Li Lin, Zhang Ruixin, Sun Jiandong, He Qian, Kong Lingzhen, Liu Xin
School of Energy and Mining Engineering, China University of Mining & Technology (Beijing), Beijing, 100083 China.
School of Safety Engineering, North China Institute of Science and Technology, Sanhe, 065201 Hebei China.
J Environ Health Sci Eng. 2021 Feb 3;19(1):401-414. doi: 10.1007/s40201-021-00613-0. eCollection 2021 Jun.
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.
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.
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.
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.
粉尘污染是目前露天矿山面临的最严重环境问题之一。与地下开采相比，露天开采有许多粉尘源，影响范围广，气象条件变化复杂，会导致粉尘浓度变化很大。因此，露天矿山粉尘浓度的预测需要进行研究，这对于减少环境污染和个人健康危害具有重要意义。
本研究基于对平朔安家岭露天煤矿总悬浮颗粒物（TSP）浓度的监测。本文提出了一种基于长短期记忆（LSTM）网络和注意力机制（LSTM-Attention）的混合模型，并将其应用于TSP浓度的预测。LSTM模型反映了输入时间序列的历史过程，注意力机制提取输入参数的内在特征，根据影响因素的重要性分配权重。自回归积分移动平均（ARIMA）模型和LSTM模型也用于预测TSP浓度。最后，使用几种误差统计度量来评估模型的准确性并进行敏感性分析。
发现一般情况下，TSP浓度在08:00-09:00时段最高，在15:00-16:00时段最低。除了气象参数和正常作业的影响外，出现这种趋势的原因是露天矿上方存在逆温层。结果表明，与ARIMA模型和LSTM模型相比，LSTM-Attention模型更稳定，预测准确率分别高出5.6%和3.0%。
该模型可应用于露天矿山粉尘浓度的预测，并为何时开展抑尘工作提供指导。它具有可扩展性，在广泛领域的应用中具有潜在价值。
