Surrogate models for design and study of underground mine ventilation

Abstract

Ventilation is vital for the production in an underground mine. Therefore, it is important to have efficient and accurate design tools in order to ensure and optimize the airflows in the mine. There are several commercial software for airflow simulation based on first principles. However, the computational cost of simulation together with integrational obstacles when connecting simulation to control strategies limits the benefit of these tools. In this paper an approach utilizing surrogate models as a complementary design tool is presented. It is shown that using surrogate models one can with rather low computational expense evaluate and benchmark different control strategies. It is also shown that the models can be used for identifying possible bottlenecks in the system in advance. Moreover, the use of surrogate models transfer the simulation into a development-friendly environment (such as Matlab). A test case is used based on a real underground mine ventilation design. Two types of surrogate models are fitted to process data; multiple least squares regression and a Gaussian process model. Sensitivity analysis on the surrogate shows the potential of using surrogate models for identifying bottlenecks. Furthermore, the surrogate is used to benchmark two different control strategies for mine ventilation.