Study on intelligent calibration of airway resistance in mine ventilation networks

Existing airway resistance calibration models for mine ventilation systems do not strictly define the constraints for the friction resistance factor and lack consideration for the magnitude of resistance adjustment, leading to low calibration efficiency. To address these issues, a dual-objective resistance calibration optimization model was proposed to minimize both the resistance correction amount and the error between measured and calculated airflow. This model strictly defined the physically feasible region for airway resistance parameters and converted the dual-objective optimization model into a single-objective function using a weighting method. This approach synergistically minimized the resistance correction amount and the error between measured and calculated airflow, maintained the physical reality of the initial resistance parameters, and improved the accuracy of the network calculation. To solve the model, the NMS-DBO algorithm was proposed by combining the geometric operations of the Nelder-Mead Simplex (NMS) algorithm with the bio-inspired optimization behavior of the Dung Beetle Optimizer (DBO). The algorithm implemented an adaptive weight fusion strategy, a fusion strategy of reflection direction perception and ball-rolling navigation, and a fusion strategy of contraction-retreat and brood-laying development to output the optimal resistance solution and ensure high-precision calibration of resistance parameters within physical constraints. A resistance calibration model was established for a mine ventilation network with 35 branches and solved using the Nelder-Mead Simplex-Dung Beetle Optimizer(NMS-DBO), Whale Optimization Algorithm（WOA）, Particle Swarm Optimization（PSO）, and DBO algorithms, respectively. The results showed that the objective function value obtained by the NMS-DBO algorithm was 0.454, which was superior to that of the WOA (0.853), PSO (0.525), and DBO (0.521) algorithms, indicating that the NMS-DBO algorithm outperformed the other three in terms of convergence accuracy. Furthermore, the similarity coefficient between the optimal resistance solved by NMS-DBO and the initial resistance reached 0.998, and the maximum error between measured and calculated airflow was reduced from 22.99 m³/s to within 3 m³/s, thereby achieving the synergistic optimization of a high-precision match between resistance parameters and airflow.