Optimization of Airflow Distribution in Mine Ventilation Networks Using the Modified Sooty Tern Optimization Algorithm

Mine ventilation energy consumption is one of the main sources of energy consumption in mining production, accounting for about one-third to one-half of the total energy consumption. Therefore, reducing the energy consumption of the mine ventilation system is crucial for mining production. With the premise of meeting the airflow requirements of the underground work areas and achieving sustainable development of mining production, this paper establishes a nonlinear optimization mathematical model for the mine ventilation network with the objective of minimizing the total power. Regarding the model, optimization of the decision variables was carried out using the method of minimum spanning tree. The constraints for air flow balance, wind pressure balance, and fan operating conditions were optimized using the exterior penalty function method, thereby transforming the model into a nonlinear unconstrained model. Based on this model, a modified sooty tern optimization algorithm (mSTOA) was proposed to achieve optimization. Improved the sooty tern optimization algorithm (STOA) by using the uniform reverse strategy, fitness value-distance balance selection strategy, and mutation strategy. The STOA, mSTOA, and three other classical optimization algorithms were applied to the optimization of a ventilation system of an actual mine. The experimental results show that after using mSTOA to optimize the ventilation network airflow distribution in the mine, the total energy consumption can be reduced by about 35.06% while meeting the constraints and demands of the ventilation network regulation and usage. This is of great value to mine roadway operation environment safety and clean production.