Optimisation of underground ventilation systems by means of neural networks

The optimisation of the auxiliary ventilation network of underground mines entails the right choice of fans and duct characteristics. Once the decision-making team has selected the appropriate set of ventilation hardware, the duct line is installed across a main drivage and ends up to the most productive working areas. However, when there are many possible duct line routes from the auxiliary fan to the duct line open end, as is the case in the room and pillar mining method, the choice of a cost effective duct line route, in terms of the total length and bending of the ducts involved, is important for the minimisation of the operating cost of ventilation. In this paper, the problem of finding a cost effective duct line route, among a definite number of possibilities, is tackled with a modified discrete Hopfield network. It is shown that the network provides the optimum result for a trivial case. The implementation of the network on a realistic case is shown to be superior to results from the A* algorithm. However, the feasibility of the proposed solution is highly dependent on mining issues and on the right choice of number and distribution of control points that realise all possible routes from the fan to the headings.