms of system composition, principle and function. Based on the real-time monitoring data of multiple sensors, a calculation method for local ventilation parameters and a ventilation system power consumption analysis method are proposed. By analyzing the dynamic distribution of air duct resistance, the air duct resistance and power consumption abnormalities are studied and quickly located. The monitoring parameters are used to simulate the air volume in advance to determine the best supply-demand matching control scheme. According to the law of gas emission from the working face, an intelligent air regulation scheme based on gas emission monitoring and ventilator frequency regulation gas diluting is proposed, and five local ventilator frequency conversion regulation rules are formulated to realize the intelligent matching of local ventilation supply and demand. The Bayesian network algorithm is used to diagnose the working status of local ventilators and sensor equipment, and the rough set and genetic algorithm are used to extract the characteristic samples and precursor information of the normal air supply and fault conditions of the local ventilation. The support vector machine is used to establish local ventilation fault decision-making rules, and local ventilation abnormality diagnosis and early warning model is established to realize the diagnosis and early warning of local ventilation status and development trend. Taking the local ventilation of a mine driving working face as an example, the calculation method of the ventilation parameters of the system has been verified, which provides the basic data for the diagnosis and early warning of local ventilation abnormalities.
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