Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm

Purpose Dust pollution is currently one of the most serious environmental problems faced by open-pit mines. Compared with underground mining, open-pit mining has many dust sources, and a wide area of influence and complicated changes in meteorological conditions can result in great variations in dust concentration. Therefore, the prediction of dust concentrations in open-pit mines requires research and is of great significance for reducing environmental pollution and personal health hazards. Methods This study is based on monitoring of the concentration of total suspended particulate (TSP) in the Anjialing open-pit coal mine in Pingshuo. This paper proposes a hybrid model based on a long short-term memory (LSTM) network and the attention mechanism (LSTM-Attention) and applies it to the prediction of TSP concentration. The LSTM model reflects the historical process of an input time series, and the attention mechanism extracts the inherent characteristics of the input parameters to assign weights based on the importance of the influencing factors. The autoregressive integrated moving average (ARIMA) and LSTM models are also used to predict the TSP concentration. Finally, several statistical measures of error are used to evaluate the accuracy of the model and perform a sensitivity analysis. Results It was found that, in general, the TSP concentration was highest in the period 08:00-09:00 and lowest in the period 15:00-16:00. In addition to the influence of meteorological parameters and normal operations, the reason for this trend is the presence of an inversion layer above the open-pit mine. The results show that, compared with the ARIMA and LSTM models, the LSTM-Attention model is more stable and has a prediction accuracy that is 5.6% and 3.0% greater, respectively. Conclusion This model can be applied to the prediction of dust concentrations in open-pit mines and provide guidance on when to carry out dust-suppression work. It has expansibility and is potentially valuable for application in a wide range of areas.