No abstract

A novel bare-bones particle swarm optimization (BBPSO) algorithm is proposed to realize intelligent mine ventilation decision-making and overcome the problems of low precision, low speed, and difficulty in converging on an optimal global solution. The proposed method determines the decision objective function based on the minimal power consumption and maximal air demand. Three penalty terms, namely, dynamic ventilation condition, the supplied air volume at the location where the air is required, and roadway wind speed, are established. The particle construction method of "wind resistance" instead of "wind resistance & air volume" is proposed to reduce the calculation dimension effectively. Three optimization strategies, namely the contraction factor, optimal initial value, and elastic mirror image, are proposed to avoid premature convergence of the algorithm. The application flow of intelligent decision-making in the field and the parallel computing architecture are also discussed. Five methods are used to solve the problems. The results reveal that the improved parallel BBPSO algorithm (BBPSO-Para-Improved) outperforms other algorithms in terms of convergence efficiency, convergence time, and global optimization performance and meets the requirements of large ventilation systems for achieving economic and safety targets.

The interaction between ventilation and fire development in diagonal pipe networks makes the study of temperature characteristics extremely complex. The thermodynamic effect caused by high temperature will change the original ventilation state, cause smoke flow retrogression and airflow reversal, and expand the disaster range. Therefore, exploring the temperature attenuation characteristics in diagonal pipe networks is necessary. In this article, the temperature distribution and attenuation in a diagonal pipe network are studied using the numerical simulation method based on the theoretical model of temperature attenuation in a single roadway. In the diagonal branch, the St number in the temperature attenuation model is optimized. The temperature attenuation of the left and right paths can be divided into two stages. The optimal St number of the temperature attenuation model under different wind speeds in the left way is determined. The fitting relationship of wind speed, distance, and temperature in the first stage of the right way is established, and the fire source distance in the second stage of the right way has the most significant influence on the temperature attenuation by using the method of multivariate statistics. The temperature of the smoke backflow front in the left and right paths decreases gradually with the increase in the fire source, and the temperature of the smoke backflow front in the left way is higher than that in the right way.