Optimisation strategy for multi-branch joint on-demand regulation of complex mine ventilation networks based on ε -CSO algorithm and sensitivity theory

A trade-off exists in mine ventilation systems to balance the ventilation needs of multiple underground stopes while minimizing energy consumption to the greatest extent possible. However, the intricate network structure, nonlinear constraint optimization models, and variable regulator positions render existing ventilation control technologies inadequate. This paper therefore proposes a multi-branch joint on-demand optimization control strategy. To address the challenges posed by complex network structures and nonlinear optimization models, we employ the Competitive Swarm Optimization algorithm with an ε-constrained method (ε-CSO), which efficiently tackles the ventilation network optimization problem with the aim of minimizing energy consumption. Notably, the strong interconnectivity between branches in complex networks means that the number and positioning of regulators significantly influence the minimization energy consumption strategy. This study introduces the second-order sensitivity theory to describe the sensitivity changes between branches during the airflow regulation process and determines the position of the regulator. Ultimately, an indirect optimization method was implemented to achieve multi-branch joint on-demand control of mine airflow. Case analysis indicates that, compared to existing algorithms, the ε-CSO algorithm yields the lowest optimized and regulated energy consumption, amounting to 123663.82W. The constraint values for each circuit are all below 3.723E-12. The optimal regulator positions were validated through solving the optimal control strategies for four ventilation requirements. This demonstrates that the ε-CSO algorithm possesses a competitive edge in addressing complex ventilation network optimization challenges. This study offers an effective strategy for reducing ventilation costs in mines with complex ventilation networks and Intelligent Ventilation on Demand (IVOD).