Department of Computer and Systems Engineering, Universidad Católica del Norte, Antofagasta 1270709, Chile.
Department of Chemical Engineering, Universidad Católica del Norte, Antofagasta 1270709, Chile.
Sensors (Basel). 2021 Mar 17;21(6):2119. doi: 10.3390/s21062119.
The copper mining industry is increasingly using artificial intelligence methods to improve copper production processes. Recent studies reveal the use of algorithms, such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in Northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew's correlation coefficient (mcc). This paper describes the dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real values. Finally, the obtained models have the following mean values: acc = 0.943, p = 88.47, r = 0.995, and mcc = 0.232. These values are highly competitive when compared with those obtained in similar studies using other approaches in the context.
铜矿开采行业越来越多地使用人工智能方法来改进铜生产工艺。最近的研究揭示了使用算法，如人工神经网络、支持向量机和随机森林等，来开发预测产品质量的模型。其他研究比较了整个采矿业中使用这些机器学习算法开发的预测模型。然而，发表的关于铜回收预测的机器学习技术结果的比较研究并不多。本研究使用来自智利北部矿区的四个数据集，对三种通过浸出预测铜回收的模型进行了详细比较。用于开发模型的算法是随机森林、支持向量机和人工神经网络。为了验证这些模型，使用了四个指标或择优值：准确性 (acc)、精度 (p)、召回率 (r) 和马修斯相关系数 (mcc)。本文描述了数据集的准备和对预测变量的阈值值的细化，该变量对类（铜回收）最有影响。结果表明，精度超过 98.50%，并且模型在预测值和实际值之间的表现最好。最后，得到的模型的平均值为：acc = 0.943、p = 88.47、r = 0.995 和 mcc = 0.232。与在类似背景下使用其他方法获得的结果相比，这些值具有很强的竞争力。
