Abstract
Static and dynamic simulation models of a section of a mine ventilation network in order to research a sequential neural control scheme of mine airflow are developed in this paper. The techniques of neural network training set creation for both simulation models, a structure of neural network and its training algorithm are described. The simulation modeling results using static and dynamic models have showed good potential capabilities of neural control approach.
Highlights
A problem of allowable concentration control of dangerous gases CH4 and CO is very urgent in coal mines and other closed environments due to safety of the people working in such areas
During the experiments the neural network (NN) is trained on 400 vectors
It tested on 225 testing vectors which did not included in the training set
Summary
A problem of allowable concentration control of dangerous gases CH4 and CO is very urgent in coal mines and other closed environments due to safety of the people working in such areas. Coal mining industry is a tough industry in every country. In 2001 there were 6.63 fatalities per million tons of coal equivalent (mtce) produced in China's mines, 0.02 fatalities per mtce in Australia, 0.83 in Russia, and 0.48 in India [1]. Development of an Automated Control Systems for coal-mine ventilation in order to prevent fatalities is a crucial issue today. It is obvious, that recent advances in science and technology should be used to fulfill this task. We should account two properties of such automatic ventilation control system at least: (i) the sensors must supply the system by accurate information in order to provide precise ventilation control and (ii) the system should provide adaptive ventilation control in normal and unexpected exploitation conditions
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