An intelligent airflow perception model for metal mines based on CNN-LSTM architecture

Abstract

In view of the harsh underground working environment and difficulties of airflow monitoring sensors placement, implementing an intelligent ventilation strategy is crucial for ensuring ventilation safety during mining process. The key issue lies in obtaining global real-time airflow parameters for ventilation safety and intelligent control. Thus, we propose an intelligent perception approach based on artificial intelligence (AI) method for acquiring airflow parameters, which operates by leveraging partial airflow data from specific monitoring points to predict airflow at undisclosed locations. Initially, this approach is facilitated by the training of an AI database through numerical simulations of ventilation networks. Subsequently, an intelligent airflow perception model is constructed, incorporating convolution neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM architectures. Through iterative updates and enhancements, these models demonstrate average deviations between predicted and actual airflow parameters of less than 5% in both simulated scenarios and empirical applications. Furthermore, in the case study, the CNN-LSTM architecture model exhibits superior performance for intelligent airflow perception. This architecture combing with airflow monitoring system, and utilizing partial real-time data inputs to obtain perception point outputs, can effectively enhance employee productivity, reduce energy consumption, and prevent resource wastage.