Metallurgical process optimization using pulp chemistry data

使用纸浆化学数据优化冶金工艺

• 批准号: 571697-2021
• 负责人: Gibson, CharlotteCE
• 金额: $ 5.46万
• 依托单位: Queen's University
• 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=761104
• 关键词: Metallurgical; process; optimization; using; pulp

The rejection of pyrrhotite from copper-nickel (Cu-Ni) concentrates has long been a pain point for the Canadian mining industry. The presence of pyrrhotite in the Ni-Cu concentrate takes up smelter capacity, adversely affects energy efficiency, and generates large quantities of sulphur dioxide. Canada produces ~6-7 % of the world's nickel and together, Vale Base Metals Canada and Glencore's Sudbury Integrated Nickel Operations mined and concentrated ~96 % of the nickel produced in Canada in 2020. It is well established that flotation pulp chemistry influences Cu-Ni mineral flotation performance and pyrrhotite rejection; however, many pulp chemistry parameters (such as dissolved oxygen and pulp potential) go unmeasured in industrial mineral processing plants. In plants where measurements are taken, the data are rarely used to predict or control the process, despite their known influence. Advances in online pulp chemistry sensors present the opportunity to leverage pulp chemistry data for the development of statistical and machine learning models that can better predict and control pyrrhotite behaviour in Cu-Ni flotation circuits. This project is a collaboration between Queen's University, automation specialist SK Godelius, Vale Canada Base Metals, and Glenore's Sudbury Integrated Nickel Operations. The short term objective is to develop models that can predict pyrrhotite content (and other ore characteristics) and corresponding flotation behaviour in the flotation of Cu-Ni ores using pulp chemistry data. Long term, this work will form the foundation of a plant optimization platform that uses process monitoring, modeling, and prediction at the industrial scale to improve plant performance and reduce the carbon footprint of Canadian nickel operations.

从铜镍精矿中去除磁黄铁矿长期以来一直是加拿大采矿业的痛点。镍铜精矿中的磁黄铁矿占用了冶炼能力，对能源效率产生不利影响，并产生大量二氧化硫。加拿大的镍产量约占全球的6- 7%，加拿大淡水河谷基础金属公司和嘉能可的萨德伯里综合镍业务在2020年开采和浓缩了加拿大镍产量的约96%。众所周知，浮选矿浆化学影响铜镍矿物浮选性能和磁黄铁矿的截留率;然而，许多矿浆化学参数（如溶解氧和矿浆电位）在工业矿物加工厂中无法测量。在进行测量的工厂中，数据很少用于预测或控制过程，尽管它们具有已知的影响。在线纸浆化学传感器的进步为利用纸浆化学数据开发统计和机器学习模型提供了机会，这些模型可以更好地预测和控制Cu-Ni浮选回路中的磁黄铁矿行为。该项目是女王大学、自动化专家SK Godelius、加拿大淡水河谷基础金属公司和Glenore的萨德伯里综合镍业务公司之间的合作。短期目标是利用矿浆化学数据，开发能够预测磁黄铁矿含量（和其他矿石特性）以及铜镍矿石浮选中相应浮选行为的模型。从长远来看，这项工作将形成工厂优化平台的基础，该平台在工业规模上使用过程监测、建模和预测，以提高工厂性能并减少加拿大镍业务的碳足迹。