Existing techniques for monitoring and controlling the ventilation system in underground mines are limited; since they only detect areas of low oxygen level or use software to model systems based on standardized data, but not, they evaluate the factors and identify the causes that generate the deficiency in the system. For this reason, a predictive method of factors influencing the airflow of the ventilation system is proposed as a possible solution with the use of artificial neural networks (ANN) to strengthen the monitoring and control process. The methodology proposed in this research includes the analysis of air flow factors in critical mining areas to identify the study parameters. In the case study, a database of records of ventilation conditions of a mine was used. A test of 11 predictive neural networks was developed, with approximately a base of 250 standardized data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.