Geometallurgical modelling: algorithms for spatial prediction

地质冶金建模：空间预测算法

• 批准号: RGPIN-2017-04200
• 负责人: Ortiz, Julián
• 金额: $ 2.48万
• 依托单位: Queen's University
• 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=761117
• 关键词: Geometallurgical; modelling; algorithms; spatial; prediction

Mining projects base decisions about the mining method, plan, schedule, equipment and setting, and the mine operation on estimates of the properties of the ore body, drawn from very limited sample and metallurgical test data. This leads to a low capacity to anticipate consequences of changes in the materials properties, during mining and processing, with significant economic and environmental consequences. In order to move from a reactive response to these changes, to a predictive setting, geometallurgical modelling is introduced. Geometallurgy combines geological, mining and metallurgical information to create spatially-based predictive models for mining, mineral processing and metallurgy that can be used to optimize these decisions, given all other key project constraints such as environmental restrictions, water availability and energy efficiency. The long term objective of this research is to build the analytical tools to model the integrated process from the materials' characterization to the performance when subject to a mining or metallurgical process. This will allow to predict and consequently plan and optimize the integrated mining process, improving recoveries, and minimizing losses and waste generation. This goal is addressed in the short term by designing the analytical algorithms that constitute building blocks and generating the modelling workflows to perform sensitivity analysis and optimization in geometallurgical applications.This will be achieved by focusing on three issues: (1) Domaining: the definition of the domains, that is of spatially homogeneous volumes where estimation can be performed, for geometallurgical modelling has a large impact in the final predictive capability of the model. Particular metrics for clustering data must be developed to identify domains with homogeneous behavior that integrate spatial continuity and multivariate correlations. This will allow for constraining the domains with geological and metallurgical information;2) Scaling: blending of materials does not always perform as expected, since their properties do not average linearly. New scaling models are required and power models and non linear prediction will be investigated along with kernel estimation to account for the variability in the properties of the blended materials; and (3) Predictive modelling: research into the most recent machine learning techniques (convolutional networs and Bayesian networks) is expected to provide some new avenues for modelling the complex relationships between the input variables and the responses, managing the uncertainties of the intermediate steps of the processes. The program involves training of 2 PhD and 4 Master's students, and developing research with potential application in the metal and oil sands mining, which may have a significant impact for the mining industry in Canada and abroad.

采矿项目根据对矿体性质的估计（从非常有限的样本和冶金测试数据中得出）来决定采矿方法、计划、进度、设备和设置以及采矿作业。这导致在采矿和加工过程中预测材料性质变化后果的能力较低，具有重大的经济和环境后果。为了从对这些变化的反应性反应转向预测性设置，引入了几何建模。几何学结合地质、采矿和冶金信息，为采矿、矿物加工和冶金创建基于空间的预测模型，可用于优化这些决策，考虑到所有其他关键项目的限制，如环境限制、水资源可用性和能源效率。本研究的长期目标是建立分析工具，以模拟采矿或冶金过程中从材料表征到性能的综合过程。这将允许预测并因此规划和优化综合采矿过程，提高回收率，并最大限度地减少损失和废物的产生。这一目标在短期内通过设计构成构件的分析算法和生成建模工作流程来实现，以在几何应用中进行灵敏度分析和优化。域的定义，即可以执行估计的空间均匀体积的定义，几何建模对模型的最终预测能力有很大影响。必须开发用于聚类数据的特定度量，以识别具有整合空间连续性和多变量相关性的同质行为的域。这将允许用地质和冶金信息来约束域;2）缩放：材料的混合并不总是如预期的那样进行，因为它们的属性不线性平均。需要新的缩放模型，并且将沿着核估计来研究幂模型和非线性预测，以说明混合材料的性质的可变性;以及（3）预测建模：研究最新的机器学习技术（卷积神经网络和贝叶斯网络）预计将提供一些新的途径，模拟输入变量和响应之间的复杂关系，管理过程的中间步骤的不确定性。该计划涉及2名博士和4名硕士生的培训，并开发在金属和油砂开采，这可能对加拿大和国外的采矿业产生重大影响的潜在应用研究。