Predicting rock fragmentation and blast-induced damage

预测岩石破碎和爆炸引起的损坏

• 批准号: RGPIN-2020-06525
• 负责人: Fillion, MarieHélène
• 金额: $ 1.89万
• 依托单位: Laurentian University of Sudbury
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753207
• 关键词: Predicting; rock; fragmentation; blast; induced

In mining, the stability of excavations is paramount for the safety of workers and achieving adequate rock fragment sizes can considerably increase productivity. Blasting is a common method used for rock fragmentation and excavation. Empirical guidelines for blast design are widely used, even if these involve a degree of uncertainty and, consequently, potential instabilities and inadequate fragmentation. Optimal blast results often require multiple iterations of blast design and this practice is costly and time-consuming. The size of the in-situ blocks formed by the intersection of natural joints in the rock mass is required to predict rock fragmentation. Discrete fracture networks (DFN) are 3D joint systems representing the distribution of in-situ block sizes. Previous research demonstrated the potential of DFNs, coupled with hybrid Finite/Discrete element methods, for the prediction of rock fragmentation in a block caving operation. This method has potential applications in blasting to obtain valuable knowledge of the role of in-situ joint networks in the overall blast outcome. The principal objective of this research is to develop predictive models for blast-induced damage and rock fragmentation using DFNs and numerical modelling tools. The secondary objectives are to establish a procedure for the field assessment of blast-induced fragmentation and damage and to provide practical recommendations regarding the optimal blast design. This project will benefit from the collaboration with mining companies that recognize the value of this research and agreed to provide access to mine sites. The state of the art DFN tool MoFrac will provide realistic fracture networks to determine the in-situ block sizes and blast-induced fracture intensity. Using DFNs as an input to numerical simulations of blast-induced fragmentation will preserve fracture properties through the modelling process. A comparison of the simulation results with field measurements will calibrate the prediction model. Previous research contributions in optimizing geotechnical data collection campaigns at mine sites are key aspects for the selection of reliable parameters for numerical modelling. General modelling practice often overlook the potential gaps in the collected data. A major contribution of this research is the development of DFN-based predictive models for blast-induced damage and fragmentation, with the main advantage of providing realistic fracture networks for numerical modelling. Another valuable research outcome is the development of practical, quantified and user-friendly recommendations based on rock properties and joint orientation. These will benefit engineers, blasters and mine management; and will increased safety for the personnel working on site. A better definition of the blast damage zone can lead to improved stability analyses. Greater control on rock fragmentation increases mine productivity, with easier and cheaper materials handling.

在采矿中，挖掘的稳定性对于工人的安全至关重要，并且获得足够的岩石碎片尺寸可以显着提高生产率。爆破是岩石破碎和开挖的常用方法。爆破设计的经验准则被广泛使用，即使这些准则涉及一定程度的不确定性，从而导致潜在的不稳定和不充分的破碎。最佳爆破结果通常需要多次迭代爆破设计，这种做法成本高昂且耗时。 需要通过岩体中自然节理相交形成的原位块体的尺寸来预测岩石破碎。离散裂缝网络 (DFN) 是代表原位区块尺寸分布的 3D 节理系统。先前的研究证明了 DFN 与混合有限/离散元方法相结合在预测崩落崩落作业中岩石破碎方面的潜力。该方法在爆破中具有潜在的应用，以获得有关现场接头网络在整体爆破结果中的作用的宝贵知识。本研究的主要目标是使用 DFN 和数值建模工具开发爆炸损伤和岩石破碎的预测模型。次要目标是建立现场评估爆炸引起的破碎和损坏的程序，并提供有关最佳爆炸设计的实用建议。 该项目将受益于与认识到这项研究的价值并同意提供进入矿场的矿业公司的合作。最先进的 DFN 工具 MoFrac 将提供真实的裂缝网络，以确定原位区块尺寸和爆炸引起的裂缝强度。使用 DFN 作为爆炸引起破碎数值模拟的输入将在建模过程中保留断裂特性。模拟结果与现场测量的比较将校准预测模型。先前在优化矿场岩土数据收集活动方面的研究贡献是为数值建模选择可靠参数的关键方面。一般建模实践常常忽略所收集数据中的潜在差距。这项研究的一个主要贡献是开发了基于 DFN 的爆炸损伤和破碎预测模型，其主要优点是为数值建模提供真实的裂缝网络。另一个有价值的研究成果是根据岩石特性和节理方向提出实用、量化和用户友好的建议。这些将使工程师、爆破工和矿山管理人员受益；并将提高现场工作人员的安全。更好地定义爆炸损伤区域可以改进稳定性分析。对岩石破碎的更好控制可以提高矿山生产率，使材料处理变得更容易、更便宜。