State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, China.
School of Mines, China University of Mining and Technology, Xuzhou, China.
Environ Sci Pollut Res Int. 2023 Nov;30(53):114591-114609. doi: 10.1007/s11356-023-30443-6. Epub 2023 Oct 20.
Mine dust pollution poses a hindrance to achieving green and climate-smart mining. This paper uses weather forecast data and mine production intensity data as model inputs to develop a novel model for forecasting daily dust concentration values in open pit mines by employing and integrating multiple machine learning techniques. The results show that the forecast model exhibits high accuracy, with a Pearson correlation coefficient exceeding 0.87. The PM2.5 forecast model performs best, followed by the total suspended particle and PM10 models. The inclusion of production intensity significantly enhances model performance. Total column water vapor exerts the most significant impact on the model's predictive performance, while the impacts of rock production and coal production are also notable. The proposed daily forecast model leverages production intensity data to predict future dust concentrations accurately. This tool offers valuable insights for optimizing mine design parameters, enabling informed decisions based on real-time forecasts. It effectively prevents severe pollution in the mining area while maximizing the use of natural meteorological conditions for effective dust removal and diffusion.
矿山粉尘污染阻碍了绿色和气候智能采矿的实现。本文使用天气预报数据和矿山生产强度数据作为模型输入，采用和集成多种机器学习技术，开发了一种新的露天矿日粉尘浓度值预测模型。结果表明，该预测模型具有很高的准确性，皮尔逊相关系数超过 0.87。PM2.5 预测模型表现最好，其次是总悬浮颗粒物和 PM10 模型。生产强度的纳入显著提高了模型性能。总柱水汽对模型预测性能的影响最大，而岩石产量和煤炭产量的影响也很显著。所提出的日度预测模型利用生产强度数据来准确预测未来的粉尘浓度。该工具为优化矿山设计参数提供了有价值的见解，使基于实时预测做出明智决策成为可能。它可以有效地防止矿区的严重污染，同时最大限度地利用自然气象条件进行有效的除尘和扩散。
