Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment

Abstract

Mining safety heavily depends on ventilation, which constitutes a significant portion of the energy costs in operations. Optimizing mine ventilation systems (MVSO) is crucial for minimizing this energy expenditure. However, current algorithms encounter challenges when applied to large-scale mines, primarily due to the complexity of variables and limited attention to optimizing main fans. This study introduces a theoretical knowledge enhanced genetic algorithm for MVSO, incorporating main fan adjustments. The algorithm models changes in the main fan’s operational status and integrates ventilation network equivalent simplification (VNES) and the minimum spanning tree (MST) to reduce the number of variables in the mine ventilation network. Additionally, leveraging mine ventilation sensitivity theory (MVST) enhances the quality of the initial algorithmic population. A simple case and two engineering cases collectively validated that the algorithm consistently provides effective and reliable optimization solutions for mine ventilation systems across varying scales. Specifically, the algorithm reduced energy consumption from 326.94 to 186.99 kW, 433.14 to 239.48 kW, and 520.53 to 324.90 kW across three different scales of mine ventilation systems. Comparative analysis with four other algorithms shows that, although this algorithm has a longer runtime due to the need to identify the minimum spanning tree during iterations, its ability to reduce problem dimensionality and improve population quality results in more stable and superior convergence performance, especially for large-scale mine ventilation systems.

Graphical abstract