Neural Network Approach to Automatic Control of Mining Ventilation

The problem of increasing underground mine ventilation efficiency is still of top priority. The complication of geological conditions with the depth of underground mining processes and reinforcement of requirements to technological and ecological safety are extorting the necessity of solving this complex problem with using of new information technologies. There exist different approaches to the ventilation problem. One of them, which the authors adhere to, is based on the concept of monitoring (under special mining and geological conditions-automatic control) of mining atmosphere and operative re-distribution of underground mining air among consumer , in accordance with the criterion of optimality [ 1.3 1. ] The set of tasks which should be decided in the frame of con- crete ventilation system depends on the peculiarities of deposit, technological schemes and so on. Nevertheless, some ventilation tasks such as: defining of ventilation network parameters; finding of dependence between these parametrs; operative redistribution of air streems, could be interpreted as universal ones. Main aspects of practical solving of different ventilation problems and their classification were investigated and considered in detail before [I ,2 ]. Some years ago we have began our investigations to develope quite simple and effective methods for decision of ventilation tasks on the base of neural network technology. It is well known that ventilation net, formed by various mine workings, is the main component of underground mining ventilation system. In general, network air distribution may be described by some functional F(Q,R,H), where Q- is a vector of air consumptions; H- is a vector of depressions; R- is a vector of airdynamic resistences. (Q,R,H) - are the informative entities , characterising every branch of the net. There exist some fundamental tasks which should be decided during the process of control of mining ventilation: calculating of natural air distribution Q = Q,{R, H,}, or Q = Q2(R, Q?}, where H, - is the maximal depression of the ventilation net, Q, - is the air consumption in the ventilation shaft; identifying of airdynamic resistance for some branches on the base either of air-depression removal or operative monitoring: [ ]