Geometallurgical modelling: algorithms for spatial prediction

采矿项目基于对矿体性质的估计，对采矿方法、计划、进度、设备和设置以及矿山运营的决策，这些估计来自非常有限的样本和冶金测试数据。这导致预测采矿和加工过程中材料性质变化的后果的能力很低，从而产生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移到预测性环境，引入了几何力学模型。几何冶金将地质、采矿和冶金信息结合在一起，为采矿、选矿和冶金创建基于空间的预测模型，可用于优化这些决策，同时考虑到所有其他关键项目限制，如环境限制、水资源供应和能源效率。*这项研究的长期目标是建立分析工具，以模拟从材料的表征到采矿或冶金过程中的性能的综合过程。这将允许预测并随后规划和优化综合采矿过程，提高回收率，并将损失和废物产生降至最低。这一目标将在短期内通过设计构成构件的分析算法和生成在几何应用中执行灵敏度分析和优化的建模工作流来实现。*这将通过集中于三个问题来实现：*(1)域的定义，即可以进行估计的空间均匀体积的域的定义，因为几何几何建模对模型的最终预测能力有很大影响。必须开发用于聚集数据的特定度量，以识别具有集成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约束区域；*2)比例：材料的混合并不总是像预期的那样执行，因为它们的性质不是线性平均的。需要新的比例模型，将研究功率模型和非线性预测以及核估计，以说明混合材料性质的可变性；以及*(3)预测建模：对最新的机器学习技术(卷积网络和贝叶斯网络)的研究有望为建模输入变量和响应之间的复杂关系，管理过程中间步骤的不确定性提供一些新的途径。*该计划包括培训2名博士生和4名硕士研究生，并开发在金属和油砂开采方面具有潜在应用的研究，这可能会对加拿大和国外的采矿业产生重大影响。

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.