Analysis On Mining Safety Mechanism Of Underground Metal Mine And Its Disaster Intelligent Prediction

The metal market of China has entered into international market since China became one of WTO and rapid devolepment of economic globalization, and influences from changes of metal mineral resources market the world and development of international metal mineral resources mining are enormous, therefore, the metal mineral resources industry in China will encounter a very flinty challenge. Obviously, it is a most vital and urgently solved problem for the metal mineral resources industry how to reduce accident loss, how to lower safety management costs and production costs and how to improve the economy benefits and competition capability in the metal mineral resources exploitation.But nonlinear science theory and artificial intelligent method including multidisciplinary design optimization, human factors engineering, the theory of differential delay equations, the theory of neural network, the theory of entropy and the theory of fuzzy are of unique superiority in solving the mentioned problems and will be able to offer a new method for solving them.The main research task in this paper such as safety production scale model of underground metal mine, evolution mechanism of man machine safe nonlinear dynamic for mining mineral resources in the underground metal mine, instability identification about large scale underground mined-out area in the metal mine, safety entropy analysis on man machine environment system for mining mineral resources in the underground metal mine, multi factor coupling disaster prediction in the underground metal mine and disaster evidence parameter time series prediction in the underground metal mine were studied. And the research fund is obtained from the national "eleven five" science and technology support program(namely survey on metal mine area hidden cavity and research on the key technologies of fault identification[2007BAK22B04-12]) and the National Natural Science Fund Projects(namely study on chaos discrimination for acoustic emission signals from roof caving in metal mine and its intelligent forecasting[51274250]), the main innovations and achievements of the paper are expressed as:(1)An analysis model of safety production scale of underground metal mine was established by using of some methods such as making income of production, safety and environmental impact as the subsystem objective function and using adaptive mutative scale chaos immunization optimization algorithm to solve multidisciplinary design optimization model, and safety production scale of underground metal mine was analyzed validly and optimized overally. Practical results show that multidisciplinary design optimization on production scale of an underground lead and zinc mine is more realistically reflect the actual operating conditions, the production scale is about1.25million t/a, the economic life approximately is14a, corresponding coefficient of production profits can be increased to15.13%, safety factor can be increased to5.4%and environmental impact coefficient can be reduced by9.52%.(2)Considering time delay when sloppy level influencing on safety level, the safety nonlinear dynamic evolutionary model of man machine system for mining mineral resources in the underground metal mine was proposed based on human sloppy and safety and its validty was tested by using of simulation results. Qualitative analysis on nonlinear dynamic evolution of man machine system for mining mineral resources in the underground metal mine and its trend reveal evolutionary patterns of interaction of sloppy and safety qualitatively in four dynamic regions. And some theory basis for safety evaluation and control to the underground metal mining mechanical system is provided by the research results.(3)A safety entropy thresholds model of man machine environment system for mining mineral resources in the underground metal mine was established and analyzized qualitatively based on dissipative structure theory and entropy change equation and the results reaveled that man machine environment system for mining mineral resources in the underground metal mine will collapse when The initial total safety entropy is too high. Moreover, safety entropy analysis model of man machine environment system for mining mineral resources in the underground metal mine was established, the result shows that the main factor which affects the safety for the man machine environment system is that the safety for man machine environment system doesn’t develop coordinately and leads to entropy production into man machine environment system in the underground metal mine.(4)The parameters of radial basis function were determined through clustering method and prediction model of multi factor coupling disaster from underground metal mining based on adaptive variable weight FRBFNN model was built. The prediction model of multi factor coupling disaster from underground metal mining was trained, tested and applied. The results show that adaptive variable weight FRBFNN model has high training accuracy and generalization ability. Correctness of analysis about adaptive variable weight FRBFNN model was proved by the practical application results about instability discrimination of surrounding rock in large-scale underground mined-out area of a metal mine in south China.(5)After a functional link neural network forecasting method was established by using of the method that it made some forecasting values from different single forecasting model being extended according to orthogonal trigonometric function and the weight of functional link neural network was calculated based on fuzzy adaptive variable weight algorithm fuzzy adaptive variable weight algorithm, prediction method of disaster warning parameter time series in underground metal mining based on fuzzy adaptive variable weight function link neural network was brought up. The success forecasting results of happening rate of acoustic emission in some lead&zinc mine reveal that the functional link neural network forecasting method based on fuzzy adaptive variable weight algorithm is higher than that of other forecasting model. The functional link neural network forecasting is very useful for predict roof caving.