Saldaña Manuel, Gálvez Edelmira, Navarra Alessandro, Toro Norman, Cisternas Luis A
Faculty of Engineering and Architecture, Universidad Arturo Prat, Iquique 1110939, Chile.
Departamento de Ingeniería Química y Procesos de Minerales, Universidad de Antofagasta, Antofagasta 1270300, Chile.
Materials (Basel). 2023 Apr 19;16(8):3220. doi: 10.3390/ma16083220.
Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process's productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.
考虑到生产成本的持续上升和资源优化，在铜矿业中，不止一个战略目标变得势在必行。在寻求提高资源利用效率的过程中，本研究利用统计分析和机器学习（ML）技术（回归、决策树和人工神经网络）开发了半自磨机（SAG）模型。所研究的假设旨在改善该过程的生产指标，如产量和能源消耗。数字模型的模拟显示，作为矿物粒度的函数，产量增加了4.42%，同时通过降低磨机转速有提高产量的潜力，对于所有线性老化配置，这将使能源消耗降低7.62%。考虑到机器学习在调整诸如SAG磨矿等复杂模型方面的性能，这些工具在矿物加工行业的应用有可能通过提高生产指标或节约能源消耗来提高这些过程的效率。最后，将这些技术纳入诸如从矿山到磨机范式等过程的综合管理，或开发考虑解释变量不确定性的模型，可能会在工业规模上进一步提高生产指标的性能。
