CHENG Xiaozhi, WANG Kai, HAO Haiqing, et al. Research on intelligent regulation and control system and key technology of mine local ventilatio[J]. Industry and Mine Automation, 2021, 47(9): 18-24.. doi: 10.13272/j.issn.1671-251x.17825
Citation: CHENG Xiaozhi, WANG Kai, HAO Haiqing, et al. Research on intelligent regulation and control system and key technology of mine local ventilatio[J]. Industry and Mine Automation, 2021, 47(9): 18-24.. doi: 10.13272/j.issn.1671-251x.17825

Research on intelligent regulation and control system and key technology of mine local ventilatio

doi: 10.13272/j.issn.1671-251x.17825
  • Publish Date: 2021-09-20
  • 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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      沈阳化工大学材料科学与工程学院 沈阳 110142

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