Tailings storage facility surface moisture mapping for prediction of dusting events – and other benefits

James Thompson, L. McDonald, Joshua Kelcey, J. Dixon
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Abstract

The fine-grained nature of some tailings materials can lead to dusting on tailings storage facilities (TSFs). Dusting has the potential to negatively affect the health of workers, nearby communities, and the surrounding environment which may adversely affect a mining operator’s ability to operate. Mitigation measures to prevent dusting can be expensive and aren’t always successful. Advances in monitoring systems allow the relative surface moisture of tailings beaches to be monitored during operations providing the opportunity to identify and forward plan the depositional strategy to minimise the risk of dusting. This paper presents the outcomes of a study that utilised predictive methods based on advanced synthetic aperture radar and multispectral data coupled with Google Earth Engine to develop a model for particular TSFs based on historical records, observations, and material types. Google Earth Engine brings the first opportunity to use a systematic and comprehensive combination of radar and visual-infrared satellite data. It was found that the synergy between the two data types could be used to offset the individual ambiguities of each, and the resulting method delivered a predictive dryness probability map and visual moisture/water depth/presence indicators that were able to be verified and made operational almost immediately. On-ground visual records and aerial imagery provided qualitative verification of the approach. The methodology allows TSF operators a free, open source platform with which to monitor and map surface moisture, enabling proactive deposition decision-making to mitigate the risk of tailings dusting. Additional benefits realised include increased data on beach formation, channel and pond location (extent and to some degree depth), improving the accuracy of the TSF water balance. For the particular TSFs studied, the water balance is a critical control from a safety perspective to mitigate potential failure mechanisms, and from an operational perspective to maximise tailings density and water return to the plant.
尾矿储存库地表水分测绘预测粉尘事件和其他好处
一些尾矿材料的细粒性会导致尾矿储存设施(TSFs)产生粉尘。粉尘有可能对工人、附近社区和周围环境的健康产生负面影响,这可能对采矿经营者的作业能力产生不利影响。防止粉尘的缓解措施可能很昂贵,而且并不总是成功。监测系统的进步使在作业期间能够监测尾矿滩的相对表面湿度,从而有机会确定和提出沉积战略计划,以尽量减少粉尘的风险。本文介绍了一项研究的结果,该研究利用基于先进合成孔径雷达和多光谱数据的预测方法,结合谷歌地球引擎,基于历史记录、观测和材料类型,为特定的tsf开发了一个模型。谷歌地球引擎首次带来了使用雷达和视红外卫星数据的系统和全面组合的机会。研究发现,两种数据类型之间的协同作用可以用来抵消每种数据类型的个别模糊性,由此产生的方法提供了预测干燥概率图和视觉湿度/水深/存在指标,这些指标几乎可以立即得到验证和操作。地面视觉记录和航空图像对该方法进行了定性验证。该方法为TSF运营商提供了一个免费的开源平台,通过该平台可以监测和绘制表面湿度图,从而实现主动沉降决策,以减轻尾矿粉尘的风险。实现的其他好处包括增加了海滩形成、水道和池塘位置(范围和某种程度上的深度)的数据,提高了TSF水平衡的准确性。对于所研究的特定tsf,从安全的角度来看,水平衡是一个关键的控制,可以减轻潜在的故障机制,从运营的角度来看,可以最大限度地提高尾矿密度和水返回工厂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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