Migration time prediction and assessment of toxic fumes under forced ventilation in underground mines  

作　　者：Jinrui Zhang Tingting Zhang Chuanqi Li 

机构地区：[1]School of Resources&Civil Engineering,Northeastern University,Shenyang 110819,China [2]Laboratory 3SR,CNRS UMR 5521,Grenoble Alpes University,Grenoble 38000,France

出　　处：《Underground Space》2024年第5期273-294,共22页地下空间（英文）

基　　金：The authors were funded by China Scholarship Council(Grant Nos.202106370038,and 201906690049);National Key Research and Development Program of China(Grant No.2021YFC3001300).

摘　　要：This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural network(BPNN)models optimized by honey badger algorithm(HBA)with four chaos mapping(CM)functions(i.e.,Chebyshev(Che)map,Circle(Cir)map,Logistic(Log)map,and Piecewise(Pie)map)are developed to predict the migration time.125 simulations by the computational fluid dynamics(CFD)method are used to train and test the developed models.The determination coefficient(R2),the variance accounted for(VAF),the Willmott’s index(WI),the root mean square error(RMSE),the mean absolute percentage error(MAPE),and the sum of squares error(SSE)are utilized to evaluate the model performance.The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2(0.9945),WI(0.9986),VAF(99.4811%),and the lowest values of RMSE(15.7600),MAPE(0.0343)and SSE(6209.4),respectively.The wind velocity in roadway(Wv)is the most important feature for predicting the migration time of toxic fumes.Furthermore,the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.

关 键 词：Migration time Underground mines Honey badger algorithm Chaos mapping Back propagation neural network 

分 类 号：O17[理学—数学]