Research On Localization Method Of Fire Source In Mine Goaf

During the comprehensive mechanized sub-levil caving process, because of the large mining height, low advancing speed and long goaf compaction time, the remaining loose coal is oxidable to the leaking air, that results in the higher risk of coal spontaneous combustion. Harmful gas generated by the coal spontaneous combustion not only jeopardizes the workers’ health, but also easily to induce gas and dust explosion accidents, which cause serious resourses waste as well as adverse impact on caving processes and economy operations. The key point of fire prevention work in goaf, is to fast and precisely locate the spontaneous combustion source.This paper summarizes the present research status of detection and localization methods of fire source in goaf, and considers the temperature as the observing object, which is the most direct representation of the fire source, designs a goaf temperature sensing system. For the sake of precisely locating the fire source in goaf, the information of distributed mornitoring sources need to be processed. The fire source locating problem can be concluded as:(1) physical mathematics modeling of temperature fields distribution in goaf(direct problem);(2) fire source location deduction by inversly using the model mensioned above(inverse problem).In direct problem, the goaf temperature fields distribution theory is introduced. By analyzing the spontaneous combustion source distribution rule and heat conduction form, this paper designs the physical mathematics model in goaf and consequently analyzes the two dimensional transient heat conduction problem as well as researches the computational method of heat conduction direct problem. Finite difference method is used to directly simulate temperature fields distribution in goaf with matlab. This method provides the simulation data and computational method for the subsequent inverse problem.In inverse problem, the goaf fire source searching problem is classified as the source searching of heat conduction problem, which is to find a solution approaching the observed value. To find a solution to high non-linear ill-posed inverse problem, this paper uses least-square method to transform the inverse problem to the optimization problem. In view of the features of Particle Swarm Optimization(PSO) algorithm and its improved algorithm, which are strong global searching capability, high rate of the convergence, few parameters, easy adjustment, simple flow and easy implementation, this paper studies the PSO algorithm. Quantum-behaved Particle Swarm Optimization(QPSO) algorithm and its improved algorithm so as to solve the localization problem of fire source.Th simulation results shows that: both PSO and QPSO algorithms are capable to locate the fire source. In the view of stability, QPSO surpasses PSO algorithm. Aiming at the slow convergence in later period of QPSO algorithm, this paper proposes a compund QPSO algorithm which combines the natural evolution principle. The experiment results shows that, this algorithm is well adapted to detect fire source.The research on localization method of fire source in goaf provides technical support for coal mine fire prevention, assures the safe and efficient production in coal mine.