Using machine learning methods to predict truck productivity in oil sands mining

使用机器学习方法预测油砂开采中的卡车生产率

• 批准号: 566724-2021
• 负责人: Liu, WeiVictorWV
• 金额: $ 4.4万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=748969
• 关键词: Using; machine; learning; methods; predict

The research program led by Dr. Liu will provide a fundamental understanding of the significant relationship between truck productivity and observational features (associated with real site operating conditions). The findings from this research program will play a vital role in assisting the prediction of mine production, giving essential guidance for the strategic planning of mine production. The proposed research will help oil sands companies take the lead in applying data science to mine operations. In addition to oil sands mining, the results can be broadly applied to other mines (both national and international) supplying a wide range of commodities (e.g., gold, copper, iron, aggregates, and coal).

由刘博士领导的研究项目将提供对卡车生产率和观测特征（与实际现场操作条件相关）之间重要关系的基本理解。研究结果对矿山生产预测具有重要的辅助作用，对矿山生产战略规划具有重要的指导意义。拟议中的研究将帮助油砂公司率先将数据科学应用于采矿作业。除油砂开采外，研究结果还可广泛应用于供应各种商品（如金、铜、铁、集料和煤）的其他矿山（国内和国际）。