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Machine Learning and Deep Learning Methods in Mining Operations: a Data-Driven SAG Mill Energy Consumption Prediction Application
Mining, Metallurgy & Exploration ( IF 1.9 ) Pub Date : 2020-06-16 , DOI: 10.1007/s42461-020-00238-1
Sebastian Avalos , Willy Kracht , Julian M. Ortiz

Semi-autogenous grinding mills play a critical role in the processing stage of many mining operations. They are also one of the most intensive energy consumers of the entire process. Current forecasting techniques of energy consumption base their inferences on feeding ore mineralogical features, SAG dimensions, and operational variables. Experts recognize their capabilities to provide adequate guidelines but also their lack of accuracy when real-time forecasting is desired. As an alternative, we propose the use of real-time operational variables (feed tonnage, bearing pressure, and spindle speed) to forecast the upcoming energy consumption via machine learning and deep learning techniques. Several predictive methods were studied: polynomial regression, k-nearest neighbor, support vector machine, multilayer perceptron, long short-term memory, and gated recurrent units. A step-by-step workflow on how to deal with real datasets, and how to find optimum models and final model selection is presented. In particular, recurrent neural networks achieved the best forecasting metrics in the energy consumption prediction task. The workflow has the potential of being extended to any other temporal and multivariate mineral processing datasets.

中文翻译:

采矿作业中的机器学习和深度学习方法:数据驱动的半自磨机能耗预测应用

半自动磨机在许多采矿作业的加工阶段发挥着关键作用。它们也是整个过程中最密集的能源消耗者之一。当前的能源消耗预测技术基于给矿矿物学特征、SAG 维度和操作变量的推断。专家们认识到他们有能力提供足够的指导,但在需要实时预测时也缺乏准确性。作为替代方案,我们建议使用实时操作变量(进给吨位、轴承压力和主轴速度)通过机器学习和深度学习技术预测即将到来的能源消耗。研究了几种预测方法:多项式回归、k-最近邻、支持向量机、多层感知器、长短期记忆、和门控循环单元。介绍了有关如何处理真实数据集以及如何找到最佳模型和最终模型选择的分步工作流程。特别是,循环神经网络在能耗预测任务中实现了最佳预测指标。该工作流程有可能扩展到任何其他时间和多元矿物加工数据集。
更新日期:2020-06-16
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