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

Abstract

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 (W_v) 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.

Keywords

Migration time / Underground mines / Honey badger algorithm / Chaos mapping / Back propagation neural network

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Jinrui Zhang: Writing - original draft, Visualization, Software, Methodology, Conceptualization. Tingting Zhang: Writing - review & editing, Supervision, Methodology, Funding acquisition, Data curation. Chuanqi Li: Writing - review & editing, Writing - original draft, Visualization, Validation, Supervision, Software, Methodology, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors were funded by China Scholarship Council (Grant Nos. 202106370038, and 201906690049) and National Key Research and Development Program of China (Grant No. 2021YFC3001300). Besides, the authors want to thank all the members who give us lots of help.

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